Photogrammetric Point Clouds Generation in Urban Areas from Integrated Image Matching and Segmentation

NASA Astrophysics Data System (ADS)

Ye, L.; Wu, B.

2017-09-01

High-resolution imagery is an attractive option for surveying and mapping applications due to the advantages of high quality imaging, short revisit time, and lower cost. Automated reliable and dense image matching is essential for photogrammetric 3D data derivation. Such matching, in urban areas, however, is extremely difficult, owing to the complexity of urban textures and severe occlusion problems on the images caused by tall buildings. Aimed at exploiting high-resolution imagery for 3D urban modelling applications, this paper presents an integrated image matching and segmentation approach for reliable dense matching of high-resolution imagery in urban areas. The approach is based on the framework of our existing self-adaptive triangulation constrained image matching (SATM), but incorporates three novel aspects to tackle the image matching difficulties in urban areas: 1) occlusion filtering based on image segmentation, 2) segment-adaptive similarity correlation to reduce the similarity ambiguity, 3) improved dense matching propagation to provide more reliable matches in urban areas. Experimental analyses were conducted using aerial images of Vaihingen, Germany and high-resolution satellite images in Hong Kong. The photogrammetric point clouds were generated, from which digital surface models (DSMs) were derived. They were compared with the corresponding airborne laser scanning data and the DSMs generated from the Semi-Global matching (SGM) method. The experimental results show that the proposed approach is able to produce dense and reliable matches comparable to SGM in flat areas, while for densely built-up areas, the proposed method performs better than SGM. The proposed method offers an alternative solution for 3D surface reconstruction in urban areas.

Robust image matching via ORB feature and VFC for mismatch removal

NASA Astrophysics Data System (ADS)

Ma, Tao; Fu, Wenxing; Fang, Bin; Hu, Fangyu; Quan, Siwen; Ma, Jie

2018-03-01

Image matching is at the base of many image processing and computer vision problems, such as object recognition or structure from motion. Current methods rely on good feature descriptors and mismatch removal strategies for detection and matching. In this paper, we proposed a robust image match approach based on ORB feature and VFC for mismatch removal. ORB (Oriented FAST and Rotated BRIEF) is an outstanding feature, it has the same performance as SIFT with lower cost. VFC (Vector Field Consensus) is a state-of-the-art mismatch removing method. The experiment results demonstrate that our method is efficient and robust.

A novel iris patterns matching algorithm of weighted polar frequency correlation

NASA Astrophysics Data System (ADS)

Zhao, Weijie; Jiang, Linhua

2014-11-01

Iris recognition is recognized as one of the most accurate techniques for biometric authentication. In this paper, we present a novel correlation method - Weighted Polar Frequency Correlation(WPFC) - to match and evaluate two iris images, actually it can also be used for evaluating the similarity of any two images. The WPFC method is a novel matching and evaluating method for iris image matching, which is complete different from the conventional methods. For instance, the classical John Daugman's method of iris recognition uses 2D Gabor wavelets to extract features of iris image into a compact bit stream, and then matching two bit streams with hamming distance. Our new method is based on the correlation in the polar coordinate system in frequency domain with regulated weights. The new method is motivated by the observation that the pattern of iris that contains far more information for recognition is fine structure at high frequency other than the gross shapes of iris images. Therefore, we transform iris images into frequency domain and set different weights to frequencies. Then calculate the correlation of two iris images in frequency domain. We evaluate the iris images by summing the discrete correlation values with regulated weights, comparing the value with preset threshold to tell whether these two iris images are captured from the same person or not. Experiments are carried out on both CASIA database and self-obtained images. The results show that our method is functional and reliable. Our method provides a new prospect for iris recognition system.

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics

PubMed Central

2012-01-01

Background Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. Methods The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. Results The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. Conclusion The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. Virtual Slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923. PMID:23035717

A novel image registration approach via combining local features and geometric invariants

PubMed Central

Lu, Yan; Gao, Kun; Zhang, Tinghua; Xu, Tingfa

2018-01-01

Image registration is widely used in many fields, but the adaptability of the existing methods is limited. This work proposes a novel image registration method with high precision for various complex applications. In this framework, the registration problem is divided into two stages. First, we detect and describe scale-invariant feature points using modified computer vision-oriented fast and rotated brief (ORB) algorithm, and a simple method to increase the performance of feature points matching is proposed. Second, we develop a new local constraint of rough selection according to the feature distances. Evidence shows that the existing matching techniques based on image features are insufficient for the images with sparse image details. Then, we propose a novel matching algorithm via geometric constraints, and establish local feature descriptions based on geometric invariances for the selected feature points. Subsequently, a new price function is constructed to evaluate the similarities between points and obtain exact matching pairs. Finally, we employ the progressive sample consensus method to remove wrong matches and calculate the space transform parameters. Experimental results on various complex image datasets verify that the proposed method is more robust and significantly reduces the rate of false matches while retaining more high-quality feature points. PMID:29293595

Robust and efficient method for matching features in omnidirectional images

NASA Astrophysics Data System (ADS)

Zhu, Qinyi; Zhang, Zhijiang; Zeng, Dan

2018-04-01

Binary descriptors have been widely used in many real-time applications due to their efficiency. These descriptors are commonly designed for perspective images but perform poorly on omnidirectional images, which are severely distorted. To address this issue, this paper proposes tangent plane BRIEF (TPBRIEF) and adapted log polar grid-based motion statistics (ALPGMS). TPBRIEF projects keypoints to a unit sphere and applies the fixed test set in BRIEF descriptor on the tangent plane of the unit sphere. The fixed test set is then backprojected onto the original distorted images to construct the distortion invariant descriptor. TPBRIEF directly enables keypoint detecting and feature describing on original distorted images, whereas other approaches correct the distortion through image resampling, which introduces artifacts and adds time cost. With ALPGMS, omnidirectional images are divided into circular arches named adapted log polar grids. Whether a match is true or false is then determined by simply thresholding the match numbers in a grid pair where the two matched points located. Experiments show that TPBRIEF greatly improves the feature matching accuracy and ALPGMS robustly removes wrong matches. Our proposed method outperforms the state-of-the-art methods.

Illumination invariant feature point matching for high-resolution planetary remote sensing images

NASA Astrophysics Data System (ADS)

Wu, Bo; Zeng, Hai; Hu, Han

2018-03-01

Despite its success with regular close-range and remote-sensing images, the scale-invariant feature transform (SIFT) algorithm is essentially not invariant to illumination differences due to the use of gradients for feature description. In planetary remote sensing imagery, which normally lacks sufficient textural information, salient regions are generally triggered by the shadow effects of keypoints, reducing the matching performance of classical SIFT. Based on the observation of dual peaks in a histogram of the dominant orientations of SIFT keypoints, this paper proposes an illumination-invariant SIFT matching method for high-resolution planetary remote sensing images. First, as the peaks in the orientation histogram are generally aligned closely with the sub-solar azimuth angle at the time of image collection, an adaptive suppression Gaussian function is tuned to level the histogram and thereby alleviate the differences in illumination caused by a changing solar angle. Next, the suppression function is incorporated into the original SIFT procedure for obtaining feature descriptors, which are used for initial image matching. Finally, as the distribution of feature descriptors changes after anisotropic suppression, and the ratio check used for matching and outlier removal in classical SIFT may produce inferior results, this paper proposes an improved matching procedure based on cross-checking and template image matching. The experimental results for several high-resolution remote sensing images from both the Moon and Mars, with illumination differences of 20°-180°, reveal that the proposed method retrieves about 40%-60% more matches than the classical SIFT method. The proposed method is of significance for matching or co-registration of planetary remote sensing images for their synergistic use in various applications. It also has the potential to be useful for flyby and rover images by integrating with the affine invariant feature detectors.

A fast image matching algorithm based on key points

NASA Astrophysics Data System (ADS)

Wang, Huilin; Wang, Ying; An, Ru; Yan, Peng

2014-05-01

Image matching is a very important technique in image processing. It has been widely used for object recognition and tracking, image retrieval, three-dimensional vision, change detection, aircraft position estimation, and multi-image registration. Based on the requirements of matching algorithm for craft navigation, such as speed, accuracy and adaptability, a fast key point image matching method is investigated and developed. The main research tasks includes: (1) Developing an improved celerity key point detection approach using self-adapting threshold of Features from Accelerated Segment Test (FAST). A method of calculating self-adapting threshold was introduced for images with different contrast. Hessian matrix was adopted to eliminate insecure edge points in order to obtain key points with higher stability. This approach in detecting key points has characteristics of small amount of computation, high positioning accuracy and strong anti-noise ability; (2) PCA-SIFT is utilized to describe key point. 128 dimensional vector are formed based on the SIFT method for the key points extracted. A low dimensional feature space was established by eigenvectors of all the key points, and each eigenvector was projected onto the feature space to form a low dimensional eigenvector. These key points were re-described by dimension-reduced eigenvectors. After reducing the dimension by the PCA, the descriptor was reduced to 20 dimensions from the original 128. This method can reduce dimensions of searching approximately near neighbors thereby increasing overall speed; (3) Distance ratio between the nearest neighbour and second nearest neighbour searching is regarded as the measurement criterion for initial matching points from which the original point pairs matched are obtained. Based on the analysis of the common methods (e.g. RANSAC (random sample consensus) and Hough transform cluster) used for elimination false matching point pairs, a heuristic local geometric restriction strategy is adopted to discard false matched point pairs further; and (4) Affine transformation model is introduced to correct coordinate difference between real-time image and reference image. This resulted in the matching of the two images. SPOT5 Remote sensing images captured at different date and airborne images captured with different flight attitude were used to test the performance of the method from matching accuracy, operation time and ability to overcome rotation. Results show the effectiveness of the approach.

Fan fault diagnosis based on symmetrized dot pattern analysis and image matching

NASA Astrophysics Data System (ADS)

Xu, Xiaogang; Liu, Haixiao; Zhu, Hao; Wang, Songling

2016-07-01

To detect the mechanical failure of fans, a new diagnostic method based on the symmetrized dot pattern (SDP) analysis and image matching is proposed. Vibration signals of 13 kinds of running states are acquired on a centrifugal fan test bed and reconstructed by the SDP technique. The SDP pattern templates of each running state are established. An image matching method is performed to diagnose the fault. In order to improve the diagnostic accuracy, the single template, multiple templates and clustering fault templates are used to perform the image matching.

Fast image matching algorithm based on projection characteristics

NASA Astrophysics Data System (ADS)

Zhou, Lijuan; Yue, Xiaobo; Zhou, Lijun

2011-06-01

Based on analyzing the traditional template matching algorithm, this paper identified the key factors restricting the speed of matching and put forward a brand new fast matching algorithm based on projection. Projecting the grayscale image, this algorithm converts the two-dimensional information of the image into one-dimensional one, and then matches and identifies through one-dimensional correlation, meanwhile, because of normalization has been done, when the image brightness or signal amplitude increasing in proportion, it could also perform correct matching. Experimental results show that the projection characteristics based image registration method proposed in this article could greatly improve the matching speed, which ensuring the matching accuracy as well.

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics.

PubMed

Sharma, Harshita; Alekseychuk, Alexander; Leskovsky, Peter; Hellwich, Olaf; Anand, R S; Zerbe, Norman; Hufnagl, Peter

2012-10-04

Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923.

a Target Aware Texture Mapping for Sculpture Heritage Modeling

NASA Astrophysics Data System (ADS)

Yang, C.; Zhang, F.; Huang, X.; Li, D.; Zhu, Y.

2017-08-01

In this paper, we proposed a target aware image to model registration method using silhouette as the matching clues. The target sculpture object in natural environment can be automatically detected from image with complex background with assistant of 3D geometric data. Then the silhouette can be automatically extracted and applied in image to model matching. Due to the user don't need to deliberately draw target area, the time consumption for precisely image to model matching operation can be greatly reduced. To enhance the function of this method, we also improved the silhouette matching algorithm to support conditional silhouette matching. Two experiments using a stone lion sculpture of Ming Dynasty and a potable relic in museum are given to evaluate the method we proposed. The method we proposed in this paper is extended and developed into a mature software applied in many culture heritage documentation projects.

Incorrect Match Detection Method for Arctic Sea-Ice Reconstruction Using Uav Images

NASA Astrophysics Data System (ADS)

Kim, J.-I.; Kim, H.-C.

2018-05-01

Shapes and surface roughness, which are considered as key indicators in understanding Arctic sea-ice, can be measured from the digital surface model (DSM) of the target area. Unmanned aerial vehicle (UAV) flying at low altitudes enables theoretically accurate DSM generation. However, the characteristics of sea-ice with textureless surface and incessant motion make image matching difficult for DSM generation. In this paper, we propose a method for effectively detecting incorrect matches before correcting a sea-ice DSM derived from UAV images. The proposed method variably adjusts the size of search window to analyze the matching results of DSM generated and distinguishes incorrect matches. Experimental results showed that the sea-ice DSM produced large errors along the textureless surfaces, and that the incorrect matches could be effectively detected by the proposed method.

On the integral use of foundational concepts in verifying validity during skull-photo superimposition.

PubMed

Jayaprakash, Paul T

2017-09-01

Often cited reliability test on video superimposition method integrated scaling face-images in relation to skull-images, tragus-auditory meatus relationship in addition to exocanthion-Whitnall's tubercle relationship when orientating the skull-image and wipe mode imaging in addition to mix mode imaging when obtaining skull-face image overlay and evaluating the goodness of match. However, a report that found higher false positive matches in computer assisted superimposition method transited from the above foundational concepts and relied on images of unspecified sizes that are lesser than 'life-size', frontal plane landmarks in the skull- and face- images alone for orientating the skull-image and mix images alone for evaluating the goodness of match. Recently, arguing the use of 'life-size' images as 'archaic', the authors who tested the reliability in the computer assisted superimposition method have denied any method transition. This article describes that the use of images of unspecified sizes at lesser than 'life-size' eliminates the only possibility to quantify parameters during superimposition which alone enables dynamic skull orientation when overlaying a skull-image with a face-image in an anatomically acceptable orientation. The dynamic skull orientation process mandatorily requires aligning the tragus in the 2D face-image with the auditory meatus in the 3D skull-image for anatomically orientating the skull-image in relation to the posture in the face-image, a step not mentioned by the authors describing the computer assisted superimposition method. Furthermore, mere reliance on mix type images during image overlay eliminates the possibility to assess the relationship between the leading edges of the skull- and face-image outlines as also specific area match among the corresponding craniofacial organs during superimposition. Indicating the possibility of increased false positive matches as a consequence of the above method transitions, the need for testing the reliability in the superimposition method adopting concepts that are considered safe is stressed. Copyright © 2017 Elsevier B.V. All rights reserved.

Dense real-time stereo matching using memory efficient semi-global-matching variant based on FPGAs

NASA Astrophysics Data System (ADS)

Buder, Maximilian

2012-06-01

This paper presents a stereo image matching system that takes advantage of a global image matching method. The system is designed to provide depth information for mobile robotic applications. Typical tasks of the proposed system are to assist in obstacle avoidance, SLAM and path planning. Mobile robots pose strong requirements about size, energy consumption, reliability and output quality of the image matching subsystem. Current available systems either rely on active sensors or on local stereo image matching algorithms. The first are only suitable in controlled environments while the second suffer from low quality depth-maps. Top ranking quality results are only achieved by an iterative approach using global image matching and color segmentation techniques which are computationally demanding and therefore difficult to be executed in realtime. Attempts were made to still reach realtime performance with global methods by simplifying the routines. The depth maps are at the end almost comparable to local methods. An equally named semi-global algorithm was proposed earlier that shows both very good image matching results and relatively simple operations. A memory efficient variant of the Semi-Global-Matching algorithm is reviewed and adopted for an implementation based on reconfigurable hardware. The implementation is suitable for realtime execution in the field of robotics. It will be shown that the modified version of the efficient Semi-Global-Matching method is delivering equivalent result compared to the original algorithm based on the Middlebury dataset. The system has proven to be capable of processing VGA sized images with a disparity resolution of 64 pixel at 33 frames per second based on low cost to mid-range hardware. In case the focus is shifted to a higher image resolution, 1024×1024-sized stereo frames may be processed with the same hardware at 10 fps. The disparity resolution settings stay unchanged. A mobile system that covers preprocessing, matching and interfacing operations is also presented.

3D temporal subtraction on multislice CT images using nonlinear warping technique

NASA Astrophysics Data System (ADS)

Ishida, Takayuki; Katsuragawa, Shigehiko; Kawashita, Ikuo; Kim, Hyounseop; Itai, Yoshinori; Awai, Kazuo; Li, Qiang; Doi, Kunio

2007-03-01

The detection of very subtle lesions and/or lesions overlapped with vessels on CT images is a time consuming and difficult task for radiologists. In this study, we have developed a 3D temporal subtraction method to enhance interval changes between previous and current multislice CT images based on a nonlinear image warping technique. Our method provides a subtraction CT image which is obtained by subtraction of a previous CT image from a current CT image. Reduction of misregistration artifacts is important in the temporal subtraction method. Therefore, our computerized method includes global and local image matching techniques for accurate registration of current and previous CT images. For global image matching, we selected the corresponding previous section image for each current section image by using 2D cross-correlation between a blurred low-resolution current CT image and a blurred previous CT image. For local image matching, we applied the 3D template matching technique with translation and rotation of volumes of interests (VOIs) which were selected in the current and the previous CT images. The local shift vector for each VOI pair was determined when the cross-correlation value became the maximum in the 3D template matching. The local shift vectors at all voxels were determined by interpolation of shift vectors of VOIs, and then the previous CT image was nonlinearly warped according to the shift vector for each voxel. Finally, the warped previous CT image was subtracted from the current CT image. The 3D temporal subtraction method was applied to 19 clinical cases. The normal background structures such as vessels, ribs, and heart were removed without large misregistration artifacts. Thus, interval changes due to lung diseases were clearly enhanced as white shadows on subtraction CT images.

Rotation and scale change invariant point pattern relaxation matching by the Hopfield neural network

NASA Astrophysics Data System (ADS)

Sang, Nong; Zhang, Tianxu

1997-12-01

Relaxation matching is one of the most relevant methods for image matching. The original relaxation matching technique using point patterns is sensitive to rotations and scale changes. We improve the original point pattern relaxation matching technique to be invariant to rotations and scale changes. A method that makes the Hopfield neural network perform this matching process is discussed. An advantage of this is that the relaxation matching process can be performed in real time with the neural network's massively parallel capability to process information. Experimental results with large simulated images demonstrate the effectiveness and feasibility of the method to perform point patten relaxation matching invariant to rotations and scale changes and the method to perform this matching by the Hopfield neural network. In addition, we show that the method presented can be tolerant to small random error.

Brain medical image diagnosis based on corners with importance-values.

PubMed

Gao, Linlin; Pan, Haiwei; Li, Qing; Xie, Xiaoqin; Zhang, Zhiqiang; Han, Jinming; Zhai, Xiao

2017-11-21

Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis. Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image. In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection method utilizing the diagnostic information from neurologists and a corner matching method based on the uncertainty and structure of brain medical images. Additionally, we present a similarity calculation method for brain image classification. Experimental results on two brain image sets show the proposed corner-based brain medical image classifier outperforms the state-of-the-art studies.

Evaluation of Existing Image Matching Methods for Deriving Glacier Surface Displacements Globally from Optical Satellite Imagery

NASA Astrophysics Data System (ADS)

Heid, T.; Kääb, A.

2011-12-01

Automatic matching of images from two different times is a method that is often used to derive glacier surface velocity. Nearly global repeat coverage of the Earth's surface by optical satellite sensors now opens the possibility for global-scale mapping and monitoring of glacier flow with a number of applications in, for example, glacier physics, glacier-related climate change and impact assessment, and glacier hazard management. The purpose of this study is to compare and evaluate different existing image matching methods for glacier flow determination over large scales. The study compares six different matching methods: normalized cross-correlation (NCC), the phase correlation algorithm used in the COSI-Corr software, and four other Fourier methods with different normalizations. We compare the methods over five regions of the world with different representative glacier characteristics: Karakoram, the European Alps, Alaska, Pine Island (Antarctica) and southwest Greenland. Landsat images are chosen for matching because they expand back to 1972, they cover large areas, and at the same time their spatial resolution is as good as 15 m for images after 1999 (ETM+ pan). Cross-correlation on orientation images (CCF-O) outperforms the three similar Fourier methods, both in areas with high and low visual contrast. NCC experiences problems in areas with low visual contrast, areas with thin clouds or changing snow conditions between the images. CCF-O has problems on narrow outlet glaciers where small window sizes (about 16 pixels by 16 pixels or smaller) are needed, and it also obtains fewer correct matches than COSI-Corr in areas with low visual contrast. COSI-Corr has problems on narrow outlet glaciers and it obtains fewer correct matches compared to CCF-O when thin clouds cover the surface, or if one of the images contains snow dunes. In total, we consider CCF-O and COSI-Corr to be the two most robust matching methods for global-scale mapping and monitoring of glacier velocities. If combining CCF-O with locally adaptive template sizes and by filtering the matching results automatically by comparing the displacement matrix to its low pass filtered version, the matching process can be automated to a large degree. This allows the derivation of glacier velocities with minimal (but not without!) user interaction and hence also opens up the possibility of global-scale mapping and monitoring of glacier flow.

Application of Time-Frequency Domain Transform to Three-Dimensional Interpolation of Medical Images.

PubMed

Lv, Shengqing; Chen, Yimin; Li, Zeyu; Lu, Jiahui; Gao, Mingke; Lu, Rongrong

2017-11-01

Medical image three-dimensional (3D) interpolation is an important means to improve the image effect in 3D reconstruction. In image processing, the time-frequency domain transform is an efficient method. In this article, several time-frequency domain transform methods are applied and compared in 3D interpolation. And a Sobel edge detection and 3D matching interpolation method based on wavelet transform is proposed. We combine wavelet transform, traditional matching interpolation methods, and Sobel edge detection together in our algorithm. What is more, the characteristics of wavelet transform and Sobel operator are used. They deal with the sub-images of wavelet decomposition separately. Sobel edge detection 3D matching interpolation method is used in low-frequency sub-images under the circumstances of ensuring high frequency undistorted. Through wavelet reconstruction, it can get the target interpolation image. In this article, we make 3D interpolation of the real computed tomography (CT) images. Compared with other interpolation methods, our proposed method is verified to be effective and superior.

A theory of phase singularities for image representation and its applications to object tracking and image matching.

PubMed

Qiao, Yu; Wang, Wei; Minematsu, Nobuaki; Liu, Jianzhuang; Takeda, Mitsuo; Tang, Xiaoou

2009-10-01

This paper studies phase singularities (PSs) for image representation. We show that PSs calculated with Laguerre-Gauss filters contain important information and provide a useful tool for image analysis. PSs are invariant to image translation and rotation. We introduce several invariant features to characterize the core structures around PSs and analyze the stability of PSs to noise addition and scale change. We also study the characteristics of PSs in a scale space, which lead to a method to select key scales along phase singularity curves. We demonstrate two applications of PSs: object tracking and image matching. In object tracking, we use the iterative closest point algorithm to determine the correspondences of PSs between two adjacent frames. The use of PSs allows us to precisely determine the motions of tracked objects. In image matching, we combine PSs and scale-invariant feature transform (SIFT) descriptor to deal with the variations between two images and examine the proposed method on a benchmark database. The results indicate that our method can find more correct matching pairs with higher repeatability rates than some well-known methods.

Medical microscopic image matching based on relativity

NASA Astrophysics Data System (ADS)

Xie, Fengying; Zhu, Liangen; Jiang, Zhiguo

2003-12-01

In this paper, an effective medical micro-optical image matching algorithm based on relativity is described. The algorithm includes the following steps: Firstly, selecting a sub-area that has obvious character in one of the two images as standard image; Secondly, finding the right matching position in the other image; Thirdly, applying coordinate transformation to merge the two images together. As a kind of application of image matching in medical micro-optical image, this method overcomes the shortcoming of microscope whose visual field is little and makes it possible to watch a big object or many objects in one view. Simultaneously it implements adaptive selection of standard image, and has a satisfied matching speed and result.

Matching methods evaluation framework for stereoscopic breast x-ray images.

PubMed

Rousson, Johanna; Naudin, Mathieu; Marchessoux, Cédric

2016-01-01

Three-dimensional (3-D) imaging has been intensively studied in the past few decades. Depth information is an important added value of 3-D systems over two-dimensional systems. Special focuses were devoted to the development of stereo matching methods for the generation of disparity maps (i.e., depth information within a 3-D scene). Dedicated frameworks were designed to evaluate and rank the performance of different stereo matching methods but never considering x-ray medical images. Yet, 3-D x-ray acquisition systems and 3-D medical displays have already been introduced into the diagnostic market. To access the depth information within x-ray stereoscopic images, computing accurate disparity maps is essential. We aimed at developing a framework dedicated to x-ray stereoscopic breast images used to evaluate and rank several stereo matching methods. A multiresolution pyramid optimization approach was integrated to the framework to increase the accuracy and the efficiency of the stereo matching techniques. Finally, a metric was designed to score the results of the stereo matching compared with the ground truth. Eight methods were evaluated and four of them [locally scaled sum of absolute differences (LSAD), zero mean sum of absolute differences, zero mean sum of squared differences, and locally scaled mean sum of squared differences] appeared to perform equally good with an average error score of 0.04 (0 is the perfect matching). LSAD was selected for generating the disparity maps.

Evaluation of Deep Learning Based Stereo Matching Methods: from Ground to Aerial Images

NASA Astrophysics Data System (ADS)

Liu, J.; Ji, S.; Zhang, C.; Qin, Z.

2018-05-01

Dense stereo matching has been extensively studied in photogrammetry and computer vision. In this paper we evaluate the application of deep learning based stereo methods, which were raised from 2016 and rapidly spread, on aerial stereos other than ground images that are commonly used in computer vision community. Two popular methods are evaluated. One learns matching cost with a convolutional neural network (known as MC-CNN); the other produces a disparity map in an end-to-end manner by utilizing both geometry and context (known as GC-net). First, we evaluate the performance of the deep learning based methods for aerial stereo images by a direct model reuse. The models pre-trained on KITTI 2012, KITTI 2015 and Driving datasets separately, are directly applied to three aerial datasets. We also give the results of direct training on target aerial datasets. Second, the deep learning based methods are compared to the classic stereo matching method, Semi-Global Matching(SGM), and a photogrammetric software, SURE, on the same aerial datasets. Third, transfer learning strategy is introduced to aerial image matching based on the assumption of a few target samples available for model fine tuning. It experimentally proved that the conventional methods and the deep learning based methods performed similarly, and the latter had greater potential to be explored.

Fast template matching with polynomials.

PubMed

Omachi, Shinichiro; Omachi, Masako

2007-08-01

Template matching is widely used for many applications in image and signal processing. This paper proposes a novel template matching algorithm, called algebraic template matching. Given a template and an input image, algebraic template matching efficiently calculates similarities between the template and the partial images of the input image, for various widths and heights. The partial image most similar to the template image is detected from the input image for any location, width, and height. In the proposed algorithm, a polynomial that approximates the template image is used to match the input image instead of the template image. The proposed algorithm is effective especially when the width and height of the template image differ from the partial image to be matched. An algorithm using the Legendre polynomial is proposed for efficient approximation of the template image. This algorithm not only reduces computational costs, but also improves the quality of the approximated image. It is shown theoretically and experimentally that the computational cost of the proposed algorithm is much smaller than the existing methods.

A 3D terrain reconstruction method of stereo vision based quadruped robot navigation system

NASA Astrophysics Data System (ADS)

Ge, Zhuo; Zhu, Ying; Liang, Guanhao

2017-01-01

To provide 3D environment information for the quadruped robot autonomous navigation system during walking through rough terrain, based on the stereo vision, a novel 3D terrain reconstruction method is presented. In order to solve the problem that images collected by stereo sensors have large regions with similar grayscale and the problem that image matching is poor at real-time performance, watershed algorithm and fuzzy c-means clustering algorithm are combined for contour extraction. Aiming at the problem of error matching, duel constraint with region matching and pixel matching is established for matching optimization. Using the stereo matching edge pixel pairs, the 3D coordinate algorithm is estimated according to the binocular stereo vision imaging model. Experimental results show that the proposed method can yield high stereo matching ratio and reconstruct 3D scene quickly and efficiently.

A spot-matching method using cumulative frequency matrix in 2D gel images

PubMed Central

Han, Chan-Myeong; Park, Joon-Ho; Chang, Chu-Seok; Ryoo, Myung-Chun

2014-01-01

A new method for spot matching in two-dimensional gel electrophoresis images using a cumulative frequency matrix is proposed. The method improves on the weak points of the previous method called ‘spot matching by topological patterns of neighbour spots’. It accumulates the frequencies of neighbour spot pairs produced through the entire matching process and determines spot pairs one by one in order of higher frequency. Spot matching by frequencies of neighbour spot pairs shows a fairly better performance. However, it can give researchers a hint for whether the matching results can be trustworthy or not, which can save researchers a lot of effort for verification of the results. PMID:26019609

Computing global minimizers to a constrained B-spline image registration problem from optimal l1 perturbations to block match data

PubMed Central

Castillo, Edward; Castillo, Richard; Fuentes, David; Guerrero, Thomas

2014-01-01

Purpose: Block matching is a well-known strategy for estimating corresponding voxel locations between a pair of images according to an image similarity metric. Though robust to issues such as image noise and large magnitude voxel displacements, the estimated point matches are not guaranteed to be spatially accurate. However, the underlying optimization problem solved by the block matching procedure is similar in structure to the class of optimization problem associated with B-spline based registration methods. By exploiting this relationship, the authors derive a numerical method for computing a global minimizer to a constrained B-spline registration problem that incorporates the robustness of block matching with the global smoothness properties inherent to B-spline parameterization. Methods: The method reformulates the traditional B-spline registration problem as a basis pursuit problem describing the minimal l1-perturbation to block match pairs required to produce a B-spline fitting error within a given tolerance. The sparsity pattern of the optimal perturbation then defines a voxel point cloud subset on which the B-spline fit is a global minimizer to a constrained variant of the B-spline registration problem. As opposed to traditional B-spline algorithms, the optimization step involving the actual image data is addressed by block matching. Results: The performance of the method is measured in terms of spatial accuracy using ten inhale/exhale thoracic CT image pairs (available for download at www.dir-lab.com) obtained from the COPDgene dataset and corresponding sets of expert-determined landmark point pairs. The results of the validation procedure demonstrate that the method can achieve a high spatial accuracy on a significantly complex image set. Conclusions: The proposed methodology is demonstrated to achieve a high spatial accuracy and is generalizable in that in can employ any displacement field parameterization described as a least squares fit to block match generated estimates. Thus, the framework allows for a wide range of image similarity block match metric and physical modeling combinations. PMID:24694135

A new template matching method based on contour information

NASA Astrophysics Data System (ADS)

Cai, Huiying; Zhu, Feng; Wu, Qingxiao; Li, Sicong

2014-11-01

Template matching is a significant approach in machine vision due to its effectiveness and robustness. However, most of the template matching methods are so time consuming that they can't be used to many real time applications. The closed contour matching method is a popular kind of template matching methods. This paper presents a new closed contour template matching method which is suitable for two dimensional objects. Coarse-to-fine searching strategy is used to improve the matching efficiency and a partial computation elimination scheme is proposed to further speed up the searching process. The method consists of offline model construction and online matching. In the process of model construction, triples and distance image are obtained from the template image. A certain number of triples which are composed by three points are created from the contour information that is extracted from the template image. The rule to select the three points is that the template contour is divided equally into three parts by these points. The distance image is obtained here by distance transform. Each point on the distance image represents the nearest distance between current point and the points on the template contour. During the process of matching, triples of the searching image are created with the same rule as the triples of the model. Through the similarity that is invariant to rotation, translation and scaling between triangles, the triples corresponding to the triples of the model are found. Then we can obtain the initial RST (rotation, translation and scaling) parameters mapping the searching contour to the template contour. In order to speed up the searching process, the points on the searching contour are sampled to reduce the number of the triples. To verify the RST parameters, the searching contour is projected into the distance image, and the mean distance can be computed rapidly by simple operations of addition and multiplication. In the fine searching process, the initial RST parameters are discrete to obtain the final accurate pose of the object. Experimental results show that the proposed method is reasonable and efficient, and can be used in many real time applications.

An adaptive clustering algorithm for image matching based on corner feature

NASA Astrophysics Data System (ADS)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-04-01

The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.

Research on sparse feature matching of improved RANSAC algorithm

NASA Astrophysics Data System (ADS)

Kong, Xiangsi; Zhao, Xian

2018-04-01

In this paper, a sparse feature matching method based on modified RANSAC algorithm is proposed to improve the precision and speed. Firstly, the feature points of the images are extracted using the SIFT algorithm. Then, the image pair is matched roughly by generating SIFT feature descriptor. At last, the precision of image matching is optimized by the modified RANSAC algorithm,. The RANSAC algorithm is improved from three aspects: instead of the homography matrix, this paper uses the fundamental matrix generated by the 8 point algorithm as the model; the sample is selected by a random block selecting method, which ensures the uniform distribution and the accuracy; adds sequential probability ratio test(SPRT) on the basis of standard RANSAC, which cut down the overall running time of the algorithm. The experimental results show that this method can not only get higher matching accuracy, but also greatly reduce the computation and improve the matching speed.

Precision 3d Surface Reconstruction from Lro Nac Images Using Semi-Global Matching with Coupled Epipolar Rectification

NASA Astrophysics Data System (ADS)

Hu, H.; Wu, B.

2017-07-01

The Narrow-Angle Camera (NAC) on board the Lunar Reconnaissance Orbiter (LRO) comprises of a pair of closely attached high-resolution push-broom sensors, in order to improve the swath coverage. However, the two image sensors do not share the same lenses and cannot be modelled geometrically using a single physical model. Thus, previous works on dense matching of stereo pairs of NAC images would generally create two to four stereo models, each with an irregular and overlapping region of varying size. Semi-Global Matching (SGM) is a well-known dense matching method and has been widely used for image-based 3D surface reconstruction. SGM is a global matching algorithm relying on global inference in a larger context rather than individual pixels to establish stable correspondences. The stereo configuration of LRO NAC images causes severe problem for image matching methods such as SGM, which emphasizes global matching strategy. Aiming at using SGM for image matching of LRO NAC stereo pairs for precision 3D surface reconstruction, this paper presents a coupled epipolar rectification methods for LRO NAC stereo images, which merges the image pair in the disparity space and in this way, only one stereo model will be estimated. For a stereo pair (four) of NAC images, the method starts with the boresight calibration by finding correspondence in the small overlapping stripe between each pair of NAC images and bundle adjustment of the stereo pair, in order to clean the vertical disparities. Then, the dominate direction of the images are estimated by project the center of the coverage area to the reference image and back-projected to the bounding box plane determined by the image orientation parameters iteratively. The dominate direction will determine an affine model, by which the pair of NAC images are warped onto the object space with a given ground resolution and in the meantime, a mask is produced indicating the owner of each pixel. SGM is then used to generate a disparity map for the stereo pair and each correspondence is transformed back to the owner and 3D points are derived through photogrammetric space intersection. Experimental results reveal that the proposed method is able to reduce gaps and inconsistencies caused by the inaccurate boresight offsets between the two NAC cameras and the irregular overlapping regions, and finally generate precise and consistent 3D surface models from the NAC stereo images automatically.

A new approach for automatic matching of ground control points in urban areas from heterogeneous images

NASA Astrophysics Data System (ADS)

Cong, Chao; Liu, Dingsheng; Zhao, Lingjun

2008-12-01

This paper discusses a new method for the automatic matching of ground control points (GCPs) between satellite remote sensing Image and digital raster graphic (DRG) in urban areas. The key of this method is to automatically extract tie point pairs according to geographic characters from such heterogeneous images. Since there are big differences between such heterogeneous images respect to texture and corner features, more detail analyzations are performed to find similarities and differences between high resolution remote sensing Image and (DRG). Furthermore a new algorithms based on the fuzzy-c means (FCM) method is proposed to extract linear feature in remote sensing Image. Based on linear feature, crossings and corners extracted from these features are chosen as GCPs. On the other hand, similar method was used to find same features from DRGs. Finally, Hausdorff Distance was adopted to pick matching GCPs from above two GCP groups. Experiences shown the method can extract GCPs from such images with a reasonable RMS error.

Heterogeneous iris image hallucination using sparse representation on a learned heterogeneous patch dictionary

NASA Astrophysics Data System (ADS)

Li, Yung-Hui; Zheng, Bo-Ren; Ji, Dai-Yan; Tien, Chung-Hao; Liu, Po-Tsun

2014-09-01

Cross sensor iris matching may seriously degrade the recognition performance because of the sensor mis-match problem of iris images between the enrollment and test stage. In this paper, we propose two novel patch-based heterogeneous dictionary learning method to attack this problem. The first method applies the latest sparse representation theory while the second method tries to learn the correspondence relationship through PCA in heterogeneous patch space. Both methods learn the basic atoms in iris textures across different image sensors and build connections between them. After such connections are built, at test stage, it is possible to hallucinate (synthesize) iris images across different sensors. By matching training images with hallucinated images, the recognition rate can be successfully enhanced. The experimental results showed the satisfied results both visually and in terms of recognition rate. Experimenting with an iris database consisting of 3015 images, we show that the EER is decreased 39.4% relatively by the proposed method.

Stereo matching algorithm based on double components model

NASA Astrophysics Data System (ADS)

Zhou, Xiao; Ou, Kejun; Zhao, Jianxin; Mou, Xingang

2018-03-01

The tiny wires are the great threat to the safety of the UAV flight. Because they have only several pixels isolated far from the background, while most of the existing stereo matching methods require a certain area of the support region to improve the robustness, or assume the depth dependence of the neighboring pixels to meet requirement of global or semi global optimization method. So there will be some false alarms even failures when images contains tiny wires. A new stereo matching algorithm is approved in the paper based on double components model. According to different texture types the input image is decomposed into two independent component images. One contains only sparse wire texture image and another contains all remaining parts. Different matching schemes are adopted for each component image pairs. Experiment proved that the algorithm can effectively calculate the depth image of complex scene of patrol UAV, which can detect tiny wires besides the large size objects. Compared with the current mainstream method it has obvious advantages.

Gun bore flaw image matching based on improved SIFT descriptor

NASA Astrophysics Data System (ADS)

Zeng, Luan; Xiong, Wei; Zhai, You

2013-01-01

In order to increase the operation speed and matching ability of SIFT algorithm, the SIFT descriptor and matching strategy are improved. First, a method of constructing feature descriptor based on sector area is proposed. By computing the gradients histogram of location bins which are parted into 6 sector areas, a descriptor with 48 dimensions is constituted. It can reduce the dimension of feature vector and decrease the complexity of structuring descriptor. Second, it introduce a strategy that partitions the circular region into 6 identical sector areas starting from the dominate orientation. Consequently, the computational complexity is reduced due to cancellation of rotation operation for the area. The experimental results indicate that comparing with the OpenCV SIFT arithmetic, the average matching speed of the new method increase by about 55.86%. The matching veracity can be increased even under some variation of view point, illumination, rotation, scale and out of focus. The new method got satisfied results in gun bore flaw image matching. Keywords: Metrology, Flaw image matching, Gun bore, Feature descriptor

Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features

PubMed Central

Mousavi Kahaki, Seyed Mostafa; Nordin, Md Jan; Ashtari, Amir H.; J. Zahra, Sophia

2016-01-01

An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics—such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient—are insufficient for achieving adequate results under different image deformations. Thus, new descriptor’s similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence. PMID:26985996

A long baseline global stereo matching based upon short baseline estimation

NASA Astrophysics Data System (ADS)

Li, Jing; Zhao, Hong; Li, Zigang; Gu, Feifei; Zhao, Zixin; Ma, Yueyang; Fang, Meiqi

2018-05-01

In global stereo vision, balancing the matching efficiency and computing accuracy seems to be impossible because they contradict each other. In the case of a long baseline, this contradiction becomes more prominent. In order to solve this difficult problem, this paper proposes a novel idea to improve both the efficiency and accuracy in global stereo matching for a long baseline. In this way, the reference images located between the long baseline image pairs are firstly chosen to form the new image pairs with short baselines. The relationship between the disparities of pixels in the image pairs with different baselines is revealed by considering the quantized error so that the disparity search range under the long baseline can be reduced by guidance of the short baseline to gain matching efficiency. Then, the novel idea is integrated into the graph cuts (GCs) to form a multi-step GC algorithm based on the short baseline estimation, by which the disparity map under the long baseline can be calculated iteratively on the basis of the previous matching. Furthermore, the image information from the pixels that are non-occluded under the short baseline but are occluded for the long baseline can be employed to improve the matching accuracy. Although the time complexity of the proposed method depends on the locations of the chosen reference images, it is usually much lower for a long baseline stereo matching than when using the traditional GC algorithm. Finally, the validity of the proposed method is examined by experiments based on benchmark datasets. The results show that the proposed method is superior to the traditional GC method in terms of efficiency and accuracy, and thus it is suitable for long baseline stereo matching.

Matching CCD images to a stellar catalog using locality-sensitive hashing

NASA Astrophysics Data System (ADS)

Liu, Bo; Yu, Jia-Zong; Peng, Qing-Yu

2018-02-01

The usage of a subset of observed stars in a CCD image to find their corresponding matched stars in a stellar catalog is an important issue in astronomical research. Subgraph isomorphic-based algorithms are the most widely used methods in star catalog matching. When more subgraph features are provided, the CCD images are recognized better. However, when the navigation feature database is large, the method requires more time to match the observing model. To solve this problem, this study investigates further and improves subgraph isomorphic matching algorithms. We present an algorithm based on a locality-sensitive hashing technique, which allocates quadrilateral models in the navigation feature database into different hash buckets and reduces the search range to the bucket in which the observed quadrilateral model is located. Experimental results indicate the effectivity of our method.

An improved finger-vein recognition algorithm based on template matching

NASA Astrophysics Data System (ADS)

Liu, Yueyue; Di, Si; Jin, Jian; Huang, Daoping

2016-10-01

Finger-vein recognition has became the most popular biometric identify methods. The investigation on the recognition algorithms always is the key point in this field. So far, there are many applicable algorithms have been developed. However, there are still some problems in practice, such as the variance of the finger position which may lead to the image distortion and shifting; during the identification process, some matching parameters determined according to experience may also reduce the adaptability of algorithm. Focus on above mentioned problems, this paper proposes an improved finger-vein recognition algorithm based on template matching. In order to enhance the robustness of the algorithm for the image distortion, the least squares error method is adopted to correct the oblique finger. During the feature extraction, local adaptive threshold method is adopted. As regard as the matching scores, we optimized the translation preferences as well as matching distance between the input images and register images on the basis of Naoto Miura algorithm. Experimental results indicate that the proposed method can improve the robustness effectively under the finger shifting and rotation conditions.

MR Image Reconstruction Using Block Matching and Adaptive Kernel Methods.

PubMed

Schmidt, Johannes F M; Santelli, Claudio; Kozerke, Sebastian

2016-01-01

An approach to Magnetic Resonance (MR) image reconstruction from undersampled data is proposed. Undersampling artifacts are removed using an iterative thresholding algorithm applied to nonlinearly transformed image block arrays. Each block array is transformed using kernel principal component analysis where the contribution of each image block to the transform depends in a nonlinear fashion on the distance to other image blocks. Elimination of undersampling artifacts is achieved by conventional principal component analysis in the nonlinear transform domain, projection onto the main components and back-mapping into the image domain. Iterative image reconstruction is performed by interleaving the proposed undersampling artifact removal step and gradient updates enforcing consistency with acquired k-space data. The algorithm is evaluated using retrospectively undersampled MR cardiac cine data and compared to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ1-SPIRiT reconstruction. Evaluation of image quality and root-mean-squared-error (RMSE) reveal improved image reconstruction for up to 8-fold undersampled data with the proposed approach relative to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ1-SPIRiT. In conclusion, block matching and kernel methods can be used for effective removal of undersampling artifacts in MR image reconstruction and outperform methods using standard compressed sensing and ℓ1-regularized parallel imaging methods.

A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching

PubMed Central

Chen, Cheng; Wang, Wei; Ozolek, John A.; Rohde, Gustavo K.

2013-01-01

We describe a new supervised learning-based template matching approach for segmenting cell nuclei from microscopy images. The method uses examples selected by a user for building a statistical model which captures the texture and shape variations of the nuclear structures from a given dataset to be segmented. Segmentation of subsequent, unlabeled, images is then performed by finding the model instance that best matches (in the normalized cross correlation sense) local neighborhood in the input image. We demonstrate the application of our method to segmenting nuclei from a variety of imaging modalities, and quantitatively compare our results to several other methods. Quantitative results using both simulated and real image data show that, while certain methods may work well for certain imaging modalities, our software is able to obtain high accuracy across several imaging modalities studied. Results also demonstrate that, relative to several existing methods, the template-based method we propose presents increased robustness in the sense of better handling variations in illumination, variations in texture from different imaging modalities, providing more smooth and accurate segmentation borders, as well as handling better cluttered nuclei. PMID:23568787

Image Mosaic Method Based on SIFT Features of Line Segment

PubMed Central

Zhu, Jun; Ren, Mingwu

2014-01-01

This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform) feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling. PMID:24511326

Optimization of incremental structure from motion combining a random k-d forest and pHash for unordered images in a complex scene

NASA Astrophysics Data System (ADS)

Zhan, Zongqian; Wang, Chendong; Wang, Xin; Liu, Yi

2018-01-01

On the basis of today's popular virtual reality and scientific visualization, three-dimensional (3-D) reconstruction is widely used in disaster relief, virtual shopping, reconstruction of cultural relics, etc. In the traditional incremental structure from motion (incremental SFM) method, the time cost of the matching is one of the main factors restricting the popularization of this method. To make the whole matching process more efficient, we propose a preprocessing method before the matching process: (1) we first construct a random k-d forest with the large-scale scale-invariant feature transform features in the images and combine this with the pHash method to obtain a value of relatedness, (2) we then construct a connected weighted graph based on the relatedness value, and (3) we finally obtain a planned sequence of adding images according to the principle of the minimum spanning tree. On this basis, we attempt to thin the minimum spanning tree to reduce the number of matchings and ensure that the images are well distributed. The experimental results show a great reduction in the number of matchings with enough object points, with only a small influence on the inner stability, which proves that this method can quickly and reliably improve the efficiency of the SFM method with unordered multiview images in complex scenes.

Photovoltaic panel extraction from very high-resolution aerial imagery using region-line primitive association analysis and template matching

NASA Astrophysics Data System (ADS)

Wang, Min; Cui, Qi; Sun, Yujie; Wang, Qiao

2018-07-01

In object-based image analysis (OBIA), object classification performance is jointly determined by image segmentation, sample or rule setting, and classifiers. Typically, as a crucial step to obtain object primitives, image segmentation quality significantly influences subsequent feature extraction and analyses. By contrast, template matching extracts specific objects from images and prevents shape defects caused by image segmentation. However, creating or editing templates is tedious and sometimes results in incomplete or inaccurate templates. In this study, we combine OBIA and template matching techniques to address these problems and aim for accurate photovoltaic panel (PVP) extraction from very high-resolution (VHR) aerial imagery. The proposed method is based on the previously proposed region-line primitive association framework, in which complementary information between region (segment) and line (straight line) primitives is utilized to achieve a more powerful performance than routine OBIA. Several novel concepts, including the mutual fitting ratio and best-fitting template based on region-line primitive association analyses, are proposed. Automatic template generation and matching method for PVP extraction from VHR imagery are designed for concept and model validation. Results show that the proposed method can successfully extract PVPs without any user-specified matching template or training sample. High user independency and accuracy are the main characteristics of the proposed method in comparison with routine OBIA and template matching techniques.

Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing

PubMed Central

Jung, Jaewook; Sohn, Gunho; Bang, Kiin; Wichmann, Andreas; Armenakis, Costas; Kada, Martin

2016-01-01

A city is a dynamic entity, which environment is continuously changing over time. Accordingly, its virtual city models also need to be regularly updated to support accurate model-based decisions for various applications, including urban planning, emergency response and autonomous navigation. A concept of continuous city modeling is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. A first critical step for continuous city modeling is to coherently register remotely sensed data taken at different epochs with existing building models. This paper presents a new model-to-image registration method using a context-based geometric hashing (CGH) method to align a single image with existing 3D building models. This model-to-image registration process consists of three steps: (1) feature extraction; (2) similarity measure; and matching, and (3) estimating exterior orientation parameters (EOPs) of a single image. For feature extraction, we propose two types of matching cues: edged corner features representing the saliency of building corner points with associated edges, and contextual relations among the edged corner features within an individual roof. A set of matched corners are found with given proximity measure through geometric hashing, and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on collinearity equations. The result shows that acceptable accuracy of EOPs of a single image can be achievable using the proposed registration approach as an alternative to a labor-intensive manual registration process. PMID:27338410

Convolutional auto-encoder for image denoising of ultra-low-dose CT.

PubMed

Nishio, Mizuho; Nagashima, Chihiro; Hirabayashi, Saori; Ohnishi, Akinori; Sasaki, Kaori; Sagawa, Tomoyuki; Hamada, Masayuki; Yamashita, Tatsuo

2017-08-01

The purpose of this study was to validate a patch-based image denoising method for ultra-low-dose CT images. Neural network with convolutional auto-encoder and pairs of standard-dose CT and ultra-low-dose CT image patches were used for image denoising. The performance of the proposed method was measured by using a chest phantom. Standard-dose and ultra-low-dose CT images of the chest phantom were acquired. The tube currents for standard-dose and ultra-low-dose CT were 300 and 10 mA, respectively. Ultra-low-dose CT images were denoised with our proposed method using neural network, large-scale nonlocal mean, and block-matching and 3D filtering. Five radiologists and three technologists assessed the denoised ultra-low-dose CT images visually and recorded their subjective impressions of streak artifacts, noise other than streak artifacts, visualization of pulmonary vessels, and overall image quality. For the streak artifacts, noise other than streak artifacts, and visualization of pulmonary vessels, the results of our proposed method were statistically better than those of block-matching and 3D filtering (p-values < 0.05). On the other hand, the difference in the overall image quality between our proposed method and block-matching and 3D filtering was not statistically significant (p-value = 0.07272). The p-values obtained between our proposed method and large-scale nonlocal mean were all less than 0.05. Neural network with convolutional auto-encoder could be trained using pairs of standard-dose and ultra-low-dose CT image patches. According to the visual assessment by radiologists and technologists, the performance of our proposed method was superior to that of large-scale nonlocal mean and block-matching and 3D filtering.

MBR-SIFT: A mirror reflected invariant feature descriptor using a binary representation for image matching.

PubMed

Su, Mingzhe; Ma, Yan; Zhang, Xiangfen; Wang, Yan; Zhang, Yuping

2017-01-01

The traditional scale invariant feature transform (SIFT) method can extract distinctive features for image matching. However, it is extremely time-consuming in SIFT matching because of the use of the Euclidean distance measure. Recently, many binary SIFT (BSIFT) methods have been developed to improve matching efficiency; however, none of them is invariant to mirror reflection. To address these problems, in this paper, we present a horizontal or vertical mirror reflection invariant binary descriptor named MBR-SIFT, in addition to a novel image matching approach. First, 16 cells in the local region around the SIFT keypoint are reorganized, and then the 128-dimensional vector of the SIFT descriptor is transformed into a reconstructed vector according to eight directions. Finally, the MBR-SIFT descriptor is obtained after binarization and reverse coding. To improve the matching speed and accuracy, a fast matching algorithm that includes a coarse-to-fine two-step matching strategy in addition to two similarity measures for the MBR-SIFT descriptor are proposed. Experimental results on the UKBench dataset show that the proposed method not only solves the problem of mirror reflection, but also ensures desirable matching accuracy and speed.

MBR-SIFT: A mirror reflected invariant feature descriptor using a binary representation for image matching

PubMed Central

Su, Mingzhe; Ma, Yan; Zhang, Xiangfen; Wang, Yan; Zhang, Yuping

2017-01-01

The traditional scale invariant feature transform (SIFT) method can extract distinctive features for image matching. However, it is extremely time-consuming in SIFT matching because of the use of the Euclidean distance measure. Recently, many binary SIFT (BSIFT) methods have been developed to improve matching efficiency; however, none of them is invariant to mirror reflection. To address these problems, in this paper, we present a horizontal or vertical mirror reflection invariant binary descriptor named MBR-SIFT, in addition to a novel image matching approach. First, 16 cells in the local region around the SIFT keypoint are reorganized, and then the 128-dimensional vector of the SIFT descriptor is transformed into a reconstructed vector according to eight directions. Finally, the MBR-SIFT descriptor is obtained after binarization and reverse coding. To improve the matching speed and accuracy, a fast matching algorithm that includes a coarse-to-fine two-step matching strategy in addition to two similarity measures for the MBR-SIFT descriptor are proposed. Experimental results on the UKBench dataset show that the proposed method not only solves the problem of mirror reflection, but also ensures desirable matching accuracy and speed. PMID:28542537

Topological visual mapping in robotics.

PubMed

Romero, Anna; Cazorla, Miguel

2012-08-01

A key problem in robotics is the construction of a map from its environment. This map could be used in different tasks, like localization, recognition, obstacle avoidance, etc. Besides, the simultaneous location and mapping (SLAM) problem has had a lot of interest in the robotics community. This paper presents a new method for visual mapping, using topological instead of metric information. For that purpose, we propose prior image segmentation into regions in order to group the extracted invariant features in a graph so that each graph defines a single region of the image. Although others methods have been proposed for visual SLAM, our method is complete, in the sense that it makes all the process: it presents a new method for image matching; it defines a way to build the topological map; and it also defines a matching criterion for loop-closing. The matching process will take into account visual features and their structure using the graph transformation matching (GTM) algorithm, which allows us to process the matching and to remove out the outliers. Then, using this image comparison method, we propose an algorithm for constructing topological maps. During the experimentation phase, we will test the robustness of the method and its ability constructing topological maps. We have also introduced new hysteresis behavior in order to solve some problems found building the graph.

An efficient photogrammetric stereo matching method for high-resolution images

NASA Astrophysics Data System (ADS)

Li, Yingsong; Zheng, Shunyi; Wang, Xiaonan; Ma, Hao

2016-12-01

Stereo matching of high-resolution images is a great challenge in photogrammetry. The main difficulty is the enormous processing workload that involves substantial computing time and memory consumption. In recent years, the semi-global matching (SGM) method has been a promising approach for solving stereo problems in different data sets. However, the time complexity and memory demand of SGM are proportional to the scale of the images involved, which leads to very high consumption when dealing with large images. To solve it, this paper presents an efficient hierarchical matching strategy based on the SGM algorithm using single instruction multiple data instructions and structured parallelism in the central processing unit. The proposed method can significantly reduce the computational time and memory required for large scale stereo matching. The three-dimensional (3D) surface is reconstructed by triangulating and fusing redundant reconstruction information from multi-view matching results. Finally, three high-resolution aerial date sets are used to evaluate our improvement. Furthermore, precise airborne laser scanner data of one data set is used to measure the accuracy of our reconstruction. Experimental results demonstrate that our method remarkably outperforms in terms of time and memory savings while maintaining the density and precision of the 3D cloud points derived.

Image matching as a data source for forest inventory - Comparison of Semi-Global Matching and Next-Generation Automatic Terrain Extraction algorithms in a typical managed boreal forest environment

NASA Astrophysics Data System (ADS)

Kukkonen, M.; Maltamo, M.; Packalen, P.

2017-08-01

Image matching is emerging as a compelling alternative to airborne laser scanning (ALS) as a data source for forest inventory and management. There is currently an open discussion in the forest inventory community about whether, and to what extent, the new method can be applied to practical inventory campaigns. This paper aims to contribute to this discussion by comparing two different image matching algorithms (Semi-Global Matching [SGM] and Next-Generation Automatic Terrain Extraction [NGATE]) and ALS in a typical managed boreal forest environment in southern Finland. Spectral features from unrectified aerial images were included in the modeling and the potential of image matching in areas without a high resolution digital terrain model (DTM) was also explored. Plot level predictions for total volume, stem number, basal area, height of basal area median tree and diameter of basal area median tree were modeled using an area-based approach. Plot level dominant tree species were predicted using a random forest algorithm, also using an area-based approach. The statistical difference between the error rates from different datasets was evaluated using a bootstrap method. Results showed that ALS outperformed image matching with every forest attribute, even when a high resolution DTM was used for height normalization and spectral information from images was included. Dominant tree species classification with image matching achieved accuracy levels similar to ALS regardless of the resolution of the DTM when spectral metrics were used. Neither of the image matching algorithms consistently outperformed the other, but there were noticeably different error rates depending on the parameter configuration, spectral band, resolution of DTM, or response variable. This study showed that image matching provides reasonable point cloud data for forest inventory purposes, especially when a high resolution DTM is available and information from the understory is redundant.

X-ray Moiré deflectometry using synthetic reference images

DOE PAGES

Stutman, Dan; Valdivia, Maria Pia; Finkenthal, Michael

2015-06-25

Moiré fringe deflectometry with grating interferometers is a technique that enables refraction-based x-ray imaging using a single exposure of an object. To obtain the refraction image, the method requires a reference fringe pattern (without the object). Our study shows that, in order to avoid artifacts, the reference pattern must be exactly matched in phase with the object fringe pattern. In experiments, however, it is difficult to produce a perfectly matched reference pattern due to unavoidable interferometer drifts. We present a simple method to obtain matched reference patterns using a phase-scan procedure to generate synthetic Moiré images. As a result, themore » method will enable deflectometric diagnostics of transient phenomena such as laser-produced plasmas and could improve the sensitivity and accuracy of medical phase-contrast imaging.« less

Color Image Classification Using Block Matching and Learning

NASA Astrophysics Data System (ADS)

Kondo, Kazuki; Hotta, Seiji

In this paper, we propose block matching and learning for color image classification. In our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of keypoints.

Rapid multi-modality preregistration based on SIFT descriptor.

PubMed

Chen, Jian; Tian, Jie

2006-01-01

This paper describes the scale invariant feature transform (SIFT) method for rapid preregistration of medical image. This technique originates from Lowe's method wherein preregistration is achieved by matching the corresponding keypoints between two images. The computational complexity has been reduced when we applied SIFT preregistration method before refined registration due to its O(n) exponential calculations. The features of SIFT are highly distinctive and invariant to image scaling and rotation, and partially invariant to change in illumination and contrast, it is robust and repeatable for cursorily matching two images. We also altered the descriptor so our method can deal with multimodality preregistration.

Seamless image stitching by homography refinement and structure deformation using optimal seam pair detection

NASA Astrophysics Data System (ADS)

Lee, Daeho; Lee, Seohyung

2017-11-01

We propose an image stitching method that can remove ghost effects and realign the structure misalignments that occur in common image stitching methods. To reduce the artifacts caused by different parallaxes, an optimal seam pair is selected by comparing the cross correlations from multiple seams detected by variable cost weights. Along the optimal seam pair, a histogram of oriented gradients is calculated, and feature points for matching are detected. The homography is refined using the matching points, and the remaining misalignment is eliminated using the propagation of deformation vectors calculated from matching points. In multiband blending, the overlapping regions are determined from a distance between the matching points to remove overlapping artifacts. The experimental results show that the proposed method more robustly eliminates misalignments and overlapping artifacts than the existing method that uses single seam detection and gradient features.

Content based image retrieval for matching images of improvised explosive devices in which snake initialization is viewed as an inverse problem

NASA Astrophysics Data System (ADS)

Acton, Scott T.; Gilliam, Andrew D.; Li, Bing; Rossi, Adam

2008-02-01

Improvised explosive devices (IEDs) are common and lethal instruments of terrorism, and linking a terrorist entity to a specific device remains a difficult task. In the effort to identify persons associated with a given IED, we have implemented a specialized content based image retrieval system to search and classify IED imagery. The system makes two contributions to the art. First, we introduce a shape-based matching technique exploiting shape, color, and texture (wavelet) information, based on novel vector field convolution active contours and a novel active contour initialization method which treats coarse segmentation as an inverse problem. Second, we introduce a unique graph theoretic approach to match annotated printed circuit board images for which no schematic or connectivity information is available. The shape-based image retrieval method, in conjunction with the graph theoretic tool, provides an efficacious system for matching IED images. For circuit imagery, the basic retrieval mechanism has a precision of 82.1% and the graph based method has a precision of 98.1%. As of the fall of 2007, the working system has processed over 400,000 case images.

a Band Selection Method for High Precision Registration of Hyperspectral Image

NASA Astrophysics Data System (ADS)

Yang, H.; Li, X.

2018-04-01

During the registration of hyperspectral images and high spatial resolution images, too much bands in a hyperspectral image make it difficult to select bands with good registration performance. Terrible bands are possible to reduce matching speed and accuracy. To solve this problem, an algorithm based on Cram'er-Rao lower bound theory is proposed to select good matching bands in this paper. The algorithm applies the Cram'er-Rao lower bound theory to the study of registration accuracy, and selects good matching bands by CRLB parameters. Experiments show that the algorithm in this paper can choose good matching bands and provide better data for the registration of hyperspectral image and high spatial resolution image.

Coupled binary embedding for large-scale image retrieval.

PubMed

Zheng, Liang; Wang, Shengjin; Tian, Qi

2014-08-01

Visual matching is a crucial step in image retrieval based on the bag-of-words (BoW) model. In the baseline method, two keypoints are considered as a matching pair if their SIFT descriptors are quantized to the same visual word. However, the SIFT visual word has two limitations. First, it loses most of its discriminative power during quantization. Second, SIFT only describes the local texture feature. Both drawbacks impair the discriminative power of the BoW model and lead to false positive matches. To tackle this problem, this paper proposes to embed multiple binary features at indexing level. To model correlation between features, a multi-IDF scheme is introduced, through which different binary features are coupled into the inverted file. We show that matching verification methods based on binary features, such as Hamming embedding, can be effectively incorporated in our framework. As an extension, we explore the fusion of binary color feature into image retrieval. The joint integration of the SIFT visual word and binary features greatly enhances the precision of visual matching, reducing the impact of false positive matches. Our method is evaluated through extensive experiments on four benchmark datasets (Ukbench, Holidays, DupImage, and MIR Flickr 1M). We show that our method significantly improves the baseline approach. In addition, large-scale experiments indicate that the proposed method requires acceptable memory usage and query time compared with other approaches. Further, when global color feature is integrated, our method yields competitive performance with the state-of-the-arts.

Image quality and dose differences caused by vendor-specific image processing of neonatal radiographs.

PubMed

Sensakovic, William F; O'Dell, M Cody; Letter, Haley; Kohler, Nathan; Rop, Baiywo; Cook, Jane; Logsdon, Gregory; Varich, Laura

2016-10-01

Image processing plays an important role in optimizing image quality and radiation dose in projection radiography. Unfortunately commercial algorithms are black boxes that are often left at or near vendor default settings rather than being optimized. We hypothesize that different commercial image-processing systems, when left at or near default settings, create significant differences in image quality. We further hypothesize that image-quality differences can be exploited to produce images of equivalent quality but lower radiation dose. We used a portable radiography system to acquire images on a neonatal chest phantom and recorded the entrance surface air kerma (ESAK). We applied two image-processing systems (Optima XR220amx, by GE Healthcare, Waukesha, WI; and MUSICA(2) by Agfa HealthCare, Mortsel, Belgium) to the images. Seven observers (attending pediatric radiologists and radiology residents) independently assessed image quality using two methods: rating and matching. Image-quality ratings were independently assessed by each observer on a 10-point scale. Matching consisted of each observer matching GE-processed images and Agfa-processed images with equivalent image quality. A total of 210 rating tasks and 42 matching tasks were performed and effective dose was estimated. Median Agfa-processed image-quality ratings were higher than GE-processed ratings. Non-diagnostic ratings were seen over a wider range of doses for GE-processed images than for Agfa-processed images. During matching tasks, observers matched image quality between GE-processed images and Agfa-processed images acquired at a lower effective dose (11 ± 9 μSv; P < 0.0001). Image-processing methods significantly impact perceived image quality. These image-quality differences can be exploited to alter protocols and produce images of equivalent image quality but lower doses. Those purchasing projection radiography systems or third-party image-processing software should be aware that image processing can significantly impact image quality when settings are left near default values.

A blur-invariant local feature for motion blurred image matching

NASA Astrophysics Data System (ADS)

Tong, Qiang; Aoki, Terumasa

2017-07-01

Image matching between a blurred (caused by camera motion, out of focus, etc.) image and a non-blurred image is a critical task for many image/video applications. However, most of the existing local feature schemes fail to achieve this work. This paper presents a blur-invariant descriptor and a novel local feature scheme including the descriptor and the interest point detector based on moment symmetry - the authors' previous work. The descriptor is based on a new concept - center peak moment-like element (CPME) which is robust to blur and boundary effect. Then by constructing CPMEs, the descriptor is also distinctive and suitable for image matching. Experimental results show our scheme outperforms state of the art methods for blurred image matching

Motion tracking in MR-guided liver therapy by using navigator echoes and projection profile matching.

PubMed

Tokuda, Junichi; Morikawa, Shigehiro; Dohi, Takeyoshi; Hata, Nobuhiko

2004-01-01

Image registration in magnetic resonance (MR) image-guided liver therapy enhances surgical guidance by fusing preoperative multimodality images with intraoperative images, or by fusing intramodality images to correlate serial intraoperative images to monitor the effect of therapy. The objective of this paper is to describe the application of navigator echo and projection profile matching to fast two-dimensional image registration for MR-guided liver therapy. We obtain navigator echoes along the read-out and phase-encoding directions by using modified gradient echo imaging. This registration is made possible by masking out the liver profile from the image and performing profile matching with cross-correlation or mutual information as similarity measures. The set of experiments include a phantom study with a 2.0-T experimental MR scanner, and a volunteer and a clinical study with a 0.5-T open-configuration MR scanner, and these evaluate the accuracy and effectiveness of this method for liver therapy. Both the phantom and volunteer study indicate that this method can perform registration in 34 ms with root-mean-square error of 1.6 mm when the given misalignment of a liver is 30 mm. The clinical studies demonstrate that the method can track liver motion of up to approximately 40 mm. Matching profiles with cross-correlation information perform better than with mutual information in terms of robustness and speed. The proposed image registration method has potential clinical impact on and advantages for MR-guided liver therapy.

n-SIFT: n-dimensional scale invariant feature transform.

PubMed

Cheung, Warren; Hamarneh, Ghassan

2009-09-01

We propose the n-dimensional scale invariant feature transform (n-SIFT) method for extracting and matching salient features from scalar images of arbitrary dimensionality, and compare this method's performance to other related features. The proposed features extend the concepts used for 2-D scalar images in the computer vision SIFT technique for extracting and matching distinctive scale invariant features. We apply the features to images of arbitrary dimensionality through the use of hyperspherical coordinates for gradients and multidimensional histograms to create the feature vectors. We analyze the performance of a fully automated multimodal medical image matching technique based on these features, and successfully apply the technique to determine accurate feature point correspondence between pairs of 3-D MRI images and dynamic 3D + time CT data.

Qualitative evaluations and comparisons of six night-vision colorization methods

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Reese, Kristopher; Blasch, Erik; McManamon, Paul

2013-05-01

Current multispectral night vision (NV) colorization techniques can manipulate images to produce colorized images that closely resemble natural scenes. The colorized NV images can enhance human perception by improving observer object classification and reaction times especially for low light conditions. This paper focuses on the qualitative (subjective) evaluations and comparisons of six NV colorization methods. The multispectral images include visible (Red-Green- Blue), near infrared (NIR), and long wave infrared (LWIR) images. The six colorization methods are channel-based color fusion (CBCF), statistic matching (SM), histogram matching (HM), joint-histogram matching (JHM), statistic matching then joint-histogram matching (SM-JHM), and the lookup table (LUT). Four categries of quality measurements are used for the qualitative evaluations, which are contrast, detail, colorfulness, and overall quality. The score of each measurement is rated from 1 to 3 scale to represent low, average, and high quality, respectively. Specifically, high contrast (of rated score 3) means an adequate level of brightness and contrast. The high detail represents high clarity of detailed contents while maintaining low artifacts. The high colorfulness preserves more natural colors (i.e., closely resembles the daylight image). Overall quality is determined from the NV image compared to the reference image. Nine sets of multispectral NV images were used in our experiments. For each set, the six colorized NV images (produced from NIR and LWIR images) are concurrently presented to users along with the reference color (RGB) image (taken at daytime). A total of 67 subjects passed a screening test ("Ishihara Color Blindness Test") and were asked to evaluate the 9-set colorized images. The experimental results showed the quality order of colorization methods from the best to the worst: CBCF < SM < SM-JHM < LUT < JHM < HM. It is anticipated that this work will provide a benchmark for NV colorization and for quantitative evaluation using an objective metric such as objective evaluation index (OEI).

Relative Pose Estimation Using Image Feature Triplets

NASA Astrophysics Data System (ADS)

Chuang, T. Y.; Rottensteiner, F.; Heipke, C.

2015-03-01

A fully automated reconstruction of the trajectory of image sequences using point correspondences is turning into a routine practice. However, there are cases in which point features are hardly detectable, cannot be localized in a stable distribution, and consequently lead to an insufficient pose estimation. This paper presents a triplet-wise scheme for calibrated relative pose estimation from image point and line triplets, and investigates the effectiveness of the feature integration upon the relative pose estimation. To this end, we employ an existing point matching technique and propose a method for line triplet matching in which the relative poses are resolved during the matching procedure. The line matching method aims at establishing hypotheses about potential minimal line matches that can be used for determining the parameters of relative orientation (pose estimation) of two images with respect to the reference one; then, quantifying the agreement using the estimated orientation parameters. Rather than randomly choosing the line candidates in the matching process, we generate an associated lookup table to guide the selection of potential line matches. In addition, we integrate the homologous point and line triplets into a common adjustment procedure. In order to be able to also work with image sequences the adjustment is formulated in an incremental manner. The proposed scheme is evaluated with both synthetic and real datasets, demonstrating its satisfactory performance and revealing the effectiveness of image feature integration.

Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

NASA Astrophysics Data System (ADS)

Senthil Kumar, A.; Keerthi, V.; Manjunath, A. S.; Werff, Harald van der; Meer, Freek van der

2010-08-01

Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously.

Template match using local feature with view invariance

NASA Astrophysics Data System (ADS)

Lu, Cen; Zhou, Gang

2013-10-01

Matching the template image in the target image is the fundamental task in the field of computer vision. Aiming at the deficiency in the traditional image matching methods and inaccurate matching in scene image with rotation, illumination and view changing, a novel matching algorithm using local features are proposed in this paper. The local histograms of the edge pixels (LHoE) are extracted as the invariable feature to resist view and brightness changing. The merits of the LHoE is that the edge points have been little affected with view changing, and the LHoE can resist not only illumination variance but also the polution of noise. For the process of matching are excuded only on the edge points, the computation burden are highly reduced. Additionally, our approach is conceptually simple, easy to implement and do not need the training phase. The view changing can be considered as the combination of rotation, illumination and shear transformation. Experimental results on simulated and real data demonstrated that the proposed approach is superior to NCC(Normalized cross-correlation) and Histogram-based methods with view changing.

Inspection of aeronautical mechanical parts with a pan-tilt-zoom camera: an approach guided by the computer-aided design model

NASA Astrophysics Data System (ADS)

Viana, Ilisio; Orteu, Jean-José; Cornille, Nicolas; Bugarin, Florian

2015-11-01

We focus on quality control of mechanical parts in aeronautical context using a single pan-tilt-zoom (PTZ) camera and a computer-aided design (CAD) model of the mechanical part. We use the CAD model to create a theoretical image of the element to be checked, which is further matched with the sensed image of the element to be inspected, using a graph theory-based approach. The matching is carried out in two stages. First, the two images are used to create two attributed graphs representing the primitives (ellipses and line segments) in the images. In the second stage, the graphs are matched using a similarity function built from the primitive parameters. The similarity scores of the matching are injected in the edges of a bipartite graph. A best-match-search procedure in the bipartite graph guarantees the uniqueness of the match solution. The method achieves promising performance in tests with synthetic data including missing elements, displaced elements, size changes, and combinations of these cases. The results open good prospects for using the method with realistic data.

Least Median of Squares Filtering of Locally Optimal Point Matches for Compressible Flow Image Registration

PubMed Central

Castillo, Edward; Castillo, Richard; White, Benjamin; Rojo, Javier; Guerrero, Thomas

2012-01-01

Compressible flow based image registration operates under the assumption that the mass of the imaged material is conserved from one image to the next. Depending on how the mass conservation assumption is modeled, the performance of existing compressible flow methods is limited by factors such as image quality, noise, large magnitude voxel displacements, and computational requirements. The Least Median of Squares Filtered Compressible Flow (LFC) method introduced here is based on a localized, nonlinear least squares, compressible flow model that describes the displacement of a single voxel that lends itself to a simple grid search (block matching) optimization strategy. Spatially inaccurate grid search point matches, corresponding to erroneous local minimizers of the nonlinear compressible flow model, are removed by a novel filtering approach based on least median of squares fitting and the forward search outlier detection method. The spatial accuracy of the method is measured using ten thoracic CT image sets and large samples of expert determined landmarks (available at www.dir-lab.com). The LFC method produces an average error within the intra-observer error on eight of the ten cases, indicating that the method is capable of achieving a high spatial accuracy for thoracic CT registration. PMID:22797602

Edge Detection Method Based on Neural Networks for COMS MI Images

NASA Astrophysics Data System (ADS)

Lee, Jin-Ho; Park, Eun-Bin; Woo, Sun-Hee

2016-12-01

Communication, Ocean And Meteorological Satellite (COMS) Meteorological Imager (MI) images are processed for radiometric and geometric correction from raw image data. When intermediate image data are matched and compared with reference landmark images in the geometrical correction process, various techniques for edge detection can be applied. It is essential to have a precise and correct edged image in this process, since its matching with the reference is directly related to the accuracy of the ground station output images. An edge detection method based on neural networks is applied for the ground processing of MI images for obtaining sharp edges in the correct positions. The simulation results are analyzed and characterized by comparing them with the results of conventional methods, such as Sobel and Canny filters.

Tie Points Extraction for SAR Images Based on Differential Constraints

NASA Astrophysics Data System (ADS)

Xiong, X.; Jin, G.; Xu, Q.; Zhang, H.

2018-04-01

Automatically extracting tie points (TPs) on large-size synthetic aperture radar (SAR) images is still challenging because the efficiency and correct ratio of the image matching need to be improved. This paper proposes an automatic TPs extraction method based on differential constraints for large-size SAR images obtained from approximately parallel tracks, between which the relative geometric distortions are small in azimuth direction and large in range direction. Image pyramids are built firstly, and then corresponding layers of pyramids are matched from the top to the bottom. In the process, the similarity is measured by the normalized cross correlation (NCC) algorithm, which is calculated from a rectangular window with the long side parallel to the azimuth direction. False matches are removed by the differential constrained random sample consensus (DC-RANSAC) algorithm, which appends strong constraints in azimuth direction and weak constraints in range direction. Matching points in the lower pyramid images are predicted with the local bilinear transformation model in range direction. Experiments performed on ENVISAT ASAR and Chinese airborne SAR images validated the efficiency, correct ratio and accuracy of the proposed method.

An Integrated Ransac and Graph Based Mismatch Elimination Approach for Wide-Baseline Image Matching

NASA Astrophysics Data System (ADS)

Hasheminasab, M.; Ebadi, H.; Sedaghat, A.

2015-12-01

In this paper we propose an integrated approach in order to increase the precision of feature point matching. Many different algorithms have been developed as to optimizing the short-baseline image matching while because of illumination differences and viewpoints changes, wide-baseline image matching is so difficult to handle. Fortunately, the recent developments in the automatic extraction of local invariant features make wide-baseline image matching possible. The matching algorithms which are based on local feature similarity principle, using feature descriptor as to establish correspondence between feature point sets. To date, the most remarkable descriptor is the scale-invariant feature transform (SIFT) descriptor , which is invariant to image rotation and scale, and it remains robust across a substantial range of affine distortion, presence of noise, and changes in illumination. The epipolar constraint based on RANSAC (random sample consensus) method is a conventional model for mismatch elimination, particularly in computer vision. Because only the distance from the epipolar line is considered, there are a few false matches in the selected matching results based on epipolar geometry and RANSAC. Aguilariu et al. proposed Graph Transformation Matching (GTM) algorithm to remove outliers which has some difficulties when the mismatched points surrounded by the same local neighbor structure. In this study to overcome these limitations, which mentioned above, a new three step matching scheme is presented where the SIFT algorithm is used to obtain initial corresponding point sets. In the second step, in order to reduce the outliers, RANSAC algorithm is applied. Finally, to remove the remained mismatches, based on the adjacent K-NN graph, the GTM is implemented. Four different close range image datasets with changes in viewpoint are utilized to evaluate the performance of the proposed method and the experimental results indicate its robustness and capability.

Improved colour matching technique for fused nighttime imagery with daytime colours

NASA Astrophysics Data System (ADS)

Hogervorst, Maarten A.; Toet, Alexander

2016-10-01

Previously, we presented a method for applying daytime colours to fused nighttime (e.g., intensified and LWIR) imagery (Toet and Hogervorst, Opt.Eng. 51(1), 2012). Our colour mapping not only imparts a natural daylight appearance to multiband nighttime images but also enhances the contrast and visibility of otherwise obscured details. As a result, this colourizing method leads to increased ease of interpretation, better discrimination and identification of materials, faster reaction times and ultimately improved situational awareness (Toet e.a., Opt.Eng.53(4), 2014). A crucial step in this colouring process is the choice of a suitable colour mapping scheme. When daytime colour images and multiband sensor images of the same scene are available the colour mapping can be derived from matching image samples (i.e., by relating colour values to sensor signal intensities). When no exact matching reference images are available the colour transformation can be derived from the first-order statistical properties of the reference image and the multiband sensor image (Toet, Info. Fus. 4(3), 2003). In the current study we investigated new colour fusion schemes that combine the advantages of the both methods, using the correspondence between multiband sensor values and daytime colours (1st method) in a smooth transformation (2nd method). We designed and evaluated three new fusion schemes that focus on: i) a closer match with the daytime luminances, ii) improved saliency of hot targets and iii) improved discriminability of materials

An accurate algorithm to match imperfectly matched images for lung tumor detection without markers

PubMed Central

Rozario, Timothy; Bereg, Sergey; Yan, Yulong; Chiu, Tsuicheng; Liu, Honghuan; Kearney, Vasant; Jiang, Lan

2015-01-01

In order to locate lung tumors on kV projection images without internal markers, digitally reconstructed radiographs (DRRs) are created and compared with projection images. However, lung tumors always move due to respiration and their locations change on projection images while they are static on DRRs. In addition, global image intensity discrepancies exist between DRRs and projections due to their different image orientations, scattering, and noises. This adversely affects comparison accuracy. A simple but efficient comparison algorithm is reported to match imperfectly matched projection images and DRRs. The kV projection images were matched with different DRRs in two steps. Preprocessing was performed in advance to generate two sets of DRRs. The tumors were removed from the planning 3D CT for a single phase of planning 4D CT images using planning contours of tumors. DRRs of background and DRRs of tumors were generated separately for every projection angle. The first step was to match projection images with DRRs of background signals. This method divided global images into a matrix of small tiles and similarities were evaluated by calculating normalized cross‐correlation (NCC) between corresponding tiles on projections and DRRs. The tile configuration (tile locations) was automatically optimized to keep the tumor within a single projection tile that had a bad matching with the corresponding DRR tile. A pixel‐based linear transformation was determined by linear interpolations of tile transformation results obtained during tile matching. The background DRRs were transformed to the projection image level and subtracted from it. The resulting subtracted image now contained only the tumor. The second step was to register DRRs of tumors to the subtracted image to locate the tumor. This method was successfully applied to kV fluoro images (about 1000 images) acquired on a Vero (BrainLAB) for dynamic tumor tracking on phantom studies. Radiation opaque markers were implanted and used as ground truth for tumor positions. Although other organs and bony structures introduced strong signals superimposed on tumors at some angles, this method accurately located tumors on every projection over 12 gantry angles. The maximum error was less than 2.2 mm, while the total average error was less than 0.9 mm. This algorithm was capable of detecting tumors without markers, despite strong background signals. PACS numbers: 87.57.cj, 87.57.cp87.57.nj, 87.57.np, 87.57.Q‐, 87.59.bf, 87.63.lm

Human Expertise Helps Computer Classify Images

NASA Technical Reports Server (NTRS)

Rorvig, Mark E.

1991-01-01

Two-domain method of computational classification of images requires less computation than other methods for computational recognition, matching, or classification of images or patterns. Does not require explicit computational matching of features, and incorporates human expertise without requiring translation of mental processes of classification into language comprehensible to computer. Conceived to "train" computer to analyze photomicrographs of microscope-slide specimens of leucocytes from human peripheral blood to distinguish between specimens from healthy and specimens from traumatized patients.

Improved LSB matching steganography with histogram characters reserved

NASA Astrophysics Data System (ADS)

Chen, Zhihong; Liu, Wenyao

2008-03-01

This letter bases on the researches of LSB (least significant bit, i.e. the last bit of a binary pixel value) matching steganographic method and the steganalytic method which aims at histograms of cover images, and proposes a modification to LSB matching. In the LSB matching, if the LSB of the next cover pixel matches the next bit of secret data, do nothing; otherwise, choose to add or subtract one from the cover pixel value at random. In our improved method, a steganographic information table is defined and records the changes which embedded secrete bits introduce in. Through the table, the next LSB which has the same pixel value will be judged to add or subtract one dynamically in order to ensure the histogram's change of cover image is minimized. Therefore, the modified method allows embedding the same payload as the LSB matching but with improved steganographic security and less vulnerability to attacks compared with LSB matching. The experimental results of the new method show that the histograms maintain their attributes, such as peak values and alternative trends, in an acceptable degree and have better performance than LSB matching in the respects of histogram distortion and resistance against existing steganalysis.

Neural-net-based image matching

NASA Astrophysics Data System (ADS)

Jerebko, Anna K.; Barabanov, Nikita E.; Luciv, Vadim R.; Allinson, Nigel M.

2000-04-01

The paper describes a neural-based method for matching spatially distorted image sets. The matching of partially overlapping images is important in many applications-- integrating information from images formed from different spectral ranges, detecting changes in a scene and identifying objects of differing orientations and sizes. Our approach consists of extracting contour features from both images, describing the contour curves as sets of line segments, comparing these sets, determining the corresponding curves and their common reference points, calculating the image-to-image co-ordinate transformation parameters on the basis of the most successful variant of the derived curve relationships. The main steps are performed by custom neural networks. The algorithms describe in this paper have been successfully tested on a large set of images of the same terrain taken in different spectral ranges, at different seasons and rotated by various angles. In general, this experimental verification indicates that the proposed method for image fusion allows the robust detection of similar objects in noisy, distorted scenes where traditional approaches often fail.

a Probability-Based Statistical Method to Extract Water Body of TM Images with Missing Information

NASA Astrophysics Data System (ADS)

Lian, Shizhong; Chen, Jiangping; Luo, Minghai

2016-06-01

Water information cannot be accurately extracted using TM images because true information is lost in some images because of blocking clouds and missing data stripes, thereby water information cannot be accurately extracted. Water is continuously distributed in natural conditions; thus, this paper proposed a new method of water body extraction based on probability statistics to improve the accuracy of water information extraction of TM images with missing information. Different disturbing information of clouds and missing data stripes are simulated. Water information is extracted using global histogram matching, local histogram matching, and the probability-based statistical method in the simulated images. Experiments show that smaller Areal Error and higher Boundary Recall can be obtained using this method compared with the conventional methods.

Automatic Matching of Large Scale Images and Terrestrial LIDAR Based on App Synergy of Mobile Phone

NASA Astrophysics Data System (ADS)

Xia, G.; Hu, C.

2018-04-01

The digitalization of Cultural Heritage based on ground laser scanning technology has been widely applied. High-precision scanning and high-resolution photography of cultural relics are the main methods of data acquisition. The reconstruction with the complete point cloud and high-resolution image requires the matching of image and point cloud, the acquisition of the homonym feature points, the data registration, etc. However, the one-to-one correspondence between image and corresponding point cloud depends on inefficient manual search. The effective classify and management of a large number of image and the matching of large image and corresponding point cloud will be the focus of the research. In this paper, we propose automatic matching of large scale images and terrestrial LiDAR based on APP synergy of mobile phone. Firstly, we develop an APP based on Android, take pictures and record related information of classification. Secondly, all the images are automatically grouped with the recorded information. Thirdly, the matching algorithm is used to match the global and local image. According to the one-to-one correspondence between the global image and the point cloud reflection intensity image, the automatic matching of the image and its corresponding laser radar point cloud is realized. Finally, the mapping relationship between global image, local image and intensity image is established according to homonym feature point. So we can establish the data structure of the global image, the local image in the global image, the local image corresponding point cloud, and carry on the visualization management and query of image.

SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wahi-Anwar, M; Lo, P; Kim, H

Purpose: The use of Quantitative Imaging (QI) methods in Clinical Trials requires both verification of adherence to a specified protocol and an assessment of scanner performance under that protocol, which are currently accomplished manually. This work introduces automated phantom identification and image QA measure extraction towards a fully-automated CT phantom QA system to perform these functions and facilitate the use of Quantitative Imaging methods in clinical trials. Methods: This study used a retrospective cohort of CT phantom scans from existing clinical trial protocols - totaling 84 phantoms, across 3 phantom types using various scanners and protocols. The QA system identifiesmore » the input phantom scan through an ensemble of threshold-based classifiers. Each classifier - corresponding to a phantom type - contains a template slice, which is compared to the input scan on a slice-by-slice basis, resulting in slice-wise similarity metric values for each slice compared. Pre-trained thresholds (established from a training set of phantom images matching the template type) are used to filter the similarity distribution, and the slice with the most optimal local mean similarity, with local neighboring slices meeting the threshold requirement, is chosen as the classifier’s matched slice (if it existed). The classifier with the matched slice possessing the most optimal local mean similarity is then chosen as the ensemble’s best matching slice. If the best matching slice exists, image QA algorithm and ROIs corresponding to the matching classifier extracted the image QA measures. Results: Automated phantom identification performed with 84.5% accuracy and 88.8% sensitivity on 84 phantoms. Automated image quality measurements (following standard protocol) on identified water phantoms (n=35) matched user QA decisions with 100% accuracy. Conclusion: We provide a fullyautomated CT phantom QA system consistent with manual QA performance. Further work will include parallel component to automatically verify image acquisition parameters and automated adherence to specifications. Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant support from: U01 CA181156.« less

Fixed-pattern noise correction method based on improved moment matching for a TDI CMOS image sensor.

PubMed

Xu, Jiangtao; Nie, Huafeng; Nie, Kaiming; Jin, Weimin

2017-09-01

In this paper, an improved moment matching method based on a spatial correlation filter (SCF) and bilateral filter (BF) is proposed to correct the fixed-pattern noise (FPN) of a time-delay-integration CMOS image sensor (TDI-CIS). First, the values of row FPN (RFPN) and column FPN (CFPN) are estimated and added to the original image through SCF and BF, respectively. Then the filtered image will be processed by an improved moment matching method with a moving window. Experimental results based on a 128-stage TDI-CIS show that, after correcting the FPN in the image captured under uniform illumination, the standard deviation of row mean vector (SDRMV) decreases from 5.6761 LSB to 0.1948 LSB, while the standard deviation of the column mean vector (SDCMV) decreases from 15.2005 LSB to 13.1949LSB. In addition, for different images captured by different TDI-CISs, the average decrease of SDRMV and SDCMV is 5.4922/2.0357 LSB, respectively. Comparative experimental results indicate that the proposed method can effectively correct the FPNs of different TDI-CISs while maintaining image details without any auxiliary equipment.

Detection of urban expansion in an urban-rural landscape with multitemporal QuickBird images

PubMed Central

Lu, Dengsheng; Hetrick, Scott; Moran, Emilio; Li, Guiying

2011-01-01

Accurately detecting urban expansion with remote sensing techniques is a challenge due to the complexity of urban landscapes. This paper explored methods for detecting urban expansion with multitemporal QuickBird images in Lucas do Rio Verde, Mato Grosso, Brazil. Different techniques, including image differencing, principal component analysis (PCA), and comparison of classified impervious surface images with the matched filtering method, were used to examine urbanization detection. An impervious surface image classified with the hybrid method was used to modify the urbanization detection results. As a comparison, the original multispectral image and segmentation-based mean-spectral images were used during the detection of urbanization. This research indicates that the comparison of classified impervious surface images with matched filtering method provides the best change detection performance, followed by the image differencing method based on segmentation-based mean spectral images. The PCA is not a good method for urban change detection in this study. Shadows and high spectral variation within the impervious surfaces represent major challenges to the detection of urban expansion when high spatial resolution images are used. PMID:21799706

Accuracy of DSM based on digital aerial image matching. (Polish Title: Dokładność NMPT tworzonego metodą automatycznego dopasowania cyfrowych zdjęć lotniczych)

NASA Astrophysics Data System (ADS)

Kubalska, J. L.; Preuss, R.

2013-12-01

Digital Surface Models (DSM) are used in GIS data bases as single product more often. They are also necessary to create other products such as3D city models, true-ortho and object-oriented classification. This article presents results of DSM generation for classification of vegetation in urban areas. Source data allowed producing DSM with using of image matching method and ALS data. The creation of DSM from digital images, obtained by Ultra Cam-D digital Vexcel camera, was carried out in Match-T by INPHO. This program optimizes the configuration of images matching process, which ensures high accuracy and minimize gap areas. The analysis of the accuracy of this process was made by comparison of DSM generated in Match-T with DSM generated from ALS data. Because of further purpose of generated DSM it was decided to create model in GRID structure with cell size of 1 m. With this parameter differential model from both DSMs was also built that allowed determining the relative accuracy of the compared models. The analysis indicates that the generation of DSM with multi-image matching method is competitive for the same surface model creation from ALS data. Thus, when digital images with high overlap are available, the additional registration of ALS data seems to be unnecessary.

Context-dependent logo matching and recognition.

PubMed

Sahbi, Hichem; Ballan, Lamberto; Serra, Giuseppe; Del Bimbo, Alberto

2013-03-01

We contribute, through this paper, to the design of a novel variational framework able to match and recognize multiple instances of multiple reference logos in image archives. Reference logos and test images are seen as constellations of local features (interest points, regions, etc.) and matched by minimizing an energy function mixing: 1) a fidelity term that measures the quality of feature matching, 2) a neighborhood criterion that captures feature co-occurrence/geometry, and 3) a regularization term that controls the smoothness of the matching solution. We also introduce a detection/recognition procedure and study its theoretical consistency. Finally, we show the validity of our method through extensive experiments on the challenging MICC-Logos dataset. Our method overtakes, by 20%, baseline as well as state-of-the-art matching/recognition procedures.

Technical Note: Method to correlate whole-specimen histopathology of radical prostatectomy with diagnostic MR imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

McGrath, Deirdre M., E-mail: d.mcgrath@sheffield.ac.uk; Lee, Jenny; Foltz, Warren D.

Purpose: Validation of MRI-guided tumor boundary delineation for targeted prostate cancer therapy is achieved via correlation with gold-standard histopathology of radical prostatectomy specimens. Challenges to accurate correlation include matching the pathology sectioning plane with the in vivo imaging slice plane and correction for the deformation that occurs between in vivo imaging and histology. A methodology is presented for matching of the histological sectioning angle and position to the in vivo imaging slices. Methods: Patients (n = 4) with biochemical failure following external beam radiotherapy underwent diagnostic MRI to confirm localized recurrence of prostate cancer, followed by salvage radical prostatectomy. High-resolutionmore » 3-D MRI of the ex vivo specimens was acquired to determine the pathology sectioning angle that best matched the in vivo imaging slice plane, using matching anatomical features and implanted fiducials. A novel sectioning device was developed to guide sectioning at the correct angle, and to assist the insertion of reference dye marks to aid in histopathology reconstruction. Results: The percentage difference in the positioning of the urethra in the ex vivo pathology sections compared to the positioning in in vivo images was reduced from 34% to 7% through slicing at the best match angle. Reference dye marks were generated, which were visible in ex vivo imaging, in the tissue sections before and after processing, and in histology sections. Conclusions: The method achieved an almost fivefold reduction in the slice-matching error and is readily implementable in combination with standard MRI technology. The technique will be employed to generate datasets for correlation of whole-specimen prostate histopathology with in vivo diagnostic MRI using 3-D deformable registration, allowing assessment of the sensitivity and specificity of MRI parameters for prostate cancer. Although developed specifically for prostate, the method is readily adaptable to other types of whole tissue specimen, such as mastectomy or liver resection.« less

Image matching algorithms for breech face marks and firing pins in a database of spent cartridge cases of firearms.

PubMed

Geradts, Z J; Bijhold, J; Hermsen, R; Murtagh, F

2001-06-01

On the market several systems exist for collecting spent ammunition data for forensic investigation. These databases store images of cartridge cases and the marks on them. Image matching is used to create hit lists that show which marks on a cartridge case are most similar to another cartridge case. The research in this paper is focused on the different methods of feature selection and pattern recognition that can be used for optimizing the results of image matching. The images are acquired by side light images for the breech face marks and by ring light for the firing pin impression. For these images a standard way of digitizing the images used. For the side light images and ring light images this means that the user has to position the cartridge case in the same position according to a protocol. The positioning is important for the sidelight, since the image that is obtained of a striation mark depends heavily on the angle of incidence of the light. In practice, it appears that the user positions the cartridge case with +/-10 degrees accuracy. We tested our algorithms using 49 cartridge cases of 19 different firearms, where the examiner determined that they were shot with the same firearm. For testing, these images were mixed with a database consisting of approximately 4900 images that were available from the Drugfire database of different calibers.In cases where the registration and the light conditions among those matching pairs was good, a simple computation of the standard deviation of the subtracted gray levels, delivered the best-matched images. For images that were rotated and shifted, we have implemented a "brute force" way of registration. The images are translated and rotated until the minimum of the standard deviation of the difference is found. This method did not result in all relevant matches in the top position. This is caused by the effect that shadows and highlights are compared in intensity. Since the angle of incidence of the light will give a different intensity profile, this method is not optimal. For this reason a preprocessing of the images was required. It appeared that the third scale of the "à trous" wavelet transform gives the best results in combination with brute force. Matching the contents of the images is less sensitive to the variation of the lighting. The problem with the brute force method is however that the time for calculation for 49 cartridge cases to compare between them, takes over 1 month of computing time on a Pentium II-computer with 333MHz. For this reason a faster approach is implemented: correlation in log polar coordinates. This gave similar results as the brute force calculation, however it was computed in 24h for a complete database with 4900 images.A fast pre-selection method based on signatures is carried out that is based on the Kanade Lucas Tomasi (KLT) equation. The positions of the points computed with this method are compared. In this way, 11 of the 49 images were in the top position in combination with the third scale of the à trous equation. It depends however on the light conditions and the prominence of the marks if correct matches are found in the top ranked position. All images were retrieved in the top 5% of the database. This method takes only a few minutes for the complete database if, and can be optimized for comparison in seconds if the location of points are stored in files. For further improvement, it is useful to have the refinement in which the user selects the areas that are relevant on the cartridge case for their marks. This is necessary if this cartridge case is damaged and other marks that are not from the firearm appear on it.

A new registration method with voxel-matching technique for temporal subtraction images

NASA Astrophysics Data System (ADS)

Itai, Yoshinori; Kim, Hyoungseop; Ishikawa, Seiji; Katsuragawa, Shigehiko; Doi, Kunio

2008-03-01

A temporal subtraction image, which is obtained by subtraction of a previous image from a current one, can be used for enhancing interval changes on medical images by removing most of normal structures. One of the important problems in temporal subtraction is that subtraction images commonly include artifacts created by slight differences in the size, shape, and/or location of anatomical structures. In this paper, we developed a new registration method with voxel-matching technique for substantially removing the subtraction artifacts on the temporal subtraction image obtained from multiple-detector computed tomography (MDCT). With this technique, the voxel value in a warped (or non-warped) previous image is replaced by a voxel value within a kernel, such as a small cube centered at a given location, which would be closest (identical or nearly equal) to the voxel value in the corresponding location in the current image. Our new method was examined on 16 clinical cases with MDCT images. Preliminary results indicated that interval changes on the subtraction images were enhanced considerably, with a substantial reduction of misregistration artifacts. The temporal subtraction images obtained by use of the voxel-matching technique would be very useful for radiologists in the detection of interval changes on MDCT images.

Binocular stereo matching method based on structure tensor

NASA Astrophysics Data System (ADS)

Song, Xiaowei; Yang, Manyi; Fan, Yubo; Yang, Lei

2016-10-01

In a binocular visual system, to recover the three-dimensional information of the object, the most important step is to acquire matching points. Structure tensor is the vector representation of each point in its local neighborhood. Therefore, structure tensor performs well in region detection of local structure, and it is very suitable for detecting specific graphics such as pedestrians, cars and road signs in the image. In this paper, the structure tensor is combined with the luminance information to form the extended structure tensor. The directional derivatives of luminance in x and y directions are calculated, so that the local structure of the image is more prominent. Meanwhile, the Euclidean distance between the eigenvectors of key points is used as the similarity determination metric of key points in the two images. By matching, the coordinates of the matching points in the detected target are precisely acquired. In this paper, experiments were performed on the captured left and right images. After the binocular calibration, image matching was done to acquire the matching points, and then the target depth was calculated according to these matching points. By comparison, it is proved that the structure tensor can accurately acquire the matching points in binocular stereo matching.

Nonrigid synthetic aperture radar and optical image coregistration by combining local rigid transformations using a Kohonen network.

PubMed

Salehpour, Mehdi; Behrad, Alireza

2017-10-01

This study proposes a new algorithm for nonrigid coregistration of synthetic aperture radar (SAR) and optical images. The proposed algorithm employs point features extracted by the binary robust invariant scalable keypoints algorithm and a new method called weighted bidirectional matching for initial correspondence. To refine false matches, we assume that the transformation between SAR and optical images is locally rigid. This property is used to refine false matches by assigning scores to matched pairs and clustering local rigid transformations using a two-layer Kohonen network. Finally, the thin plate spline algorithm and mutual information are used for nonrigid coregistration of SAR and optical images.

Research and implementation of finger-vein recognition algorithm

NASA Astrophysics Data System (ADS)

Pang, Zengyao; Yang, Jie; Chen, Yilei; Liu, Yin

2017-06-01

In finger vein image preprocessing, finger angle correction and ROI extraction are important parts of the system. In this paper, we propose an angle correction algorithm based on the centroid of the vein image, and extract the ROI region according to the bidirectional gray projection method. Inspired by the fact that features in those vein areas have similar appearance as valleys, a novel method was proposed to extract center and width of palm vein based on multi-directional gradients, which is easy-computing, quick and stable. On this basis, an encoding method was designed to determine the gray value distribution of texture image. This algorithm could effectively overcome the edge of the texture extraction error. Finally, the system was equipped with higher robustness and recognition accuracy by utilizing fuzzy threshold determination and global gray value matching algorithm. Experimental results on pairs of matched palm images show that, the proposed method has a EER with 3.21% extracts features at the speed of 27ms per image. It can be concluded that the proposed algorithm has obvious advantages in grain extraction efficiency, matching accuracy and algorithm efficiency.

An intergrated image matching algorithm and its application in the production of lunar map based on Chang'E-2 images

NASA Astrophysics Data System (ADS)

Wang, F.; Ren, X.; Liu, J.; Li, C.

2012-12-01

An accurate topographic map is a requisite for nearly every phase of research on lunar surface, as well as an essential tool for spacecraft mission planning and operating. Automatic image matching is a key component in this process that could ensure both quality and efficiency in the production of digital topographic map for the whole lunar coverage. It also provides the basis for lunar photographic surveying block adjustment. Image matching is relatively easy when encountered with good image texture conditions. However, on lunar images with characteristics such as constantly changing lighting conditions, large rotation angle, few or homogeneous texture and low image contrasts, it becomes a difficult and challenging job. Thus, we require a robust algorithm that is capable of dealing with light effect and image deformation to fulfill this task. In order to obtain a comprehensive review of currently dominated feature point extraction operators and test whether they are suitable for lunar images, we applied several operators, such as Harris, Forstner, Moravec, SIFT, to images from Chang'E-2 spacecraft. We found that SITF (Scale Invariant Feature Transform) is a scale invariant interest point detector that can provide robustness against errors caused by image distortions from scale, orientation or illumination condition changes. Meanwhile, its capability in detecting blob-like interest points satisfies the image characteristics of Chang'E-2. However, the uneven distributed and low accurate matching results cannot meet the practical requirements in lunar photogrammetry. In contrast, some high-precision corner detectors, such as Harris, Forstner, Moravec, are limited in their sensitivities to geometric rotation. Therefore, this paper proposed a least square matching algorithm that combines the advantages of both local feature detector and corner detector. We experiment this novel method in several sites. The accuracy assessment shows that the overall matching error is within 0.3 pixel and the matching reliability can reach 98%, which proves its robustness. This method had been successfully applied to over 700 scenes of lunar images that cover the entire moon, in finding corresponding pixels in a pair of images from adjacent tracks and aiding the automatic lunar image mosaicing. The completion of the 7 meter resolution lunar map shows the promise of this least square matching algorithm in applications with a large quantity of images to be processed.

Deblurring traffic sign images based on exemplars

PubMed Central

Qiu, Tianshuang; Luan, Shengyang; Song, Haiyu; Wu, Linxiu

2018-01-01

Motion blur appearing in traffic sign images may lead to poor recognition results, and therefore it is of great significance to study how to deblur the images. In this paper, a novel method for deblurring traffic sign is proposed based on exemplars and several related approaches are also made. First, an exemplar dataset construction method is proposed based on multiple-size partition strategy to lower calculation cost of exemplar matching. Second, a matching criterion based on gradient information and entropy correlation coefficient is also proposed to enhance the matching accuracy. Third, L0.5-norm is introduced as the regularization item to maintain the sparsity of blur kernel. Experiments verify the superiority of the proposed approaches and extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm. PMID:29513677

A New Test Method of Circuit Breaker Spring Telescopic Characteristics Based Image Processing

NASA Astrophysics Data System (ADS)

Huang, Huimin; Wang, Feifeng; Lu, Yufeng; Xia, Xiaofei; Su, Yi

2018-06-01

This paper applied computer vision technology to the fatigue condition monitoring of springs, and a new telescopic characteristics test method is proposed for circuit breaker operating mechanism spring based on image processing technology. High-speed camera is utilized to capture spring movement image sequences when high voltage circuit breaker operated. Then the image-matching method is used to obtain the deformation-time curve and speed-time curve, and the spring expansion and deformation parameters are extracted from it, which will lay a foundation for subsequent spring force analysis and matching state evaluation. After performing simulation tests at the experimental site, this image analyzing method could solve the complex problems of traditional mechanical sensor installation and monitoring online, status assessment of the circuit breaker spring.

Rigid shape matching by segmentation averaging.

PubMed

Wang, Hongzhi; Oliensis, John

2010-04-01

We use segmentations to match images by shape. The new matching technique does not require point-to-point edge correspondence and is robust to small shape variations and spatial shifts. To address the unreliability of segmentations computed bottom-up, we give a closed form approximation to an average over all segmentations. Our method has many extensions, yielding new algorithms for tracking, object detection, segmentation, and edge-preserving smoothing. For segmentation, instead of a maximum a posteriori approach, we compute the "central" segmentation minimizing the average distance to all segmentations of an image. For smoothing, instead of smoothing images based on local structures, we smooth based on the global optimal image structures. Our methods for segmentation, smoothing, and object detection perform competitively, and we also show promising results in shape-based tracking.

Thin plate spline feature point matching for organ surfaces in minimally invasive surgery imaging

NASA Astrophysics Data System (ADS)

Lin, Bingxiong; Sun, Yu; Qian, Xiaoning

2013-03-01

Robust feature point matching for images with large view angle changes in Minimally Invasive Surgery (MIS) is a challenging task due to low texture and specular reflections in these images. This paper presents a new approach that can improve feature matching performance by exploiting the inherent geometric property of the organ surfaces. Recently, intensity based template image tracking using a Thin Plate Spline (TPS) model has been extended for 3D surface tracking with stereo cameras. The intensity based tracking is also used here for 3D reconstruction of internal organ surfaces. To overcome the small displacement requirement of intensity based tracking, feature point correspondences are used for proper initialization of the nonlinear optimization in the intensity based method. Second, we generate simulated images from the reconstructed 3D surfaces under all potential view positions and orientations, and then extract feature points from these simulated images. The obtained feature points are then filtered and re-projected to the common reference image. The descriptors of the feature points under different view angles are stored to ensure that the proposed method can tolerate a large range of view angles. We evaluate the proposed method with silicon phantoms and in vivo images. The experimental results show that our method is much more robust with respect to the view angle changes than other state-of-the-art methods.

A convergence algorithm for correlation of breech face images based on the congruent matching cells (CMC) method.

PubMed

Chen, Zhe; Song, John; Chu, Wei; Soons, Johannes A; Zhao, Xuezeng

2017-11-01

The Congruent Matching Cells (CMC) method was invented at the National Institute of Standards and Technology (NIST) for accurate firearm evidence identification and error rate estimation. The CMC method is based on the principle of discretization. The toolmark image of the reference sample is divided into correlation cells. Each cell is registered to the cell-sized area of the compared image that has maximum surface topography similarity. For each resulting cell pair, one parameter quantifies the similarity of the cell surface topography and three parameters quantify the pattern congruency of the registration position and orientation. An identification (declared match) requires a significant number of CMCs, that is, cell pairs that meet both similarity and pattern congruency requirements. The use of cell correlations reduces the effects of "invalid regions" in the compared image pairs and increases the correlation accuracy. The identification accuracy of the CMC method can be further improved by considering a feature named "convergence," that is, the tendency of the x-y registration positions of the correlated cell pairs to converge at the correct registration angle when comparing same-source samples at different relative orientations. In this paper, the difference of the convergence feature between known matching (KM) and known non-matching (KNM) image pairs is characterized, based on which an improved algorithm is developed for breech face image correlations using the CMC method. Its advantage is demonstrated by comparison with three existing CMC algorithms using four datasets. The datasets address three different brands of consecutively manufactured pistol slides, with significant differences in the distribution overlap of cell pair topography similarity for KM and KNM image pairs. For the same CMC threshold values, the convergence algorithm demonstrates noticeably improved results by reducing the number of false-positive or false-negative CMCs in a comparison. Published by Elsevier B.V.

Automatic structural matching of 3D image data

NASA Astrophysics Data System (ADS)

Ponomarev, Svjatoslav; Lutsiv, Vadim; Malyshev, Igor

2015-10-01

A new image matching technique is described. It is implemented as an object-independent hierarchical structural juxtaposition algorithm based on an alphabet of simple object-independent contour structural elements. The structural matching applied implements an optimized method of walking through a truncated tree of all possible juxtapositions of two sets of structural elements. The algorithm was initially developed for dealing with 2D images such as the aerospace photographs, and it turned out to be sufficiently robust and reliable for matching successfully the pictures of natural landscapes taken in differing seasons from differing aspect angles by differing sensors (the visible optical, IR, and SAR pictures, as well as the depth maps and geographical vector-type maps). At present (in the reported version), the algorithm is enhanced based on additional use of information on third spatial coordinates of observed points of object surfaces. Thus, it is now capable of matching the images of 3D scenes in the tasks of automatic navigation of extremely low flying unmanned vehicles or autonomous terrestrial robots. The basic principles of 3D structural description and matching of images are described, and the examples of image matching are presented.

Cerenkov luminescence tomography based on preconditioning orthogonal matching pursuit

NASA Astrophysics Data System (ADS)

Liu, Haixiao; Hu, Zhenhua; Wang, Kun; Tian, Jie; Yang, Xin

2015-03-01

Cerenkov luminescence imaging (CLI) is a novel optical imaging method and has been proved to be a potential substitute of the traditional radionuclide imaging such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT). This imaging method inherits the high sensitivity of nuclear medicine and low cost of optical molecular imaging. To obtain the depth information of the radioactive isotope, Cerenkov luminescence tomography (CLT) is established and the 3D distribution of the isotope is reconstructed. However, because of the strong absorption and scatter, the reconstruction of the CLT sources is always converted to an ill-posed linear system which is hard to be solved. In this work, the sparse nature of the light source was taken into account and the preconditioning orthogonal matching pursuit (POMP) method was established to effectively reduce the ill-posedness and obtain better reconstruction accuracy. To prove the accuracy and speed of this algorithm, a heterogeneous numerical phantom experiment and an in vivo mouse experiment were conducted. Both the simulation result and the mouse experiment showed that our reconstruction method can provide more accurate reconstruction result compared with the traditional Tikhonov regularization method and the ordinary orthogonal matching pursuit (OMP) method. Our reconstruction method will provide technical support for the biological application for Cerenkov luminescence.

Perception for rugged terrain

NASA Technical Reports Server (NTRS)

Kweon, In SO; Hebert, Martial; Kanade, Takeo

1989-01-01

A three-dimensional perception system for building a geometrical description of rugged terrain environments from range image data is presented with reference to the exploration of the rugged terrain of Mars. An intermediate representation consisting of an elevation map that includes an explicit representation of uncertainty and labeling of the occluded regions is proposed. The locus method used to convert range image to an elevation map is introduced, along with an uncertainty model based on this algorithm. Both the elevation map and the locus method are the basis of a terrain matching algorithm which does not assume any correspondences between range images. The two-stage algorithm consists of a feature-based matching algorithm to compute an initial transform and an iconic terrain matching algorithm to merge multiple range images into a uniform representation. Terrain modeling results on real range images of rugged terrain are presented. The algorithms considered are a fundamental part of the perception system for the Ambler, a legged locomotor.

An improved schlieren method for measurement and automatic reconstruction of the far-field focal spot

PubMed Central

Wang, Zhengzhou; Hu, Bingliang; Yin, Qinye

2017-01-01

The schlieren method of measuring far-field focal spots offers many advantages at the Shenguang III laser facility such as low cost and automatic laser-path collimation. However, current methods of far-field focal spot measurement often suffer from low precision and efficiency when the final focal spot is merged manually, thereby reducing the accuracy of reconstruction. In this paper, we introduce an improved schlieren method to construct the high dynamic-range image of far-field focal spots and improve the reconstruction accuracy and efficiency. First, a detection method based on weak light beam sampling and magnification imaging was designed; images of the main and side lobes of the focused laser irradiance in the far field were obtained using two scientific CCD cameras. Second, using a self-correlation template matching algorithm, a circle the same size as the schlieren ball was dug from the main lobe cutting image and used to change the relative region of the main lobe cutting image within a 100×100 pixel region. The position that had the largest correlation coefficient between the side lobe cutting image and the main lobe cutting image when a circle was dug was identified as the best matching point. Finally, the least squares method was used to fit the center of the side lobe schlieren small ball, and the error was less than 1 pixel. The experimental results show that this method enables the accurate, high-dynamic-range measurement of a far-field focal spot and automatic image reconstruction. Because the best matching point is obtained through image processing rather than traditional reconstruction methods based on manual splicing, this method is less sensitive to the efficiency of focal-spot reconstruction and thus offers better experimental precision. PMID:28207758

Planarity constrained multi-view depth map reconstruction for urban scenes

NASA Astrophysics Data System (ADS)

Hou, Yaolin; Peng, Jianwei; Hu, Zhihua; Tao, Pengjie; Shan, Jie

2018-05-01

Multi-view depth map reconstruction is regarded as a suitable approach for 3D generation of large-scale scenes due to its flexibility and scalability. However, there are challenges when this technique is applied to urban scenes where apparent man-made regular shapes may present. To address this need, this paper proposes a planarity constrained multi-view depth (PMVD) map reconstruction method. Starting with image segmentation and feature matching for each input image, the main procedure is iterative optimization under the constraints of planar geometry and smoothness. A set of candidate local planes are first generated by an extended PatchMatch method. The image matching costs are then computed and aggregated by an adaptive-manifold filter (AMF), whereby the smoothness constraint is applied to adjacent pixels through belief propagation. Finally, multiple criteria are used to eliminate image matching outliers. (Vertical) aerial images, oblique (aerial) images and ground images are used for qualitative and quantitative evaluations. The experiments demonstrated that the PMVD outperforms the popular multi-view depth map reconstruction with an accuracy two times better for the aerial datasets and achieves an outcome comparable to the state-of-the-art for ground images. As expected, PMVD is able to preserve the planarity for piecewise flat structures in urban scenes and restore the edges in depth discontinuous areas.

Parallax handling of image stitching using dominant-plane homography

NASA Astrophysics Data System (ADS)

Pang, Zhaofeng; Li, Cheng; Zhao, Baojun; Tang, Linbo

2015-10-01

In this paper, we present a novel image stitching method to handle parallax in practical application. For images with significant amount of parallax, the more effective approach is to align roughly and globally the overlapping regions and then apply a seam-cutting method to composite naturally stitched images. It is well known that images can be modeled by various planes result from the projective parallax under non-ideal imaging condition. The dominant-plane homography has important advantages of warping an image globally and avoiding some local distortions. The proposed method primarily addresses large parallax problem through two steps: (1) selecting matching point pairs located on the dominant plane, by clustering matching correspondences and then measuring the cost of each cluster; and (2) in order to obtain a plausible seam, edge maps of overlapped area incorporation arithmetic is adopted to modify the standard seam-cutting method. Furthermore, our approach is demonstrated to achieve reliable performance of handling parallax through a mass of experimental comparisons with state-of-the-art methods.

Content Based Image Matching for Planetary Science

NASA Astrophysics Data System (ADS)

Deans, M. C.; Meyer, C.

2006-12-01

Planetary missions generate large volumes of data. With the MER rovers still functioning on Mars, PDS contains over 7200 released images from the Microscopic Imagers alone. These data products are only searchable by keys such as the Sol, spacecraft clock, or rover motion counter index, with little connection to the semantic content of the images. We have developed a method for matching images based on the visual textures in images. For every image in a database, a series of filters compute the image response to localized frequencies and orientations. Filter responses are turned into a low dimensional descriptor vector, generating a 37 dimensional fingerprint. For images such as the MER MI, this represents a compression ratio of 99.9965% (the fingerprint is approximately 0.0035% the size of the original image). At query time, fingerprints are quickly matched to find images with similar appearance. Image databases containing several thousand images are preprocessed offline in a matter of hours. Image matches from the database are found in a matter of seconds. We have demonstrated this image matching technique using three sources of data. The first database consists of 7200 images from the MER Microscopic Imager. The second database consists of 3500 images from the Narrow Angle Mars Orbital Camera (MOC-NA), which were cropped into 1024×1024 sub-images for consistency. The third database consists of 7500 scanned archival photos from the Apollo Metric Camera. Example query results from all three data sources are shown. We have also carried out user tests to evaluate matching performance by hand labeling results. User tests verify approximately 20% false positive rate for the top 14 results for MOC NA and MER MI data. This means typically 10 to 12 results out of 14 match the query image sufficiently. This represents a powerful search tool for databases of thousands of images where the a priori match probability for an image might be less than 1%. Qualitatively, correct matches can also be confirmed by verifying MI images taken in the same z-stack, or MOC image tiles taken from the same image strip. False negatives are difficult to quantify as it would mean finding matches in the database of thousands of images that the algorithm did not detect.

Conceptual transitions in methods of skull-photo superimposition that impact the reliability of identification: a review.

PubMed

Jayaprakash, Paul T

2015-01-01

Establishing identification during skull-photo superimposition relies on correlating the salient morphological features of an unidentified skull with those of a face-image of a suspected dead individual using image overlay processes. Technical progression in the process of overlay has included the incorporation of video cameras, image-mixing devices and software that enables real-time vision-mixing. Conceptual transitions occur in the superimposition methods that involve 'life-size' images, that achieve orientation of the skull to the posture of the face in the photograph and that assess the extent of match. A recent report on the reliability of identification using the superimposition method adopted the currently prevalent methods and suggested an increased rate of failures when skulls were compared with related and unrelated face images. The reported reduction in the reliability of the superimposition method prompted a review of the transition in the concepts that are involved in skull-photo superimposition. The prevalent popular methods for visualizing the superimposed images at less than 'life-size', overlaying skull-face images by relying on the cranial and facial landmarks in the frontal plane when orienting the skull for matching and evaluating the match on a morphological basis by relying on mix-mode alone are the major departures in the methodology that may have reduced the identification reliability. The need to reassess the reliability of the method that incorporates the concepts which have been considered appropriate by the practitioners is stressed. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Brute Force Matching Between Camera Shots and Synthetic Images from Point Clouds

NASA Astrophysics Data System (ADS)

Boerner, R.; Kröhnert, M.

2016-06-01

3D point clouds, acquired by state-of-the-art terrestrial laser scanning techniques (TLS), provide spatial information about accuracies up to several millimetres. Unfortunately, common TLS data has no spectral information about the covered scene. However, the matching of TLS data with images is important for monoplotting purposes and point cloud colouration. Well-established methods solve this issue by matching of close range images and point cloud data by fitting optical camera systems on top of laser scanners or rather using ground control points. The approach addressed in this paper aims for the matching of 2D image and 3D point cloud data from a freely moving camera within an environment covered by a large 3D point cloud, e.g. a 3D city model. The key advantage of the free movement affects augmented reality applications or real time measurements. Therefore, a so-called real image, captured by a smartphone camera, has to be matched with a so-called synthetic image which consists of reverse projected 3D point cloud data to a synthetic projection centre whose exterior orientation parameters match the parameters of the image, assuming an ideal distortion free camera.

Phi-s correlation and dynamic time warping - Two methods for tracking ice floes in SAR images

NASA Technical Reports Server (NTRS)

Mcconnell, Ross; Kober, Wolfgang; Kwok, Ronald; Curlander, John C.; Pang, Shirley S.

1991-01-01

The authors present two algorithms for performing shape matching on ice floe boundaries in SAR (synthetic aperture radar) images. These algorithms quickly produce a set of ice motion and rotation vectors that can be used to guide a pixel value correlator. The algorithms match a shape descriptor known as the Phi-s curve. The first algorithm uses normalized correlation to match the Phi-s curves, while the second uses dynamic programming to compute an elastic match that better accommodates ice floe deformation. Some empirical data on the performance of the algorithms on Seasat SAR images are presented.

A registration-based segmentation method with application to adiposity analysis of mice microCT images

NASA Astrophysics Data System (ADS)

Bai, Bing; Joshi, Anand; Brandhorst, Sebastian; Longo, Valter D.; Conti, Peter S.; Leahy, Richard M.

2014-04-01

Obesity is a global health problem, particularly in the U.S. where one third of adults are obese. A reliable and accurate method of quantifying obesity is necessary. Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) are two measures of obesity that reflect different associated health risks, but accurate measurements in humans or rodent models are difficult. In this paper we present an automatic, registration-based segmentation method for mouse adiposity studies using microCT images. We co-register the subject CT image and a mouse CT atlas. Our method is based on surface matching of the microCT image and an atlas. Surface-based elastic volume warping is used to match the internal anatomy. We acquired a whole body scan of a C57BL6/J mouse injected with contrast agent using microCT and created a whole body mouse atlas by manually delineate the boundaries of the mouse and major organs. For method verification we scanned a C57BL6/J mouse from the base of the skull to the distal tibia. We registered the obtained mouse CT image to our atlas. Preliminary results show that we can warp the atlas image to match the posture and shape of the subject CT image, which has significant differences from the atlas. We plan to use this software tool in longitudinal obesity studies using mouse models.

SU-E-J-89: Deformable Registration Method Using B-TPS in Radiotherapy.

PubMed

Xie, Y

2012-06-01

A novel deformable registration method for four-dimensional computed tomography (4DCT) images is developed in radiation therapy. The proposed method combines the thin plate spline (TPS) and B-spline together to achieve high accuracy and high efficiency. The method consists of two steps. First, TPS is used as a global registration method to deform large unfit regions in the moving image to match counterpart in the reference image. Then B-spline is used for local registration, the previous deformed moving image is further deformed to match the reference image more accurately. Two clinical CT image sets, including one pair of lung and one pair of liver, are simulated using the proposed algorithm, which results in a tremendous improvement in both run-time and registration quality, compared with the conventional methods solely using either TPS or B-spline. The proposed method can combine the efficiency of TPS and the accuracy of B-spline, performing good adaptively and robust in registration of clinical 4DCT image. © 2012 American Association of Physicists in Medicine.

Compact hybrid optoelectrical unit for image processing and recognition

NASA Astrophysics Data System (ADS)

Cheng, Gang; Jin, Guofan; Wu, Minxian; Liu, Haisong; He, Qingsheng; Yuan, ShiFu

1998-07-01

In this paper a compact opto-electric unit (CHOEU) for digital image processing and recognition is proposed. The central part of CHOEU is an incoherent optical correlator, which is realized with a SHARP QA-1200 8.4 inch active matrix TFT liquid crystal display panel which is used as two real-time spatial light modulators for both the input image and reference template. CHOEU can do two main processing works. One is digital filtering; the other is object matching. Using CHOEU an edge-detection operator is realized to extract the edges from the input images. Then the reprocessed images are sent into the object recognition unit for identifying the important targets. A novel template- matching method is proposed for gray-tome image recognition. A positive and negative cycle-encoding method is introduced to realize the absolute difference measurement pixel- matching on a correlator structure simply. The system has god fault-tolerance ability for rotation distortion, Gaussian noise disturbance or information losing. The experiments are given at the end of this paper.

Quasi-Epipolar Resampling of High Resolution Satellite Stereo Imagery for Semi Global Matching

NASA Astrophysics Data System (ADS)

Tatar, N.; Saadatseresht, M.; Arefi, H.; Hadavand, A.

2015-12-01

Semi-global matching is a well-known stereo matching algorithm in photogrammetric and computer vision society. Epipolar images are supposed as input of this algorithm. Epipolar geometry of linear array scanners is not a straight line as in case of frame camera. Traditional epipolar resampling algorithms demands for rational polynomial coefficients (RPCs), physical sensor model or ground control points. In this paper we propose a new solution for epipolar resampling method which works without the need for these information. In proposed method, automatic feature extraction algorithms are employed to generate corresponding features for registering stereo pairs. Also original images are divided into small tiles. In this way by omitting the need for extra information, the speed of matching algorithm increased and the need for high temporal memory decreased. Our experiments on GeoEye-1 stereo pair captured over Qom city in Iran demonstrates that the epipolar images are generated with sub-pixel accuracy.

Multi-Sensor Registration of Earth Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Cole-Rhodes, Arlene; Eastman, Roger; Johnson, Kisha; Morisette, Jeffrey; Netanyahu, Nathan S.; Stone, Harold S.; Zavorin, Ilya; Zukor, Dorothy (Technical Monitor)

2001-01-01

Assuming that approximate registration is given within a few pixels by a systematic correction system, we develop automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy. Automatic image registration is usually defined by three steps; feature extraction, feature matching, and data resampling or fusion. Our previous work focused on image correlation methods based on the use of different features. In this paper, we study different feature matching techniques and present five algorithms where the features are either original gray levels or wavelet-like features, and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information. These algorithms are tested and compared on several multi-sensor datasets covering one of the EOS Core Sites, the Konza Prairie in Kansas, from four different sensors: IKONOS (4m), Landsat-7/ETM+ (30m), MODIS (500m), and SeaWIFS (1000m).

Block matching and Wiener filtering approach to optical turbulence mitigation and its application to simulated and real imagery with quantitative error analysis

NASA Astrophysics Data System (ADS)

Hardie, Russell C.; Rucci, Michael A.; Dapore, Alexander J.; Karch, Barry K.

2017-07-01

We present a block-matching and Wiener filtering approach to atmospheric turbulence mitigation for long-range imaging of extended scenes. We evaluate the proposed method, along with some benchmark methods, using simulated and real-image sequences. The simulated data are generated with a simulation tool developed by one of the authors. These data provide objective truth and allow for quantitative error analysis. The proposed turbulence mitigation method takes a sequence of short-exposure frames of a static scene and outputs a single restored image. A block-matching registration algorithm is used to provide geometric correction for each of the individual input frames. The registered frames are then averaged, and the average image is processed with a Wiener filter to provide deconvolution. An important aspect of the proposed method lies in how we model the degradation point spread function (PSF) for the purposes of Wiener filtering. We use a parametric model that takes into account the level of geometric correction achieved during image registration. This is unlike any method we are aware of in the literature. By matching the PSF to the level of registration in this way, the Wiener filter is able to fully exploit the reduced blurring achieved by registration. We also describe a method for estimating the atmospheric coherence diameter (or Fried parameter) from the estimated motion vectors. We provide a detailed performance analysis that illustrates how the key tuning parameters impact system performance. The proposed method is relatively simple computationally, yet it has excellent performance in comparison with state-of-the-art benchmark methods in our study.

Terrain matching image pre-process and its format transform in autonomous underwater navigation

NASA Astrophysics Data System (ADS)

Cao, Xuejun; Zhang, Feizhou; Yang, Dongkai; Yang, Bogang

2007-06-01

Underwater passive navigation technology is one of the important development orientations in the field of modern navigation. With the advantage of high self-determination, stealth at sea, anti-jamming and high precision, passive navigation is completely meet with actual navigation requirements. Therefore passive navigation has become a specific navigating method for underwater vehicles. The scientists and researchers in the navigating field paid more attention to it. The underwater passive navigation can provide accurate navigation information with main Inertial Navigation System (INS) for a long period, such as location and speed. Along with the development of micro-electronics technology, the navigation of AUV is given priority to INS assisted with other navigation methods, such as terrain matching navigation. It can provide navigation ability for a long period, correct the errors of INS and make AUV not emerge from the seabed termly. With terrain matching navigation technique, in the assistance of digital charts and ocean geographical characteristics sensors, we carry through underwater image matching assistant navigation to obtain the higher location precision, therefore it is content with the requirement of underwater, long-term, high precision and all-weather of the navigation system for Autonomous Underwater Vehicles. Tertian-assistant navigation (TAN) is directly dependent on the image information (map information) in the navigating field to assist the primary navigation system according to the path appointed in advance. In TAN, a factor coordinative important with the system operation is precision and practicability of the storable images and the database which produce the image data. If the data used for characteristics are not suitable, the system navigation precision will be low. Comparing with terrain matching assistant navigation system, image matching navigation system is a kind of high precision and low cost assistant navigation system, and its matching precision directly influences the final precision of integrated navigation system. Image matching assistant navigation is spatially matching and aiming at two underwater scenery images coming from two different sensors matriculating of the same scenery in order to confirm the relative displacement of the two images. In this way, we can obtain the vehicle's location in fiducial image known geographical relation, and the precise location information given from image matching location is transmitted to INS to eliminate its location error and greatly enhance the navigation precision of vehicle. Digital image data analysis and processing of image matching in underwater passive navigation is important. In regard to underwater geographic data analysis, we focus on the acquirement, disposal, analysis, expression and measurement of database information. These analysis items structure one of the important contents of underwater terrain matching and are propitious to know the seabed terrain configuration of navigation areas so that the best advantageous seabed terrain district and dependable navigation algorithm can be selected. In this way, we can improve the precision and reliability of terrain assistant navigation system. The pre-process and format transformation of digital image during underwater image matching are expatiated in this paper. The information of the terrain status in navigation areas need further study to provide the reliable data terrain characteristic and underwater overcast for navigation. Through realizing the choice of sea route, danger district prediction and navigating algorithm analysis, TAN can obtain more high location precision and probability, hence provide technological support for image matching of underwater passive navigation.

Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier.

PubMed

Memari, Nogol; Ramli, Abd Rahman; Bin Saripan, M Iqbal; Mashohor, Syamsiah; Moghbel, Mehrdad

2017-01-01

The structure and appearance of the blood vessel network in retinal fundus images is an essential part of diagnosing various problems associated with the eyes, such as diabetes and hypertension. In this paper, an automatic retinal vessel segmentation method utilizing matched filter techniques coupled with an AdaBoost classifier is proposed. The fundus image is enhanced using morphological operations, the contrast is increased using contrast limited adaptive histogram equalization (CLAHE) method and the inhomogeneity is corrected using Retinex approach. Then, the blood vessels are enhanced using a combination of B-COSFIRE and Frangi matched filters. From this preprocessed image, different statistical features are computed on a pixel-wise basis and used in an AdaBoost classifier to extract the blood vessel network inside the image. Finally, the segmented images are postprocessed to remove the misclassified pixels and regions. The proposed method was validated using publicly accessible Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE) and Child Heart and Health Study in England (CHASE_DB1) datasets commonly used for determining the accuracy of retinal vessel segmentation methods. The accuracy of the proposed segmentation method was comparable to other state of the art methods while being very close to the manual segmentation provided by the second human observer with an average accuracy of 0.972, 0.951 and 0.948 in DRIVE, STARE and CHASE_DB1 datasets, respectively.

SU-E-J-23: An Accurate Algorithm to Match Imperfectly Matched Images for Lung Tumor Detection Without Markers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rozario, T; Bereg, S; Chiu, T

Purpose: In order to locate lung tumors on projection images without internal markers, digitally reconstructed radiograph (DRR) is created and compared with projection images. Since lung tumors always move and their locations change on projection images while they are static on DRRs, a special DRR (background DRR) is generated based on modified anatomy from which lung tumors are removed. In addition, global discrepancies exist between DRRs and projections due to their different image originations, scattering, and noises. This adversely affects comparison accuracy. A simple but efficient comparison algorithm is reported. Methods: This method divides global images into a matrix ofmore » small tiles and similarities will be evaluated by calculating normalized cross correlation (NCC) between corresponding tiles on projections and DRRs. The tile configuration (tile locations) will be automatically optimized to keep the tumor within a single tile which has bad matching with the corresponding DRR tile. A pixel based linear transformation will be determined by linear interpolations of tile transformation results obtained during tile matching. The DRR will be transformed to the projection image level and subtracted from it. The resulting subtracted image now contains only the tumor. A DRR of the tumor is registered to the subtracted image to locate the tumor. Results: This method has been successfully applied to kV fluoro images (about 1000 images) acquired on a Vero (Brainlab) for dynamic tumor tracking on phantom studies. Radiation opaque markers are implanted and used as ground truth for tumor positions. Although, other organs and bony structures introduce strong signals superimposed on tumors at some angles, this method accurately locates tumors on every projection over 12 gantry angles. The maximum error is less than 2.6 mm while the total average error is 1.0 mm. Conclusion: This algorithm is capable of detecting tumor without markers despite strong background signals.« less

MO-C-17A-11: A Segmentation and Point Matching Enhanced Deformable Image Registration Method for Dose Accumulation Between HDR CT Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhen, X; Chen, H; Zhou, L

2014-06-15

Purpose: To propose and validate a novel and accurate deformable image registration (DIR) scheme to facilitate dose accumulation among treatment fractions of high-dose-rate (HDR) gynecological brachytherapy. Method: We have developed a method to adapt DIR algorithms to gynecologic anatomies with HDR applicators by incorporating a segmentation step and a point-matching step into an existing DIR framework. In the segmentation step, random walks algorithm is used to accurately segment and remove the applicator region (AR) in the HDR CT image. A semi-automatic seed point generation approach is developed to obtain the incremented foreground and background point sets to feed the randommore » walks algorithm. In the subsequent point-matching step, a feature-based thin-plate spline-robust point matching (TPS-RPM) algorithm is employed for AR surface point matching. With the resulting mapping, a DVF characteristic of the deformation between the two AR surfaces is generated by B-spline approximation, which serves as the initial DVF for the following Demons DIR between the two AR-free HDR CT images. Finally, the calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. Results: The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative results as well as the visual inspection of the DIR indicate that our proposed method can suppress the interference of the applicator with the DIR algorithm, and accurately register HDR CT images as well as deform and add interfractional HDR doses. Conclusions: We have developed a novel and robust DIR scheme that can perform registration between HDR gynecological CT images and yield accurate registration results. This new DIR scheme has potential for accurate interfractional HDR dose accumulation. This work is supported in part by the National Natural ScienceFoundation of China (no 30970866 and no 81301940)« less

Exploring the Validity of Assessment in Anatomy: Do Images Influence Cognitive Processes Used in Answering Extended Matching Questions?

ERIC Educational Resources Information Center

Vorstenbosch, Marc A. T. M.; Bouter, Shifra T.; van den Hurk, Marianne M.; Kooloos, Jan G. M.; Bolhuis, Sanneke M.; Laan, Roland F. J. M.

2014-01-01

Assessment is an important aspect of medical education because it tests students' competence and motivates them to study. Various assessment methods, with and without images, are used in the study of anatomy. In this study, we investigated the use of extended matching questions (EMQs). To gain insight into the influence of images on the…

Component-based target recognition inspired by human vision

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Agyepong, Kwabena

2009-05-01

In contrast with machine vision, human can recognize an object from complex background with great flexibility. For example, given the task of finding and circling all cars (no further information) in a picture, you may build a virtual image in mind from the task (or target) description before looking at the picture. Specifically, the virtual car image may be composed of the key components such as driver cabin and wheels. In this paper, we propose a component-based target recognition method by simulating the human recognition process. The component templates (equivalent to the virtual image in mind) of the target (car) are manually decomposed from the target feature image. Meanwhile, the edges of the testing image can be extracted by using a difference of Gaussian (DOG) model that simulates the spatiotemporal response in visual process. A phase correlation matching algorithm is then applied to match the templates with the testing edge image. If all key component templates are matched with the examining object, then this object is recognized as the target. Besides the recognition accuracy, we will also investigate if this method works with part targets (half cars). In our experiments, several natural pictures taken on streets were used to test the proposed method. The preliminary results show that the component-based recognition method is very promising.

A novel scheme for automatic nonrigid image registration using deformation invariant feature and geometric constraint

NASA Astrophysics Data System (ADS)

Deng, Zhipeng; Lei, Lin; Zhou, Shilin

2015-10-01

Automatic image registration is a vital yet challenging task, particularly for non-rigid deformation images which are more complicated and common in remote sensing images, such as distorted UAV (unmanned aerial vehicle) images or scanning imaging images caused by flutter. Traditional non-rigid image registration methods are based on the correctly matched corresponding landmarks, which usually needs artificial markers. It is a rather challenging task to locate the accurate position of the points and get accurate homonymy point sets. In this paper, we proposed an automatic non-rigid image registration algorithm which mainly consists of three steps: To begin with, we introduce an automatic feature point extraction method based on non-linear scale space and uniform distribution strategy to extract the points which are uniform distributed along the edge of the image. Next, we propose a hybrid point matching algorithm using DaLI (Deformation and Light Invariant) descriptor and local affine invariant geometric constraint based on triangulation which is constructed by K-nearest neighbor algorithm. Based on the accurate homonymy point sets, the two images are registrated by the model of TPS (Thin Plate Spline). Our method is demonstrated by three deliberately designed experiments. The first two experiments are designed to evaluate the distribution of point set and the correctly matching rate on synthetic data and real data respectively. The last experiment is designed on the non-rigid deformation remote sensing images and the three experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm compared with other traditional methods.

Modifications in SIFT-based 3D reconstruction from image sequence

NASA Astrophysics Data System (ADS)

Wei, Zhenzhong; Ding, Boshen; Wang, Wei

2014-11-01

In this paper, we aim to reconstruct 3D points of the scene from related images. Scale Invariant Feature Transform( SIFT) as a feature extraction and matching algorithm has been proposed and improved for years and has been widely used in image alignment and stitching, image recognition and 3D reconstruction. Because of the robustness and reliability of the SIFT's feature extracting and matching algorithm, we use it to find correspondences between images. Hence, we describe a SIFT-based method to reconstruct 3D sparse points from ordered images. In the process of matching, we make a modification in the process of finding the correct correspondences, and obtain a satisfying matching result. By rejecting the "questioned" points before initial matching could make the final matching more reliable. Given SIFT's attribute of being invariant to the image scale, rotation, and variable changes in environment, we propose a way to delete the multiple reconstructed points occurred in sequential reconstruction procedure, which improves the accuracy of the reconstruction. By removing the duplicated points, we avoid the possible collapsed situation caused by the inexactly initialization or the error accumulation. The limitation of some cases that all reprojected points are visible at all times also does not exist in our situation. "The small precision" could make a big change when the number of images increases. The paper shows the contrast between the modified algorithm and not. Moreover, we present an approach to evaluate the reconstruction by comparing the reconstructed angle and length ratio with actual value by using a calibration target in the scene. The proposed evaluation method is easy to be carried out and with a great applicable value. Even without the Internet image datasets, we could evaluate our own results. In this paper, the whole algorithm has been tested on several image sequences both on the internet and in our shots.

Keyhole imaging method for dynamic objects behind the occlusion area

NASA Astrophysics Data System (ADS)

Hao, Conghui; Chen, Xi; Dong, Liquan; Zhao, Yuejin; Liu, Ming; Kong, Lingqin; Hui, Mei; Liu, Xiaohua; Wu, Hong

2018-01-01

A method of keyhole imaging based on camera array is realized to obtain the video image behind a keyhole in shielded space at a relatively long distance. We get the multi-angle video images by using a 2×2 CCD camera array to take the images behind the keyhole in four directions. The multi-angle video images are saved in the form of frame sequences. This paper presents a method of video frame alignment. In order to remove the non-target area outside the aperture, we use the canny operator and morphological method to realize the edge detection of images and fill the images. The image stitching of four images is accomplished on the basis of the image stitching algorithm of two images. In the image stitching algorithm of two images, the SIFT method is adopted to accomplish the initial matching of images, and then the RANSAC algorithm is applied to eliminate the wrong matching points and to obtain a homography matrix. A method of optimizing transformation matrix is proposed in this paper. Finally, the video image with larger field of view behind the keyhole can be synthesized with image frame sequence in which every single frame is stitched. The results show that the screen of the video is clear and natural, the brightness transition is smooth. There is no obvious artificial stitching marks in the video, and it can be applied in different engineering environment .

False match elimination for face recognition based on SIFT algorithm

NASA Astrophysics Data System (ADS)

Gu, Xuyuan; Shi, Ping; Shao, Meide

2011-06-01

The SIFT (Scale Invariant Feature Transform) is a well known algorithm used to detect and describe local features in images. It is invariant to image scale, rotation and robust to the noise and illumination. In this paper, a novel method used for face recognition based on SIFT is proposed, which combines the optimization of SIFT, mutual matching and Progressive Sample Consensus (PROSAC) together and can eliminate the false matches of face recognition effectively. Experiments on ORL face database show that many false matches can be eliminated and better recognition rate is achieved.

Lung motion estimation using dynamic point shifting: An innovative model based on a robust point matching algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yi, Jianbing, E-mail: yijianbing8@163.com; Yang, Xuan, E-mail: xyang0520@263.net; Li, Yan-Ran, E-mail: lyran@szu.edu.cn

2015-10-15

Purpose: Image-guided radiotherapy is an advanced 4D radiotherapy technique that has been developed in recent years. However, respiratory motion causes significant uncertainties in image-guided radiotherapy procedures. To address these issues, an innovative lung motion estimation model based on a robust point matching is proposed in this paper. Methods: An innovative robust point matching algorithm using dynamic point shifting is proposed to estimate patient-specific lung motion during free breathing from 4D computed tomography data. The correspondence of the landmark points is determined from the Euclidean distance between the landmark points and the similarity between the local images that are centered atmore » points at the same time. To ensure that the points in the source image correspond to the points in the target image during other phases, the virtual target points are first created and shifted based on the similarity between the local image centered at the source point and the local image centered at the virtual target point. Second, the target points are shifted by the constrained inverse function mapping the target points to the virtual target points. The source point set and shifted target point set are used to estimate the transformation function between the source image and target image. Results: The performances of the authors’ method are evaluated on two publicly available DIR-lab and POPI-model lung datasets. For computing target registration errors on 750 landmark points in six phases of the DIR-lab dataset and 37 landmark points in ten phases of the POPI-model dataset, the mean and standard deviation by the authors’ method are 1.11 and 1.11 mm, but they are 2.33 and 2.32 mm without considering image intensity, and 1.17 and 1.19 mm with sliding conditions. For the two phases of maximum inhalation and maximum exhalation in the DIR-lab dataset with 300 landmark points of each case, the mean and standard deviation of target registration errors on the 3000 landmark points of ten cases by the authors’ method are 1.21 and 1.04 mm. In the EMPIRE10 lung registration challenge, the authors’ method ranks 24 of 39. According to the index of the maximum shear stretch, the authors’ method is also efficient to describe the discontinuous motion at the lung boundaries. Conclusions: By establishing the correspondence of the landmark points in the source phase and the other target phases combining shape matching and image intensity matching together, the mismatching issue in the robust point matching algorithm is adequately addressed. The target registration errors are statistically reduced by shifting the virtual target points and target points. The authors’ method with consideration of sliding conditions can effectively estimate the discontinuous motion, and the estimated motion is natural. The primary limitation of the proposed method is that the temporal constraints of the trajectories of voxels are not introduced into the motion model. However, the proposed method provides satisfactory motion information, which results in precise tumor coverage by the radiation dose during radiotherapy.« less

Manta Matcher: automated photographic identification of manta rays using keypoint features.

PubMed

Town, Christopher; Marshall, Andrea; Sethasathien, Nutthaporn

2013-07-01

For species which bear unique markings, such as natural spot patterning, field work has become increasingly more reliant on visual identification to recognize and catalog particular specimens or to monitor individuals within populations. While many species of interest exhibit characteristic markings that in principle allow individuals to be identified from photographs, scientists are often faced with the task of matching observations against databases of hundreds or thousands of images. We present a novel technique for automated identification of manta rays (Manta alfredi and Manta birostris) by means of a pattern-matching algorithm applied to images of their ventral surface area. Automated visual identification has recently been developed for several species. However, such methods are typically limited to animals that can be photographed above water, or whose markings exhibit high contrast and appear in regular constellations. While manta rays bear natural patterning across their ventral surface, these patterns vary greatly in their size, shape, contrast, and spatial distribution. Our method is the first to have proven successful at achieving high matching accuracies on a large corpus of manta ray images taken under challenging underwater conditions. Our method is based on automated extraction and matching of keypoint features using the Scale-Invariant Feature Transform (SIFT) algorithm. In order to cope with the considerable variation in quality of underwater photographs, we also incorporate preprocessing and image enhancement steps. Furthermore, we use a novel pattern-matching approach that results in better accuracy than the standard SIFT approach and other alternative methods. We present quantitative evaluation results on a data set of 720 images of manta rays taken under widely different conditions. We describe a novel automated pattern representation and matching method that can be used to identify individual manta rays from photographs. The method has been incorporated into a website (mantamatcher.org) which will serve as a global resource for ecological and conservation research. It will allow researchers to manage and track sightings data to establish important life-history parameters as well as determine other ecological data such as abundance, range, movement patterns, and structure of manta ray populations across the world.

Manta Matcher: automated photographic identification of manta rays using keypoint features

PubMed Central

Town, Christopher; Marshall, Andrea; Sethasathien, Nutthaporn

2013-01-01

For species which bear unique markings, such as natural spot patterning, field work has become increasingly more reliant on visual identification to recognize and catalog particular specimens or to monitor individuals within populations. While many species of interest exhibit characteristic markings that in principle allow individuals to be identified from photographs, scientists are often faced with the task of matching observations against databases of hundreds or thousands of images. We present a novel technique for automated identification of manta rays (Manta alfredi and Manta birostris) by means of a pattern-matching algorithm applied to images of their ventral surface area. Automated visual identification has recently been developed for several species. However, such methods are typically limited to animals that can be photographed above water, or whose markings exhibit high contrast and appear in regular constellations. While manta rays bear natural patterning across their ventral surface, these patterns vary greatly in their size, shape, contrast, and spatial distribution. Our method is the first to have proven successful at achieving high matching accuracies on a large corpus of manta ray images taken under challenging underwater conditions. Our method is based on automated extraction and matching of keypoint features using the Scale-Invariant Feature Transform (SIFT) algorithm. In order to cope with the considerable variation in quality of underwater photographs, we also incorporate preprocessing and image enhancement steps. Furthermore, we use a novel pattern-matching approach that results in better accuracy than the standard SIFT approach and other alternative methods. We present quantitative evaluation results on a data set of 720 images of manta rays taken under widely different conditions. We describe a novel automated pattern representation and matching method that can be used to identify individual manta rays from photographs. The method has been incorporated into a website (mantamatcher.org) which will serve as a global resource for ecological and conservation research. It will allow researchers to manage and track sightings data to establish important life-history parameters as well as determine other ecological data such as abundance, range, movement patterns, and structure of manta ray populations across the world. PMID:23919138

Relevance of Whitnall's tubercle and auditory meatus in diagnosing exclusions during skull-photo superimposition.

PubMed

Jayaprakash, Paul T; Hashim, Natassha; Yusop, Ridzuan Abd Aziz Mohd

2015-08-01

Video vision mixer based skull-photo superimposition is a popular method for identifying skulls retrieved from unidentified human remains. A report on the reliability of the superimposition method suggested increased failure rates of 17.3 to 32% to exclude and 15 to 20% to include skulls while using related and unrelated face photographs. Such raise in failures prompted an analysis of the methods employed for the research. The protocols adopted for assessing the reliability are seen to vary from those suggested by the practitioners in the field. The former include overlaying the skull- and face-images on the basis of morphology by relying on anthropometric landmarks on the front plane of the face-images and evaluating the goodness of match depending on mix-mode images; the latter consist of orienting the skull considering landmarks on both the eye and ear planes of the face- and skull-images and evaluating the match utilizing images seen in wipe-mode in addition to those in mix-mode. Superimposition of a skull with face-images of five living individuals in two sets of experiments, one following the procedure described for the research on reliability and the other applying the methods suggested by the practitioners has shown that overlaying the images on the basis of morphology depending on the landmarks on the front plane alone and assessing the match in mix-mode fails to exclude the skull. However, orienting the skull relying on the relationship between the anatomical landmarks on the skull- and face-images such as Whitnall's tubercle and exocanthus in the front (eye) plane and the porion and tragus in the rear (ear) plane as well as assessing the match using wipe-mode images enables excluding that skull while superimposing with the same set of face-images. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A novel retinal vessel extraction algorithm based on matched filtering and gradient vector flow

NASA Astrophysics Data System (ADS)

Yu, Lei; Xia, Mingliang; Xuan, Li

2013-10-01

The microvasculature network of retina plays an important role in the study and diagnosis of retinal diseases (age-related macular degeneration and diabetic retinopathy for example). Although it is possible to noninvasively acquire high-resolution retinal images with modern retinal imaging technologies, non-uniform illumination, the low contrast of thin vessels and the background noises all make it difficult for diagnosis. In this paper, we introduce a novel retinal vessel extraction algorithm based on gradient vector flow and matched filtering to segment retinal vessels with different likelihood. Firstly, we use isotropic Gaussian kernel and adaptive histogram equalization to smooth and enhance the retinal images respectively. Secondly, a multi-scale matched filtering method is adopted to extract the retinal vessels. Then, the gradient vector flow algorithm is introduced to locate the edge of the retinal vessels. Finally, we combine the results of matched filtering method and gradient vector flow algorithm to extract the vessels at different likelihood levels. The experiments demonstrate that our algorithm is efficient and the intensities of vessel images exactly represent the likelihood of the vessels.

Four-dimensional dose reconstruction through in vivo phase matching of cine images of electronic portal imaging device

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, Jihyung; Jung, Jae Won, E-mail: jungj@ecu.ed

Purpose: A method is proposed to reconstruct a four-dimensional (4D) dose distribution using phase matching of measured cine images to precalculated images of electronic portal imaging device (EPID). Methods: (1) A phantom, designed to simulate a tumor in lung (a polystyrene block with a 3 cm diameter embedded in cork), was placed on a sinusoidally moving platform with an amplitude of 1 cm and a period of 4 s. Ten-phase 4D computed tomography (CT) images of the phantom were acquired. A planning target volume (PTV) was created by adding a margin of 1 cm around the internal target volume ofmore » the tumor. (2) Three beams were designed, which included a static beam, a theoretical dynamic beam, and a planning-optimized dynamic beam (PODB). While the theoretical beam was made by manually programming a simplistic sliding leaf motion, the planning-optimized beam was obtained from treatment planning. From the three beams, three-dimensional (3D) doses on the phantom were calculated; 4D dose was calculated by means of the ten phase images (integrated over phases afterward); serving as “reference” images, phase-specific EPID dose images under the lung phantom were also calculated for each of the ten phases. (3) Cine EPID images were acquired while the beams were irradiated to the moving phantom. (4) Each cine image was phase-matched to a phase-specific CT image at which common irradiation occurred by intercomparing the cine image with the reference images. (5) Each cine image was used to reconstruct dose in the phase-matched CT image, and the reconstructed doses were summed over all phases. (6) The summation was compared with forwardly calculated 4D and 3D dose distributions. Accounting for realistic situations, intratreatment breathing irregularity was simulated by assuming an amplitude of 0.5 cm for the phantom during a portion of breathing trace in which the phase matching could not be performed. Intertreatment breathing irregularity between the time of treatment and the time of planning CT was considered by utilizing the same reduced amplitude when the phantom was irradiated. To examine the phase matching in a humanoid environment, the matching was also performed in a digital phantom (4D XCAT phantom). Results: For the static, the theoretical, and the planning-optimized dynamic beams, the 4D reconstructed doses showed agreement with the forwardly calculated 4D doses within the gamma pass rates of 92.7%, 100%, and 98.1%, respectively, at the isocenter plane given by 3%/3 mm criteria. Excellent agreement in dose volume histogram of PTV and lung-PTV was also found between the two 4D doses, while substantial differences were found between the 3D and the 4D doses. The significant breathing irregularities modeled in this study were found not to be noticeably affecting the reconstructed dose. The phase matching was performed equally well in a digital phantom. Conclusions: The method of retrospective phase determination of a moving object under irradiation provided successful 4D dose reconstruction. This method will provide accurate quality assurance and facilitate adaptive therapy when distinguishable objects such as well-defined tumors, diaphragm, and organs with markers (pancreas and liver) are covered by treatment beam apertures.« less

A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments

PubMed Central

Herrera, Pedro Javier; Pajares, Gonzalo; Guijarro, Maria; Ruz, José J.; Cruz, Jesús M.; Montes, Fernando

2009-01-01

This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion. PMID:22303134

Ultrasound of the fingers for human identification using biometrics.

PubMed

Narayanasamy, Ganesh; Fowlkes, J Brian; Kripfgans, Oliver D; Jacobson, Jon A; De Maeseneer, Michel; Schmitt, Rainer M; Carson, Paul L

2008-03-01

It was hypothesized that the use of internal finger structure as imaged using commercially available ultrasound (US) scanners could act as a supplement to standard methods of biometric identification, as well as a means of assessing physiological and cardiovascular status. Anatomical structures in the finger including bone contour, tendon and features along the interphalangeal joint were investigated as potential biometric identifiers. Thirty-six pairs of three-dimensional (3D) gray-scale images of second to fourth finger (index, middle and ring) data taken from 20 individuals were spatially registered using MIAMI-Fuse software developed at our institution and also visually matched by four readers. The image-based registration met the criteria for matching successfully in 14 out of 15 image pairs on the same individual and did not meet criteria for matching in any of the 12 image pairs from different subjects, providing a sensitivity and specificity of 0.93 and 1.00, respectively. Visual matching of all image pairs by four readers yielded 96% successful match. Power Doppler imaging was performed to calculate the change in color pixel density due to physical exercise as a surrogate of stress level and to provide basic physiological information. (E-mail: gnarayan@umich.edu).

On the simulation and mitigation of anisoplanatic optical turbulence for long range imaging

NASA Astrophysics Data System (ADS)

Hardie, Russell C.; LeMaster, Daniel A.

2017-05-01

We describe a numerical wave propagation method for simulating long range imaging of an extended scene under anisoplanatic conditions. Our approach computes an array of point spread functions (PSFs) for a 2D grid on the object plane. The PSFs are then used in a spatially varying weighted sum operation, with an ideal image, to produce a simulated image with realistic optical turbulence degradation. To validate the simulation we compare simulated outputs with the theoretical anisoplanatic tilt correlation and differential tilt variance. This is in addition to comparing the long- and short-exposure PSFs, and isoplanatic angle. Our validation analysis shows an excellent match between the simulation statistics and the theoretical predictions. The simulation tool is also used here to quantitatively evaluate a recently proposed block- matching and Wiener filtering (BMWF) method for turbulence mitigation. In this method block-matching registration algorithm is used to provide geometric correction for each of the individual input frames. The registered frames are then averaged and processed with a Wiener filter for restoration. A novel aspect of the proposed BMWF method is that the PSF model used for restoration takes into account the level of geometric correction achieved during image registration. This way, the Wiener filter is able fully exploit the reduced blurring achieved by registration. The BMWF method is relatively simple computationally, and yet, has excellent performance in comparison to state-of-the-art benchmark methods.

Radargrammetric DSM generation in mountainous areas through adaptive-window least squares matching constrained by enhanced epipolar geometry

NASA Astrophysics Data System (ADS)

Dong, Yuting; Zhang, Lu; Balz, Timo; Luo, Heng; Liao, Mingsheng

2018-03-01

Radargrammetry is a powerful tool to construct digital surface models (DSMs) especially in heavily vegetated and mountainous areas where SAR interferometry (InSAR) technology suffers from decorrelation problems. In radargrammetry, the most challenging step is to produce an accurate disparity map through massive image matching, from which terrain height information can be derived using a rigorous sensor orientation model. However, precise stereoscopic SAR (StereoSAR) image matching is a very difficult task in mountainous areas due to the presence of speckle noise and dissimilar geometric/radiometric distortions. In this article, an adaptive-window least squares matching (AW-LSM) approach with an enhanced epipolar geometric constraint is proposed to robustly identify homologous points after compensation for radiometric discrepancies and geometric distortions. The matching procedure consists of two stages. In the first stage, the right image is re-projected into the left image space to generate epipolar images using rigorous imaging geometries enhanced with elevation information extracted from the prior DEM data e.g. SRTM DEM instead of the mean height of the mapped area. Consequently, the dissimilarities in geometric distortions between the left and right images are largely reduced, and the residual disparity corresponds to the height difference between true ground surface and the prior DEM. In the second stage, massive per-pixel matching between StereoSAR epipolar images identifies the residual disparity. To ensure the reliability and accuracy of the matching results, we develop an iterative matching scheme in which the classic cross correlation matching is used to obtain initial results, followed by the least squares matching (LSM) to refine the matching results. An adaptively resizing search window strategy is adopted during the dense matching step to help find right matching points. The feasibility and effectiveness of the proposed approach is demonstrated using Stripmap and Spotlight mode TerraSAR-X stereo data pairs covering Mount Song in central China. Experimental results show that the proposed method can provide a robust and effective matching tool for radargrammetry in mountainous areas.

Statistical lamb wave localization based on extreme value theory

NASA Astrophysics Data System (ADS)

Harley, Joel B.

2018-04-01

Guided wave localization methods based on delay-and-sum imaging, matched field processing, and other techniques have been designed and researched to create images that locate and describe structural damage. The maximum value of these images typically represent an estimated damage location. Yet, it is often unclear if this maximum value, or any other value in the image, is a statistically significant indicator of damage. Furthermore, there are currently few, if any, approaches to assess the statistical significance of guided wave localization images. As a result, we present statistical delay-and-sum and statistical matched field processing localization methods to create statistically significant images of damage. Our framework uses constant rate of false alarm statistics and extreme value theory to detect damage with little prior information. We demonstrate our methods with in situ guided wave data from an aluminum plate to detect two 0.75 cm diameter holes. Our results show an expected improvement in statistical significance as the number of sensors increase. With seventeen sensors, both methods successfully detect damage with statistical significance.

Probabilistic seismic history matching using binary images

NASA Astrophysics Data System (ADS)

Davolio, Alessandra; Schiozer, Denis Jose

2018-02-01

Currently, the goal of history-matching procedures is not only to provide a model matching any observed data but also to generate multiple matched models to properly handle uncertainties. One such approach is a probabilistic history-matching methodology based on the discrete Latin Hypercube sampling algorithm, proposed in previous works, which was particularly efficient for matching well data (production rates and pressure). 4D seismic (4DS) data have been increasingly included into history-matching procedures. A key issue in seismic history matching (SHM) is to transfer data into a common domain: impedance, amplitude or pressure, and saturation. In any case, seismic inversions and/or modeling are required, which can be time consuming. An alternative to avoid these procedures is using binary images in SHM as they allow the shape, rather than the physical values, of observed anomalies to be matched. This work presents the incorporation of binary images in SHM within the aforementioned probabilistic history matching. The application was performed with real data from a segment of the Norne benchmark case that presents strong 4D anomalies, including softening signals due to pressure build up. The binary images are used to match the pressurized zones observed in time-lapse data. Three history matchings were conducted using: only well data, well and 4DS data, and only 4DS. The methodology is very flexible and successfully utilized the addition of binary images for seismic objective functions. Results proved the good convergence of the method in few iterations for all three cases. The matched models of the first two cases provided the best results, with similar well matching quality. The second case provided models presenting pore pressure changes according to the expected dynamic behavior (pressurized zones) observed on 4DS data. The use of binary images in SHM is relatively new with few examples in the literature. This work enriches this discussion by presenting a new application to match pressure in a reservoir segment with complex pressure behavior.

A robust correspondence matching algorithm of ground images along the optic axis

NASA Astrophysics Data System (ADS)

Jia, Fengman; Kang, Zhizhong

2013-10-01

Facing challenges of nontraditional geometry, multiple resolutions and the same features sensed from different angles, there are more difficulties of robust correspondence matching for ground images along the optic axis. A method combining SIFT algorithm and the geometric constraint of the ratio of coordinate differences between image point and image principal point is proposed in this paper. As it can provide robust matching across a substantial range of affine distortion addition of change in 3D viewpoint and noise, we use SIFT algorithm to tackle the problem of image distortion. By analyzing the nontraditional geometry of ground image along the optic axis, this paper derivates that for one correspondence pair, the ratio of distances between image point and image principal point in an image pair should be a value not far from 1. Therefore, a geometric constraint for gross points detection is formed. The proposed approach is tested with real image data acquired by Kodak. The results show that with SIFT and the proposed geometric constraint, the robustness of correspondence matching on the ground images along the optic axis can be effectively improved, and thus prove the validity of the proposed algorithm.

Ultrasound sounding in air by fast-moving receiver

NASA Astrophysics Data System (ADS)

Sukhanov, D.; Erzakova, N.

2018-05-01

A method of ultrasound imaging in the air for a fast receiver. The case, when the speed of movement of the receiver can not be neglected with respect to the speed of sound. In this case, the Doppler effect is significant, making it difficult for matched filtering of the backscattered signal. The proposed method does not use a continuous repetitive noise-sounding signal. generalized approach applies spatial matched filtering in the time domain to recover the ultrasonic tomographic images.

Respiratory motion compensation algorithm of ultrasound hepatic perfusion data acquired in free-breathing

NASA Astrophysics Data System (ADS)

Wu, Kaizhi; Zhang, Xuming; Chen, Guangxie; Weng, Fei; Ding, Mingyue

2013-10-01

Images acquired in free breathing using contrast enhanced ultrasound exhibit a periodic motion that needs to be compensated for if a further accurate quantification of the hepatic perfusion analysis is to be executed. In this work, we present an algorithm to compensate the respiratory motion by effectively combining the PCA (Principal Component Analysis) method and block matching method. The respiratory kinetics of the ultrasound hepatic perfusion image sequences was firstly extracted using the PCA method. Then, the optimal phase of the obtained respiratory kinetics was detected after normalizing the motion amplitude and determining the image subsequences of the original image sequences. The image subsequences were registered by the block matching method using cross-correlation as the similarity. Finally, the motion-compensated contrast images can be acquired by using the position mapping and the algorithm was evaluated by comparing the TICs extracted from the original image sequences and compensated image subsequences. Quantitative comparisons demonstrated that the average fitting error estimated of ROIs (region of interest) was reduced from 10.9278 +/- 6.2756 to 5.1644 +/- 3.3431 after compensating.

A real-time TV logo tracking method using template matching

NASA Astrophysics Data System (ADS)

Li, Zhi; Sang, Xinzhu; Yan, Binbin; Leng, Junmin

2012-11-01

A fast and accurate TV Logo detection method is presented based on real-time image filtering, noise eliminating and recognition of image features including edge and gray level information. It is important to accurately extract the optical template using the time averaging method from the sample video stream, and then different templates are used to match different logos in separated video streams with different resolution based on the topology features of logos. 12 video streams with different logos are used to verify the proposed method, and the experimental result demonstrates that the achieved accuracy can be up to 99%.

Lung motion estimation using dynamic point shifting: An innovative model based on a robust point matching algorithm.

PubMed

Yi, Jianbing; Yang, Xuan; Chen, Guoliang; Li, Yan-Ran

2015-10-01

Image-guided radiotherapy is an advanced 4D radiotherapy technique that has been developed in recent years. However, respiratory motion causes significant uncertainties in image-guided radiotherapy procedures. To address these issues, an innovative lung motion estimation model based on a robust point matching is proposed in this paper. An innovative robust point matching algorithm using dynamic point shifting is proposed to estimate patient-specific lung motion during free breathing from 4D computed tomography data. The correspondence of the landmark points is determined from the Euclidean distance between the landmark points and the similarity between the local images that are centered at points at the same time. To ensure that the points in the source image correspond to the points in the target image during other phases, the virtual target points are first created and shifted based on the similarity between the local image centered at the source point and the local image centered at the virtual target point. Second, the target points are shifted by the constrained inverse function mapping the target points to the virtual target points. The source point set and shifted target point set are used to estimate the transformation function between the source image and target image. The performances of the authors' method are evaluated on two publicly available DIR-lab and POPI-model lung datasets. For computing target registration errors on 750 landmark points in six phases of the DIR-lab dataset and 37 landmark points in ten phases of the POPI-model dataset, the mean and standard deviation by the authors' method are 1.11 and 1.11 mm, but they are 2.33 and 2.32 mm without considering image intensity, and 1.17 and 1.19 mm with sliding conditions. For the two phases of maximum inhalation and maximum exhalation in the DIR-lab dataset with 300 landmark points of each case, the mean and standard deviation of target registration errors on the 3000 landmark points of ten cases by the authors' method are 1.21 and 1.04 mm. In the EMPIRE10 lung registration challenge, the authors' method ranks 24 of 39. According to the index of the maximum shear stretch, the authors' method is also efficient to describe the discontinuous motion at the lung boundaries. By establishing the correspondence of the landmark points in the source phase and the other target phases combining shape matching and image intensity matching together, the mismatching issue in the robust point matching algorithm is adequately addressed. The target registration errors are statistically reduced by shifting the virtual target points and target points. The authors' method with consideration of sliding conditions can effectively estimate the discontinuous motion, and the estimated motion is natural. The primary limitation of the proposed method is that the temporal constraints of the trajectories of voxels are not introduced into the motion model. However, the proposed method provides satisfactory motion information, which results in precise tumor coverage by the radiation dose during radiotherapy.

An Improved Algorithm of Congruent Matching Cells (CMC) Method for Firearm Evidence Identifications

PubMed Central

Tong, Mingsi; Song, John; Chu, Wei

2015-01-01

The Congruent Matching Cells (CMC) method was invented at the National Institute of Standards and Technology (NIST) for firearm evidence identifications. The CMC method divides the measured image of a surface area, such as a breech face impression from a fired cartridge case, into small correlation cells and uses four identification parameters to identify correlated cell pairs originating from the same firearm. The CMC method was validated by identification tests using both 3D topography images and optical images captured from breech face impressions of 40 cartridge cases fired from a pistol with 10 consecutively manufactured slides. In this paper, we discuss the processing of the cell correlations and propose an improved algorithm of the CMC method which takes advantage of the cell correlations at a common initial phase angle and combines the forward and backward correlations to improve the identification capability. The improved algorithm is tested by 780 pairwise correlations using the same optical images and 3D topography images as the initial validation. PMID:26958441

An Improved Algorithm of Congruent Matching Cells (CMC) Method for Firearm Evidence Identifications.

PubMed

Tong, Mingsi; Song, John; Chu, Wei

2015-01-01

The Congruent Matching Cells (CMC) method was invented at the National Institute of Standards and Technology (NIST) for firearm evidence identifications. The CMC method divides the measured image of a surface area, such as a breech face impression from a fired cartridge case, into small correlation cells and uses four identification parameters to identify correlated cell pairs originating from the same firearm. The CMC method was validated by identification tests using both 3D topography images and optical images captured from breech face impressions of 40 cartridge cases fired from a pistol with 10 consecutively manufactured slides. In this paper, we discuss the processing of the cell correlations and propose an improved algorithm of the CMC method which takes advantage of the cell correlations at a common initial phase angle and combines the forward and backward correlations to improve the identification capability. The improved algorithm is tested by 780 pairwise correlations using the same optical images and 3D topography images as the initial validation.

Image database for digital hand atlas

NASA Astrophysics Data System (ADS)

Cao, Fei; Huang, H. K.; Pietka, Ewa; Gilsanz, Vicente; Dey, Partha S.; Gertych, Arkadiusz; Pospiech-Kurkowska, Sywia

2003-05-01

Bone age assessment is a procedure frequently performed in pediatric patients to evaluate their growth disorder. A commonly used method is atlas matching by a visual comparison of a hand radiograph with a small reference set of old Greulich-Pyle atlas. We have developed a new digital hand atlas with a large set of clinically normal hand images of diverse ethnic groups. In this paper, we will present our system design and implementation of the digital atlas database to support the computer-aided atlas matching for bone age assessment. The system consists of a hand atlas image database, a computer-aided diagnostic (CAD) software module for image processing and atlas matching, and a Web user interface. Users can use a Web browser to push DICOM images, directly or indirectly from PACS, to the CAD server for a bone age assessment. Quantitative features on the examined image, which reflect the skeletal maturity, are then extracted and compared with patterns from the atlas image database to assess the bone age. The digital atlas method built on a large image database and current Internet technology provides an alternative to supplement or replace the traditional one for a quantitative, accurate and cost-effective assessment of bone age.

Monoplane 3D-2D registration of cerebral angiograms based on multi-objective stratified optimization

NASA Astrophysics Data System (ADS)

Aksoy, T.; Špiclin, Ž.; Pernuš, F.; Unal, G.

2017-12-01

Registration of 3D pre-interventional to 2D intra-interventional medical images has an increasingly important role in surgical planning, navigation and treatment, because it enables the physician to co-locate depth information given by pre-interventional 3D images with the live information in intra-interventional 2D images such as x-ray. Most tasks during image-guided interventions are carried out under a monoplane x-ray, which is a highly ill-posed problem for state-of-the-art 3D to 2D registration methods. To address the problem of rigid 3D-2D monoplane registration we propose a novel multi-objective stratified parameter optimization, wherein a small set of high-magnitude intensity gradients are matched between the 3D and 2D images. The stratified parameter optimization matches rotation templates to depth templates, first sampled from projected 3D gradients and second from the 2D image gradients, so as to recover 3D rigid-body rotations and out-of-plane translation. The objective for matching was the gradient magnitude correlation coefficient, which is invariant to in-plane translation. The in-plane translations are then found by locating the maximum of the gradient phase correlation between the best matching pair of rotation and depth templates. On twenty pairs of 3D and 2D images of ten patients undergoing cerebral endovascular image-guided intervention the 3D to monoplane 2D registration experiments were setup with a rather high range of initial mean target registration error from 0 to 100 mm. The proposed method effectively reduced the registration error to below 2 mm, which was further refined by a fast iterative method and resulted in a high final registration accuracy (0.40 mm) and high success rate (> 96%). Taking into account a fast execution time below 10 s, the observed performance of the proposed method shows a high potential for application into clinical image-guidance systems.

Automatic Marker-free Longitudinal Infrared Image Registration by Shape Context Based Matching and Competitive Winner-guided Optimal Corresponding

PubMed Central

Lee, Chia-Yen; Wang, Hao-Jen; Lai, Jhih-Hao; Chang, Yeun-Chung; Huang, Chiun-Sheng

2017-01-01

Long-term comparisons of infrared image can facilitate the assessment of breast cancer tissue growth and early tumor detection, in which longitudinal infrared image registration is a necessary step. However, it is hard to keep markers attached on a body surface for weeks, and rather difficult to detect anatomic fiducial markers and match them in the infrared image during registration process. The proposed study, automatic longitudinal infrared registration algorithm, develops an automatic vascular intersection detection method and establishes feature descriptors by shape context to achieve robust matching, as well as to obtain control points for the deformation model. In addition, competitive winner-guided mechanism is developed for optimal corresponding. The proposed algorithm is evaluated in two ways. Results show that the algorithm can quickly lead to accurate image registration and that the effectiveness is superior to manual registration with a mean error being 0.91 pixels. These findings demonstrate that the proposed registration algorithm is reasonably accurate and provide a novel method of extracting a greater amount of useful data from infrared images. PMID:28145474

SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qiu, J; Yang, D

2015-06-15

Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets,more » and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from Varian Medical System.« less

Discriminability limits in spatio-temporal stereo block matching.

PubMed

Jain, Ankit K; Nguyen, Truong Q

2014-05-01

Disparity estimation is a fundamental task in stereo imaging and is a well-studied problem. Recently, methods have been adapted to the video domain where motion is used as a matching criterion to help disambiguate spatially similar candidates. In this paper, we analyze the validity of the underlying assumptions of spatio-temporal disparity estimation, and determine the extent to which motion aids the matching process. By analyzing the error signal for spatio-temporal block matching under the sum of squared differences criterion and treating motion as a stochastic process, we determine the probability of a false match as a function of image features, motion distribution, image noise, and number of frames in the spatio-temporal patch. This performance quantification provides insight into when spatio-temporal matching is most beneficial in terms of the scene and motion, and can be used as a guide to select parameters for stereo matching algorithms. We validate our results through simulation and experiments on stereo video.

Refractive-index-matched hydrogel materials for measuring flow-structure interactions

NASA Astrophysics Data System (ADS)

Byron, Margaret L.; Variano, Evan A.

2013-02-01

In imaging-based studies of flow around solid objects, it is useful to have materials that are refractive-index-matched to the surrounding fluid. However, materials currently in use are usually rigid and matched to liquids that are either expensive or highly viscous. This does not allow for measurements at high Reynolds number, nor accurate modeling of flexible structures. This work explores the use of two hydrogels (agarose and polyacrylamide) as refractive-index-matched models in water. These hydrogels are inexpensive, can be cast into desired shapes, and have flexibility that can be tuned to match biological materials. The use of water as the fluid phase allows this method to be implemented immediately in many experimental facilities and permits investigation of high-Reynolds-number phenomena. We explain fabrication methods and present a summary of the physical and optical properties of both gels, and then show measurements demonstrating the use of hydrogel models in quantitative imaging.

Spot the match – wildlife photo-identification using information theory

PubMed Central

Speed, Conrad W; Meekan, Mark G; Bradshaw, Corey JA

2007-01-01

Background Effective approaches for the management and conservation of wildlife populations require a sound knowledge of population demographics, and this is often only possible through mark-recapture studies. We applied an automated spot-recognition program (I3S) for matching natural markings of wildlife that is based on a novel information-theoretic approach to incorporate matching uncertainty. Using a photo-identification database of whale sharks (Rhincodon typus) as an example case, the information criterion (IC) algorithm we developed resulted in a parsimonious ranking of potential matches of individuals in an image library. Automated matches were compared to manual-matching results to test the performance of the software and algorithm. Results Validation of matched and non-matched images provided a threshold IC weight (approximately 0.2) below which match certainty was not assured. Most images tested were assigned correctly; however, scores for the by-eye comparison were lower than expected, possibly due to the low sample size. The effect of increasing horizontal angle of sharks in images reduced matching likelihood considerably. There was a negative linear relationship between the number of matching spot pairs and matching score, but this relationship disappeared when using the IC algorithm. Conclusion The software and use of easily applied information-theoretic scores of match parsimony provide a reliable and freely available method for individual identification of wildlife, with wide applications and the potential to improve mark-recapture studies without resorting to invasive marking techniques. PMID:17227581

Robust Point Set Matching for Partial Face Recognition.

PubMed

Weng, Renliang; Lu, Jiwen; Tan, Yap-Peng

2016-03-01

Over the past three decades, a number of face recognition methods have been proposed in computer vision, and most of them use holistic face images for person identification. In many real-world scenarios especially some unconstrained environments, human faces might be occluded by other objects, and it is difficult to obtain fully holistic face images for recognition. To address this, we propose a new partial face recognition approach to recognize persons of interest from their partial faces. Given a pair of gallery image and probe face patch, we first detect keypoints and extract their local textural features. Then, we propose a robust point set matching method to discriminatively match these two extracted local feature sets, where both the textural information and geometrical information of local features are explicitly used for matching simultaneously. Finally, the similarity of two faces is converted as the distance between these two aligned feature sets. Experimental results on four public face data sets show the effectiveness of the proposed approach.

An Efficient Method to Detect Mutual Overlap of a Large Set of Unordered Images for Structure-From

NASA Astrophysics Data System (ADS)

Wang, X.; Zhan, Z. Q.; Heipke, C.

2017-05-01

Recently, low-cost 3D reconstruction based on images has become a popular focus of photogrammetry and computer vision research. Methods which can handle an arbitrary geometric setup of a large number of unordered and convergent images are of particular interest. However, determining the mutual overlap poses a considerable challenge. We propose a new method which was inspired by and improves upon methods employing random k-d forests for this task. Specifically, we first derive features from the images and then a random k-d forest is used to find the nearest neighbours in feature space. Subsequently, the degree of similarity between individual images, the image overlaps and thus images belonging to a common block are calculated as input to a structure-from-motion (sfm) pipeline. In our experiments we show the general applicability of the new method and compare it with other methods by analyzing the time efficiency. Orientations and 3D reconstructions were successfully conducted with our overlap graphs by sfm. The results show a speed-up of a factor of 80 compared to conventional pairwise matching, and of 8 and 2 compared to the VocMatch approach using 1 and 4 CPU, respectively.

3D deformable image matching: a hierarchical approach over nested subspaces

NASA Astrophysics Data System (ADS)

Musse, Olivier; Heitz, Fabrice; Armspach, Jean-Paul

2000-06-01

This paper presents a fast hierarchical method to perform dense deformable inter-subject matching of 3D MR Images of the brain. To recover the complex morphological variations in neuroanatomy, a hierarchy of 3D deformations fields is estimated, by minimizing a global energy function over a sequence of nested subspaces. The nested subspaces, generated from a single scaling function, consist of deformation fields constrained at different scales. The highly non linear energy function, describing the interactions between the target and the source images, is minimized using a coarse-to-fine continuation strategy over this hierarchy. The resulting deformable matching method shows low sensitivity to local minima and is able to track large non-linear deformations, with moderate computational load. The performances of the approach are assessed both on simulated 3D transformations and on a real data base of 3D brain MR Images from different individuals. The method has shown efficient in putting into correspondence the principle anatomical structures of the brain. An application to atlas-based MRI segmentation, by transporting a labeled segmentation map on patient data, is also presented.

Spectral characterization of near-infrared acousto-optic tunable filter (AOTF) hyperspectral imaging systems using standard calibration materials.

PubMed

Bürmen, Miran; Pernuš, Franjo; Likar, Boštjan

2011-04-01

In this study, we propose and evaluate a method for spectral characterization of acousto-optic tunable filter (AOTF) hyperspectral imaging systems in the near-infrared (NIR) spectral region from 900 nm to 1700 nm. The proposed spectral characterization method is based on the SRM-2035 standard reference material, exhibiting distinct spectral features, which enables robust non-rigid matching of the acquired and reference spectra. The matching is performed by simultaneously optimizing the parameters of the AOTF tuning curve, spectral resolution, baseline, and multiplicative effects. In this way, the tuning curve (frequency-wavelength characteristics) and the corresponding spectral resolution of the AOTF hyperspectral imaging system can be characterized simultaneously. Also, the method enables simple spectral characterization of the entire imaging plane of hyperspectral imaging systems. The results indicate that the method is accurate and efficient and can easily be integrated with systems operating in diffuse reflection or transmission modes. Therefore, the proposed method is suitable for characterization, calibration, or validation of AOTF hyperspectral imaging systems. © 2011 Society for Applied Spectroscopy

Inter-image matching

NASA Technical Reports Server (NTRS)

Wolfe, R. H., Jr.; Juday, R. D.

1982-01-01

Interimage matching is the process of determining the geometric transformation required to conform spatially one image to another. In principle, the parameters of that transformation are varied until some measure of some difference between the two images is minimized or some measure of sameness (e.g., cross-correlation) is maximized. The number of such parameters to vary is faily large (six for merely an affine transformation), and it is customary to attempt an a priori transformation reducing the complexity of the residual transformation or subdivide the image into small enough match zones (control points or patches) that a simple transformation (e.g., pure translation) is applicable, yet large enough to facilitate matching. In the latter case, a complex mapping function is fit to the results (e.g., translation offsets) in all the patches. The methods reviewed have all chosen one or both of the above options, ranging from a priori along-line correction for line-dependent effects (the high-frequency correction) to a full sensor-to-geobase transformation with subsequent subdivision into a grid of match points.

A novel point cloud registration using 2D image features

NASA Astrophysics Data System (ADS)

Lin, Chien-Chou; Tai, Yen-Chou; Lee, Jhong-Jin; Chen, Yong-Sheng

2017-01-01

Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.

Epipolar Rectification for CARTOSAT-1 Stereo Images Using SIFT and RANSAC

NASA Astrophysics Data System (ADS)

Akilan, A.; Sudheer Reddy, D.; Nagasubramanian, V.; Radhadevi, P. V.; Varadan, G.

2014-11-01

Cartosat-1 provides stereo images of spatial resolution 2.5 m with high fidelity of geometry. Stereo camera on the spacecraft has look angles of +26 degree and -5 degree respectively that yields effective along track stereo. Any DSM generation algorithm can use the stereo images for accurate 3D reconstruction and measurement of ground. Dense match points and pixel-wise matching are prerequisite in DSM generation to capture discontinuities and occlusions for accurate 3D modelling application. Epipolar image matching reduces the computational effort from two dimensional area searches to one dimensional. Thus, epipolar rectification is preferred as a pre-processing step for accurate DSM generation. In this paper we explore a method based on SIFT and RANSAC for epipolar rectification of cartosat-1 stereo images.

Building dynamic population graph for accurate correspondence detection.

PubMed

Du, Shaoyi; Guo, Yanrong; Sanroma, Gerard; Ni, Dong; Wu, Guorong; Shen, Dinggang

2015-12-01

In medical imaging studies, there is an increasing trend for discovering the intrinsic anatomical difference across individual subjects in a dataset, such as hand images for skeletal bone age estimation. Pair-wise matching is often used to detect correspondences between each individual subject and a pre-selected model image with manually-placed landmarks. However, the large anatomical variability across individual subjects can easily compromise such pair-wise matching step. In this paper, we present a new framework to simultaneously detect correspondences among a population of individual subjects, by propagating all manually-placed landmarks from a small set of model images through a dynamically constructed image graph. Specifically, we first establish graph links between models and individual subjects according to pair-wise shape similarity (called as forward step). Next, we detect correspondences for the individual subjects with direct links to any of model images, which is achieved by a new multi-model correspondence detection approach based on our recently-published sparse point matching method. To correct those inaccurate correspondences, we further apply an error detection mechanism to automatically detect wrong correspondences and then update the image graph accordingly (called as backward step). After that, all subject images with detected correspondences are included into the set of model images, and the above two steps of graph expansion and error correction are repeated until accurate correspondences for all subject images are established. Evaluations on real hand X-ray images demonstrate that our proposed method using a dynamic graph construction approach can achieve much higher accuracy and robustness, when compared with the state-of-the-art pair-wise correspondence detection methods as well as a similar method but using static population graph. Copyright © 2015 Elsevier B.V. All rights reserved.

Illumination Invariant Change Detection (iicd): from Earth to Mars

NASA Astrophysics Data System (ADS)

Wan, X.; Liu, J.; Qin, M.; Li, S. Y.

2018-04-01

Multi-temporal Earth Observation and Mars orbital imagery data with frequent repeat coverage provide great capability for planetary surface change detection. When comparing two images taken at different times of day or in different seasons for change detection, the variation of topographic shades and shadows caused by the change of sunlight angle can be so significant that it overwhelms the real object and environmental changes, making automatic detection unreliable. An effective change detection algorithm therefore has to be robust to the illumination variation. This paper presents our research on developing and testing an Illumination Invariant Change Detection (IICD) method based on the robustness of phase correlation (PC) to the variation of solar illumination for image matching. The IICD is based on two key functions: i) initial change detection based on a saliency map derived from pixel-wise dense PC matching and ii) change quantization which combines change type identification, motion estimation and precise appearance change identification. Experiment using multi-temporal Landsat 7 ETM+ satellite images, Rapid eye satellite images and Mars HiRiSE images demonstrate that our frequency based image matching method can reach sub-pixel accuracy and thus the proposed IICD method can effectively detect and precisely segment large scale change such as landslide as well as small object change such as Mars rover, under daily and seasonal sunlight changes.

Automated extraction of pleural effusion in three-dimensional thoracic CT images

NASA Astrophysics Data System (ADS)

Kido, Shoji; Tsunomori, Akinori

2009-02-01

It is important for diagnosis of pulmonary diseases to measure volume of accumulating pleural effusion in threedimensional thoracic CT images quantitatively. However, automated extraction of pulmonary effusion correctly is difficult. Conventional extraction algorithm using a gray-level based threshold can not extract pleural effusion from thoracic wall or mediastinum correctly, because density of pleural effusion in CT images is similar to those of thoracic wall or mediastinum. So, we have developed an automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion. Our method used a template of lung obtained from a normal lung for segmentation of lungs with pleural effusions. Registration process consisted of two steps. First step was a global matching processing between normal and abnormal lungs of organs such as bronchi, bones (ribs, sternum and vertebrae) and upper surfaces of livers which were extracted using a region-growing algorithm. Second step was a local matching processing between normal and abnormal lungs which were deformed by the parameter obtained from the global matching processing. Finally, we segmented a lung with pleural effusion by use of the template which was deformed by two parameters obtained from the global matching processing and the local matching processing. We compared our method with a conventional extraction method using a gray-level based threshold and two published methods. The extraction rates of pleural effusions obtained from our method were much higher than those obtained from other methods. Automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion is promising for diagnosis of pulmonary diseases by providing quantitative volume of accumulating pleural effusion.

Experiment research on inertia-aided adaptive electronic image stabilization of optical stable platform

NASA Astrophysics Data System (ADS)

Lu, Xiaodong; Wu, Tianze; Zhou, Jun; Zhao, Bin; Ma, Xiaoyuan; Tang, Xiucheng

2016-03-01

An electronic image stabilization method compounded with inertia information, which can compensate the coupling interference caused by the pitch-yaw movement of the optical stable platform system, has been proposed in this paper. Firstly the mechanisms of coning rotation and lever-arm translation of line of sight (LOS) are analyzed during the stabilization process under moving carriers, and the mathematical model which describes the relationship between LOS rotation angle and platform attitude angle are derived. Then the image spin angle caused by coning rotation is estimated by using inertia information. Furthermore, an adaptive block matching method, which based on image edge and angular point, is proposed to smooth the jitter created by the lever-arm translation. This method optimizes the matching process and strategies. Finally, the results of hardware-in-the-loop simulation verified the effectiveness and real-time performance of the proposed method.

Computer face-matching technology using two-dimensional photographs accurately matches the facial gestalt of unrelated individuals with the same syndromic form of intellectual disability.

PubMed

Dudding-Byth, Tracy; Baxter, Anne; Holliday, Elizabeth G; Hackett, Anna; O'Donnell, Sheridan; White, Susan M; Attia, John; Brunner, Han; de Vries, Bert; Koolen, David; Kleefstra, Tjitske; Ratwatte, Seshika; Riveros, Carlos; Brain, Steve; Lovell, Brian C

2017-12-19

Massively parallel genetic sequencing allows rapid testing of known intellectual disability (ID) genes. However, the discovery of novel syndromic ID genes requires molecular confirmation in at least a second or a cluster of individuals with an overlapping phenotype or similar facial gestalt. Using computer face-matching technology we report an automated approach to matching the faces of non-identical individuals with the same genetic syndrome within a database of 3681 images [1600 images of one of 10 genetic syndrome subgroups together with 2081 control images]. Using the leave-one-out method, two research questions were specified: 1) Using two-dimensional (2D) photographs of individuals with one of 10 genetic syndromes within a database of images, did the technology correctly identify more than expected by chance: i) a top match? ii) at least one match within the top five matches? or iii) at least one in the top 10 with an individual from the same syndrome subgroup? 2) Was there concordance between correct technology-based matches and whether two out of three clinical geneticists would have considered the diagnosis based on the image alone? The computer face-matching technology correctly identifies a top match, at least one correct match in the top five and at least one in the top 10 more than expected by chance (P < 0.00001). There was low agreement between the technology and clinicians, with higher accuracy of the technology when results were discordant (P < 0.01) for all syndromes except Kabuki syndrome. Although the accuracy of the computer face-matching technology was tested on images of individuals with known syndromic forms of intellectual disability, the results of this pilot study illustrate the potential utility of face-matching technology within deep phenotyping platforms to facilitate the interpretation of DNA sequencing data for individuals who remain undiagnosed despite testing the known developmental disorder genes.

Fast and accurate face recognition based on image compression

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Blasch, Erik

2017-05-01

Image compression is desired for many image-related applications especially for network-based applications with bandwidth and storage constraints. The face recognition community typical reports concentrate on the maximal compression rate that would not decrease the recognition accuracy. In general, the wavelet-based face recognition methods such as EBGM (elastic bunch graph matching) and FPB (face pattern byte) are of high performance but run slowly due to their high computation demands. The PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) algorithms run fast but perform poorly in face recognition. In this paper, we propose a novel face recognition method based on standard image compression algorithm, which is termed as compression-based (CPB) face recognition. First, all gallery images are compressed by the selected compression algorithm. Second, a mixed image is formed with the probe and gallery images and then compressed. Third, a composite compression ratio (CCR) is computed with three compression ratios calculated from: probe, gallery and mixed images. Finally, the CCR values are compared and the largest CCR corresponds to the matched face. The time cost of each face matching is about the time of compressing the mixed face image. We tested the proposed CPB method on the "ASUMSS face database" (visible and thermal images) from 105 subjects. The face recognition accuracy with visible images is 94.76% when using JPEG compression. On the same face dataset, the accuracy of FPB algorithm was reported as 91.43%. The JPEG-compressionbased (JPEG-CPB) face recognition is standard and fast, which may be integrated into a real-time imaging device.

Estimating error rates for firearm evidence identifications in forensic science

PubMed Central

Song, John; Vorburger, Theodore V.; Chu, Wei; Yen, James; Soons, Johannes A.; Ott, Daniel B.; Zhang, Nien Fan

2018-01-01

Estimating error rates for firearm evidence identification is a fundamental challenge in forensic science. This paper describes the recently developed congruent matching cells (CMC) method for image comparisons, its application to firearm evidence identification, and its usage and initial tests for error rate estimation. The CMC method divides compared topography images into correlation cells. Four identification parameters are defined for quantifying both the topography similarity of the correlated cell pairs and the pattern congruency of the registered cell locations. A declared match requires a significant number of CMCs, i.e., cell pairs that meet all similarity and congruency requirements. Initial testing on breech face impressions of a set of 40 cartridge cases fired with consecutively manufactured pistol slides showed wide separation between the distributions of CMC numbers observed for known matching and known non-matching image pairs. Another test on 95 cartridge cases from a different set of slides manufactured by the same process also yielded widely separated distributions. The test results were used to develop two statistical models for the probability mass function of CMC correlation scores. The models were applied to develop a framework for estimating cumulative false positive and false negative error rates and individual error rates of declared matches and non-matches for this population of breech face impressions. The prospect for applying the models to large populations and realistic case work is also discussed. The CMC method can provide a statistical foundation for estimating error rates in firearm evidence identifications, thus emulating methods used for forensic identification of DNA evidence. PMID:29331680

Estimating error rates for firearm evidence identifications in forensic science.

PubMed

Song, John; Vorburger, Theodore V; Chu, Wei; Yen, James; Soons, Johannes A; Ott, Daniel B; Zhang, Nien Fan

2018-03-01

Estimating error rates for firearm evidence identification is a fundamental challenge in forensic science. This paper describes the recently developed congruent matching cells (CMC) method for image comparisons, its application to firearm evidence identification, and its usage and initial tests for error rate estimation. The CMC method divides compared topography images into correlation cells. Four identification parameters are defined for quantifying both the topography similarity of the correlated cell pairs and the pattern congruency of the registered cell locations. A declared match requires a significant number of CMCs, i.e., cell pairs that meet all similarity and congruency requirements. Initial testing on breech face impressions of a set of 40 cartridge cases fired with consecutively manufactured pistol slides showed wide separation between the distributions of CMC numbers observed for known matching and known non-matching image pairs. Another test on 95 cartridge cases from a different set of slides manufactured by the same process also yielded widely separated distributions. The test results were used to develop two statistical models for the probability mass function of CMC correlation scores. The models were applied to develop a framework for estimating cumulative false positive and false negative error rates and individual error rates of declared matches and non-matches for this population of breech face impressions. The prospect for applying the models to large populations and realistic case work is also discussed. The CMC method can provide a statistical foundation for estimating error rates in firearm evidence identifications, thus emulating methods used for forensic identification of DNA evidence. Published by Elsevier B.V.

A new phase-correlation-based iris matching for degraded images.

PubMed

Krichen, Emine; Garcia-Salicetti, Sonia; Dorizzi, Bernadette

2009-08-01

In this paper, we present a new phase-correlation-based iris matching approach in order to deal with degradations in iris images due to unconstrained acquisition procedures. Our matching system is a fusion of global and local Gabor phase-correlation schemes. The main originality of our local approach is that we do not only consider the correlation peak amplitudes but also their locations in different regions of the images. Results on several degraded databases, namely, the CASIA-BIOSECURE and Iris Challenge Evaluation 2005 databases, show the improvement of our method compared to two available reference systems, Masek and Open Source for Iris (OSRIS), in verification mode.

Three-dimensional object surface identification

NASA Astrophysics Data System (ADS)

Celenk, Mehmet

1995-03-01

This paper describes a computationally efficient matching method for inspecting 3D objects using their serial cross sections. Object regions of interest in cross-sectional binary images of successive slices are aligned with those of the models. Cross-sectional differences between the object and the models are measured in the direction of the gradient of the cross section boundary. This is repeated in all the cross-sectional images. The model with minimum average cross-sectional difference is selected as the best match to the given object (i.e., no defect). The method is tested using various computer generated surfaces and matching results are presented. It is also demonstrated using Symult S-2010 16-node system that the method is suitable for parallel implementation in massage passing processors with the maximum attainable speedup (close to 16 for S-2010).

Naturalness preservation image contrast enhancement via histogram modification

NASA Astrophysics Data System (ADS)

Tian, Qi-Chong; Cohen, Laurent D.

2018-04-01

Contrast enhancement is a technique for enhancing image contrast to obtain better visual quality. Since many existing contrast enhancement algorithms usually produce over-enhanced results, the naturalness preservation is needed to be considered in the framework of image contrast enhancement. This paper proposes a naturalness preservation contrast enhancement method, which adopts the histogram matching to improve the contrast and uses the image quality assessment to automatically select the optimal target histogram. The contrast improvement and the naturalness preservation are both considered in the target histogram, so this method can avoid the over-enhancement problem. In the proposed method, the optimal target histogram is a weighted sum of the original histogram, the uniform histogram, and the Gaussian-shaped histogram. Then the structural metric and the statistical naturalness metric are used to determine the weights of corresponding histograms. At last, the contrast-enhanced image is obtained via matching the optimal target histogram. The experiments demonstrate the proposed method outperforms the compared histogram-based contrast enhancement algorithms.

Ranging through Gabor logons-a consistent, hierarchical approach.

PubMed

Chang, C; Chatterjee, S

1993-01-01

In this work, the correspondence problem in stereo vision is handled by matching two sets of dense feature vectors. Inspired by biological evidence, these feature vectors are generated by a correlation between a bank of Gabor sensors and the intensity image. The sensors consist of two-dimensional Gabor filters at various scales (spatial frequencies) and orientations, which bear close resemblance to the receptive field profiles of simple V1 cells in visual cortex. A hierarchical, stochastic relaxation method is then used to obtain the dense stereo disparities. Unlike traditional hierarchical methods for stereo, feature based hierarchical processing yields consistent disparities. To avoid false matchings due to static occlusion, a dual matching, based on the imaging geometry, is used.

a Robust Descriptor Based on Spatial and Frequency Structural Information for Visible and Thermal Infrared Image Matching

NASA Astrophysics Data System (ADS)

Fu, Z.; Qin, Q.; Wu, C.; Chang, Y.; Luo, B.

2017-09-01

Due to the differences of imaging principles, image matching between visible and thermal infrared images still exist new challenges and difficulties. Inspired by the complementary spatial and frequency information of geometric structural features, a robust descriptor is proposed for visible and thermal infrared images matching. We first divide two different spatial regions to the region around point of interest, using the histogram of oriented magnitudes, which corresponds to the 2-D structural shape information to describe the larger region and the edge oriented histogram to describe the spatial distribution for the smaller region. Then the two vectors are normalized and combined to a higher feature vector. Finally, our proposed descriptor is obtained by applying principal component analysis (PCA) to reduce the dimension of the combined high feature vector to make our descriptor more robust. Experimental results showed that our proposed method was provided with significant improvements in correct matching numbers and obvious advantages by complementing information within spatial and frequency structural information.

Recognition and Matching of Clustered Mature Litchi Fruits Using Binocular Charge-Coupled Device (CCD) Color Cameras

PubMed Central

Wang, Chenglin; Tang, Yunchao; Zou, Xiangjun; Luo, Lufeng; Chen, Xiong

2017-01-01

Recognition and matching of litchi fruits are critical steps for litchi harvesting robots to successfully grasp litchi. However, due to the randomness of litchi growth, such as clustered growth with uncertain number of fruits and random occlusion by leaves, branches and other fruits, the recognition and matching of the fruit become a challenge. Therefore, this study firstly defined mature litchi fruit as three clustered categories. Then an approach for recognition and matching of clustered mature litchi fruit was developed based on litchi color images acquired by binocular charge-coupled device (CCD) color cameras. The approach mainly included three steps: (1) calibration of binocular color cameras and litchi image acquisition; (2) segmentation of litchi fruits using four kinds of supervised classifiers, and recognition of the pre-defined categories of clustered litchi fruit using a pixel threshold method; and (3) matching the recognized clustered fruit using a geometric center-based matching method. The experimental results showed that the proposed recognition method could be robust against the influences of varying illumination and occlusion conditions, and precisely recognize clustered litchi fruit. In the tested 432 clustered litchi fruits, the highest and lowest average recognition rates were 94.17% and 92.00% under sunny back-lighting and partial occlusion, and sunny front-lighting and non-occlusion conditions, respectively. From 50 pairs of tested images, the highest and lowest matching success rates were 97.37% and 91.96% under sunny back-lighting and non-occlusion, and sunny front-lighting and partial occlusion conditions, respectively. PMID:29112177

Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment.

PubMed

Mezgec, Simon; Eftimov, Tome; Bucher, Tamara; Koroušić Seljak, Barbara

2018-04-06

The present study tested the combination of an established and a validated food-choice research method (the 'fake food buffet') with a new food-matching technology to automate the data collection and analysis. The methodology combines fake-food image recognition using deep learning and food matching and standardization based on natural language processing. The former is specific because it uses a single deep learning network to perform both the segmentation and the classification at the pixel level of the image. To assess its performance, measures based on the standard pixel accuracy and Intersection over Union were applied. Food matching firstly describes each of the recognized food items in the image and then matches the food items with their compositional data, considering both their food names and their descriptors. The final accuracy of the deep learning model trained on fake-food images acquired by 124 study participants and providing fifty-five food classes was 92·18 %, while the food matching was performed with a classification accuracy of 93 %. The present findings are a step towards automating dietary assessment and food-choice research. The methodology outperforms other approaches in pixel accuracy, and since it is the first automatic solution for recognizing the images of fake foods, the results could be used as a baseline for possible future studies. As the approach enables a semi-automatic description of recognized food items (e.g. with respect to FoodEx2), these can be linked to any food composition database that applies the same classification and description system.

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

The Effect of Shadow Area on Sgm Algorithm and Disparity Map Refinement from High Resolution Satellite Stereo Images

NASA Astrophysics Data System (ADS)

Tatar, N.; Saadatseresht, M.; Arefi, H.

2017-09-01

Semi Global Matching (SGM) algorithm is known as a high performance and reliable stereo matching algorithm in photogrammetry community. However, there are some challenges using this algorithm especially for high resolution satellite stereo images over urban areas and images with shadow areas. As it can be seen, unfortunately the SGM algorithm computes highly noisy disparity values for shadow areas around the tall neighborhood buildings due to mismatching in these lower entropy areas. In this paper, a new method is developed to refine the disparity map in shadow areas. The method is based on the integration of potential of panchromatic and multispectral image data to detect shadow areas in object level. In addition, a RANSAC plane fitting and morphological filtering are employed to refine the disparity map. The results on a stereo pair of GeoEye-1 captured over Qom city in Iran, shows a significant increase in the rate of matched pixels compared to standard SGM algorithm.

Automatic target detection using binary template matching

NASA Astrophysics Data System (ADS)

Jun, Dong-San; Sun, Sun-Gu; Park, HyunWook

2005-03-01

This paper presents a new automatic target detection (ATD) algorithm to detect targets such as battle tanks and armored personal carriers in ground-to-ground scenarios. Whereas most ATD algorithms were developed for forward-looking infrared (FLIR) images, we have developed an ATD algorithm for charge-coupled device (CCD) images, which have superior quality to FLIR images in daylight. The proposed algorithm uses fast binary template matching with an adaptive binarization, which is robust to various light conditions in CCD images and saves computation time. Experimental results show that the proposed method has good detection performance.

Multi-Image Registration for an Enhanced Vision System

NASA Technical Reports Server (NTRS)

Hines, Glenn; Rahman, Zia-Ur; Jobson, Daniel; Woodell, Glenn

2002-01-01

An Enhanced Vision System (EVS) utilizing multi-sensor image fusion is currently under development at the NASA Langley Research Center. The EVS will provide enhanced images of the flight environment to assist pilots in poor visibility conditions. Multi-spectral images obtained from a short wave infrared (SWIR), a long wave infrared (LWIR), and a color visible band CCD camera, are enhanced and fused using the Retinex algorithm. The images from the different sensors do not have a uniform data structure: the three sensors not only operate at different wavelengths, but they also have different spatial resolutions, optical fields of view (FOV), and bore-sighting inaccuracies. Thus, in order to perform image fusion, the images must first be co-registered. Image registration is the task of aligning images taken at different times, from different sensors, or from different viewpoints, so that all corresponding points in the images match. In this paper, we present two methods for registering multiple multi-spectral images. The first method performs registration using sensor specifications to match the FOVs and resolutions directly through image resampling. In the second method, registration is obtained through geometric correction based on a spatial transformation defined by user selected control points and regression analysis.

Generation of synthetic image sequences for the verification of matching and tracking algorithms for deformation analysis

NASA Astrophysics Data System (ADS)

Bethmann, F.; Jepping, C.; Luhmann, T.

2013-04-01

This paper reports on a method for the generation of synthetic image data for almost arbitrary static or dynamic 3D scenarios. Image data generation is based on pre-defined 3D objects, object textures, camera orientation data and their imaging properties. The procedure does not focus on the creation of photo-realistic images under consideration of complex imaging and reflection models as they are used by common computer graphics programs. In contrast, the method is designed with main emphasis on geometrically correct synthetic images without radiometric impact. The calculation process includes photogrammetric distortion models, hence cameras with arbitrary geometric imaging characteristics can be applied. Consequently, image sets can be created that are consistent to mathematical photogrammetric models to be used as sup-pixel accurate data for the assessment of high-precision photogrammetric processing methods. In the first instance the paper describes the process of image simulation under consideration of colour value interpolation, MTF/PSF and so on. Subsequently the geometric quality of the synthetic images is evaluated with ellipse operators. Finally, simulated image sets are used to investigate matching and tracking algorithms as they have been developed at IAPG for deformation measurement in car safety testing.

A novel methodology for querying web images

NASA Astrophysics Data System (ADS)

Prabhakara, Rashmi; Lee, Ching Cheng

2005-01-01

Ever since the advent of Internet, there has been an immense growth in the amount of image data that is available on the World Wide Web. With such a magnitude of image availability, an efficient and effective image retrieval system is required to make use of this information. This research presents an effective image matching and indexing technique that improvises on existing integrated image retrieval methods. The proposed technique follows a two-phase approach, integrating query by topic and query by example specification methods. The first phase consists of topic-based image retrieval using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. It consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. The second phase uses the query by example specification to perform a low-level content-based image match for the retrieval of smaller and relatively closer results of the example image. Information related to the image feature is automatically extracted from the query image by the image processing system. A technique that is not computationally intensive based on color feature is used to perform content-based matching of images. The main goal is to develop a functional image search and indexing system and to demonstrate that better retrieval results can be achieved with this proposed hybrid search technique.

A novel methodology for querying web images

NASA Astrophysics Data System (ADS)

Prabhakara, Rashmi; Lee, Ching Cheng

2004-12-01

Ever since the advent of Internet, there has been an immense growth in the amount of image data that is available on the World Wide Web. With such a magnitude of image availability, an efficient and effective image retrieval system is required to make use of this information. This research presents an effective image matching and indexing technique that improvises on existing integrated image retrieval methods. The proposed technique follows a two-phase approach, integrating query by topic and query by example specification methods. The first phase consists of topic-based image retrieval using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. It consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. The second phase uses the query by example specification to perform a low-level content-based image match for the retrieval of smaller and relatively closer results of the example image. Information related to the image feature is automatically extracted from the query image by the image processing system. A technique that is not computationally intensive based on color feature is used to perform content-based matching of images. The main goal is to develop a functional image search and indexing system and to demonstrate that better retrieval results can be achieved with this proposed hybrid search technique.

Matching forensic sketches to mug shot photos.

PubMed

Klare, Brendan F; Li, Zhifeng; Jain, Anil K

2011-03-01

The problem of matching a forensic sketch to a gallery of mug shot images is addressed in this paper. Previous research in sketch matching only offered solutions to matching highly accurate sketches that were drawn while looking at the subject (viewed sketches). Forensic sketches differ from viewed sketches in that they are drawn by a police sketch artist using the description of the subject provided by an eyewitness. To identify forensic sketches, we present a framework called local feature-based discriminant analysis (LFDA). In LFDA, we individually represent both sketches and photos using SIFT feature descriptors and multiscale local binary patterns (MLBP). Multiple discriminant projections are then used on partitioned vectors of the feature-based representation for minimum distance matching. We apply this method to match a data set of 159 forensic sketches against a mug shot gallery containing 10,159 images. Compared to a leading commercial face recognition system, LFDA offers substantial improvements in matching forensic sketches to the corresponding face images. We were able to further improve the matching performance using race and gender information to reduce the target gallery size. Additional experiments demonstrate that the proposed framework leads to state-of-the-art accuracys when matching viewed sketches.

Personal authentication using hand vein triangulation and knuckle shape.

PubMed

Kumar, Ajay; Prathyusha, K Venkata

2009-09-01

This paper presents a new approach to authenticate individuals using triangulation of hand vein images and simultaneous extraction of knuckle shape information. The proposed method is fully automated and employs palm dorsal hand vein images acquired from the low-cost, near infrared, contactless imaging. The knuckle tips are used as key points for the image normalization and extraction of region of interest. The matching scores are generated in two parallel stages: (i) hierarchical matching score from the four topologies of triangulation in the binarized vein structures and (ii) from the geometrical features consisting of knuckle point perimeter distances in the acquired images. The weighted score level combination from these two matching scores are used to authenticate the individuals. The achieved experimental results from the proposed system using contactless palm dorsal-hand vein images are promising (equal error rate of 1.14%) and suggest more user friendly alternative for user identification.

Derivation of planetary topography using multi-image shape-from-shading

USGS Publications Warehouse

Lohse, V.; Heipke, C.; Kirk, R.L.

2006-01-01

In many cases, the derivation of high-resolution digital terrain models (DTMs) from planetary surfaces using conventional digital image matching is a problem. The matching methods need at least one stereo pair of images with sufficient texture. However, many space missions provide only a few stereo images and planetary surfaces often possess insufficient texture. This paper describes a method for the generation of high-resolution DTMs from planetary surfaces, which has the potential to overcome the described problem. The suggested method, developed by our group, is based on shape-from-shading using an arbitrary number of digital optical images, and is termed "multi-image shape-from-shading" (MI-SFS). The paper contains an explanation of the theory of MI-SFS, followed by a presentation of current results, which were obtained using images from NASA's lunar mission Clementine, and constitute the first practical application with our method using extraterrestrial imagery. The lunar surface is reconstructed under the assumption of different kinds of reflectance models (e.g. Lommel-Seeliger and Lambert). The represented results show that the derivation of a high-resolution DTM of real digital planetary images by means of MI-SFS is feasible. ?? 2006 Elsevier Ltd. All rights reserved.

Joint histogram-based cost aggregation for stereo matching.

PubMed

Min, Dongbo; Lu, Jiangbo; Do, Minh N

2013-10-01

This paper presents a novel method for performing efficient cost aggregation in stereo matching. The cost aggregation problem is reformulated from the perspective of a histogram, giving us the potential to reduce the complexity of the cost aggregation in stereo matching significantly. Differently from previous methods which have tried to reduce the complexity in terms of the size of an image and a matching window, our approach focuses on reducing the computational redundancy that exists among the search range, caused by a repeated filtering for all the hypotheses. Moreover, we also reduce the complexity of the window-based filtering through an efficient sampling scheme inside the matching window. The tradeoff between accuracy and complexity is extensively investigated by varying the parameters used in the proposed method. Experimental results show that the proposed method provides high-quality disparity maps with low complexity and outperforms existing local methods. This paper also provides new insights into complexity-constrained stereo-matching algorithm design.

Image-based computer-assisted diagnosis system for benign paroxysmal positional vertigo

NASA Astrophysics Data System (ADS)

Kohigashi, Satoru; Nakamae, Koji; Fujioka, Hiromu

2005-04-01

We develop the image based computer assisted diagnosis system for benign paroxysmal positional vertigo (BPPV) that consists of the balance control system simulator, the 3D eye movement simulator, and the extraction method of nystagmus response directly from an eye movement image sequence. In the system, the causes and conditions of BPPV are estimated by searching the database for record matching with the nystagmus response for the observed eye image sequence of the patient with BPPV. The database includes the nystagmus responses for simulated eye movement sequences. The eye movement velocity is obtained by using the balance control system simulator that allows us to simulate BPPV under various conditions such as canalithiasis, cupulolithiasis, number of otoconia, otoconium size, and so on. Then the eye movement image sequence is displayed on the CRT by the 3D eye movement simulator. The nystagmus responses are extracted from the image sequence by the proposed method and are stored in the database. In order to enhance the diagnosis accuracy, the nystagmus response for a newly simulated sequence is matched with that for the observed sequence. From the matched simulation conditions, the causes and conditions of BPPV are estimated. We apply our image based computer assisted diagnosis system to two real eye movement image sequences for patients with BPPV to show its validity.

Object-Based Dense Matching Method for Maintaining Structure Characteristics of Linear Buildings

PubMed Central

Yan, Yiming; Qiu, Mingjie; Zhao, Chunhui; Wang, Liguo

2018-01-01

In this paper, we proposed a novel object-based dense matching method specially for the high-precision disparity map of building objects in urban areas, which can maintain accurate object structure characteristics. The proposed framework mainly includes three stages. Firstly, an improved edge line extraction method is proposed for the edge segments to fit closely to building outlines. Secondly, a fusion method is proposed for the outlines under the constraint of straight lines, which can maintain the building structural attribute with parallel or vertical edges, which is very useful for the dense matching method. Finally, we proposed an edge constraint and outline compensation (ECAOC) dense matching method to maintain building object structural characteristics in the disparity map. In the proposed method, the improved edge lines are used to optimize matching search scope and matching template window, and the high-precision building outlines are used to compensate the shape feature of building objects. Our method can greatly increase the matching accuracy of building objects in urban areas, especially at building edges. For the outline extraction experiments, our fusion method verifies the superiority and robustness on panchromatic images of different satellites and different resolutions. For the dense matching experiments, our ECOAC method shows great advantages for matching accuracy of building objects in urban areas compared with three other methods. PMID:29596393

Four-dimensional dose reconstruction through in vivo phase matching of cine images of electronic portal imaging device.

PubMed

Yoon, Jihyung; Jung, Jae Won; Kim, Jong Oh; Yi, Byong Yong; Yeo, Inhwan

2016-07-01

A method is proposed to reconstruct a four-dimensional (4D) dose distribution using phase matching of measured cine images to precalculated images of electronic portal imaging device (EPID). (1) A phantom, designed to simulate a tumor in lung (a polystyrene block with a 3 cm diameter embedded in cork), was placed on a sinusoidally moving platform with an amplitude of 1 cm and a period of 4 s. Ten-phase 4D computed tomography (CT) images of the phantom were acquired. A planning target volume (PTV) was created by adding a margin of 1 cm around the internal target volume of the tumor. (2) Three beams were designed, which included a static beam, a theoretical dynamic beam, and a planning-optimized dynamic beam (PODB). While the theoretical beam was made by manually programming a simplistic sliding leaf motion, the planning-optimized beam was obtained from treatment planning. From the three beams, three-dimensional (3D) doses on the phantom were calculated; 4D dose was calculated by means of the ten phase images (integrated over phases afterward); serving as "reference" images, phase-specific EPID dose images under the lung phantom were also calculated for each of the ten phases. (3) Cine EPID images were acquired while the beams were irradiated to the moving phantom. (4) Each cine image was phase-matched to a phase-specific CT image at which common irradiation occurred by intercomparing the cine image with the reference images. (5) Each cine image was used to reconstruct dose in the phase-matched CT image, and the reconstructed doses were summed over all phases. (6) The summation was compared with forwardly calculated 4D and 3D dose distributions. Accounting for realistic situations, intratreatment breathing irregularity was simulated by assuming an amplitude of 0.5 cm for the phantom during a portion of breathing trace in which the phase matching could not be performed. Intertreatment breathing irregularity between the time of treatment and the time of planning CT was considered by utilizing the same reduced amplitude when the phantom was irradiated. To examine the phase matching in a humanoid environment, the matching was also performed in a digital phantom (4D XCAT phantom). For the static, the theoretical, and the planning-optimized dynamic beams, the 4D reconstructed doses showed agreement with the forwardly calculated 4D doses within the gamma pass rates of 92.7%, 100%, and 98.1%, respectively, at the isocenter plane given by 3%/3 mm criteria. Excellent agreement in dose volume histogram of PTV and lung-PTV was also found between the two 4D doses, while substantial differences were found between the 3D and the 4D doses. The significant breathing irregularities modeled in this study were found not to be noticeably affecting the reconstructed dose. The phase matching was performed equally well in a digital phantom. The method of retrospective phase determination of a moving object under irradiation provided successful 4D dose reconstruction. This method will provide accurate quality assurance and facilitate adaptive therapy when distinguishable objects such as well-defined tumors, diaphragm, and organs with markers (pancreas and liver) are covered by treatment beam apertures.

Iris Matching Based on Personalized Weight Map.

PubMed

Dong, Wenbo; Sun, Zhenan; Tan, Tieniu

2011-09-01

Iris recognition typically involves three steps, namely, iris image preprocessing, feature extraction, and feature matching. The first two steps of iris recognition have been well studied, but the last step is less addressed. Each human iris has its unique visual pattern and local image features also vary from region to region, which leads to significant differences in robustness and distinctiveness among the feature codes derived from different iris regions. However, most state-of-the-art iris recognition methods use a uniform matching strategy, where features extracted from different regions of the same person or the same region for different individuals are considered to be equally important. This paper proposes a personalized iris matching strategy using a class-specific weight map learned from the training images of the same iris class. The weight map can be updated online during the iris recognition procedure when the successfully recognized iris images are regarded as the new training data. The weight map reflects the robustness of an encoding algorithm on different iris regions by assigning an appropriate weight to each feature code for iris matching. Such a weight map trained by sufficient iris templates is convergent and robust against various noise. Extensive and comprehensive experiments demonstrate that the proposed personalized iris matching strategy achieves much better iris recognition performance than uniform strategies, especially for poor quality iris images.

Matching shapes with self-intersections: application to leaf classification.

PubMed

Mokhtarian, Farzin; Abbasi, Sadegh

2004-05-01

We address the problem of two-dimensional (2-D) shape representation and matching in presence of self-intersection for large image databases. This may occur when part of an object is hidden behind another part and results in a darker section in the gray level image of the object. The boundary contour of the object must include the boundary of this part which is entirely inside the outline of the object. The Curvature Scale Space (CSS) image of a shape is a multiscale organization of its inflection points as it is smoothed. The CSS-based shape representation method has been selected for MPEG-7 standardization. We study the effects of contour self-intersection on the Curvature Scale Space image. When there is no self-intersection, the CSS image contains several arch shape contours, each related to a concavity or a convexity of the shape. Self intersections create contours with minima as well as maxima in the CSS image. An efficient shape representation method has been introduced in this paper which describes a shape using the maxima as well as the minima of its CSS contours. This is a natural generalization of the conventional method which only includes the maxima of the CSS image contours. The conventional matching algorithm has also been modified to accommodate the new information about the minima. The method has been successfully used in a real world application to find, for an unknown leaf, similar classes from a database of classified leaf images representing different varieties of chrysanthemum. For many classes of leaves, self-intersection is inevitable during the scanning of the image. Therefore the original contributions of this paper is the generalization of the Curvature Scale Space representation to the class of 2-D contours with self-intersection, and its application to the classification of Chrysanthemum leaves.

Improved Seam-Line Searching Algorithm for UAV Image Mosaic with Optical Flow.

PubMed

Zhang, Weilong; Guo, Bingxuan; Li, Ming; Liao, Xuan; Li, Wenzhuo

2018-04-16

Ghosting and seams are two major challenges in creating unmanned aerial vehicle (UAV) image mosaic. In response to these problems, this paper proposes an improved method for UAV image seam-line searching. First, an image matching algorithm is used to extract and match the features of adjacent images, so that they can be transformed into the same coordinate system. Then, the gray scale difference, the gradient minimum, and the optical flow value of pixels in adjacent image overlapped area in a neighborhood are calculated, which can be applied to creating an energy function for seam-line searching. Based on that, an improved dynamic programming algorithm is proposed to search the optimal seam-lines to complete the UAV image mosaic. This algorithm adopts a more adaptive energy aggregation and traversal strategy, which can find a more ideal splicing path for adjacent UAV images and avoid the ground objects better. The experimental results show that the proposed method can effectively solve the problems of ghosting and seams in the panoramic UAV images.

Practical method for appearance match between soft copy and hard copy

NASA Astrophysics Data System (ADS)

Katoh, Naoya

1994-04-01

CRT monitors are often used as a soft proofing device for the hard copy image output. However, what the user sees on the monitor does not match its output, even if the monitor and the output device are calibrated with CIE/XYZ or CIE/Lab. This is especially obvious when correlated color temperature (CCT) of CRT monitor's white point significantly differs from ambient light. In a typical office environment, one uses a computer graphic monitor having a CCT of 9300K in a room of white fluorescent light of 4150K CCT. In such a case, human visual system is partially adapted to the CRT monitor's white point and partially to the ambient light. The visual experiments were performed on the effect of the ambient lighting. Practical method for soft copy color reproduction that matches the hard copy image in appearance is presented in this paper. This method is fundamentally based on a simple von Kries' adaptation model and takes into account the human visual system's partial adaptation and contrast matching.

Using image mapping towards biomedical and biological data sharing

PubMed Central

2013-01-01

Image-based data integration in eHealth and life sciences is typically concerned with the method used for anatomical space mapping, needed to retrieve, compare and analyse large volumes of biomedical data. In mapping one image onto another image, a mechanism is used to match and find the corresponding spatial regions which have the same meaning between the source and the matching image. Image-based data integration is useful for integrating data of various information structures. Here we discuss a broad range of issues related to data integration of various information structures, review exemplary work on image representation and mapping, and discuss the challenges that these techniques may bring. PMID:24059352

Local motion compensation in image sequences degraded by atmospheric turbulence: a comparative analysis of optical flow vs. block matching methods

NASA Astrophysics Data System (ADS)

Huebner, Claudia S.

2016-10-01

As a consequence of fluctuations in the index of refraction of the air, atmospheric turbulence causes scintillation, spatial and temporal blurring as well as global and local image motion creating geometric distortions. To mitigate these effects many different methods have been proposed. Global as well as local motion compensation in some form or other constitutes an integral part of many software-based approaches. For the estimation of motion vectors between consecutive frames simple methods like block matching are preferable to more complex algorithms like optical flow, at least when challenged with near real-time requirements. However, the processing power of commercially available computers continues to increase rapidly and the more powerful optical flow methods have the potential to outperform standard block matching methods. Therefore, in this paper three standard optical flow algorithms, namely Horn-Schunck (HS), Lucas-Kanade (LK) and Farnebäck (FB), are tested for their suitability to be employed for local motion compensation as part of a turbulence mitigation system. Their qualitative performance is evaluated and compared with that of three standard block matching methods, namely Exhaustive Search (ES), Adaptive Rood Pattern Search (ARPS) and Correlation based Search (CS).

Advanced Tie Feature Matching for the Registration of Mobile Mapping Imaging Data and Aerial Imagery

NASA Astrophysics Data System (ADS)

Jende, P.; Peter, M.; Gerke, M.; Vosselman, G.

2016-06-01

Mobile Mapping's ability to acquire high-resolution ground data is opposing unreliable localisation capabilities of satellite-based positioning systems in urban areas. Buildings shape canyons impeding a direct line-of-sight to navigation satellites resulting in a deficiency to accurately estimate the mobile platform's position. Consequently, acquired data products' positioning quality is considerably diminished. This issue has been widely addressed in the literature and research projects. However, a consistent compliance of sub-decimetre accuracy as well as a correction of errors in height remain unsolved. We propose a novel approach to enhance Mobile Mapping (MM) image orientation based on the utilisation of highly accurate orientation parameters derived from aerial imagery. In addition to that, the diminished exterior orientation parameters of the MM platform will be utilised as they enable the application of accurate matching techniques needed to derive reliable tie information. This tie information will then be used within an adjustment solution to correct affected MM data. This paper presents an advanced feature matching procedure as a prerequisite to the aforementioned orientation update. MM data is ortho-projected to gain a higher resemblance to aerial nadir data simplifying the images' geometry for matching. By utilising MM exterior orientation parameters, search windows may be used in conjunction with a selective keypoint detection and template matching. Originating from different sensor systems, however, difficulties arise with respect to changes in illumination, radiometry and a different original perspective. To respond to these challenges for feature detection, the procedure relies on detecting keypoints in only one image. Initial tests indicate a considerable improvement in comparison to classic detector/descriptor approaches in this particular matching scenario. This method leads to a significant reduction of outliers due to the limited availability of putative matches and the utilisation of templates instead of feature descriptors. In our experiments discussed in this paper, typical urban scenes have been used for evaluating the proposed method. Even though no additional outlier removal techniques have been used, our method yields almost 90% of correct correspondences. However, repetitive image patterns may still induce ambiguities which cannot be fully averted by this technique. Hence and besides, possible advancements will be briefly presented.

Image multiplexing and authentication based on double phase retrieval in fresnel transform domain

NASA Astrophysics Data System (ADS)

Chang, Hsuan-Ting; Lin, Che-Hsian; Chen, Chien-Yue

2017-04-01

An image multiplexing and authentication method based on the double-phase retrieval algorithm (DPRA) with the manipulations of wavelength and position in the Fresnel transform (FrT) domain is proposed in this study. The DPRA generates two matched phase-only functions (POFs) in the different planes so that the corresponding image can be reconstructed at the output plane. Given a number of target images, all the sets of matched POFs are used to generate the phase-locked system through the phase modulation and synthesis to achieve the multiplexing purpose. To reconstruct a target image, the corresponding phase key and all the correct parameters in the FrT are required. Therefore, the authentication system with high-level security can be achieved. The computer simulation verifies the validity of the proposed method and also shows good resistance to the crosstalk among the reconstructed images.

Non-imaged based method for matching brains in a common anatomical space for cellular imagery.

PubMed

Midroit, Maëllie; Thevenet, Marc; Fournel, Arnaud; Sacquet, Joelle; Bensafi, Moustafa; Breton, Marine; Chalençon, Laura; Cavelius, Matthias; Didier, Anne; Mandairon, Nathalie

2018-04-22

Cellular imagery using histology sections is one of the most common techniques used in Neuroscience. However, this inescapable technique has severe limitations due to the need to delineate regions of interest on each brain, which is time consuming and variable across experimenters. We developed algorithms based on a vectors field elastic registration allowing fast, automatic realignment of experimental brain sections and associated labeling in a brain atlas with high accuracy and in a streamlined way. Thereby, brain areas of interest can be finely identified without outlining them and different experimental groups can be easily analyzed using conventional tools. This method directly readjusts labeling in the brain atlas without any intermediate manipulation of images. We mapped the expression of cFos, in the mouse brain (C57Bl/6J) after olfactory stimulation or a non-stimulated control condition and found an increased density of cFos-positive cells in the primary olfactory cortex but not in non-olfactory areas of the odor-stimulated animals compared to the controls. Existing methods of matching are based on image registration which often requires expensive material (two-photon tomography mapping or imaging with iDISCO) or are less accurate since they are based on mutual information contained in the images. Our new method is non-imaged based and relies only on the positions of detected labeling and the external contours of sections. We thus provide a new method that permits automated matching of histology sections of experimental brains with a brain reference atlas. Copyright © 2018 Elsevier B.V. All rights reserved.

A target detection multi-layer matched filter for color and hyperspectral cameras

NASA Astrophysics Data System (ADS)

Miyanishi, Tomoya; Preece, Bradley L.; Reynolds, Joseph P.

2018-05-01

In this article, a method for applying matched filters to a 3-dimentional hyperspectral data cube is discussed. In many applications, color visible cameras or hyperspectral cameras are used for target detection where the color or spectral optical properties of the imaged materials are partially known in advance. Therefore, the use of matched filtering with spectral data along with shape data is an effective method for detecting certain targets. Since many methods for 2D image filtering have been researched, we propose a multi-layer filter where ordinary spatially matched filters are used before the spectral filters. We discuss a way to layer the spectral filters for a 3D hyperspectral data cube, accompanied by a detectability metric for calculating the SNR of the filter. This method is appropriate for visible color cameras and hyperspectral cameras. We also demonstrate an analysis using the Night Vision Integrated Performance Model (NV-IPM) and a Monte Carlo simulation in order to confirm the effectiveness of the filtering in providing a higher output SNR and a lower false alarm rate.

Skeletal camera network embedded structure-from-motion for 3D scene reconstruction from UAV images

NASA Astrophysics Data System (ADS)

Xu, Zhihua; Wu, Lixin; Gerke, Markus; Wang, Ran; Yang, Huachao

2016-11-01

Structure-from-Motion (SfM) techniques have been widely used for 3D scene reconstruction from multi-view images. However, due to the large computational costs of SfM methods there is a major challenge in processing highly overlapping images, e.g. images from unmanned aerial vehicles (UAV). This paper embeds a novel skeletal camera network (SCN) into SfM to enable efficient 3D scene reconstruction from a large set of UAV images. First, the flight control data are used within a weighted graph to construct a topologically connected camera network (TCN) to determine the spatial connections between UAV images. Second, the TCN is refined using a novel hierarchical degree bounded maximum spanning tree to generate a SCN, which contains a subset of edges from the TCN and ensures that each image is involved in at least a 3-view configuration. Third, the SCN is embedded into the SfM to produce a novel SCN-SfM method, which allows performing tie-point matching only for the actually connected image pairs. The proposed method was applied in three experiments with images from two fixed-wing UAVs and an octocopter UAV, respectively. In addition, the SCN-SfM method was compared to three other methods for image connectivity determination. The comparison shows a significant reduction in the number of matched images if our method is used, which leads to less computational costs. At the same time the achieved scene completeness and geometric accuracy are comparable.

New development of the image matching algorithm

NASA Astrophysics Data System (ADS)

Zhang, Xiaoqiang; Feng, Zhao

2018-04-01

To study the image matching algorithm, algorithm four elements are described, i.e., similarity measurement, feature space, search space and search strategy. Four common indexes for evaluating the image matching algorithm are described, i.e., matching accuracy, matching efficiency, robustness and universality. Meanwhile, this paper describes the principle of image matching algorithm based on the gray value, image matching algorithm based on the feature, image matching algorithm based on the frequency domain analysis, image matching algorithm based on the neural network and image matching algorithm based on the semantic recognition, and analyzes their characteristics and latest research achievements. Finally, the development trend of image matching algorithm is discussed. This study is significant for the algorithm improvement, new algorithm design and algorithm selection in practice.

A Parallel Point Matching Algorithm for Landmark Based Image Registration Using Multicore Platform

PubMed Central

Yang, Lin; Gong, Leiguang; Zhang, Hong; Nosher, John L.; Foran, David J.

2013-01-01

Point matching is crucial for many computer vision applications. Establishing the correspondence between a large number of data points is a computationally intensive process. Some point matching related applications, such as medical image registration, require real time or near real time performance if applied to critical clinical applications like image assisted surgery. In this paper, we report a new multicore platform based parallel algorithm for fast point matching in the context of landmark based medical image registration. We introduced a non-regular data partition algorithm which utilizes the K-means clustering algorithm to group the landmarks based on the number of available processing cores, which optimize the memory usage and data transfer. We have tested our method using the IBM Cell Broadband Engine (Cell/B.E.) platform. The results demonstrated a significant speed up over its sequential implementation. The proposed data partition and parallelization algorithm, though tested only on one multicore platform, is generic by its design. Therefore the parallel algorithm can be extended to other computing platforms, as well as other point matching related applications. PMID:24308014

3D range-gated super-resolution imaging based on stereo matching for moving platforms and targets

NASA Astrophysics Data System (ADS)

Sun, Liang; Wang, Xinwei; Zhou, Yan

2017-11-01

3D range-gated superresolution imaging is a novel 3D reconstruction technique for target detection and recognition with good real-time performance. However, for moving targets or platforms such as airborne, shipborne, remote operated vehicle and autonomous vehicle, 3D reconstruction has a large error or failure. In order to overcome this drawback, we propose a method of stereo matching for 3D range-gated superresolution reconstruction algorithm. In experiment, the target is a doll of Mario with a height of 38cm at the location of 34m, and we obtain two successive frame images of the Mario. To confirm our method is effective, we transform the original images with translation, rotation, scale and perspective, respectively. The experimental result shows that our method has a good result of 3D reconstruction for moving targets or platforms.

A fully convolutional networks (FCN) based image segmentation algorithm in binocular imaging system

NASA Astrophysics Data System (ADS)

Long, Zourong; Wei, Biao; Feng, Peng; Yu, Pengwei; Liu, Yuanyuan

2018-01-01

This paper proposes an image segmentation algorithm with fully convolutional networks (FCN) in binocular imaging system under various circumstance. Image segmentation is perfectly solved by semantic segmentation. FCN classifies the pixels, so as to achieve the level of image semantic segmentation. Different from the classical convolutional neural networks (CNN), FCN uses convolution layers instead of the fully connected layers. So it can accept image of arbitrary size. In this paper, we combine the convolutional neural network and scale invariant feature matching to solve the problem of visual positioning under different scenarios. All high-resolution images are captured with our calibrated binocular imaging system and several groups of test data are collected to verify this method. The experimental results show that the binocular images are effectively segmented without over-segmentation. With these segmented images, feature matching via SURF method is implemented to obtain regional information for further image processing. The final positioning procedure shows that the results are acceptable in the range of 1.4 1.6 m, the distance error is less than 10mm.

On the use of INS to improve Feature Matching

NASA Astrophysics Data System (ADS)

Masiero, A.; Guarnieri, A.; Vettore, A.; Pirotti, F.

2014-11-01

The continuous technological improvement of mobile devices opens the frontiers of Mobile Mapping systems to very compact systems, i.e. a smartphone or a tablet. This motivates the development of efficient 3D reconstruction techniques based on the sensors typically embedded in such devices, i.e. imaging sensors, GPS and Inertial Navigation System (INS). Such methods usually exploits photogrammetry techniques (structure from motion) to provide an estimation of the geometry of the scene. Actually, 3D reconstruction techniques (e.g. structure from motion) rely on use of features properly matched in different images to compute the 3D positions of objects by means of triangulation. Hence, correct feature matching is of fundamental importance to ensure good quality 3D reconstructions. Matching methods are based on the appearance of features, that can change as a consequence of variations of camera position and orientation, and environment illumination. For this reason, several methods have been developed in recent years in order to provide feature descriptors robust (ideally invariant) to such variations, e.g. Scale-Invariant Feature Transform (SIFT), Affine SIFT, Hessian affine and Harris affine detectors, Maximally Stable Extremal Regions (MSER). This work deals with the integration of information provided by the INS in the feature matching procedure: a previously developed navigation algorithm is used to constantly estimate the device position and orientation. Then, such information is exploited to estimate the transformation of feature regions between two camera views. This allows to compare regions from different images but associated to the same feature as seen by the same point of view, hence significantly easing the comparison of feature characteristics and, consequently, improving matching. SIFT-like descriptors are used in order to ensure good matching results in presence of illumination variations and to compensate the approximations related to the estimation process.

Depth map generation using a single image sensor with phase masks.

PubMed

Jang, Jinbeum; Park, Sangwoo; Jo, Jieun; Paik, Joonki

2016-06-13

Conventional stereo matching systems generate a depth map using two or more digital imaging sensors. It is difficult to use the small camera system because of their high costs and bulky sizes. In order to solve this problem, this paper presents a stereo matching system using a single image sensor with phase masks for the phase difference auto-focusing. A novel pattern of phase mask array is proposed to simultaneously acquire two pairs of stereo images. Furthermore, a noise-invariant depth map is generated from the raw format sensor output. The proposed method consists of four steps to compute the depth map: (i) acquisition of stereo images using the proposed mask array, (ii) variational segmentation using merging criteria to simplify the input image, (iii) disparity map generation using the hierarchical block matching for disparity measurement, and (iv) image matting to fill holes to generate the dense depth map. The proposed system can be used in small digital cameras without additional lenses or sensors.

Geometric registration of images by similarity transformation using two reference points

NASA Technical Reports Server (NTRS)

Kang, Yong Q. (Inventor); Jo, Young-Heon (Inventor); Yan, Xiao-Hai (Inventor)

2011-01-01

A method for registering a first image to a second image using a similarity transformation. The each image includes a plurality of pixels. The first image pixels are mapped to a set of first image coordinates and the second image pixels are mapped to a set of second image coordinates. The first image coordinates of two reference points in the first image are determined. The second image coordinates of these reference points in the second image are determined. A Cartesian translation of the set of second image coordinates is performed such that the second image coordinates of the first reference point match its first image coordinates. A similarity transformation of the translated set of second image coordinates is performed. This transformation scales and rotates the second image coordinates about the first reference point such that the second image coordinates of the second reference point match its first image coordinates.

Greedy algorithms for diffuse optical tomography reconstruction

NASA Astrophysics Data System (ADS)

Dileep, B. P. V.; Das, Tapan; Dutta, Pranab K.

2018-03-01

Diffuse optical tomography (DOT) is a noninvasive imaging modality that reconstructs the optical parameters of a highly scattering medium. However, the inverse problem of DOT is ill-posed and highly nonlinear due to the zig-zag propagation of photons that diffuses through the cross section of tissue. The conventional DOT imaging methods iteratively compute the solution of forward diffusion equation solver which makes the problem computationally expensive. Also, these methods fail when the geometry is complex. Recently, the theory of compressive sensing (CS) has received considerable attention because of its efficient use in biomedical imaging applications. The objective of this paper is to solve a given DOT inverse problem by using compressive sensing framework and various Greedy algorithms such as orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP), and stagewise orthogonal matching pursuit (StOMP), regularized orthogonal matching pursuit (ROMP) and simultaneous orthogonal matching pursuit (S-OMP) have been studied to reconstruct the change in the absorption parameter i.e, Δα from the boundary data. Also, the Greedy algorithms have been validated experimentally on a paraffin wax rectangular phantom through a well designed experimental set up. We also have studied the conventional DOT methods like least square method and truncated singular value decomposition (TSVD) for comparison. One of the main features of this work is the usage of less number of source-detector pairs, which can facilitate the use of DOT in routine applications of screening. The performance metrics such as mean square error (MSE), normalized mean square error (NMSE), structural similarity index (SSIM), and peak signal to noise ratio (PSNR) have been used to evaluate the performance of the algorithms mentioned in this paper. Extensive simulation results confirm that CS based DOT reconstruction outperforms the conventional DOT imaging methods in terms of computational efficiency. The main advantage of this study is that the forward diffusion equation solver need not be repeatedly solved.

Block matching sparsity regularization-based image reconstruction for incomplete projection data in computed tomography

NASA Astrophysics Data System (ADS)

Cai, Ailong; Li, Lei; Zheng, Zhizhong; Zhang, Hanming; Wang, Linyuan; Hu, Guoen; Yan, Bin

2018-02-01

In medical imaging many conventional regularization methods, such as total variation or total generalized variation, impose strong prior assumptions which can only account for very limited classes of images. A more reasonable sparse representation frame for images is still badly needed. Visually understandable images contain meaningful patterns, and combinations or collections of these patterns can be utilized to form some sparse and redundant representations which promise to facilitate image reconstructions. In this work, we propose and study block matching sparsity regularization (BMSR) and devise an optimization program using BMSR for computed tomography (CT) image reconstruction for an incomplete projection set. The program is built as a constrained optimization, minimizing the L1-norm of the coefficients of the image in the transformed domain subject to data observation and positivity of the image itself. To solve the program efficiently, a practical method based on the proximal point algorithm is developed and analyzed. In order to accelerate the convergence rate, a practical strategy for tuning the BMSR parameter is proposed and applied. The experimental results for various settings, including real CT scanning, have verified the proposed reconstruction method showing promising capabilities over conventional regularization.

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

PubMed

Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

2017-11-01

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

The selection of the optimal baseline in the front-view monocular vision system

NASA Astrophysics Data System (ADS)

Xiong, Bincheng; Zhang, Jun; Zhang, Daimeng; Liu, Xiaomao; Tian, Jinwen

2018-03-01

In the front-view monocular vision system, the accuracy of solving the depth field is related to the length of the inter-frame baseline and the accuracy of image matching result. In general, a longer length of the baseline can lead to a higher precision of solving the depth field. However, at the same time, the difference between the inter-frame images increases, which increases the difficulty in image matching and the decreases matching accuracy and at last may leads to the failure of solving the depth field. One of the usual practices is to use the tracking and matching method to improve the matching accuracy between images, but this algorithm is easy to cause matching drift between images with large interval, resulting in cumulative error in image matching, and finally the accuracy of solving the depth field is still very low. In this paper, we propose a depth field fusion algorithm based on the optimal length of the baseline. Firstly, we analyze the quantitative relationship between the accuracy of the depth field calculation and the length of the baseline between frames, and find the optimal length of the baseline by doing lots of experiments; secondly, we introduce the inverse depth filtering technique for sparse SLAM, and solve the depth field under the constraint of the optimal length of the baseline. By doing a large number of experiments, the results show that our algorithm can effectively eliminate the mismatch caused by image changes, and can still solve the depth field correctly in the large baseline scene. Our algorithm is superior to the traditional SFM algorithm in time and space complexity. The optimal baseline obtained by a large number of experiments plays a guiding role in the calculation of the depth field in front-view monocular.

Simultaneous 3D–2D image registration and C-arm calibration: Application to endovascular image-guided interventions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mitrović, Uroš; Pernuš, Franjo; Likar, Boštjan

Purpose: Three-dimensional to two-dimensional (3D–2D) image registration is a key to fusion and simultaneous visualization of valuable information contained in 3D pre-interventional and 2D intra-interventional images with the final goal of image guidance of a procedure. In this paper, the authors focus on 3D–2D image registration within the context of intracranial endovascular image-guided interventions (EIGIs), where the 3D and 2D images are generally acquired with the same C-arm system. The accuracy and robustness of any 3D–2D registration method, to be used in a clinical setting, is influenced by (1) the method itself, (2) uncertainty of initial pose of the 3Dmore » image from which registration starts, (3) uncertainty of C-arm’s geometry and pose, and (4) the number of 2D intra-interventional images used for registration, which is generally one and at most two. The study of these influences requires rigorous and objective validation of any 3D–2D registration method against a highly accurate reference or “gold standard” registration, performed on clinical image datasets acquired in the context of the intervention. Methods: The registration process is split into two sequential, i.e., initial and final, registration stages. The initial stage is either machine-based or template matching. The latter aims to reduce possibly large in-plane translation errors by matching a projection of the 3D vessel model and 2D image. In the final registration stage, four state-of-the-art intrinsic image-based 3D–2D registration methods, which involve simultaneous refinement of rigid-body and C-arm parameters, are evaluated. For objective validation, the authors acquired an image database of 15 patients undergoing cerebral EIGI, for which accurate gold standard registrations were established by fiducial marker coregistration. Results: Based on target registration error, the obtained success rates of 3D to a single 2D image registration after initial machine-based and template matching and final registration involving C-arm calibration were 36%, 73%, and 93%, respectively, while registration accuracy of 0.59 mm was the best after final registration. By compensating in-plane translation errors by initial template matching, the success rates achieved after the final stage improved consistently for all methods, especially if C-arm calibration was performed simultaneously with the 3D–2D image registration. Conclusions: Because the tested methods perform simultaneous C-arm calibration and 3D–2D registration based solely on anatomical information, they have a high potential for automation and thus for an immediate integration into current interventional workflow. One of the authors’ main contributions is also comprehensive and representative validation performed under realistic conditions as encountered during cerebral EIGI.« less

Color calibration and fusion of lens-free and mobile-phone microscopy images for high-resolution and accurate color reproduction

NASA Astrophysics Data System (ADS)

Zhang, Yibo; Wu, Yichen; Zhang, Yun; Ozcan, Aydogan

2016-06-01

Lens-free holographic microscopy can achieve wide-field imaging in a cost-effective and field-portable setup, making it a promising technique for point-of-care and telepathology applications. However, due to relatively narrow-band sources used in holographic microscopy, conventional colorization methods that use images reconstructed at discrete wavelengths, corresponding to e.g., red (R), green (G) and blue (B) channels, are subject to color artifacts. Furthermore, these existing RGB colorization methods do not match the chromatic perception of human vision. Here we present a high-color-fidelity and high-resolution imaging method, termed “digital color fusion microscopy” (DCFM), which fuses a holographic image acquired at a single wavelength with a color-calibrated image taken by a low-magnification lens-based microscope using a wavelet transform-based colorization method. We demonstrate accurate color reproduction of DCFM by imaging stained tissue sections. In particular we show that a lens-free holographic microscope in combination with a cost-effective mobile-phone-based microscope can generate color images of specimens, performing very close to a high numerical-aperture (NA) benchtop microscope that is corrected for color distortions and chromatic aberrations, also matching the chromatic response of human vision. This method can be useful for wide-field imaging needs in telepathology applications and in resource-limited settings, where whole-slide scanning microscopy systems are not available.

Color calibration and fusion of lens-free and mobile-phone microscopy images for high-resolution and accurate color reproduction

PubMed Central

Zhang, Yibo; Wu, Yichen; Zhang, Yun; Ozcan, Aydogan

2016-01-01

Lens-free holographic microscopy can achieve wide-field imaging in a cost-effective and field-portable setup, making it a promising technique for point-of-care and telepathology applications. However, due to relatively narrow-band sources used in holographic microscopy, conventional colorization methods that use images reconstructed at discrete wavelengths, corresponding to e.g., red (R), green (G) and blue (B) channels, are subject to color artifacts. Furthermore, these existing RGB colorization methods do not match the chromatic perception of human vision. Here we present a high-color-fidelity and high-resolution imaging method, termed “digital color fusion microscopy” (DCFM), which fuses a holographic image acquired at a single wavelength with a color-calibrated image taken by a low-magnification lens-based microscope using a wavelet transform-based colorization method. We demonstrate accurate color reproduction of DCFM by imaging stained tissue sections. In particular we show that a lens-free holographic microscope in combination with a cost-effective mobile-phone-based microscope can generate color images of specimens, performing very close to a high numerical-aperture (NA) benchtop microscope that is corrected for color distortions and chromatic aberrations, also matching the chromatic response of human vision. This method can be useful for wide-field imaging needs in telepathology applications and in resource-limited settings, where whole-slide scanning microscopy systems are not available. PMID:27283459

Color calibration and fusion of lens-free and mobile-phone microscopy images for high-resolution and accurate color reproduction.

PubMed

Zhang, Yibo; Wu, Yichen; Zhang, Yun; Ozcan, Aydogan

2016-06-10

Lens-free holographic microscopy can achieve wide-field imaging in a cost-effective and field-portable setup, making it a promising technique for point-of-care and telepathology applications. However, due to relatively narrow-band sources used in holographic microscopy, conventional colorization methods that use images reconstructed at discrete wavelengths, corresponding to e.g., red (R), green (G) and blue (B) channels, are subject to color artifacts. Furthermore, these existing RGB colorization methods do not match the chromatic perception of human vision. Here we present a high-color-fidelity and high-resolution imaging method, termed "digital color fusion microscopy" (DCFM), which fuses a holographic image acquired at a single wavelength with a color-calibrated image taken by a low-magnification lens-based microscope using a wavelet transform-based colorization method. We demonstrate accurate color reproduction of DCFM by imaging stained tissue sections. In particular we show that a lens-free holographic microscope in combination with a cost-effective mobile-phone-based microscope can generate color images of specimens, performing very close to a high numerical-aperture (NA) benchtop microscope that is corrected for color distortions and chromatic aberrations, also matching the chromatic response of human vision. This method can be useful for wide-field imaging needs in telepathology applications and in resource-limited settings, where whole-slide scanning microscopy systems are not available.

A fast and automatic mosaic method for high-resolution satellite images

NASA Astrophysics Data System (ADS)

Chen, Hongshun; He, Hui; Xiao, Hongyu; Huang, Jing

2015-12-01

We proposed a fast and fully automatic mosaic method for high-resolution satellite images. First, the overlapped rectangle is computed according to geographical locations of the reference and mosaic images and feature points on both the reference and mosaic images are extracted by a scale-invariant feature transform (SIFT) algorithm only from the overlapped region. Then, the RANSAC method is used to match feature points of both images. Finally, the two images are fused into a seamlessly panoramic image by the simple linear weighted fusion method or other method. The proposed method is implemented in C++ language based on OpenCV and GDAL, and tested by Worldview-2 multispectral images with a spatial resolution of 2 meters. Results show that the proposed method can detect feature points efficiently and mosaic images automatically.

A graph lattice approach to maintaining and learning dense collections of subgraphs as image features.

PubMed

Saund, Eric

2013-10-01

Effective object and scene classification and indexing depend on extraction of informative image features. This paper shows how large families of complex image features in the form of subgraphs can be built out of simpler ones through construction of a graph lattice—a hierarchy of related subgraphs linked in a lattice. Robustness is achieved by matching many overlapping and redundant subgraphs, which allows the use of inexpensive exact graph matching, instead of relying on expensive error-tolerant graph matching to a minimal set of ideal model graphs. Efficiency in exact matching is gained by exploitation of the graph lattice data structure. Additionally, the graph lattice enables methods for adaptively growing a feature space of subgraphs tailored to observed data. We develop the approach in the domain of rectilinear line art, specifically for the practical problem of document forms recognition. We are especially interested in methods that require only one or very few labeled training examples per category. We demonstrate two approaches to using the subgraph features for this purpose. Using a bag-of-words feature vector we achieve essentially single-instance learning on a benchmark forms database, following an unsupervised clustering stage. Further performance gains are achieved on a more difficult dataset using a feature voting method and feature selection procedure.

Sparse Feature Extraction for Pose-Tolerant Face Recognition.

PubMed

Abiantun, Ramzi; Prabhu, Utsav; Savvides, Marios

2014-10-01

Automatic face recognition performance has been steadily improving over years of research, however it remains significantly affected by a number of factors such as illumination, pose, expression, resolution and other factors that can impact matching scores. The focus of this paper is the pose problem which remains largely overlooked in most real-world applications. Specifically, we focus on one-to-one matching scenarios where a query face image of a random pose is matched against a set of gallery images. We propose a method that relies on two fundamental components: (a) A 3D modeling step to geometrically correct the viewpoint of the face. For this purpose, we extend a recent technique for efficient synthesis of 3D face models called 3D Generic Elastic Model. (b) A sparse feature extraction step using subspace modeling and ℓ1-minimization to induce pose-tolerance in coefficient space. This in return enables the synthesis of an equivalent frontal-looking face, which can be used towards recognition. We show significant performance improvements in verification rates compared to commercial matchers, and also demonstrate the resilience of the proposed method with respect to degrading input quality. We find that the proposed technique is able to match non-frontal images to other non-frontal images of varying angles.

Efficient structure from motion for oblique UAV images based on maximal spanning tree expansion

NASA Astrophysics Data System (ADS)

Jiang, San; Jiang, Wanshou

2017-10-01

The primary contribution of this paper is an efficient Structure from Motion (SfM) solution for oblique unmanned aerial vehicle (UAV) images. First, an algorithm, considering spatial relationship constraints between image footprints, is designed for match pair selection with the assistance of UAV flight control data and oblique camera mounting angles. Second, a topological connection network (TCN), represented by an undirected weighted graph, is constructed from initial match pairs, which encodes the overlap areas and intersection angles into edge weights. Then, an algorithm, termed MST-Expansion, is proposed to extract the match graph from the TCN, where the TCN is first simplified by a maximum spanning tree (MST). By further analysis of the local structure in the MST, expansion operations are performed on the vertices of the MST for match graph enhancement, which is achieved by introducing critical connections in the expansion directions. Finally, guided by the match graph, an efficient SfM is proposed. Under extensive analysis and comparison, its performance is verified by using three oblique UAV datasets captured with different multi-camera systems. Experimental results demonstrate that the efficiency of image matching is improved, with speedup ratios ranging from 19 to 35, and competitive orientation accuracy is achieved from both relative bundle adjustment (BA) without GCPs (Ground Control Points) and absolute BA with GCPs. At the same time, images in the three datasets are successfully oriented. For the orientation of oblique UAV images, the proposed method can be a more efficient solution.

Incremental Multi-view 3D Reconstruction Starting from Two Images Taken by a Stereo Pair of Cameras

NASA Astrophysics Data System (ADS)

El hazzat, Soulaiman; Saaidi, Abderrahim; Karam, Antoine; Satori, Khalid

2015-03-01

In this paper, we present a new method for multi-view 3D reconstruction based on the use of a binocular stereo vision system constituted of two unattached cameras to initialize the reconstruction process. Afterwards , the second camera of stereo vision system (characterized by varying parameters) moves to capture more images at different times which are used to obtain an almost complete 3D reconstruction. The first two projection matrices are estimated by using a 3D pattern with known properties. After that, 3D scene points are recovered by triangulation of the matched interest points between these two images. The proposed approach is incremental. At each insertion of a new image, the camera projection matrix is estimated using the 3D information already calculated and new 3D points are recovered by triangulation from the result of the matching of interest points between the inserted image and the previous image. For the refinement of the new projection matrix and the new 3D points, a local bundle adjustment is performed. At first, all projection matrices are estimated, the matches between consecutive images are detected and Euclidean sparse 3D reconstruction is obtained. So, to increase the number of matches and have a more dense reconstruction, the Match propagation algorithm, more suitable for interesting movement of the camera, was applied on the pairs of consecutive images. The experimental results show the power and robustness of the proposed approach.

a Performance Comparison of Feature Detectors for Planetary Rover Mapping and Localization

NASA Astrophysics Data System (ADS)

Wan, W.; Peng, M.; Xing, Y.; Wang, Y.; Liu, Z.; Di, K.; Teng, B.; Mao, X.; Zhao, Q.; Xin, X.; Jia, M.

2017-07-01

Feature detection and matching are key techniques in computer vision and robotics, and have been successfully implemented in many fields. So far there is no performance comparison of feature detectors and matching methods for planetary mapping and rover localization using rover stereo images. In this research, we present a comprehensive evaluation and comparison of six feature detectors, including Moravec, Förstner, Harris, FAST, SIFT and SURF, aiming for optimal implementation of feature-based matching in planetary surface environment. To facilitate quantitative analysis, a series of evaluation criteria, including distribution evenness of matched points, coverage of detected points, and feature matching accuracy, are developed in the research. In order to perform exhaustive evaluation, stereo images, simulated under different baseline, pitch angle, and interval of adjacent rover locations, are taken as experimental data source. The comparison results show that SIFT offers the best overall performance, especially it is less sensitive to changes of image taken at adjacent locations.

Impact of Personal Relevance and Contextualization on Word-Picture Matching by People with Aphasia

ERIC Educational Resources Information Center

McKelvey, Miechelle L.; Hux, Karen; Dietz, Aimee; Beukelman, David R.

2010-01-01

Purpose: To determine the effect of personal relevance and contextualization of images on the preferences and word-picture matching accuracy of people with severe aphasia. Method: Eight adults with aphasia performed 2 experimental tasks to reveal their preferences and accuracy during word-picture matching. The researchers used 3 types of visual…

Robust feature estimation by non-rigid hierarchical image registration and its application in disparity measurement

NASA Astrophysics Data System (ADS)

Badshah, Amir; Choudhry, Aadil Jaleel; Ullah, Shan

2017-03-01

Industries are moving towards automation in order to increase productivity and ensure quality. Variety of electronic and electromagnetic systems are being employed to assist human operator in fast and accurate quality inspection of products. Majority of these systems are equipped with cameras and rely on diverse image processing algorithms. Information is lost in 2D image, therefore acquiring accurate 3D data from 2D images is an open issue. FAST, SURF and SIFT are well-known spatial domain techniques for features extraction and henceforth image registration to find correspondence between images. The efficiency of these methods is measured in terms of the number of perfect matches found. A novel fast and robust technique for stereo-image processing is proposed. It is based on non-rigid registration using modified normalized phase correlation. The proposed method registers two images in hierarchical fashion using quad-tree structure. The registration process works through global to local level resulting in robust matches even in presence of blur and noise. The computed matches can further be utilized to determine disparity and depth for industrial product inspection. The same can be used in driver assistance systems. The preliminary tests on Middlebury dataset produced satisfactory results. The execution time for a 413 x 370 stereo-pair is 500ms approximately on a low cost DSP.

Image-Based Localization Aided Indoor Pedestrian Trajectory Estimation Using Smartphones

PubMed Central

Zhou, Yan; Zheng, Xianwei; Chen, Ruizhi; Xiong, Hanjiang; Guo, Sheng

2018-01-01

Accurately determining pedestrian location in indoor environments using consumer smartphones is a significant step in the development of ubiquitous localization services. Many different map-matching methods have been combined with pedestrian dead reckoning (PDR) to achieve low-cost and bias-free pedestrian tracking. However, this works only in areas with dense map constraints and the error accumulates in open areas. In order to achieve reliable localization without map constraints, an improved image-based localization aided pedestrian trajectory estimation method is proposed in this paper. The image-based localization recovers the pose of the camera from the 2D-3D correspondences between the 2D image positions and the 3D points of the scene model, previously reconstructed by a structure-from-motion (SfM) pipeline. This enables us to determine the initial location and eliminate the accumulative error of PDR when an image is successfully registered. However, the image is not always registered since the traditional 2D-to-3D matching rejects more and more correct matches when the scene becomes large. We thus adopt a robust image registration strategy that recovers initially unregistered images by integrating 3D-to-2D search. In the process, the visibility and co-visibility information is adopted to improve the efficiency when searching for the correspondences from both sides. The performance of the proposed method was evaluated through several experiments and the results demonstrate that it can offer highly acceptable pedestrian localization results in long-term tracking, with an error of only 0.56 m, without the need for dedicated infrastructures. PMID:29342123

Automated Point Cloud Correspondence Detection for Underwater Mapping Using AUVs

NASA Technical Reports Server (NTRS)

Hammond, Marcus; Clark, Ashley; Mahajan, Aditya; Sharma, Sumant; Rock, Stephen

2015-01-01

An algorithm for automating correspondence detection between point clouds composed of multibeam sonar data is presented. This allows accurate initialization for point cloud alignment techniques even in cases where accurate inertial navigation is not available, such as iceberg profiling or vehicles with low-grade inertial navigation systems. Techniques from computer vision literature are used to extract, label, and match keypoints between "pseudo-images" generated from these point clouds. Image matches are refined using RANSAC and information about the vehicle trajectory. The resulting correspondences can be used to initialize an iterative closest point (ICP) registration algorithm to estimate accumulated navigation error and aid in the creation of accurate, self-consistent maps. The results presented use multibeam sonar data obtained from multiple overlapping passes of an underwater canyon in Monterey Bay, California. Using strict matching criteria, the method detects 23 between-swath correspondence events in a set of 155 pseudo-images with zero false positives. Using less conservative matching criteria doubles the number of matches but introduces several false positive matches as well. Heuristics based on known vehicle trajectory information are used to eliminate these.

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors

PubMed Central

Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin

2018-01-01

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison. PMID:29614028

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors.

PubMed

Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin

2018-04-03

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.

An improved ASIFT algorithm for indoor panorama image matching

NASA Astrophysics Data System (ADS)

Fu, Han; Xie, Donghai; Zhong, Ruofei; Wu, Yu; Wu, Qiong

2017-07-01

The generation of 3D models for indoor objects and scenes is an attractive tool for digital city, virtual reality and SLAM purposes. Panoramic images are becoming increasingly more common in such applications due to their advantages to capture the complete environment in one single image with large field of view. The extraction and matching of image feature points are important and difficult steps in three-dimensional reconstruction, and ASIFT is a state-of-the-art algorithm to implement these functions. Compared with the SIFT algorithm, more feature points can be generated and the matching accuracy of ASIFT algorithm is higher, even for the panoramic images with obvious distortions. However, the algorithm is really time-consuming because of complex operations and performs not very well for some indoor scenes under poor light or without rich textures. To solve this problem, this paper proposes an improved ASIFT algorithm for indoor panoramic images: firstly, the panoramic images are projected into multiple normal perspective images. Secondly, the original ASIFT algorithm is simplified from the affine transformation of tilt and rotation with the images to the only tilt affine transformation. Finally, the results are re-projected to the panoramic image space. Experiments in different environments show that this method can not only ensure the precision of feature points extraction and matching, but also greatly reduce the computing time.

SU-C-207B-07: Deep Convolutional Neural Network Image Matching for Ultrasound Guidance in Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, N; Najafi, M; Hancock, S

Purpose: Robust matching of ultrasound images is a challenging problem as images of the same anatomy often present non-trivial differences. This poses an obstacle for ultrasound guidance in radiotherapy. Thus our objective is to overcome this obstacle by designing and evaluating an image blocks matching framework based on a two channel deep convolutional neural network. Methods: We extend to 3D an algorithmic structure previously introduced for 2D image feature learning [1]. To obtain the similarity between two 3D image blocks A and B, the 3D image blocks are divided into 2D patches Ai and Bi. The similarity is then calculatedmore » as the average similarity score of Ai and Bi. The neural network was then trained with public non-medical image pairs, and subsequently evaluated on ultrasound image blocks for the following scenarios: (S1) same image blocks with/without shifts (A and A-shift-x); (S2) non-related random block pairs; (S3) ground truth registration matched pairs of different ultrasound images with/without shifts (A-i and A-reg-i-shift-x). Results: For S1 the similarity scores of A and A-shift-x were 32.63, 18.38, 12.95, 9.23, 2.15 and 0.43 for x=ranging from 0 mm to 10 mm in 2 mm increments. For S2 the average similarity score for non-related block pairs was −1.15. For S3 the average similarity score of ground truth registration matched blocks A-i and A-reg-i-shift-0 (1≤i≤5) was 12.37. After translating A-reg-i-shift-0 by 0 mm, 2 mm, 4 mm, 6 mm, 8 mm, and 10 mm, the average similarity scores of A-i and A-reg-i-shift-x were 11.04, 8.42, 4.56, 2.27, and 0.29 respectively. Conclusion: The proposed method correctly assigns highest similarity to corresponding 3D ultrasound image blocks despite differences in image content and thus can form the basis for ultrasound image registration and tracking.[1] Zagoruyko, Komodakis, “Learning to compare image patches via convolutional neural networks', IEEE CVPR 2015,pp.4353–4361.« less

Efficient moving target analysis for inverse synthetic aperture radar images via joint speeded-up robust features and regular moment

NASA Astrophysics Data System (ADS)

Yang, Hongxin; Su, Fulin

2018-01-01

We propose a moving target analysis algorithm using speeded-up robust features (SURF) and regular moment in inverse synthetic aperture radar (ISAR) image sequences. In our study, we first extract interest points from ISAR image sequences by SURF. Different from traditional feature point extraction methods, SURF-based feature points are invariant to scattering intensity, target rotation, and image size. Then, we employ a bilateral feature registering model to match these feature points. The feature registering scheme can not only search the isotropic feature points to link the image sequences but also reduce the error matching pairs. After that, the target centroid is detected by regular moment. Consequently, a cost function based on correlation coefficient is adopted to analyze the motion information. Experimental results based on simulated and real data validate the effectiveness and practicability of the proposed method.

SU-E-J-140: Availability of Using Diaphragm Matching in Stereotactic Body Radiotherapy (SBRT) at the Time in Breath-Holding SBRT for Liver Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kawahara, D; Tsuda, S.; Section of Radiation Therapy, Department of Clinical Support, Hiroshima University Hospital Health

2014-06-01

Purpose: IGRT based on the bone matching may produce a larger target positioning error in terms of the reproducibility of the expiration breath hold. Therefore, the feasibility of the 3D image matching between planning CT image and pretreatment CBCT image based on the diaphragm matching was investigated. Methods: In fifteen-nine liver SBRT cases, Lipiodol, uptake after TACE was outlined as the marker of the tumor. The relative coordinate of the isocenter obtained by the contrast matching was defined as the reference coordinate. The target positioning difference between diaphragm matching and bone matching were evaluated by the relative coordinate of themore » isocenter from the reference coordinate obtained by each matching technique. In addition, we evaluated PTV margins by van Herk setup margin formula. Results: The target positioning error by the diaphragm matching and the bone matching was 1.31±0.83 and 3.10±2.80 mm in the cranial-caudal(C-C) direction, 1.04±0.95 and 1.62±1.02 mm in the anterior-posterior(A-P) direction, 0.93±1.19 and 1.12±0.94 mm in the left-right(L-R) direction, respectively. The positioning error by the diaphragm matching was significantly smaller than the bone matching in the C-C direction (p<0.05). The setup margin of diaphragm matching and bone matching that we had calculated based on van Herk margin formula was 4.5mm and 6.2mm(C-C), and 3.6mm and 6.3mm(A-P), and 2.6mm and 4.5mm(L-R), respectively. Conclusion: IGRT based on a diaphragm matching could be one alternative image matching technique for the positioning of the patients with liver tumor.« less

TU-D-209-03: Alignment of the Patient Graphic Model Using Fluoroscopic Images for Skin Dose Mapping

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oines, A; Oines, A; Kilian-Meneghin, J

2016-06-15

Purpose: The Dose Tracking System (DTS) was developed to provide realtime feedback of skin dose and dose rate during interventional fluoroscopic procedures. A color map on a 3D graphic of the patient represents the cumulative dose distribution on the skin. Automated image correlation algorithms are described which use the fluoroscopic procedure images to align and scale the patient graphic for more accurate dose mapping. Methods: Currently, the DTS employs manual patient graphic selection and alignment. To improve the accuracy of dose mapping and automate the software, various methods are explored to extract information about the beam location and patient morphologymore » from the procedure images. To match patient anatomy with a reference projection image, preprocessing is first used, including edge enhancement, edge detection, and contour detection. Template matching algorithms from OpenCV are then employed to find the location of the beam. Once a match is found, the reference graphic is scaled and rotated to fit the patient, using image registration correlation functions in Matlab. The algorithm runs correlation functions for all points and maps all correlation confidences to a surface map. The highest point of correlation is used for alignment and scaling. The transformation data is saved for later model scaling. Results: Anatomic recognition is used to find matching features between model and image and image registration correlation provides for alignment and scaling at any rotation angle with less than onesecond runtime, and at noise levels in excess of 150% of those found in normal procedures. Conclusion: The algorithm provides the necessary scaling and alignment tools to improve the accuracy of dose distribution mapping on the patient graphic with the DTS. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.« less

Nonlinear matching measure for the analysis of on-off type DNA microarray images

NASA Astrophysics Data System (ADS)

Kim, Jong D.; Park, Misun; Kim, Jongwon

2003-07-01

In this paper, we propose a new nonlinear matching measure for automatic analysis of the on-off type DNA microarray images in which the hybridized spots are detected by the template matching method. The targeting spots of HPV DNA chips are designed for genotyping the human papilloma virus(HPV). The proposed measure is obtained by binarythresholding over the whole template region and taking the number of white pixels inside the spotted area. This measure is evaluated in terms of the accuracy of the estimated marker location to show better performance than the normalized covariance.

Fuzzy based finger vein recognition with rotation invariant feature matching

NASA Astrophysics Data System (ADS)

Ezhilmaran, D.; Joseph, Rose Bindu

2017-11-01

Finger vein recognition is a promising biometric with commercial applications which is explored widely in the recent years. In this paper, a finger vein recognition system is proposed using rotation invariant feature descriptors for matching after enhancing the finger vein images with an interval type-2 fuzzy method. SIFT features are extracted and matched using a matching score based on Euclidian distance. Rotation invariance of the proposed method is verified in the experiment and the results are compared with SURF matching and minutiae matching. It is seen that rotation invariance is verified and the poor quality issues are solved efficiently with the designed system of finger vein recognition during the analysis. The experiments underlines the robustness and reliability of the interval type-2 fuzzy enhancement and SIFT feature matching.

Coregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information

NASA Astrophysics Data System (ADS)

Avbelj, Janja; Iwaszczuk, Dorota; Müller, Rupert; Reinartz, Peter; Stilla, Uwe

2015-02-01

For image fusion in remote sensing applications the georeferencing accuracy using position, attitude, and camera calibration measurements can be insufficient. Thus, image processing techniques should be employed for precise coregistration of images. In this article a method for multimodal object-based image coregistration refinement between hyperspectral images (HSI) and digital surface models (DSM) is presented. The method is divided in three parts: object outline detection in HSI and DSM, matching, and determination of transformation parameters. The novelty of our proposed coregistration refinement method is the use of material properties and height information of urban objects from HSI and DSM, respectively. We refer to urban objects as objects which are typical in urban environments and focus on buildings by describing them with 2D outlines. Furthermore, the geometric accuracy of these detected building outlines is taken into account in the matching step and for the determination of transformation parameters. Hence, a stochastic model is introduced to compute optimal transformation parameters. The feasibility of the method is shown by testing it on two aerial HSI of different spatial and spectral resolution, and two DSM of different spatial resolution. The evaluation is carried out by comparing the accuracies of the transformations parameters to the reference parameters, determined by considering object outlines at much higher resolution, and also by computing the correctness and the quality rate of the extracted outlines before and after coregistration refinement. Results indicate that using outlines of objects instead of only line segments is advantageous for coregistration of HSI and DSM. The extraction of building outlines in comparison to the line cue extraction provides a larger amount of assigned lines between the images and is more robust to outliers, i.e. false matches.

Apparatus and method for identification and recognition of an item with ultrasonic patterns from item subsurface micro-features

DOEpatents

Perkins, Richard W.; Fuller, James L.; Doctor, Steven R.; Good, Morris S.; Heasler, Patrick G.; Skorpik, James R.; Hansen, Norman H.

1995-01-01

The present invention is a means and method for identification and recognition of an item by ultrasonic imaging of material microfeatures and/or macrofeatures within the bulk volume of a material. The invention is based upon ultrasonic interrogation and imaging of material microfeatures within the body of material by accepting only reflected ultrasonic energy from a preselected plane or volume within the material. An initial interrogation produces an identification reference. Subsequent new scans are statistically compared to the identification reference for making a match/non-match decision.

Apparatus and method for identification and recognition of an item with ultrasonic patterns from item subsurface micro-features

DOEpatents

Perkins, R.W.; Fuller, J.L.; Doctor, S.R.; Good, M.S.; Heasler, P.G.; Skorpik, J.R.; Hansen, N.H.

1995-09-26

The present invention is a means and method for identification and recognition of an item by ultrasonic imaging of material microfeatures and/or macrofeatures within the bulk volume of a material. The invention is based upon ultrasonic interrogation and imaging of material microfeatures within the body of material by accepting only reflected ultrasonic energy from a preselected plane or volume within the material. An initial interrogation produces an identification reference. Subsequent new scans are statistically compared to the identification reference for making a match/non-match decision. 15 figs.

Case retrieval in medical databases by fusing heterogeneous information.

PubMed

Quellec, Gwénolé; Lamard, Mathieu; Cazuguel, Guy; Roux, Christian; Cochener, Béatrice

2011-01-01

A novel content-based heterogeneous information retrieval framework, particularly well suited to browse medical databases and support new generation computer aided diagnosis (CADx) systems, is presented in this paper. It was designed to retrieve possibly incomplete documents, consisting of several images and semantic information, from a database; more complex data types such as videos can also be included in the framework. The proposed retrieval method relies on image processing, in order to characterize each individual image in a document by their digital content, and information fusion. Once the available images in a query document are characterized, a degree of match, between the query document and each reference document stored in the database, is defined for each attribute (an image feature or a metadata). A Bayesian network is used to recover missing information if need be. Finally, two novel information fusion methods are proposed to combine these degrees of match, in order to rank the reference documents by decreasing relevance for the query. In the first method, the degrees of match are fused by the Bayesian network itself. In the second method, they are fused by the Dezert-Smarandache theory: the second approach lets us model our confidence in each source of information (i.e., each attribute) and take it into account in the fusion process for a better retrieval performance. The proposed methods were applied to two heterogeneous medical databases, a diabetic retinopathy database and a mammography screening database, for computer aided diagnosis. Precisions at five of 0.809 ± 0.158 and 0.821 ± 0.177, respectively, were obtained for these two databases, which is very promising.

Study of Automatic Image Rectification and Registration of Scanned Historical Aerial Photographs

NASA Astrophysics Data System (ADS)

Chen, H. R.; Tseng, Y. H.

2016-06-01

Historical aerial photographs directly provide good evidences of past times. The Research Center for Humanities and Social Sciences (RCHSS) of Taiwan Academia Sinica has collected and scanned numerous historical maps and aerial images of Taiwan and China. Some maps or images have been geo-referenced manually, but most of historical aerial images have not been registered since there are no GPS or IMU data for orientation assisting in the past. In our research, we developed an automatic process of matching historical aerial images by SIFT (Scale Invariant Feature Transform) for handling the great quantity of images by computer vision. SIFT is one of the most popular method of image feature extracting and matching. This algorithm extracts extreme values in scale space into invariant image features, which are robust to changing in rotation scale, noise, and illumination. We also use RANSAC (Random sample consensus) to remove outliers, and obtain good conjugated points between photographs. Finally, we manually add control points for registration through least square adjustment based on collinear equation. In the future, we can use image feature points of more photographs to build control image database. Every new image will be treated as query image. If feature points of query image match the features in database, it means that the query image probably is overlapped with control images.With the updating of database, more and more query image can be matched and aligned automatically. Other research about multi-time period environmental changes can be investigated with those geo-referenced temporal spatial data.

Deep Learning for Lowtextured Image Matching

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Fedorenko, V. V.; Fomin, N. A.

2018-05-01

Low-textured objects pose challenges for an automatic 3D model reconstruction. Such objects are common in archeological applications of photogrammetry. Most of the common feature point descriptors fail to match local patches in featureless regions of an object. Hence, automatic documentation of the archeological process using Structure from Motion (SfM) methods is challenging. Nevertheless, such documentation is possible with the aid of a human operator. Deep learning-based descriptors have outperformed most of common feature point descriptors recently. This paper is focused on the development of a new Wide Image Zone Adaptive Robust feature Descriptor (WIZARD) based on the deep learning. We use a convolutional auto-encoder to compress discriminative features of a local path into a descriptor code. We build a codebook to perform point matching on multiple images. The matching is performed using the nearest neighbor search and a modified voting algorithm. We present a new "Multi-view Amphora" (Amphora) dataset for evaluation of point matching algorithms. The dataset includes images of an Ancient Greek vase found at Taman Peninsula in Southern Russia. The dataset provides color images, a ground truth 3D model, and a ground truth optical flow. We evaluated the WIZARD descriptor on the "Amphora" dataset to show that it outperforms the SIFT and SURF descriptors on the complex patch pairs.

Deformable registration of CT and cone-beam CT with local intensity matching.

PubMed

Park, Seyoun; Plishker, William; Quon, Harry; Wong, John; Shekhar, Raj; Lee, Junghoon

2017-02-07

Cone-beam CT (CBCT) is a widely used intra-operative imaging modality in image-guided radiotherapy and surgery. A short scan followed by a filtered-backprojection is typically used for CBCT reconstruction. While data on the mid-plane (plane of source-detector rotation) is complete, off-mid-planes undergo different information deficiency and the computed reconstructions are approximate. This causes different reconstruction artifacts at off-mid-planes depending on slice locations, and therefore impedes accurate registration between CT and CBCT. In this paper, we propose a method to accurately register CT and CBCT by iteratively matching local CT and CBCT intensities. We correct CBCT intensities by matching local intensity histograms slice by slice in conjunction with intensity-based deformable registration. The correction-registration steps are repeated in an alternating way until the result image converges. We integrate the intensity matching into three different deformable registration methods, B-spline, demons, and optical flow that are widely used for CT-CBCT registration. All three registration methods were implemented on a graphics processing unit for efficient parallel computation. We tested the proposed methods on twenty five head and neck cancer cases and compared the performance with state-of-the-art registration methods. Normalized cross correlation (NCC), structural similarity index (SSIM), and target registration error (TRE) were computed to evaluate the registration performance. Our method produced overall NCC of 0.96, SSIM of 0.94, and TRE of 2.26 → 2.27 mm, outperforming existing methods by 9%, 12%, and 27%, respectively. Experimental results also show that our method performs consistently and is more accurate than existing algorithms, and also computationally efficient.

Deformable registration of CT and cone-beam CT with local intensity matching

NASA Astrophysics Data System (ADS)

Park, Seyoun; Plishker, William; Quon, Harry; Wong, John; Shekhar, Raj; Lee, Junghoon

2017-02-01

Cone-beam CT (CBCT) is a widely used intra-operative imaging modality in image-guided radiotherapy and surgery. A short scan followed by a filtered-backprojection is typically used for CBCT reconstruction. While data on the mid-plane (plane of source-detector rotation) is complete, off-mid-planes undergo different information deficiency and the computed reconstructions are approximate. This causes different reconstruction artifacts at off-mid-planes depending on slice locations, and therefore impedes accurate registration between CT and CBCT. In this paper, we propose a method to accurately register CT and CBCT by iteratively matching local CT and CBCT intensities. We correct CBCT intensities by matching local intensity histograms slice by slice in conjunction with intensity-based deformable registration. The correction-registration steps are repeated in an alternating way until the result image converges. We integrate the intensity matching into three different deformable registration methods, B-spline, demons, and optical flow that are widely used for CT-CBCT registration. All three registration methods were implemented on a graphics processing unit for efficient parallel computation. We tested the proposed methods on twenty five head and neck cancer cases and compared the performance with state-of-the-art registration methods. Normalized cross correlation (NCC), structural similarity index (SSIM), and target registration error (TRE) were computed to evaluate the registration performance. Our method produced overall NCC of 0.96, SSIM of 0.94, and TRE of 2.26 → 2.27 mm, outperforming existing methods by 9%, 12%, and 27%, respectively. Experimental results also show that our method performs consistently and is more accurate than existing algorithms, and also computationally efficient.

Improved Seam-Line Searching Algorithm for UAV Image Mosaic with Optical Flow

PubMed Central

Zhang, Weilong; Guo, Bingxuan; Liao, Xuan; Li, Wenzhuo

2018-01-01

Ghosting and seams are two major challenges in creating unmanned aerial vehicle (UAV) image mosaic. In response to these problems, this paper proposes an improved method for UAV image seam-line searching. First, an image matching algorithm is used to extract and match the features of adjacent images, so that they can be transformed into the same coordinate system. Then, the gray scale difference, the gradient minimum, and the optical flow value of pixels in adjacent image overlapped area in a neighborhood are calculated, which can be applied to creating an energy function for seam-line searching. Based on that, an improved dynamic programming algorithm is proposed to search the optimal seam-lines to complete the UAV image mosaic. This algorithm adopts a more adaptive energy aggregation and traversal strategy, which can find a more ideal splicing path for adjacent UAV images and avoid the ground objects better. The experimental results show that the proposed method can effectively solve the problems of ghosting and seams in the panoramic UAV images. PMID:29659526

Buildings Change Detection Based on Shape Matching for Multi-Resolution Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Abdessetar, M.; Zhong, Y.

2017-09-01

Buildings change detection has the ability to quantify the temporal effect, on urban area, for urban evolution study or damage assessment in disaster cases. In this context, changes analysis might involve the utilization of the available satellite images with different resolutions for quick responses. In this paper, to avoid using traditional method with image resampling outcomes and salt-pepper effect, building change detection based on shape matching is proposed for multi-resolution remote sensing images. Since the object's shape can be extracted from remote sensing imagery and the shapes of corresponding objects in multi-scale images are similar, it is practical for detecting buildings changes in multi-scale imagery using shape analysis. Therefore, the proposed methodology can deal with different pixel size for identifying new and demolished buildings in urban area using geometric properties of objects of interest. After rectifying the desired multi-dates and multi-resolutions images, by image to image registration with optimal RMS value, objects based image classification is performed to extract buildings shape from the images. Next, Centroid-Coincident Matching is conducted, on the extracted building shapes, based on the Euclidean distance measurement between shapes centroid (from shape T0 to shape T1 and vice versa), in order to define corresponding building objects. Then, New and Demolished buildings are identified based on the obtained distances those are greater than RMS value (No match in the same location).

Modeling human faces with multi-image photogrammetry

NASA Astrophysics Data System (ADS)

D'Apuzzo, Nicola

2002-03-01

Modeling and measurement of the human face have been increasing by importance for various purposes. Laser scanning, coded light range digitizers, image-based approaches and digital stereo photogrammetry are the used methods currently employed in medical applications, computer animation, video surveillance, teleconferencing and virtual reality to produce three dimensional computer models of the human face. Depending on the application, different are the requirements. Ours are primarily high accuracy of the measurement and automation in the process. The method presented in this paper is based on multi-image photogrammetry. The equipment, the method and results achieved with this technique are here depicted. The process is composed of five steps: acquisition of multi-images, calibration of the system, establishment of corresponding points in the images, computation of their 3-D coordinates and generation of a surface model. The images captured by five CCD cameras arranged in front of the subject are digitized by a frame grabber. The complete system is calibrated using a reference object with coded target points, which can be measured fully automatically. To facilitate the establishment of correspondences in the images, texture in the form of random patterns can be projected from two directions onto the face. The multi-image matching process, based on a geometrical constrained least squares matching algorithm, produces a dense set of corresponding points in the five images. Neighborhood filters are then applied on the matching results to remove the errors. After filtering the data, the three-dimensional coordinates of the matched points are computed by forward intersection using the results of the calibration process; the achieved mean accuracy is about 0.2 mm in the sagittal direction and about 0.1 mm in the lateral direction. The last step of data processing is the generation of a surface model from the point cloud and the application of smooth filters. Moreover, a color texture image can be draped over the model to achieve a photorealistic visualization. The advantage of the presented method over laser scanning and coded light range digitizers is the acquisition of the source data in a fraction of a second, allowing the measurement of human faces with higher accuracy and the possibility to measure dynamic events like the speech of a person.

Wheat gliadin: digital imaging and database construction using a 4-band reference system of agarose isoelectric focusing patterns.

PubMed

Black, J A; Waggamon, K A

1992-01-01

An isoelectric focusing method using thin-layer agarose gel has been developed for wheat gliadin. Using flat-bed units with a third electrode, up to 72 samples per gel may be analyzed. Advantages over traditional acid polyacrylamide gel electrophoresis methodology include: faster run times, nontoxic media, and greater sample capacity. The method is suitable for fingerprinting or purity testing of wheat varieties. Using digital images captured by a flat-bed scanner, a 4-band reference system using isoelectric points was devised. Software enables separated bands to be assigned pI values based upon reference tracks. Precision of assigned isoelectric points is shown to be on the order of 0.02 pH units. Captured images may be stored in a computer database and compared to unknown patterns to enable an identification. Parameters for a match with a stored pattern may be adjusted for pI interval required for a match, and number of best matches.

Improvement in the Accuracy of Matching by Different Feature Subspaces in Traffic Sign Recognition

NASA Astrophysics Data System (ADS)

Ihara, Arihito; Fujiyoshi, Hironobu; Takaki, Masanari; Kumon, Hiroaki; Tamatsu, Yukimasa

A technique for recognizing traffic signs from an image taken with an in-vehicle camera has already been proposed as driver's drive assist. SIFT feature is used for traffic sign recognition, because it is robust to changes in scaling and rotating of the traffic sign. However, it is difficult to process in real-time because the computation cost of the SIFT feature extraction and matching is expensive. This paper presents a method of traffic sign recognition based on keypoint classifier by AdaBoost using PCA-SIFT features in different feature subspaces. Each subspace is constructed from gradients of traffic sign images and general images respectively. A detected keypoint is projected to both subspaces, and then the AdaBoost employs to classy into whether the keypoint is on the traffic sign or not. Experimental results show that the computation cost for keypoint matching can be reduced to about 1/2 compared with the conventional method.

Region-Based Prediction for Image Compression in the Cloud.

PubMed

Begaint, Jean; Thoreau, Dominique; Guillotel, Philippe; Guillemot, Christine

2018-04-01

Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we use a semi-local approach to exploit inter-image correlation. The reference image is first segmented into multiple planar regions determined from matched local features and super-pixels. The geometric and photometric disparities between the matched regions of the reference image and the current image are then compensated. Finally, multiple references are generated from the estimated compensation models and organized in a pseudo-sequence to differentially encode the input image using classical video coding tools. Experimental results demonstrate that the proposed approach yields significant rate-distortion performance improvements compared with the current image inter-coding solutions such as high efficiency video coding.

Homographic Patch Feature Transform: A Robustness Registration for Gastroscopic Surgery.

PubMed

Hu, Weiling; Zhang, Xu; Wang, Bin; Liu, Jiquan; Duan, Huilong; Dai, Ning; Si, Jianmin

2016-01-01

Image registration is a key component of computer assistance in image guided surgery, and it is a challenging topic in endoscopic environments. In this study, we present a method for image registration named Homographic Patch Feature Transform (HPFT) to match gastroscopic images. HPFT can be used for tracking lesions and augmenting reality applications during gastroscopy. Furthermore, an overall evaluation scheme is proposed to validate the precision, robustness and uniformity of the registration results, which provides a standard for rejection of false matching pairs from corresponding results. Finally, HPFT is applied for processing in vivo gastroscopic data. The experimental results show that HPFT has stable performance in gastroscopic applications.

Stereotactic ultrasound for target volume definition in a patient with prostate cancer and bilateral total hip replacement.

PubMed

Boda-Heggemann, Judit; Haneder, Stefan; Ehmann, Michael; Sihono, Dwi Seno Kuncoro; Wertz, Hansjörg; Mai, Sabine; Kegel, Stefan; Heitmann, Sigrun; von Swietochowski, Sandra; Lohr, Frank; Wenz, Frederik

2015-01-01

Target-volume definition for prostate cancer in patients with bilateral metal total hip replacements (THRs) is a challenge because of metal artifacts in the planning computed tomography (CT) scans. Magnetic resonance imaging (MRI) can be used for matching and prostate delineation; however, at a spatial and temporal distance from the planning CT, identical rectal and vesical filling is difficult to achieve. In addition, MRI may also be impaired by metal artifacts, even resulting in spatial image distortion. Here, we present a method to define prostate target volumes based on ultrasound images acquired during CT simulation and online-matched to the CT data set directly at the planning CT. A 78-year-old patient with cT2cNxM0 prostate cancer with bilateral metal THRs was referred to external beam radiation therapy. T2-weighted MRI was performed on the day of the planning CT with preparation according to a protocol for reproducible bladder and rectal filling. The planning CT was obtained with the immediate acquisition of a 3-dimensional ultrasound data set with a dedicated stereotactic ultrasound system for online intermodality image matching referenced to the isocenter by ceiling-mounted infrared cameras. MRI (offline) and ultrasound images (online) were thus both matched to the CT images for planning. Daily image guided radiation therapy (IGRT) was performed with transabdominal ultrasound and compared with cone beam CT. Because of variations in bladder and rectal filling and metal-induced image distortion in MRI, soft-tissue-based matching of the MRI to CT was not sufficient for unequivocal prostate target definition. Ultrasound-based images could be matched, and prostate, seminal vesicles, and target volumes were reliably defined. Daily IGRT could be successfully completed with transabdominal ultrasound with good accordance between cone beam CT and ultrasound. For prostate cancer patients with bilateral THRs causing artifacts in planning CTs, ultrasound referenced to the isocenter of the CT simulator and acquired with intermodal online coregistration directly at the planning CT is a fast and easy method to reliably delineate the prostate and target volumes and for daily IGRT. Copyright © 2015 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Diagnostic possibilities with multidimensional images in head and neck area using efficient registration and visualization methods

NASA Astrophysics Data System (ADS)

Zeilhofer, Hans-Florian U.; Krol, Zdzislaw; Sader, Robert; Hoffmann, Karl-Heinz; Gerhardt, Paul; Schweiger, Markus; Horch, Hans-Henning

1997-05-01

For several diseases in the head and neck area different imaging modalities are applied to the same patient.Each of these image data sets has its specific advantages and disadvantages. The combination of different methods allows to make the best use of the advantageous properties of each method while minimizing the impact of its negative aspects. Soft tissue alterations can be judged better in an MRI image while it may be unrecognizable in the relating CT. Bone tissue, on the other hand, is optimally imaged in CT. Inflammatory nuclei of the bone can be detected best by their increased signal in SPECT. Only the combination of all modalities let the physical come to an exact statement on pathological processes that involve multiple tissue structures. Several surfaces and voxel based matching functions we have tested allowed a precise merging by means of numerical optimization methods like e.g. simulated annealing without the complicated assertion of fiducial markers or the localization landmarks in 2D cross sectional slice images. The quality of the registration depends on the choice of the optimization procedure according to the complexity of the matching function landscape. Precise correlation of the multimodal head and neck area images together with its 2D and 3D presentation techniques provides a valuable tool for physicians.

Fast and Robust Registration of Multimodal Remote Sensing Images via Dense Orientated Gradient Feature

NASA Astrophysics Data System (ADS)

Ye, Y.

2017-09-01

This paper presents a fast and robust method for the registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR and map). The proposed method is based on the hypothesis that structural similarity between images is preserved across different modalities. In the definition of the proposed method, we first develop a pixel-wise feature descriptor named Dense Orientated Gradient Histogram (DOGH), which can be computed effectively at every pixel and is robust to non-linear intensity differences between images. Then a fast similarity metric based on DOGH is built in frequency domain using the Fast Fourier Transform (FFT) technique. Finally, a template matching scheme is applied to detect tie points between images. Experimental results on different types of multimodal remote sensing images show that the proposed similarity metric has the superior matching performance and computational efficiency than the state-of-the-art methods. Moreover, based on the proposed similarity metric, we also design a fast and robust automatic registration system for multimodal images. This system has been evaluated using a pair of very large SAR and optical images (more than 20000 × 20000 pixels). Experimental results show that our system outperforms the two popular commercial software systems (i.e. ENVI and ERDAS) in both registration accuracy and computational efficiency.

A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback.

PubMed

Rahman, Md Mahmudur; Bhattacharya, Prabir; Desai, Bipin C

2007-01-01

A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework.

Hubble Source Catalog

NASA Astrophysics Data System (ADS)

Lubow, S.; Budavári, T.

2013-10-01

We have created an initial catalog of objects observed by the WFPC2 and ACS instruments on the Hubble Space Telescope (HST). The catalog is based on observations taken on more than 6000 visits (telescope pointings) of ACS/WFC and more than 25000 visits of WFPC2. The catalog is obtained by cross matching by position in the sky all Hubble Legacy Archive (HLA) Source Extractor source lists for these instruments. The source lists describe properties of source detections within a visit. The calculations are performed on a SQL Server database system. First we collect overlapping images into groups, e.g., Eta Car, and determine nearby (approximately matching) pairs of sources from different images within each group. We then apply a novel algorithm for improving the cross matching of pairs of sources by adjusting the astrometry of the images. Next, we combine pairwise matches into maximal sets of possible multi-source matches. We apply a greedy Bayesian method to split the maximal matches into more reliable matches. We test the accuracy of the matches by comparing the fluxes of the matched sources. The result is a set of information that ties together multiple observations of the same object. A byproduct of the catalog is greatly improved relative astrometry for many of the HST images. We also provide information on nondetections that can be used to determine dropouts. With the catalog, for the first time, one can carry out time domain, multi-wavelength studies across a large set of HST data. The catalog is publicly available. Much more can be done to expand the catalog capabilities.

Patch Based Synthesis of Whole Head MR Images: Application to EPI Distortion Correction.

PubMed

Roy, Snehashis; Chou, Yi-Yu; Jog, Amod; Butman, John A; Pham, Dzung L

2016-10-01

Different magnetic resonance imaging pulse sequences are used to generate image contrasts based on physical properties of tissues, which provide different and often complementary information about them. Therefore multiple image contrasts are useful for multimodal analysis of medical images. Often, medical image processing algorithms are optimized for particular image contrasts. If a desirable contrast is unavailable, contrast synthesis (or modality synthesis) methods try to "synthesize" the unavailable constrasts from the available ones. Most of the recent image synthesis methods generate synthetic brain images, while whole head magnetic resonance (MR) images can also be useful for many applications. We propose an atlas based patch matching algorithm to synthesize T 2 -w whole head (including brain, skull, eyes etc) images from T 1 -w images for the purpose of distortion correction of diffusion weighted MR images. The geometric distortion in diffusion MR images due to in-homogeneous B 0 magnetic field are often corrected by non-linearly registering the corresponding b = 0 image with zero diffusion gradient to an undistorted T 2 -w image. We show that our synthetic T 2 -w images can be used as a template in absence of a real T 2 -w image. Our patch based method requires multiple atlases with T 1 and T 2 to be registeLowRes to a given target T 1 . Then for every patch on the target, multiple similar looking matching patches are found on the atlas T 1 images and corresponding patches on the atlas T 2 images are combined to generate a synthetic T 2 of the target. We experimented on image data obtained from 44 patients with traumatic brain injury (TBI), and showed that our synthesized T 2 images produce more accurate distortion correction than a state-of-the-art registration based image synthesis method.

SU-C-9A-06: The Impact of CT Image Used for Attenuation Correction in 4D-PET

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Y; Bowsher, J; Yan, S

2014-06-01

Purpose: To evaluate the appropriateness of using 3D non-gated CT image for attenuation correction (AC) in a 4D-PET (gated PET) imaging protocol used in radiotherapy treatment planning simulation. Methods: The 4D-PET imaging protocol in a Siemens PET/CT simulator (Biograph mCT, Siemens Medical Solutions, Hoffman Estates, IL) was evaluated. CIRS Dynamic Thorax Phantom (CIRS Inc., Norfolk, VA) with a moving glass sphere (8 mL) in the middle of its thorax portion was used in the experiments. The glass was filled with {sup 18}F-FDG and was in a longitudinal motion derived from a real patient breathing pattern. Varian RPM system (Varian Medicalmore » Systems, Palo Alto, CA) was used for respiratory gating. Both phase-gating and amplitude-gating methods were tested. The clinical imaging protocol was modified to use three different CT images for AC in 4D-PET reconstruction: first is to use a single-phase CT image to mimic actual clinical protocol (single-CT-PET); second is to use the average intensity projection CT (AveIP-CT) derived from 4D-CT scanning (AveIP-CT-PET); third is to use 4D-CT image to do the phase-matched AC (phase-matching- PET). Maximum SUV (SUVmax) and volume of the moving target (glass sphere) with threshold of 40% SUVmax were calculated for comparison between 4D-PET images derived with different AC methods. Results: The SUVmax varied 7.3%±6.9% over the breathing cycle in single-CT-PET, compared to 2.5%±2.8% in AveIP-CT-PET and 1.3%±1.2% in phasematching PET. The SUVmax in single-CT-PET differed by up to 15% from those in phase-matching-PET. The target volumes measured from single- CT-PET images also presented variations up to 10% among different phases of 4D PET in both phase-gating and amplitude-gating experiments. Conclusion: Attenuation correction using non-gated CT in 4D-PET imaging is not optimal process for quantitative analysis. Clinical 4D-PET imaging protocols should consider phase-matched 4D-CT image if available to achieve better accuracy.« less

A dual-adaptive support-based stereo matching algorithm

NASA Astrophysics Data System (ADS)

Zhang, Yin; Zhang, Yun

2017-07-01

Many stereo matching algorithms use fixed color thresholds and a rigid cross skeleton to segment supports (viz., Cross method), which, however, does not work well for different images. To address this issue, this paper proposes a novel dual adaptive support (viz., DAS)-based stereo matching method, which uses both appearance and shape information of a local region to segment supports automatically, and, then, integrates the DAS-based cost aggregation with the absolute difference plus census transform cost, scanline optimization and disparity refinement to develop a stereo matching system. The performance of the DAS method is also evaluated in the Middlebury benchmark and by comparing with the Cross method. The results show that the average error for the DAS method 25.06% lower than that for the Cross method, indicating that the proposed method is more accurate, with fewer parameters and suitable for parallel computing.

Image alignment for tomography reconstruction from synchrotron X-ray microscopic images.

PubMed

Cheng, Chang-Chieh; Chien, Chia-Chi; Chen, Hsiang-Hsin; Hwu, Yeukuang; Ching, Yu-Tai

2014-01-01

A synchrotron X-ray microscope is a powerful imaging apparatus for taking high-resolution and high-contrast X-ray images of nanoscale objects. A sufficient number of X-ray projection images from different angles is required for constructing 3D volume images of an object. Because a synchrotron light source is immobile, a rotational object holder is required for tomography. At a resolution of 10 nm per pixel, the vibration of the holder caused by rotating the object cannot be disregarded if tomographic images are to be reconstructed accurately. This paper presents a computer method to compensate for the vibration of the rotational holder by aligning neighboring X-ray images. This alignment process involves two steps. The first step is to match the "projected feature points" in the sequence of images. The matched projected feature points in the x-θ plane should form a set of sine-shaped loci. The second step is to fit the loci to a set of sine waves to compute the parameters required for alignment. The experimental results show that the proposed method outperforms two previously proposed methods, Xradia and SPIDER. The developed software system can be downloaded from the URL, http://www.cs.nctu.edu.tw/~chengchc/SCTA or http://goo.gl/s4AMx.

Self-Similar Spin Images for Point Cloud Matching

NASA Astrophysics Data System (ADS)

Pulido, Daniel

The rapid growth of Light Detection And Ranging (Lidar) technologies that collect, process, and disseminate 3D point clouds have allowed for increasingly accurate spatial modeling and analysis of the real world. Lidar sensors can generate massive 3D point clouds of a collection area that provide highly detailed spatial and radiometric information. However, a Lidar collection can be expensive and time consuming. Simultaneously, the growth of crowdsourced Web 2.0 data (e.g., Flickr, OpenStreetMap) have provided researchers with a wealth of freely available data sources that cover a variety of geographic areas. Crowdsourced data can be of varying quality and density. In addition, since it is typically not collected as part of a dedicated experiment but rather volunteered, when and where the data is collected is arbitrary. The integration of these two sources of geoinformation can provide researchers the ability to generate products and derive intelligence that mitigate their respective disadvantages and combine their advantages. Therefore, this research will address the problem of fusing two point clouds from potentially different sources. Specifically, we will consider two problems: scale matching and feature matching. Scale matching consists of computing feature metrics of each point cloud and analyzing their distributions to determine scale differences. Feature matching consists of defining local descriptors that are invariant to common dataset distortions (e.g., rotation and translation). Additionally, after matching the point clouds they can be registered and processed further (e.g., change detection). The objective of this research is to develop novel methods to fuse and enhance two point clouds from potentially disparate sources (e.g., Lidar and crowdsourced Web 2.0 datasets). The scope of this research is to investigate both scale and feature matching between two point clouds. The specific focus of this research will be in developing a novel local descriptor based on the concept of self-similarity to aid in the scale and feature matching steps. An open problem in fusion is how best to extract features from two point clouds and then perform feature-based matching. The proposed approach for this matching step is the use of local self-similarity as an invariant measure to match features. In particular, the proposed approach is to combine the concept of local self-similarity with a well-known feature descriptor, Spin Images, and thereby define "Self-Similar Spin Images". This approach is then extended to the case of matching two points clouds in very different coordinate systems (e.g., a geo-referenced Lidar point cloud and stereo-image derived point cloud without geo-referencing). The use of Self-Similar Spin Images is again applied to address this problem by introducing a "Self-Similar Keyscale" that matches the spatial scales of two point clouds. Another open problem is how best to detect changes in content between two point clouds. A method is proposed to find changes between two point clouds by analyzing the order statistics of the nearest neighbors between the two clouds, and thereby define the "Nearest Neighbor Order Statistic" method. Note that the well-known Hausdorff distance is a special case as being just the maximum order statistic. Therefore, by studying the entire histogram of these nearest neighbors it is expected to yield a more robust method to detect points that are present in one cloud but not the other. This approach is applied at multiple resolutions. Therefore, changes detected at the coarsest level will yield large missing targets and at finer levels will yield smaller targets.

Real-time non-rigid target tracking for ultrasound-guided clinical interventions

NASA Astrophysics Data System (ADS)

Zachiu, C.; Ries, M.; Ramaekers, P.; Guey, J.-L.; Moonen, C. T. W.; de Senneville, B. Denis

2017-10-01

Biological motion is a problem for non- or mini-invasive interventions when conducted in mobile/deformable organs due to the targeted pathology moving/deforming with the organ. This may lead to high miss rates and/or incomplete treatment of the pathology. Therefore, real-time tracking of the target anatomy during the intervention would be beneficial for such applications. Since the aforementioned interventions are often conducted under B-mode ultrasound (US) guidance, target tracking can be achieved via image registration, by comparing the acquired US images to a separate image established as positional reference. However, such US images are intrinsically altered by speckle noise, introducing incoherent gray-level intensity variations. This may prove problematic for existing intensity-based registration methods. In the current study we address US-based target tracking by employing the recently proposed EVolution registration algorithm. The method is, by construction, robust to transient gray-level intensities. Instead of directly matching image intensities, EVolution aligns similar contrast patterns in the images. Moreover, the displacement is computed by evaluating a matching criterion for image sub-regions rather than on a point-by-point basis, which typically provides more robust motion estimates. However, unlike similar previously published approaches, which assume rigid displacements in the image sub-regions, the EVolution algorithm integrates the matching criterion in a global functional, allowing the estimation of an elastic dense deformation. The approach was validated for soft tissue tracking under free-breathing conditions on the abdomen of seven healthy volunteers. Contact echography was performed on all volunteers, while three of the volunteers also underwent standoff echography. Each of the two modalities is predominantly specific to a particular type of non- or mini-invasive clinical intervention. The method demonstrated on average an accuracy of  ˜1.5 mm and submillimeter precision. This, together with a computational performance of 20 images per second make the proposed method an attractive solution for real-time target tracking during US-guided clinical interventions.

Real-time non-rigid target tracking for ultrasound-guided clinical interventions.

PubMed

Zachiu, C; Ries, M; Ramaekers, P; Guey, J-L; Moonen, C T W; de Senneville, B Denis

2017-10-04

Biological motion is a problem for non- or mini-invasive interventions when conducted in mobile/deformable organs due to the targeted pathology moving/deforming with the organ. This may lead to high miss rates and/or incomplete treatment of the pathology. Therefore, real-time tracking of the target anatomy during the intervention would be beneficial for such applications. Since the aforementioned interventions are often conducted under B-mode ultrasound (US) guidance, target tracking can be achieved via image registration, by comparing the acquired US images to a separate image established as positional reference. However, such US images are intrinsically altered by speckle noise, introducing incoherent gray-level intensity variations. This may prove problematic for existing intensity-based registration methods. In the current study we address US-based target tracking by employing the recently proposed EVolution registration algorithm. The method is, by construction, robust to transient gray-level intensities. Instead of directly matching image intensities, EVolution aligns similar contrast patterns in the images. Moreover, the displacement is computed by evaluating a matching criterion for image sub-regions rather than on a point-by-point basis, which typically provides more robust motion estimates. However, unlike similar previously published approaches, which assume rigid displacements in the image sub-regions, the EVolution algorithm integrates the matching criterion in a global functional, allowing the estimation of an elastic dense deformation. The approach was validated for soft tissue tracking under free-breathing conditions on the abdomen of seven healthy volunteers. Contact echography was performed on all volunteers, while three of the volunteers also underwent standoff echography. Each of the two modalities is predominantly specific to a particular type of non- or mini-invasive clinical intervention. The method demonstrated on average an accuracy of  ∼1.5 mm and submillimeter precision. This, together with a computational performance of 20 images per second make the proposed method an attractive solution for real-time target tracking during US-guided clinical interventions.

Improved photo response non-uniformity (PRNU) based source camera identification.

PubMed

Cooper, Alan J

2013-03-10

The concept of using Photo Response Non-Uniformity (PRNU) as a reliable forensic tool to match an image to a source camera is now well established. Traditionally, the PRNU estimation methodologies have centred on a wavelet based de-noising approach. Resultant filtering artefacts in combination with image and JPEG contamination act to reduce the quality of PRNU estimation. In this paper, it is argued that the application calls for a simplified filtering strategy which at its base level may be realised using a combination of adaptive and median filtering applied in the spatial domain. The proposed filtering method is interlinked with a further two stage enhancement strategy where only pixels in the image having high probabilities of significant PRNU bias are retained. This methodology significantly improves the discrimination between matching and non-matching image data sets over that of the common wavelet filtering approach. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Wide baseline stereo matching based on double topological relationship consistency

NASA Astrophysics Data System (ADS)

Zou, Xiaohong; Liu, Bin; Song, Xiaoxue; Liu, Yang

2009-07-01

Stereo matching is one of the most important branches in computer vision. In this paper, an algorithm is proposed for wide-baseline stereo vision matching. Here, a novel scheme is presented called double topological relationship consistency (DCTR). The combination of double topological configuration includes the consistency of first topological relationship (CFTR) and the consistency of second topological relationship (CSTR). It not only sets up a more advanced model on matching, but discards mismatches by iteratively computing the fitness of the feature matches and overcomes many problems of traditional methods depending on the powerful invariance to changes in the scale, rotation or illumination across large view changes and even occlusions. Experimental examples are shown where the two cameras have been located in very different orientations. Also, epipolar geometry can be recovered using RANSAC by far the most widely method adopted possibly. By the method, we can obtain correspondences with high precision on wide baseline matching problems. Finally, the effectiveness and reliability of this method are demonstrated in wide-baseline experiments on the image pairs.

How to COAAD Images. I. Optimal Source Detection and Photometry of Point Sources Using Ensembles of Images

NASA Astrophysics Data System (ADS)

Zackay, Barak; Ofek, Eran O.

2017-02-01

Stacks of digital astronomical images are combined in order to increase image depth. The variable seeing conditions, sky background, and transparency of ground-based observations make the coaddition process nontrivial. We present image coaddition methods that maximize the signal-to-noise ratio (S/N) and optimized for source detection and flux measurement. We show that for these purposes, the best way to combine images is to apply a matched filter to each image using its own point-spread function (PSF) and only then to sum the images with the appropriate weights. Methods that either match the filter after coaddition or perform PSF homogenization prior to coaddition will result in loss of sensitivity. We argue that our method provides an increase of between a few and 25% in the survey speed of deep ground-based imaging surveys compared with weighted coaddition techniques. We demonstrate this claim using simulated data as well as data from the Palomar Transient Factory data release 2. We present a variant of this coaddition method, which is optimal for PSF or aperture photometry. We also provide an analytic formula for calculating the S/N for PSF photometry on single or multiple observations. In the next paper in this series, we present a method for image coaddition in the limit of background-dominated noise, which is optimal for any statistical test or measurement on the constant-in-time image (e.g., source detection, shape or flux measurement, or star-galaxy separation), making the original data redundant. We provide an implementation of these algorithms in MATLAB.

Global image registration using a symmetric block-matching approach

PubMed Central

Modat, Marc; Cash, David M.; Daga, Pankaj; Winston, Gavin P.; Duncan, John S.; Ourselin, Sébastien

2014-01-01

Abstract. Most medical image registration algorithms suffer from a directionality bias that has been shown to largely impact subsequent analyses. Several approaches have been proposed in the literature to address this bias in the context of nonlinear registration, but little work has been done for global registration. We propose a symmetric approach based on a block-matching technique and least-trimmed square regression. The proposed method is suitable for multimodal registration and is robust to outliers in the input images. The symmetric framework is compared with the original asymmetric block-matching technique and is shown to outperform it in terms of accuracy and robustness. The methodology presented in this article has been made available to the community as part of the NiftyReg open-source package. PMID:26158035

Model-to-image based 2D-3D registration of angiographic data

NASA Astrophysics Data System (ADS)

Mollus, Sabine; Lübke, Jördis; Walczuch, Andreas J.; Schumann, Heidrun; Weese, Jürgen

2008-03-01

We propose a novel registration method, which combines well-known vessel detection techniques with aspects of model adaptation. The proposed method is tailored to the requirements of 2D-3D-registration of interventional angiographic X-ray data such as acquired during abdominal procedures. As prerequisite, a vessel centerline is extracted out of a rotational angiography (3DRA) data set to build an individual model of the vascular tree. Following the two steps of local vessel detection and model transformation the centerline model is matched to one dynamic subtraction angiography (DSA) target image. Thereby, the in-plane position and the 3D orientation of the centerline is related to the vessel candidates found in the target image minimizing the residual error in least squares manner. In contrast to feature-based methods, no segmentation of the vessel tree in the 2D target image is required. First experiments with synthetic angiographies and clinical data sets indicate that matching with the proposed model-to-image based registration approach is accurate and robust and is characterized by a large capture range.

Block Adjustment and Image Matching of WORLDVIEW-3 Stereo Pairs and Accuracy Evaluation

NASA Astrophysics Data System (ADS)

Zuo, C.; Xiao, X.; Hou, Q.; Li, B.

2018-05-01

WorldView-3, as a high-resolution commercial earth observation satellite, which is launched by Digital Global, provides panchromatic imagery of 0.31 m resolution. The positioning accuracy is less than 3.5 meter CE90 without ground control, which can use for large scale topographic mapping. This paper presented the block adjustment for WorldView-3 based on RPC model and achieved the accuracy of 1 : 2000 scale topographic mapping with few control points. On the base of stereo orientation result, this paper applied two kinds of image matching algorithm for DSM extraction: LQM and SGM. Finally, this paper compared the accuracy of the point cloud generated by the two image matching methods with the reference data which was acquired by an airborne laser scanner. The results showed that the RPC adjustment model of WorldView-3 image with small number of GCPs could satisfy the requirement of Chinese Surveying and Mapping regulations for 1 : 2000 scale topographic maps. And the point cloud result obtained through WorldView-3 stereo image matching had higher elevation accuracy, the RMS error of elevation for bare ground area is 0.45 m, while for buildings the accuracy can almost reach 1 meter.

A coarse-to-fine kernel matching approach for mean-shift based visual tracking

NASA Astrophysics Data System (ADS)

Liangfu, L.; Zuren, F.; Weidong, C.; Ming, J.

2009-03-01

Mean shift is an efficient pattern match algorithm. It is widely used in visual tracking fields since it need not perform whole search in the image space. It employs gradient optimization method to reduce the time of feature matching and realize rapid object localization, and uses Bhattacharyya coefficient as the similarity measure between object template and candidate template. This thesis presents a mean shift algorithm based on coarse-to-fine search for the best kernel matching. This paper researches for object tracking with large motion area based on mean shift. To realize efficient tracking of such an object, we present a kernel matching method from coarseness to fine. If the motion areas of the object between two frames are very large and they are not overlapped in image space, then the traditional mean shift method can only obtain local optimal value by iterative computing in the old object window area, so the real tracking position cannot be obtained and the object tracking will be disabled. Our proposed algorithm can efficiently use a similarity measure function to realize the rough location of motion object, then use mean shift method to obtain the accurate local optimal value by iterative computing, which successfully realizes object tracking with large motion. Experimental results show its good performance in accuracy and speed when compared with background-weighted histogram algorithm in the literature.

Aerial images visual localization on a vector map using color-texture segmentation

NASA Astrophysics Data System (ADS)

Kunina, I. A.; Teplyakov, L. M.; Gladkov, A. P.; Khanipov, T. M.; Nikolaev, D. P.

2018-04-01

In this paper we study the problem of combining UAV obtained optical data and a coastal vector map in absence of satellite navigation data. The method is based on presenting the territory as a set of segments produced by color-texture image segmentation. We then find such geometric transform which gives the best match between these segments and land and water areas of the georeferenced vector map. We calculate transform consisting of an arbitrary shift relatively to the vector map and bound rotation and scaling. These parameters are estimated using the RANSAC algorithm which matches the segments contours and the contours of land and water areas of the vector map. To implement this matching we suggest computing shape descriptors robust to rotation and scaling. We performed numerical experiments demonstrating the practical applicability of the proposed method.

Demons deformable registration of CT and cone-beam CT using an iterative intensity matching approach.

PubMed

Nithiananthan, Sajendra; Schafer, Sebastian; Uneri, Ali; Mirota, Daniel J; Stayman, J Webster; Zbijewski, Wojciech; Brock, Kristy K; Daly, Michael J; Chan, Harley; Irish, Jonathan C; Siewerdsen, Jeffrey H

2011-04-01

A method of intensity-based deformable registration of CT and cone-beam CT (CBCT) images is described, in which intensity correction occurs simultaneously within the iterative registration process. The method preserves the speed and simplicity of the popular Demons algorithm while providing robustness and accuracy in the presence of large mismatch between CT and CBCT voxel values ("intensity"). A variant of the Demons algorithm was developed in which an estimate of the relationship between CT and CBCT intensity values for specific materials in the image is computed at each iteration based on the set of currently overlapping voxels. This tissue-specific intensity correction is then used to estimate the registration output for that iteration and the process is repeated. The robustness of the method was tested in CBCT images of a cadaveric head exhibiting a broad range of simulated intensity variations associated with x-ray scatter, object truncation, and/or errors in the reconstruction algorithm. The accuracy of CT-CBCT registration was also measured in six real cases, exhibiting deformations ranging from simple to complex during surgery or radiotherapy guided by a CBCT-capable C-arm or linear accelerator, respectively. The iterative intensity matching approach was robust against all levels of intensity variation examined, including spatially varying errors in voxel value of a factor of 2 or more, as can be encountered in cases of high x-ray scatter. Registration accuracy without intensity matching degraded severely with increasing magnitude of intensity error and introduced image distortion. A single histogram match performed prior to registration alleviated some of these effects but was also prone to image distortion and was quantifiably less robust and accurate than the iterative approach. Within the six case registration accuracy study, iterative intensity matching Demons reduced mean TRE to (2.5 +/- 2.8) mm compared to (3.5 +/- 3.0) mm with rigid registration. A method was developed to iteratively correct CT-CBCT intensity disparity during Demons registration, enabling fast, intensity-based registration in CBCT-guided procedures such as surgery and radiotherapy, in which CBCT voxel values may be inaccurate. Accurate CT-CBCT registration in turn facilitates registration of multimodality preoperative image and planning data to intraoperative CBCT by way of the preoperative CT, thereby linking the intraoperative frame of reference to a wealth of preoperative information that could improve interventional guidance.

Evolutionary Fuzzy Block-Matching-Based Camera Raw Image Denoising.

PubMed

Yang, Chin-Chang; Guo, Shu-Mei; Tsai, Jason Sheng-Hong

2017-09-01

An evolutionary fuzzy block-matching-based image denoising algorithm is proposed to remove noise from a camera raw image. Recently, a variance stabilization transform is widely used to stabilize the noise variance, so that a Gaussian denoising algorithm can be used to remove the signal-dependent noise in camera sensors. However, in the stabilized domain, the existed denoising algorithm may blur too much detail. To provide a better estimate of the noise-free signal, a new block-matching approach is proposed to find similar blocks by the use of a type-2 fuzzy logic system (FLS). Then, these similar blocks are averaged with the weightings which are determined by the FLS. Finally, an efficient differential evolution is used to further improve the performance of the proposed denoising algorithm. The experimental results show that the proposed denoising algorithm effectively improves the performance of image denoising. Furthermore, the average performance of the proposed method is better than those of two state-of-the-art image denoising algorithms in subjective and objective measures.

Positioning accuracy and daily dose assessment for prostate cancer treatment using in-room CT image guidance at a proton therapy facility.

PubMed

Maeda, Yoshikazu; Sato, Yoshitaka; Minami, Hiroki; Yasukawa, Yutaka; Yamamoto, Kazutaka; Tamamura, Hiroyasu; Shibata, Satoshi; Bou, Sayuri; Sasaki, Makoto; Tameshige, Yuji; Kume, Kyo; Ooto, Hiroshi; Kasahara, Shigeru; Shimizu, Yasuhiro; Saga, Yusuke; Omoya, Akira; Saitou, Makoto

2018-05-01

To evaluate the effectiveness of CT image-guided proton radiotherapy for prostate cancer by analyzing the positioning uncertainty and assessing daily dose change due to anatomical variations. Patients with prostate cancer were treated by opposed lateral proton beams based on a passive scattering method using an in-room CT image-guided system. The system employs a single couch for both CT scanning and beam delivery. The patient was positioned by matching the boundary between the prostate and the rectum's anterior region identified in the CT images to the corresponding boundary in the simulator images after bone matching. We acquired orthogonal kV x-ray images after couch movement and confirmed the body position by referring to the bony structure prior to treatment. In offline analyses, we contoured the targeted anatomical structures on 375 sets of daily in-room CT images for 10 patients. The uncertainty of the image-matching procedure was evaluated using the prostate contours and actual couch corrections. We also performed dose calculations using the same set of CT images, and evaluated daily change of dose-volume histograms (DVHs) to compare the effectiveness of the treatment using prostate matching to the bone-matching procedure. The isocenter shifts by prostate matching after bone matching were 0.5 ± 1.8 and -0.8 ± 2.6 mm along the superior-inferior (SI) and anterior-posterior (AP) directions, respectively. The body movement errors (σ) after couch movement were 0.7, 0.5, and 0.3 mm along the lateral, SI and AP direction, respectively, for 30 patients. The estimated errors (σ) in the prostate matching were 1.0 and 1.3 mm, and, in conjunction with the movement errors, the total positioning uncertainty was estimated to be 1.0 and 1.4 mm along the SI and AP directions, respectively. Daily DVH analyses showed that in the prostate matching, 98.7% and 86.1% of the total 375 irradiations maintained a dose condition of V 95%  > 95% for the prostate and a dose constraint of V 77%  < 18% for the rectum, whereas 90.4% and 66.1% of the total irradiations did so when bone matching was used. The dose constraint of the rectum and dose coverage of the prostate were better maintained by prostate matching than bone matching (P < 0.001). The daily variation in the dose to the seminal vesicles (SVs) was large, and only 40% of the total irradiations maintained the initial planned values of V 95% for high-risk treatment. Nevertheless, the deviations from the original value were -4 ± 7% and -5 ± 11% in the prostate and bone matching, respectively, and a better dose coverage of the SV was achieved by the prostate matching. The correction of repositioning along the AP and SI direction from conventional bone matching in CT image-guided proton therapy was found to be effective to maintain the dose constraint of the rectum and the dose coverage of the prostate. This work indicated that prostate cancer treatment by prostate matching using CT image guidance may be effective to reduce the rectal complications and achieve better tumor control of the prostate. However, an adaptive approach is desirable to maintain better dose coverage of the SVs. © 2018 American Association of Physicists in Medicine.

Multimodal Image Alignment via Linear Mapping between Feature Modalities.

PubMed

Jiang, Yanyun; Zheng, Yuanjie; Hou, Sujuan; Chang, Yuchou; Gee, James

2017-01-01

We propose a novel landmark matching based method for aligning multimodal images, which is accomplished uniquely by resolving a linear mapping between different feature modalities. This linear mapping results in a new measurement on similarity of images captured from different modalities. In addition, our method simultaneously solves this linear mapping and the landmark correspondences by minimizing a convex quadratic function. Our method can estimate complex image relationship between different modalities and nonlinear nonrigid spatial transformations even in the presence of heavy noise, as shown in our experiments carried out by using a variety of image modalities.

A 30-MHz piezo-composite ultrasound array for medical imaging applications.

PubMed

Ritter, Timothy A; Shrout, Thomas R; Tutwiler, Rick; Shung, K Kirk

2002-02-01

Ultrasound imaging at frequencies above 20 MHz is capable of achieving improved resolution in clinical applications requiring limited penetration depth. High frequency arrays that allow real-time imaging are desired for these applications but are not yet currently available. In this work, a method for fabricating fine-scale 2-2 composites suitable for 30-MHz linear array transducers was successfully demonstrated. High thickness coupling, low mechanical loss, and moderate electrical loss were achieved. This piezo-composite was incorporated into a 30-MHz array that included acoustic matching, an elevation focusing lens, electrical matching, and an air-filled kerf between elements. Bandwidths near 60%, 15-dB insertion loss, and crosstalk less than -30 dB were measured. Images of both a phantom and an ex vivo human eye were acquired using a synthetic aperture reconstruction method, resulting in measured lateral and axial resolutions of approximately 100 microm.

Characterization of a subwavelength-scale 3D void structure using the FDTD-based confocal laser scanning microscopic image mapping technique.

PubMed

Choi, Kyongsik; Chon, James W; Gu, Min; Lee, Byoungho

2007-08-20

In this paper, a simple confocal laser scanning microscopic (CLSM) image mapping technique based on the finite-difference time domain (FDTD) calculation has been proposed and evaluated for characterization of a subwavelength-scale three-dimensional (3D) void structure fabricated inside polymer matrix. The FDTD simulation method adopts a focused Gaussian beam incident wave, Berenger's perfectly matched layer absorbing boundary condition, and the angular spectrum analysis method. Through the well matched simulation and experimental results of the xz-scanned 3D void structure, we first characterize the exact position and the topological shape factor of the subwavelength-scale void structure, which was fabricated by a tightly focused ultrashort pulse laser. The proposed CLSM image mapping technique based on the FDTD can be widely applied from the 3D near-field microscopic imaging, optical trapping, and evanescent wave phenomenon to the state-of-the-art bio- and nanophotonics.

Towards Guided Underwater Survey Using Light Visual Odometry

NASA Astrophysics Data System (ADS)

Nawaf, M. M.; Drap, P.; Royer, J. P.; Merad, D.; Saccone, M.

2017-02-01

A light distributed visual odometry method adapted to embedded hardware platform is proposed. The aim is to guide underwater surveys in real time. We rely on image stream captured using portable stereo rig attached to the embedded system. Taken images are analyzed on the fly to assess image quality in terms of sharpness and lightness, so that immediate actions can be taken accordingly. Images are then transferred over the network to another processing unit to compute the odometry. Relying on a standard ego-motion estimation approach, we speed up points matching between image quadruplets using a low level points matching scheme relying on fast Harris operator and template matching that is invariant to illumination changes. We benefit from having the light source attached to the hardware platform to estimate a priori rough depth belief following light divergence over distance low. The rough depth is used to limit points correspondence search zone as it linearly depends on disparity. A stochastic relative bundle adjustment is applied to minimize re-projection errors. The evaluation of the proposed method demonstrates the gain in terms of computation time w.r.t. other approaches that use more sophisticated feature descriptors. The built system opens promising areas for further development and integration of embedded computer vision techniques.

Development and validation of automated 2D-3D bronchial airway matching to track changes in regional bronchial morphology using serial low-dose chest CT scans in children with chronic lung disease.

PubMed

Raman, Pavithra; Raman, Raghav; Newman, Beverley; Venkatraman, Raman; Raman, Bhargav; Robinson, Terry E

2010-12-01

To address potential concern for cumulative radiation exposure with serial spiral chest computed tomography (CT) scans in children with chronic lung disease, we developed an approach to match bronchial airways on low-dose spiral and low-dose high-resolution CT (HRCT) chest images to allow serial comparisons. An automated algorithm matches the position and orientation of bronchial airways obtained from HRCT slices with those in the spiral CT scan. To validate this algorithm, we compared manual matching vs automatic matching of bronchial airways in three pediatric patients. The mean absolute percentage difference between the manually matched spiral CT airway and the index HRCT airways were 9.4 ± 8.5% for the internal diameter measurements, 6.0 ± 4.1% for the outer diameter measurements, and 10.1 ± 9.3% for the wall thickness measurements. The mean absolute percentage difference between the automatically matched spiral CT airway measurements and index HRCT airway measurements were 9.2 ± 8.6% for the inner diameter, 5.8 ± 4.5% for the outer diameter, and 9.9 ± 9.5% for the wall thickness. The overall difference between manual and automated methods was 2.1 ± 1.2%, which was significantly less than the interuser variability of 5.1 ± 4.6% (p<0.05). Tests of equivalence had p<0.05, demonstrating no significant difference between the two methods. The time required for matching was significantly reduced in the automated method (p<0.01) and was as accurate as manual matching, allowing efficient comparison of airways obtained on low-dose spiral CT imaging with low-dose HRCT scans.

Cone beam CT dose reduction in prostate radiotherapy using Likert scale methods

PubMed Central

Newton, Louise A; Jordan, Suzanne; Smith, Ruth

2016-01-01

Objective: To use a Likert scale method to optimize image quality (IQ) for cone beam CT (CBCT) soft-tissue matching for image-guided radiotherapy of the prostate. Methods: 23 males with local/locally advanced prostate cancer had the CBCT IQ assessed using a 4-point Likert scale (4 = excellent, no artefacts; 3 = good, few artefacts; 2 = poor, just able to match; 1 = unsatisfactory, not able to match) at three levels of exposure. The lateral separations of the subjects were also measured. The Friedman test and Wilcoxon signed-rank tests were used to determine if the IQ was associated with the exposure level. We used the point-biserial correlation and a χ2 test to investigate the relationship between the separation and IQ. Results: The Friedman test showed that the IQ was related to exposure (p = 2 × 10−7) and the Wilcoxon signed-rank test demonstrated that the IQ decreased as exposure decreased (all p-values <0.005). We did not find a correlation between the IQ and the separation (correlation coefficient 0.045), but for separations <35 cm, it was possible to use the lowest exposure parameters studied. Conclusion: We can reduce exposure factors to 80% of those supplied with the system without hindering the matching process for all patients. For patients with lateral separations <35 cm, the exposure factors can be reduced further to 64% of the original values. Advances in knowledge: Likert scales are a useful tool for measuring IQ in the optimization of CBCT IQ for soft-tissue matching in radiotherapy image guidance applications. PMID:26689092

A New FPGA Architecture of FAST and BRIEF Algorithm for On-Board Corner Detection and Matching.

PubMed

Huang, Jingjin; Zhou, Guoqing; Zhou, Xiang; Zhang, Rongting

2018-03-28

Although some researchers have proposed the Field Programmable Gate Array (FPGA) architectures of Feature From Accelerated Segment Test (FAST) and Binary Robust Independent Elementary Features (BRIEF) algorithm, there is no consideration of image data storage in these traditional architectures that will result in no image data that can be reused by the follow-up algorithms. This paper proposes a new FPGA architecture that considers the reuse of sub-image data. In the proposed architecture, a remainder-based method is firstly designed for reading the sub-image, a FAST detector and a BRIEF descriptor are combined for corner detection and matching. Six pairs of satellite images with different textures, which are located in the Mentougou district, Beijing, China, are used to evaluate the performance of the proposed architecture. The Modelsim simulation results found that: (i) the proposed architecture is effective for sub-image reading from DDR3 at a minimum cost; (ii) the FPGA implementation is corrected and efficient for corner detection and matching, such as the average value of matching rate of natural areas and artificial areas are approximately 67% and 83%, respectively, which are close to PC's and the processing speed by FPGA is approximately 31 and 2.5 times faster than those by PC processing and by GPU processing, respectively.

Image-Based Localization for Indoor Environment Using Mobile Phone

NASA Astrophysics Data System (ADS)

Huang, Y.; Wang, H.; Zhan, K.; Zhao, J.; Gui, P.; Feng, T.

2015-05-01

Real-time indoor localization based on supporting infrastructures like wireless devices and QR codes are usually costly and labor intensive to implement. In this study, we explored a cheap alternative approach based on images for indoor localization. A user can localize him/herself by just shooting a photo of the surrounding indoor environment using the mobile phone. No any other equipment is required. This is achieved by employing image-matching and searching techniques with a dataset of pre-captured indoor images. In the beginning, a database of structured images of the indoor environment is constructed by using image matching and the bundle adjustment algorithm. Then each image's relative pose (its position and orientation) is estimated and the semantic locations of images are tagged. A user's location can then be determined by comparing a photo taken by the mobile phone to the database. This is done by combining quick image searching, matching and the relative orientation. This study also try to explore image acquisition plans and the processing capacity of off-the-shell mobile phones. During the whole pipeline, a collection of indoor images with both rich and poor textures are examined. Several feature detectors are used and compared. Pre-processing of complex indoor photo is also implemented on the mobile phone. The preliminary experimental results prove the feasibility of this method. In the future, we are trying to raise the efficiency of matching between indoor images and explore the fast 4G wireless communication to ensure the speed and accuracy of the localization based on a client-server framework.

a Preliminary Work on Layout Slam for Reconstruction of Indoor Corridor Environments

NASA Astrophysics Data System (ADS)

Baligh Jahromi, A.; Sohn, G.; Shahbazi, M.; Kang, J.

2017-09-01

We propose a real time indoor corridor layout estimation method based on visual Simultaneous Localization and Mapping (SLAM). The proposed method adopts the Manhattan World Assumption at indoor spaces and uses the detected single image straight line segments and their corresponding orthogonal vanishing points to improve the feature matching scheme in the adopted visual SLAM system. Using the proposed real time indoor corridor layout estimation method, the system is able to build an online sparse map of structural corner point features. The challenges presented by abrupt camera rotation in the 3D space are successfully handled through matching vanishing directions of consecutive video frames on the Gaussian sphere. Using the single image based indoor layout features for initializing the system, permitted the proposed method to perform real time layout estimation and camera localization in indoor corridor areas. For layout structural corner points matching, we adopted features which are invariant under scale, translation, and rotation. We proposed a new feature matching cost function which considers both local and global context information. The cost function consists of a unary term, which measures pixel to pixel orientation differences of the matched corners, and a binary term, which measures the amount of angle differences between directly connected layout corner features. We have performed the experiments on real scenes at York University campus buildings and the available RAWSEEDS dataset. The incoming results depict that the proposed method robustly performs along with producing very limited position and orientation errors.

Wavelet domain textual coding of Ottoman script images

NASA Astrophysics Data System (ADS)

Gerek, Oemer N.; Cetin, Enis A.; Tewfik, Ahmed H.

1996-02-01

Image coding using wavelet transform, DCT, and similar transform techniques is well established. On the other hand, these coding methods neither take into account the special characteristics of the images in a database nor are they suitable for fast database search. In this paper, the digital archiving of Ottoman printings is considered. Ottoman documents are printed in Arabic letters. Witten et al. describes a scheme based on finding the characters in binary document images and encoding the positions of the repeated characters This method efficiently compresses document images and is suitable for database research, but it cannot be applied to Ottoman or Arabic documents as the concept of character is different in Ottoman or Arabic. Typically, one has to deal with compound structures consisting of a group of letters. Therefore, the matching criterion will be according to those compound structures. Furthermore, the text images are gray tone or color images for Ottoman scripts for the reasons that are described in the paper. In our method the compound structure matching is carried out in wavelet domain which reduces the search space and increases the compression ratio. In addition to the wavelet transformation which corresponds to the linear subband decomposition, we also used nonlinear subband decomposition. The filters in the nonlinear subband decomposition have the property of preserving edges in the low resolution subband image.

A neighboring structure reconstructed matching algorithm based on LARK features

NASA Astrophysics Data System (ADS)

Xue, Taobei; Han, Jing; Zhang, Yi; Bai, Lianfa

2015-11-01

Aimed at the low contrast ratio and high noise of infrared images, and the randomness and ambient occlusion of its objects, this paper presents a neighboring structure reconstructed matching (NSRM) algorithm based on LARK features. The neighboring structure relationships of local window are considered based on a non-negative linear reconstruction method to build a neighboring structure relationship matrix. Then the LARK feature matrix and the NSRM matrix are processed separately to get two different similarity images. By fusing and analyzing the two similarity images, those infrared objects are detected and marked by the non-maximum suppression. The NSRM approach is extended to detect infrared objects with incompact structure. High performance is demonstrated on infrared body set, indicating a lower false detecting rate than conventional methods in complex natural scenes.

2D-3D registration using gradient-based MI for image guided surgery systems

NASA Astrophysics Data System (ADS)

Yim, Yeny; Chen, Xuanyi; Wakid, Mike; Bielamowicz, Steve; Hahn, James

2011-03-01

Registration of preoperative CT data to intra-operative video images is necessary not only to compare the outcome of the vocal fold after surgery with the preplanned shape but also to provide the image guidance for fusion of all imaging modalities. We propose a 2D-3D registration method using gradient-based mutual information. The 3D CT scan is aligned to 2D endoscopic images by finding the corresponding viewpoint between the real camera for endoscopic images and the virtual camera for CT scans. Even though mutual information has been successfully used to register different imaging modalities, it is difficult to robustly register the CT rendered image to the endoscopic image due to varying light patterns and shape of the vocal fold. The proposed method calculates the mutual information in the gradient images as well as original images, assigning more weight to the high gradient regions. The proposed method can emphasize the effect of vocal fold and allow a robust matching regardless of the surface illumination. To find the viewpoint with maximum mutual information, a downhill simplex method is applied in a conditional multi-resolution scheme which leads to a less-sensitive result to local maxima. To validate the registration accuracy, we evaluated the sensitivity to initial viewpoint of preoperative CT. Experimental results showed that gradient-based mutual information provided robust matching not only for two identical images with different viewpoints but also for different images acquired before and after surgery. The results also showed that conditional multi-resolution scheme led to a more accurate registration than single-resolution.

Joint sparse coding based spatial pyramid matching for classification of color medical image.

PubMed

Shi, Jun; Li, Yi; Zhu, Jie; Sun, Haojie; Cai, Yin

2015-04-01

Although color medical images are important in clinical practice, they are usually converted to grayscale for further processing in pattern recognition, resulting in loss of rich color information. The sparse coding based linear spatial pyramid matching (ScSPM) and its variants are popular for grayscale image classification, but cannot extract color information. In this paper, we propose a joint sparse coding based SPM (JScSPM) method for the classification of color medical images. A joint dictionary can represent both the color information in each color channel and the correlation between channels. Consequently, the joint sparse codes calculated from a joint dictionary can carry color information, and therefore this method can easily transform a feature descriptor originally designed for grayscale images to a color descriptor. A color hepatocellular carcinoma histological image dataset was used to evaluate the performance of the proposed JScSPM algorithm. Experimental results show that JScSPM provides significant improvements as compared with the majority voting based ScSPM and the original ScSPM for color medical image classification. Copyright © 2014 Elsevier Ltd. All rights reserved.

Scalable Integrated Region-Based Image Retrieval Using IRM and Statistical Clustering.

ERIC Educational Resources Information Center

Wang, James Z.; Du, Yanping

Statistical clustering is critical in designing scalable image retrieval systems. This paper presents a scalable algorithm for indexing and retrieving images based on region segmentation. The method uses statistical clustering on region features and IRM (Integrated Region Matching), a measure developed to evaluate overall similarity between images…

Adaptively Tuned Iterative Low Dose CT Image Denoising

PubMed Central

Hashemi, SayedMasoud; Paul, Narinder S.; Beheshti, Soosan; Cobbold, Richard S. C.

2015-01-01

Improving image quality is a critical objective in low dose computed tomography (CT) imaging and is the primary focus of CT image denoising. State-of-the-art CT denoising algorithms are mainly based on iterative minimization of an objective function, in which the performance is controlled by regularization parameters. To achieve the best results, these should be chosen carefully. However, the parameter selection is typically performed in an ad hoc manner, which can cause the algorithms to converge slowly or become trapped in a local minimum. To overcome these issues a noise confidence region evaluation (NCRE) method is used, which evaluates the denoising residuals iteratively and compares their statistics with those produced by additive noise. It then updates the parameters at the end of each iteration to achieve a better match to the noise statistics. By combining NCRE with the fundamentals of block matching and 3D filtering (BM3D) approach, a new iterative CT image denoising method is proposed. It is shown that this new denoising method improves the BM3D performance in terms of both the mean square error and a structural similarity index. Moreover, simulations and patient results show that this method preserves the clinically important details of low dose CT images together with a substantial noise reduction. PMID:26089972

Real-time automatic fiducial marker tracking in low contrast cine-MV images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Wei-Yang; Lin, Shu-Fang; Yang, Sheng-Chang

2013-01-15

Purpose: To develop a real-time automatic method for tracking implanted radiographic markers in low-contrast cine-MV patient images used in image-guided radiation therapy (IGRT). Methods: Intrafraction motion tracking using radiotherapy beam-line MV images have gained some attention recently in IGRT because no additional imaging dose is introduced. However, MV images have much lower contrast than kV images, therefore a robust and automatic algorithm for marker detection in MV images is a prerequisite. Previous marker detection methods are all based on template matching or its derivatives. Template matching needs to match object shape that changes significantly for different implantation and projection angle.more » While these methods require a large number of templates to cover various situations, they are often forced to use a smaller number of templates to reduce the computation load because their methods all require exhaustive search in the region of interest. The authors solve this problem by synergetic use of modern but well-tested computer vision and artificial intelligence techniques; specifically the authors detect implanted markers utilizing discriminant analysis for initialization and use mean-shift feature space analysis for sequential tracking. This novel approach avoids exhaustive search by exploiting the temporal correlation between consecutive frames and makes it possible to perform more sophisticated detection at the beginning to improve the accuracy, followed by ultrafast sequential tracking after the initialization. The method was evaluated and validated using 1149 cine-MV images from two prostate IGRT patients and compared with manual marker detection results from six researchers. The average of the manual detection results is considered as the ground truth for comparisons. Results: The average root-mean-square errors of our real-time automatic tracking method from the ground truth are 1.9 and 2.1 pixels for the two patients (0.26 mm/pixel). The standard deviations of the results from the 6 researchers are 2.3 and 2.6 pixels. The proposed framework takes about 128 ms to detect four markers in the first MV images and about 23 ms to track these markers in each of the subsequent images. Conclusions: The unified framework for tracking of multiple markers presented here can achieve marker detection accuracy similar to manual detection even in low-contrast cine-MV images. It can cope with shape deformations of fiducial markers at different gantry angles. The fast processing speed reduces the image processing portion of the system latency, therefore can improve the performance of real-time motion compensation.« less

Animal imaging using immersion

NASA Astrophysics Data System (ADS)

Kalogerakis, Konstantinos S.; Kotz, Kenneth T.; Rand, Kendra; Faris, Gregory W.

2003-07-01

We are using rodent animal models to study and compare contrast mechanisms for detection of breast cancer. These measurements are performed with the animals immersed in a matching scattering medium. The matching scattering medium or liquid tissue phantom comprises a mixture of Ropaque (hollow acrylic/styrene microspheres) and ink. We have previously applied matched imaging to imaging in humans. Surrounding the imaged region with a matched tissue phantom compensates for variations in tissue thickness and geometry, provides more uniform illumination, and allows better use of the dynamic range of the imaging system. If the match is good, the boundaries of the imaged region should almost vanish, enhancing the contrast from internal structure as compared to contrast from the boundaries and surface topography. For our measurements in animals, the immersion plays two additional roles. First, we can readily study tumors through tissue thickness similar to that of a human breast. Although the heterogeneity of the breast is lost, this is a practical method to study the detection of small tumors and monitor changes as they grow. Second, the immersion enhances our ability to quantify the contrast mechanisms for peripheral tumors on the animal because the boundary effects on photon migration are eliminated. We are currently developing two systems for these measurements. One is a continuous-wave (CW) system based on near-infrared LED illumination and a CCD (charge-coupled device) camera. The second system, a frequency domain system, can help quantify the changes observed with the CW system.

SU-E-J-36: A Flexible Integration of Key Technologies in Image-Guided Radiotherapy for Accurate Radiotherapy System (ARTS-IGRT).

PubMed

Jia, J; Liu, F; Ren, Q; Pei, X; Cao, R; Wu, Y

2012-06-01

Image-guided radiotherapy (IGRT) is becoming increasingly important in the planning and delivery of radiotherapy. With the aim of implementing the key technologies in a flexible and integrated way in IGRT for accurate radiotherapy system (ARTS), a prototype system named as ARTS-IGRT was designed and completed to apply main principles in image-guided radiotherapy. The basic workflow of the ARTS-IGRT software was completed with five functional modules including management of patient information, X-ray image acquisition, 2D/2D anatomy match, 2D/3D match as well as marker-based match. For 2D/2D match, an image registration method was proposed based on maximization of mutual information with multi-resolution and regions of interest. For the 2D/3D registration, optimizations have been employed to improve the existing digitally reconstructed radiography generation algorithm based on ray-casting, and also an image registration method based on implanted markers with different numbers was adopted for 3D/3D match. In additional, the kV X-Ray imaging on rail device was finished for a better internal anatomy image checking at any angle. Together with an infrared device, a positioning and tracking system was developed as well for accurate patient setup and motion monitoring during each treatment. A lot of tests were carried out based on the head phantom to testify the availability of the improved algorithms. Compared with a set of controlled experiments adopted on the released commercial IGRT platform in the hospital, the functions of both software and hardware were testified comprehensively. The results showed a validity verification of ARTS-IGRT. The accuracy and efficiency of ARTS-IGRT on both software and hardware proved to be valid. And also with a flexible and user-friendly interface it can meet the principles of clinical radiotherapy practice. Supported by the Natural Science Foundation of Anhui Province (11040606Q55) and the National Natural Science Foundation of China (30900386). © 2012 American Association of Physicists in Medicine.

An automatic bone segmentation method based on anatomical structure for the knee joint in MDCT image.

PubMed

Uozumi, Y; Nagamune, K

2013-01-01

The purpose of this study is to propose an automatic segmentation about each bone (the femur, the tibia, the patellar, and fibular) of the knee in MDCT image. The proposed method was applied for six patients (Age 33 ± 13, four males/tew females). The proposed method segmented the knee joint into each bone by using anatomical structure for the knee joint. The experiments calculate matching rate of the manual and the proposed method for evaluating it. As a result, The matching rate of the femur, the tibia, the patellar, and fibula were 95.84 ± 0.57%, 94.12 ± 1.01%, 94.49 ± 0.83%, 86.37 ± 4.28%, respectively. This study concluded that the proposed method is enough to segment the knee bones.

Registration of 3D spectral OCT volumes combining ICP with a graph-based approach

NASA Astrophysics Data System (ADS)

Niemeijer, Meindert; Lee, Kyungmoo; Garvin, Mona K.; Abràmoff, Michael D.; Sonka, Milan

2012-02-01

The introduction of spectral Optical Coherence Tomography (OCT) scanners has enabled acquisition of high resolution, 3D cross-sectional volumetric images of the retina. 3D-OCT is used to detect and manage eye diseases such as glaucoma and age-related macular degeneration. To follow-up patients over time, image registration is a vital tool to enable more precise, quantitative comparison of disease states. In this work we present a 3D registrationmethod based on a two-step approach. In the first step we register both scans in the XY domain using an Iterative Closest Point (ICP) based algorithm. This algorithm is applied to vessel segmentations obtained from the projection image of each scan. The distance minimized in the ICP algorithm includes measurements of the vessel orientation and vessel width to allow for a more robust match. In the second step, a graph-based method is applied to find the optimal translation along the depth axis of the individual A-scans in the volume to match both scans. The cost image used to construct the graph is based on the mean squared error (MSE) between matching A-scans in both images at different translations. We have applied this method to the registration of Optic Nerve Head (ONH) centered 3D-OCT scans of the same patient. First, 10 3D-OCT scans of 5 eyes with glaucoma imaged in vivo were registered for a qualitative evaluation of the algorithm performance. Then, 17 OCT data set pairs of 17 eyes with known deformation were used for quantitative assessment of the method's robustness.

A Feature-based Approach to Big Data Analysis of Medical Images

PubMed Central

Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M.

2015-01-01

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches in O(log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct. PMID:26221685

A Feature-Based Approach to Big Data Analysis of Medical Images.

PubMed

Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M

2015-01-01

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct.

Iris recognition using possibilistic fuzzy matching on local features.

PubMed

Tsai, Chung-Chih; Lin, Heng-Yi; Taur, Jinshiuh; Tao, Chin-Wang

2012-02-01

In this paper, we propose a novel possibilistic fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points. In addition, the nonlinear normalization model is adopted to provide more accurate position before matching. Moreover, an effective iris segmentation method is proposed to refine the detected inner and outer boundaries to smooth curves. For feature extraction, the Gabor filters are adopted to detect the local feature points from the segmented iris image in the Cartesian coordinate system and to generate a rotation-invariant descriptor for each detected point. After that, the proposed matching algorithm is used to compute a similarity score for two sets of feature points from a pair of iris images. The experimental results show that the performance of our system is better than those of the systems based on the local features and is comparable to those of the typical systems.

Research on three-dimensional reconstruction method based on binocular vision

NASA Astrophysics Data System (ADS)

Li, Jinlin; Wang, Zhihui; Wang, Minjun

2018-03-01

As the hot and difficult issue in computer vision, binocular stereo vision is an important form of computer vision,which has a broad application prospects in many computer vision fields,such as aerial mapping,vision navigation,motion analysis and industrial inspection etc.In this paper, a research is done into binocular stereo camera calibration, image feature extraction and stereo matching. In the binocular stereo camera calibration module, the internal parameters of a single camera are obtained by using the checkerboard lattice of zhang zhengyou the field of image feature extraction and stereo matching, adopted the SURF operator in the local feature operator and the SGBM algorithm in the global matching algorithm are used respectively, and the performance are compared. After completed the feature points matching, we can build the corresponding between matching points and the 3D object points using the camera parameters which are calibrated, which means the 3D information.

Ultrasound strain imaging using Barker code

NASA Astrophysics Data System (ADS)

Peng, Hui; Tie, Juhong; Guo, Dequan

2017-01-01

Ultrasound strain imaging is showing promise as a new way of imaging soft tissue elasticity in order to help clinicians detect lesions or cancers in tissues. In this paper, Barker code is applied to strain imaging to improve its quality. Barker code as a coded excitation signal can be used to improve the echo signal-to-noise ratio (eSNR) in ultrasound imaging system. For the Baker code of length 13, the sidelobe level of the matched filter output is -22dB, which is unacceptable for ultrasound strain imaging, because high sidelobe level will cause high decorrelation noise. Instead of using the conventional matched filter, we use the Wiener filter to decode the Barker-coded echo signal to suppress the range sidelobes. We also compare the performance of Barker code and the conventional short pulse in simulation method. The simulation results demonstrate that the performance of the Wiener filter is much better than the matched filter, and Baker code achieves higher elastographic signal-to-noise ratio (SNRe) than the short pulse in low eSNR or great depth conditions due to the increased eSNR with it.

Morphological Feature Extraction for Automatic Registration of Multispectral Images

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2007-01-01

The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.

Automated Image Registration Using Morphological Region of Interest Feature Extraction

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2005-01-01

With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.

Demons deformable registration of CT and cone-beam CT using an iterative intensity matching approach

PubMed Central

Nithiananthan, Sajendra; Schafer, Sebastian; Uneri, Ali; Mirota, Daniel J.; Stayman, J. Webster; Zbijewski, Wojciech; Brock, Kristy K.; Daly, Michael J.; Chan, Harley; Irish, Jonathan C.; Siewerdsen, Jeffrey H.

2011-01-01

Purpose: A method of intensity-based deformable registration of CT and cone-beam CT (CBCT) images is described, in which intensity correction occurs simultaneously within the iterative registration process. The method preserves the speed and simplicity of the popular Demons algorithm while providing robustness and accuracy in the presence of large mismatch between CT and CBCT voxel values (“intensity”). Methods: A variant of the Demons algorithm was developed in which an estimate of the relationship between CT and CBCT intensity values for specific materials in the image is computed at each iteration based on the set of currently overlapping voxels. This tissue-specific intensity correction is then used to estimate the registration output for that iteration and the process is repeated. The robustness of the method was tested in CBCT images of a cadaveric head exhibiting a broad range of simulated intensity variations associated with x-ray scatter, object truncation, and∕or errors in the reconstruction algorithm. The accuracy of CT-CBCT registration was also measured in six real cases, exhibiting deformations ranging from simple to complex during surgery or radiotherapy guided by a CBCT-capable C-arm or linear accelerator, respectively. Results: The iterative intensity matching approach was robust against all levels of intensity variation examined, including spatially varying errors in voxel value of a factor of 2 or more, as can be encountered in cases of high x-ray scatter. Registration accuracy without intensity matching degraded severely with increasing magnitude of intensity error and introduced image distortion. A single histogram match performed prior to registration alleviated some of these effects but was also prone to image distortion and was quantifiably less robust and accurate than the iterative approach. Within the six case registration accuracy study, iterative intensity matching Demons reduced mean TRE to (2.5±2.8) mm compared to (3.5±3.0) mm with rigid registration. Conclusions: A method was developed to iteratively correct CT-CBCT intensity disparity during Demons registration, enabling fast, intensity-based registration in CBCT-guided procedures such as surgery and radiotherapy, in which CBCT voxel values may be inaccurate. Accurate CT-CBCT registration in turn facilitates registration of multimodality preoperative image and planning data to intraoperative CBCT by way of the preoperative CT, thereby linking the intraoperative frame of reference to a wealth of preoperative information that could improve interventional guidance. PMID:21626913

Demons deformable registration of CT and cone-beam CT using an iterative intensity matching approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nithiananthan, Sajendra; Schafer, Sebastian; Uneri, Ali

2011-04-15

Purpose: A method of intensity-based deformable registration of CT and cone-beam CT (CBCT) images is described, in which intensity correction occurs simultaneously within the iterative registration process. The method preserves the speed and simplicity of the popular Demons algorithm while providing robustness and accuracy in the presence of large mismatch between CT and CBCT voxel values (''intensity''). Methods: A variant of the Demons algorithm was developed in which an estimate of the relationship between CT and CBCT intensity values for specific materials in the image is computed at each iteration based on the set of currently overlapping voxels. This tissue-specificmore » intensity correction is then used to estimate the registration output for that iteration and the process is repeated. The robustness of the method was tested in CBCT images of a cadaveric head exhibiting a broad range of simulated intensity variations associated with x-ray scatter, object truncation, and/or errors in the reconstruction algorithm. The accuracy of CT-CBCT registration was also measured in six real cases, exhibiting deformations ranging from simple to complex during surgery or radiotherapy guided by a CBCT-capable C-arm or linear accelerator, respectively. Results: The iterative intensity matching approach was robust against all levels of intensity variation examined, including spatially varying errors in voxel value of a factor of 2 or more, as can be encountered in cases of high x-ray scatter. Registration accuracy without intensity matching degraded severely with increasing magnitude of intensity error and introduced image distortion. A single histogram match performed prior to registration alleviated some of these effects but was also prone to image distortion and was quantifiably less robust and accurate than the iterative approach. Within the six case registration accuracy study, iterative intensity matching Demons reduced mean TRE to (2.5{+-}2.8) mm compared to (3.5{+-}3.0) mm with rigid registration. Conclusions: A method was developed to iteratively correct CT-CBCT intensity disparity during Demons registration, enabling fast, intensity-based registration in CBCT-guided procedures such as surgery and radiotherapy, in which CBCT voxel values may be inaccurate. Accurate CT-CBCT registration in turn facilitates registration of multimodality preoperative image and planning data to intraoperative CBCT by way of the preoperative CT, thereby linking the intraoperative frame of reference to a wealth of preoperative information that could improve interventional guidance.« less

Improvement of retinal blood vessel detection by spur removal and Gaussian matched filtering compensation

NASA Astrophysics Data System (ADS)

Xiao, Di; Vignarajan, Janardhan; An, Dong; Tay-Kearney, Mei-Ling; Kanagasingam, Yogi

2016-03-01

Retinal photography is a non-invasive and well-accepted clinical diagnosis of ocular diseases. Qualitative and quantitative assessment of retinal images is crucial in ocular diseases related clinical application. In this paper, we proposed approaches for improving the quality of blood vessel detection based on our initial blood vessel detection methods. A blood vessel spur pruning method has been developed for removing the blood vessel spurs both on vessel medial lines and binary vessel masks, which are caused by artifacts and side-effect of Gaussian matched vessel enhancement. A Gaussian matched filtering compensation method has been developed for removing incorrect vessel branches in the areas of low illumination. The proposed approaches were applied and tested on the color fundus images from one publicly available database and our diabetic retinopathy screening dataset. A preliminary result has demonstrated the robustness and good performance of the proposed approaches and their potential application for improving retinal blood vessel detection.

Automatic dynamic range adjustment for ultrasound B-mode imaging.

PubMed

Lee, Yeonhwa; Kang, Jinbum; Yoo, Yangmo

2015-02-01

In medical ultrasound imaging, dynamic range (DR) is defined as the difference between the maximum and minimum values of the displayed signal to display and it is one of the most essential parameters that determine its image quality. Typically, DR is given with a fixed value and adjusted manually by operators, which leads to low clinical productivity and high user dependency. Furthermore, in 3D ultrasound imaging, DR values are unable to be adjusted during 3D data acquisition. A histogram matching method, which equalizes the histogram of an input image based on that from a reference image, can be applied to determine the DR value. However, it could be lead to an over contrasted image. In this paper, a new Automatic Dynamic Range Adjustment (ADRA) method is presented that adaptively adjusts the DR value by manipulating input images similar to a reference image. The proposed ADRA method uses the distance ratio between the log average and each extreme value of a reference image. To evaluate the performance of the ADRA method, the similarity between the reference and input images was measured by computing a correlation coefficient (CC). In in vivo experiments, the CC values were increased by applying the ADRA method from 0.6872 to 0.9870 and from 0.9274 to 0.9939 for kidney and liver data, respectively, compared to the fixed DR case. In addition, the proposed ADRA method showed to outperform the histogram matching method with in vivo liver and kidney data. When using 3D abdominal data with 70 frames, while the CC value from the ADRA method is slightly increased (i.e., 0.6%), the proposed method showed improved image quality in the c-plane compared to its fixed counterpart, which suffered from a shadow artifact. These results indicate that the proposed method can enhance image quality in 2D and 3D ultrasound B-mode imaging by improving the similarity between the reference and input images while eliminating unnecessary manual interaction by the user. Copyright © 2014 Elsevier B.V. All rights reserved.

Research on the principle and experimentation of optical compressive spectral imaging

NASA Astrophysics Data System (ADS)

Chen, Yuheng; Chen, Xinhua; Zhou, Jiankang; Ji, Yiqun; Shen, Weimin

2013-12-01

The optical compressive spectral imaging method is a novel spectral imaging technique that draws in the inspiration of compressed sensing, which takes on the advantages such as reducing acquisition data amount, realizing snapshot imaging, increasing signal to noise ratio and so on. Considering the influence of the sampling quality on the ultimate imaging quality, researchers match the sampling interval with the modulation interval in former reported imaging system, while the depressed sampling rate leads to the loss on the original spectral resolution. To overcome that technical defect, the demand for the matching between the sampling interval and the modulation interval is disposed of and the spectral channel number of the designed experimental device increases more than threefold comparing to that of the previous method. Imaging experiment is carried out by use of the experiment installation and the spectral data cube of the shooting target is reconstructed with the acquired compressed image by use of the two-step iterative shrinkage/thresholding algorithms. The experimental result indicates that the spectral channel number increases effectively and the reconstructed data stays high-fidelity. The images and spectral curves are able to accurately reflect the spatial and spectral character of the target.

Robust stereo matching with trinary cross color census and triple image-based refinements

NASA Astrophysics Data System (ADS)

Chang, Ting-An; Lu, Xiao; Yang, Jar-Ferr

2017-12-01

For future 3D TV broadcasting systems and navigation applications, it is necessary to have accurate stereo matching which could precisely estimate depth map from two distanced cameras. In this paper, we first suggest a trinary cross color (TCC) census transform, which can help to achieve accurate disparity raw matching cost with low computational cost. The two-pass cost aggregation (TPCA) is formed to compute the aggregation cost, then the disparity map can be obtained by a range winner-take-all (RWTA) process and a white hole filling procedure. To further enhance the accuracy performance, a range left-right checking (RLRC) method is proposed to classify the results as correct, mismatched, or occluded pixels. Then, the image-based refinements for the mismatched and occluded pixels are proposed to refine the classified errors. Finally, the image-based cross voting and a median filter are employed to complete the fine depth estimation. Experimental results show that the proposed semi-global stereo matching system achieves considerably accurate disparity maps with reasonable computation cost.

A Brightness-Referenced Star Identification Algorithm for APS Star Trackers

PubMed Central

Zhang, Peng; Zhao, Qile; Liu, Jingnan; Liu, Ning

2014-01-01

Star trackers are currently the most accurate spacecraft attitude sensors. As a result, they are widely used in remote sensing satellites. Since traditional charge-coupled device (CCD)-based star trackers have a limited sensitivity range and dynamic range, the matching process for a star tracker is typically not very sensitive to star brightness. For active pixel sensor (APS) star trackers, the intensity of an imaged star is valuable information that can be used in star identification process. In this paper an improved brightness referenced star identification algorithm is presented. This algorithm utilizes the k-vector search theory and adds imaged stars' intensities to narrow the search scope and therefore increase the efficiency of the matching process. Based on different imaging conditions (slew, bright bodies, etc.) the developed matching algorithm operates in one of two identification modes: a three-star mode, and a four-star mode. If the reference bright stars (the stars brighter than three magnitude) show up, the algorithm runs the three-star mode and efficiency is further improved. The proposed method was compared with other two distinctive methods the pyramid and geometric voting methods. All three methods were tested with simulation data and actual in orbit data from the APS star tracker of ZY-3. Using a catalog composed of 1500 stars, the results show that without false stars the efficiency of this new method is 4∼5 times that of the pyramid method and 35∼37 times that of the geometric method. PMID:25299950

A brightness-referenced star identification algorithm for APS star trackers.

PubMed

Zhang, Peng; Zhao, Qile; Liu, Jingnan; Liu, Ning

2014-10-08

Star trackers are currently the most accurate spacecraft attitude sensors. As a result, they are widely used in remote sensing satellites. Since traditional charge-coupled device (CCD)-based star trackers have a limited sensitivity range and dynamic range, the matching process for a star tracker is typically not very sensitive to star brightness. For active pixel sensor (APS) star trackers, the intensity of an imaged star is valuable information that can be used in star identification process. In this paper an improved brightness referenced star identification algorithm is presented. This algorithm utilizes the k-vector search theory and adds imaged stars' intensities to narrow the search scope and therefore increase the efficiency of the matching process. Based on different imaging conditions (slew, bright bodies, etc.) the developed matching algorithm operates in one of two identification modes: a three-star mode, and a four-star mode. If the reference bright stars (the stars brighter than three magnitude) show up, the algorithm runs the three-star mode and efficiency is further improved. The proposed method was compared with other two distinctive methods the pyramid and geometric voting methods. All three methods were tested with simulation data and actual in orbit data from the APS star tracker of ZY-3. Using a catalog composed of 1500 stars, the results show that without false stars the efficiency of this new method is 4~5 times that of the pyramid method and 35~37 times that of the geometric method.

Automatic tracking of arbitrarily shaped implanted markers in kilovoltage projection images: A feasibility study

PubMed Central

Regmi, Rajesh; Lovelock, D. Michael; Hunt, Margie; Zhang, Pengpeng; Pham, Hai; Xiong, Jianping; Yorke, Ellen D.; Goodman, Karyn A.; Rimner, Andreas; Mostafavi, Hassan; Mageras, Gig S.

2014-01-01

Purpose: Certain types of commonly used fiducial markers take on irregular shapes upon implantation in soft tissue. This poses a challenge for methods that assume a predefined shape of markers when automatically tracking such markers in kilovoltage (kV) radiographs. The authors have developed a method of automatically tracking regularly and irregularly shaped markers using kV projection images and assessed its potential for detecting intrafractional target motion during rotational treatment. Methods: Template-based matching used a normalized cross-correlation with simplex minimization. Templates were created from computed tomography (CT) images for phantom studies and from end-expiration breath-hold planning CT for patient studies. The kV images were processed using a Sobel filter to enhance marker visibility. To correct for changes in intermarker relative positions between simulation and treatment that can introduce errors in automatic matching, marker offsets in three dimensions were manually determined from an approximately orthogonal pair of kV images. Two studies in anthropomorphic phantom were carried out, one using a gold cylindrical marker representing regular shape, another using a Visicoil marker representing irregular shape. Automatic matching of templates to cone beam CT (CBCT) projection images was performed to known marker positions in phantom. In patient data, automatic matching was compared to manual matching as an approximate ground truth. Positional discrepancy between automatic and manual matching of less than 2 mm was assumed as the criterion for successful tracking. Tracking success rates were examined in kV projection images from 22 CBCT scans of four pancreas, six gastroesophageal junction, and one lung cancer patients. Each patient had at least one irregularly shaped radiopaque marker implanted in or near the tumor. In addition, automatic tracking was tested in intrafraction kV images of three lung cancer patients with irregularly shaped markers during 11 volumetric modulated arc treatments. Purpose-built software developed at our institution was used to create marker templates and track the markers embedded in kV images. Results: Phantom studies showed mean ± standard deviation measurement uncertainty of automatic registration to be 0.14 ± 0.07 mm and 0.17 ± 0.08 mm for Visicoil and gold cylindrical markers, respectively. The mean success rate of automatic tracking with CBCT projections (11 frames per second, fps) of pancreas, gastroesophageal junction, and lung cancer patients was 100%, 99.1% (range 98%–100%), and 100%, respectively. With intrafraction images (approx. 0.2 fps) of lung cancer patients, the success rate was 98.2% (range 97%–100%), and 94.3% (range 93%–97%) using templates from 1.25 mm and 2.5 mm slice spacing CT scans, respectively. Correction of intermarker relative position was found to improve the success rate in two out of eight patients analyzed. Conclusions: The proposed method can track arbitrary marker shapes in kV images using templates generated from a breath-hold CT acquired at simulation. The studies indicate its feasibility for tracking tumor motion during rotational treatment. Investigation of the causes of misregistration suggests that its rate of incidence can be reduced with higher frequency of image acquisition, templates made from smaller CT slice spacing, and correction of changes in intermarker relative positions when they occur. PMID:24989384

Automatic tracking of arbitrarily shaped implanted markers in kilovoltage projection images: A feasibility study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Regmi, Rajesh; Lovelock, D. Michael; Hunt, Margie

Purpose: Certain types of commonly used fiducial markers take on irregular shapes upon implantation in soft tissue. This poses a challenge for methods that assume a predefined shape of markers when automatically tracking such markers in kilovoltage (kV) radiographs. The authors have developed a method of automatically tracking regularly and irregularly shaped markers using kV projection images and assessed its potential for detecting intrafractional target motion during rotational treatment. Methods: Template-based matching used a normalized cross-correlation with simplex minimization. Templates were created from computed tomography (CT) images for phantom studies and from end-expiration breath-hold planning CT for patient studies. Themore » kV images were processed using a Sobel filter to enhance marker visibility. To correct for changes in intermarker relative positions between simulation and treatment that can introduce errors in automatic matching, marker offsets in three dimensions were manually determined from an approximately orthogonal pair of kV images. Two studies in anthropomorphic phantom were carried out, one using a gold cylindrical marker representing regular shape, another using a Visicoil marker representing irregular shape. Automatic matching of templates to cone beam CT (CBCT) projection images was performed to known marker positions in phantom. In patient data, automatic matching was compared to manual matching as an approximate ground truth. Positional discrepancy between automatic and manual matching of less than 2 mm was assumed as the criterion for successful tracking. Tracking success rates were examined in kV projection images from 22 CBCT scans of four pancreas, six gastroesophageal junction, and one lung cancer patients. Each patient had at least one irregularly shaped radiopaque marker implanted in or near the tumor. In addition, automatic tracking was tested in intrafraction kV images of three lung cancer patients with irregularly shaped markers during 11 volumetric modulated arc treatments. Purpose-built software developed at our institution was used to create marker templates and track the markers embedded in kV images. Results: Phantom studies showed mean ± standard deviation measurement uncertainty of automatic registration to be 0.14 ± 0.07 mm and 0.17 ± 0.08 mm for Visicoil and gold cylindrical markers, respectively. The mean success rate of automatic tracking with CBCT projections (11 frames per second, fps) of pancreas, gastroesophageal junction, and lung cancer patients was 100%, 99.1% (range 98%–100%), and 100%, respectively. With intrafraction images (approx. 0.2 fps) of lung cancer patients, the success rate was 98.2% (range 97%–100%), and 94.3% (range 93%–97%) using templates from 1.25 mm and 2.5 mm slice spacing CT scans, respectively. Correction of intermarker relative position was found to improve the success rate in two out of eight patients analyzed. Conclusions: The proposed method can track arbitrary marker shapes in kV images using templates generated from a breath-hold CT acquired at simulation. The studies indicate its feasibility for tracking tumor motion during rotational treatment. Investigation of the causes of misregistration suggests that its rate of incidence can be reduced with higher frequency of image acquisition, templates made from smaller CT slice spacing, and correction of changes in intermarker relative positions when they occur.« less

Band co-registration modeling of LAPAN-A3/IPB multispectral imager based on satellite attitude

NASA Astrophysics Data System (ADS)

Hakim, P. R.; Syafrudin, A. H.; Utama, S.; Jayani, A. P. S.

2018-05-01

One of significant geometric distortion on images of LAPAN-A3/IPB multispectral imager is co-registration error between each color channel detector. Band co-registration distortion usually can be corrected by using several approaches, which are manual method, image matching algorithm, or sensor modeling and calibration approach. This paper develops another approach to minimize band co-registration distortion on LAPAN-A3/IPB multispectral image by using supervised modeling of image matching with respect to satellite attitude. Modeling results show that band co-registration error in across-track axis is strongly influenced by yaw angle, while error in along-track axis is fairly influenced by both pitch and roll angle. Accuracy of the models obtained is pretty good, which lies between 1-3 pixels error for each axis of each pair of band co-registration. This mean that the model can be used to correct the distorted images without the need of slower image matching algorithm, nor the laborious effort needed in manual approach and sensor calibration. Since the calculation can be executed in order of seconds, this approach can be used in real time quick-look image processing in ground station or even in satellite on-board image processing.

Fusion of Building Information and Range Imaging for Autonomous Location Estimation in Indoor Environments

PubMed Central

Kohoutek, Tobias K.; Mautz, Rainer; Wegner, Jan D.

2013-01-01

We present a novel approach for autonomous location estimation and navigation in indoor environments using range images and prior scene knowledge from a GIS database (CityGML). What makes this task challenging is the arbitrary relative spatial relation between GIS and Time-of-Flight (ToF) range camera further complicated by a markerless configuration. We propose to estimate the camera's pose solely based on matching of GIS objects and their detected location in image sequences. We develop a coarse-to-fine matching strategy that is able to match point clouds without any initial parameters. Experiments with a state-of-the-art ToF point cloud show that our proposed method delivers an absolute camera position with decimeter accuracy, which is sufficient for many real-world applications (e.g., collision avoidance). PMID:23435055

Study of moving object detecting and tracking algorithm for video surveillance system

NASA Astrophysics Data System (ADS)

Wang, Tao; Zhang, Rongfu

2010-10-01

This paper describes a specific process of moving target detecting and tracking in the video surveillance.Obtain high-quality background is the key to achieving differential target detecting in the video surveillance.The paper is based on a block segmentation method to build clear background,and using the method of background difference to detecing moving target,after a series of treatment we can be extracted the more comprehensive object from original image,then using the smallest bounding rectangle to locate the object.In the video surveillance system, the delay of camera and other reasons lead to tracking lag,the model of Kalman filter based on template matching was proposed,using deduced and estimated capacity of Kalman,the center of smallest bounding rectangle for predictive value,predicted the position in the next moment may appare,followed by template matching in the region as the center of this position,by calculate the cross-correlation similarity of current image and reference image,can determine the best matching center.As narrowed the scope of searching,thereby reduced the searching time,so there be achieve fast-tracking.

Method and system for detecting polygon boundaries of structures in images as particle tracks through fields of corners and pixel gradients

DOEpatents

Paglieroni, David W [Pleasanton, CA; Manay, Siddharth [Livermore, CA

2011-12-20

A stochastic method and system for detecting polygon structures in images, by detecting a set of best matching corners of predetermined acuteness .alpha. of a polygon model from a set of similarity scores based on GDM features of corners, and tracking polygon boundaries as particle tracks using a sequential Monte Carlo approach. The tracking involves initializing polygon boundary tracking by selecting pairs of corners from the set of best matching corners to define a first side of a corresponding polygon boundary; tracking all intermediate sides of the polygon boundaries using a particle filter, and terminating polygon boundary tracking by determining the last side of the tracked polygon boundaries to close the polygon boundaries. The particle tracks are then blended to determine polygon matches, which may be made available, such as to a user, for ranking and inspection.

Implementation and image processing of a multi-focusing bionic compound eye

NASA Astrophysics Data System (ADS)

Wang, Xin; Guo, Yongcai; Luo, Jiasai

2018-01-01

In this paper, a new BCE with multi-focusing microlens array (MLA) was proposed. The BCE consist of detachable micro-hole array (MHA), multi-focusing MLA and spherical substrate, thus allowing it to have a large FOV without crosstalk and stray light. The MHA was fabricated by the precision machining and the parameters of the microlens varied depend on the aperture of micro-hole, through which the implementation of the multi-focusing MLA was realized under the negative pressure. Without the pattern transfer and substrate reshaping, the whole fabrication method was capable of accomplishing within several minutes by using microinjection technology. Furthermore, the method is cost-effective and easy for operation, thus providing a feasible method for the mass production of the BCE. The corresponding image processing was used to realize the image stitching for the sub-image of each single microlens, which offering an integral image in large FOV. The image stitching was implemented through the overlap between the adjacent sub-images and the feature points between the adjacent sub-images were captured by the Harris point detection. By using the adaptive non-maximal suppression, numerous potential mismatching points were eliminated and the algorithm efficiency was proved effectively. Following this, the random sample consensus (RANSAC) was used for feature points matching, by which the relation of projection transformation of the image is obtained. The implementation of the accurate image matching was then realized after the smooth transition by weighted average method. Experimental results indicate that the image-stitching algorithm can be applied for the curved BCE in large field.

Hybrid-Based Dense Stereo Matching

NASA Astrophysics Data System (ADS)

Chuang, T. Y.; Ting, H. W.; Jaw, J. J.

2016-06-01

Stereo matching generating accurate and dense disparity maps is an indispensable technique for 3D exploitation of imagery in the fields of Computer vision and Photogrammetry. Although numerous solutions and advances have been proposed in the literature, occlusions, disparity discontinuities, sparse texture, image distortion, and illumination changes still lead to problematic issues and await better treatment. In this paper, a hybrid-based method based on semi-global matching is presented to tackle the challenges on dense stereo matching. To ease the sensitiveness of SGM cost aggregation towards penalty parameters, a formal way to provide proper penalty estimates is proposed. To this end, the study manipulates a shape-adaptive cross-based matching with an edge constraint to generate an initial disparity map for penalty estimation. Image edges, indicating the potential locations of occlusions as well as disparity discontinuities, are approved by the edge drawing algorithm to ensure the local support regions not to cover significant disparity changes. Besides, an additional penalty parameter 𝑃𝑒 is imposed onto the energy function of SGM cost aggregation to specifically handle edge pixels. Furthermore, the final disparities of edge pixels are found by weighting both values derived from the SGM cost aggregation and the U-SURF matching, providing more reliable estimates at disparity discontinuity areas. Evaluations on Middlebury stereo benchmarks demonstrate satisfactory performance and reveal the potency of the hybrid-based dense stereo matching method.

A method for automatic matching of multi-timepoint findings for enhanced clinical workflow

NASA Astrophysics Data System (ADS)

Raghupathi, Laks; Dinesh, MS; Devarakota, Pandu R.; Valadez, Gerardo Hermosillo; Wolf, Matthias

2013-03-01

Non-interventional diagnostics (CT or MR) enables early identification of diseases like cancer. Often, lesion growth assessment done during follow-up is used to distinguish between benign and malignant ones. Thus correspondences need to be found for lesions localized at each time point. Manually matching the radiological findings can be time consuming as well as tedious due to possible differences in orientation and position between scans. Also, the complicated nature of the disease makes the physicians to rely on multiple modalities (PETCT, PET-MR) where it is even more challenging. Here, we propose an automatic feature-based matching that is robust to change in organ volume, subpar or no registration that can be done with very less computations. Traditional matching methods rely mostly on accurate image registration and applying the resulting deformation map on the findings coordinates. This has disadvantages when accurate registration is time-consuming or may not be possible due to vast organ volume differences between scans. Our novel matching proposes supervised learning by taking advantage of the underlying CAD features that are already present and considering the matching as a classification problem. In addition, the matching can be done extremely fast and at reasonable accuracy even when the image registration fails for some reason. Experimental results∗ on real-world multi-time point thoracic CT data showed an accuracy of above 90% with negligible false positives on a variety of registration scenarios.

A High Spatial Resolution Depth Sensing Method Based on Binocular Structured Light

PubMed Central

Yao, Huimin; Ge, Chenyang; Xue, Jianru; Zheng, Nanning

2017-01-01

Depth information has been used in many fields because of its low cost and easy availability, since the Microsoft Kinect was released. However, the Kinect and Kinect-like RGB-D sensors show limited performance in certain applications and place high demands on accuracy and robustness of depth information. In this paper, we propose a depth sensing system that contains a laser projector similar to that used in the Kinect, and two infrared cameras located on both sides of the laser projector, to obtain higher spatial resolution depth information. We apply the block-matching algorithm to estimate the disparity. To improve the spatial resolution, we reduce the size of matching blocks, but smaller matching blocks generate lower matching precision. To address this problem, we combine two matching modes (binocular mode and monocular mode) in the disparity estimation process. Experimental results show that our method can obtain higher spatial resolution depth without loss of the quality of the range image, compared with the Kinect. Furthermore, our algorithm is implemented on a low-cost hardware platform, and the system can support the resolution of 1280 × 960, and up to a speed of 60 frames per second, for depth image sequences. PMID:28397759

Circularly polarized antennas for active holographic imaging through barriers

DOEpatents

McMakin, Douglas L [Richland, WA; Severtsen, Ronald H [Richland, WA; Lechelt, Wayne M [West Richland, WA; Prince, James M [Kennewick, WA

2011-07-26

Circularly-polarized antennas and their methods of use for active holographic imaging through barriers. The antennas are dielectrically loaded to optimally match the dielectric constant of the barrier through which images are to be produced. The dielectric loading helps to remove barrier-front surface reflections and to couple electromagnetic energy into the barrier.

Maximal likelihood correspondence estimation for face recognition across pose.

PubMed

Li, Shaoxin; Liu, Xin; Chai, Xiujuan; Zhang, Haihong; Lao, Shihong; Shan, Shiguang

2014-10-01

Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database.

Characteristic extraction and matching algorithms of ballistic missile in near-space by hyperspectral image analysis

NASA Astrophysics Data System (ADS)

Lu, Li; Sheng, Wen; Liu, Shihua; Zhang, Xianzhi

2014-10-01

The ballistic missile hyperspectral data of imaging spectrometer from the near-space platform are generated by numerical method. The characteristic of the ballistic missile hyperspectral data is extracted and matched based on two different kinds of algorithms, which called transverse counting and quantization coding, respectively. The simulation results show that two algorithms extract the characteristic of ballistic missile adequately and accurately. The algorithm based on the transverse counting has the low complexity and can be implemented easily compared to the algorithm based on the quantization coding does. The transverse counting algorithm also shows the good immunity to the disturbance signals and speed up the matching and recognition of subsequent targets.

Mobile Visual Search Based on Histogram Matching and Zone Weight Learning

NASA Astrophysics Data System (ADS)

Zhu, Chuang; Tao, Li; Yang, Fan; Lu, Tao; Jia, Huizhu; Xie, Xiaodong

2018-01-01

In this paper, we propose a novel image retrieval algorithm for mobile visual search. At first, a short visual codebook is generated based on the descriptor database to represent the statistical information of the dataset. Then, an accurate local descriptor similarity score is computed by merging the tf-idf weighted histogram matching and the weighting strategy in compact descriptors for visual search (CDVS). At last, both the global descriptor matching score and the local descriptor similarity score are summed up to rerank the retrieval results according to the learned zone weights. The results show that the proposed approach outperforms the state-of-the-art image retrieval method in CDVS.

A fast and automatic fusion algorithm for unregistered multi-exposure image sequence

NASA Astrophysics Data System (ADS)

Liu, Yan; Yu, Feihong

2014-09-01

Human visual system (HVS) can visualize all the brightness levels of the scene through visual adaptation. However, the dynamic range of most commercial digital cameras and display devices are smaller than the dynamic range of human eye. This implies low dynamic range (LDR) images captured by normal digital camera may lose image details. We propose an efficient approach to high dynamic (HDR) image fusion that copes with image displacement and image blur degradation in a computationally efficient manner, which is suitable for implementation on mobile devices. The various image registration algorithms proposed in the previous literatures are unable to meet the efficiency and performance requirements in the application of mobile devices. In this paper, we selected Oriented Brief (ORB) detector to extract local image structures. The descriptor selected in multi-exposure image fusion algorithm has to be fast and robust to illumination variations and geometric deformations. ORB descriptor is the best candidate in our algorithm. Further, we perform an improved RANdom Sample Consensus (RANSAC) algorithm to reject incorrect matches. For the fusion of images, a new approach based on Stationary Wavelet Transform (SWT) is used. The experimental results demonstrate that the proposed algorithm generates high quality images at low computational cost. Comparisons with a number of other feature matching methods show that our method gets better performance.

A comparison of image processing techniques for bird recognition.

PubMed

Nadimpalli, Uma D; Price, Randy R; Hall, Steven G; Bomma, Pallavi

2006-01-01

Bird predation is one of the major concerns for fish culture in open ponds. A novel method for dispersing birds is the use of autonomous vehicles. Image recognition software can improve their efficiency. Several image processing techniques for recognition of birds have been tested. A series of morphological operations were implemented. We divided images into 3 types, Type 1, Type 2, and Type 3, based on the level of difficulty of recognizing birds. Type 1 images were clear; Type 2 images were medium clear, and Type 3 images were unclear. Local thresholding has been implemented using HSV (Hue, Saturation, and Value), GRAY, and RGB (Red, Green, and Blue) color models on all three sections of images and results were tabulated. Template matching using normal correlation and artificial neural networks (ANN) are the other methods that have been developed in this study in addition to image morphology. Template matching produced satisfactory results irrespective of the difficulty level of images, but artificial neural networks produced accuracies of 100, 60, and 50% on Type 1, Type 2, and Type 3 images, respectively. Correct classification rate can be increased by further training. Future research will focus on testing the recognition algorithms in natural or aquacultural settings on autonomous boats. Applications of such techniques to industrial, agricultural, or related areas are additional future possibilities.

Measuring the Coseismic Displacements of 2010 Ms7.1 Yushu Earthquake by Using SAR and High Resolution Optical Satellite Images

NASA Astrophysics Data System (ADS)

Zhang, L.; Wu, J.; Shi, F.

2017-09-01

After the 2010, Mw7.1, Yushu earthquake, many researchers have conducted detail investigations of the surface rupture zone by optical image interpretation, field surveying and inversion of seismic waves. However, how larger of the crustal deformation area caused by the earthquake and the quantitative co-seismic displacements are still not available. In this paper, we first take advantage of D-InSAR, MAI, and optical image matching methods to determine the whole co-seismic displacement fields. Two PALSAR images and two SPOT5 images before and after the earthquake are processed and the co-seismic displacements at the surface rupture zone and far field are obtained. The results are consistent with the field investigations, which illustrates the rationality of the application of optical image matching technology in the earthquake.

Linking crime guns: the impact of ballistics imaging technology on the productivity of the Boston Police Department's Ballistics Unit.

PubMed

Braga, Anthony A; Pierce, Glenn L

2004-07-01

Ballistics imaging technology has received national attention as a potent tool for moving the law enforcement response to violent gun criminals forward by linking multiple crime scenes to one firearm. This study examines the impact of ballistics imaging technology on the productivity of the Boston Police Department's Ballistics Unit. Using negative binomial regression models to analyze times series data on ballistics matches, we find that ballistics imaging technology was associated with a more than sixfold increase in the monthly number of ballistics matches made by the Boston Police Department's Ballistics Unit. Cost-effectiveness estimates and qualitative evidence also suggest that ballistics imaging technology allows law enforcement agencies to make hits that would not have been possible using traditional ballistics methods.

Speckle attenuation by adaptive singular value shrinking with generalized likelihood matching in optical coherence tomography

NASA Astrophysics Data System (ADS)

Chen, Huaiguang; Fu, Shujun; Wang, Hong; Lv, Hongli; Zhang, Caiming

2018-03-01

As a high-resolution imaging mode of biological tissues and materials, optical coherence tomography (OCT) is widely used in medical diagnosis and analysis. However, OCT images are often degraded by annoying speckle noise inherent in its imaging process. Employing the bilateral sparse representation an adaptive singular value shrinking method is proposed for its highly sparse approximation of image data. Adopting the generalized likelihood ratio as similarity criterion for block matching and an adaptive feature-oriented backward projection strategy, the proposed algorithm can restore better underlying layered structures and details of the OCT image with effective speckle attenuation. The experimental results demonstrate that the proposed algorithm achieves a state-of-the-art despeckling performance in terms of both quantitative measurement and visual interpretation.

Drape simulation and subjective assessment of virtual drape

NASA Astrophysics Data System (ADS)

Buyukaslan, E.; Kalaoglu, F.; Jevsnik, S.

2017-10-01

In this study, a commercial 3D virtual garment simulation software (Optitex) is used to simulate drape behaviours of five different fabrics. Mechanical properties of selected fabrics are measured by Fabric Assurance by Simple Testing (FAST) method. Measured bending, shear and extension properties of fabrics are inserted to the simulation software to achieve more realistic simulations. Simulation images of fabrics are shown to 27 people and they are asked to match real drape images of fabrics with simulated drape images. Fabric simulations of two fabrics were correctly matched by the majority of the test group. However, the other three fabrics’ simulations were mismatched by most of the people.

Validation of the Gatortail method for accurate sizing of pulmonary vessels from 3D medical images.

PubMed

O'Dell, Walter G; Gormaley, Anne K; Prida, David A

2017-12-01

Detailed characterization of changes in vessel size is crucial for the diagnosis and management of a variety of vascular diseases. Because clinical measurement of vessel size is typically dependent on the radiologist's subjective interpretation of the vessel borders, it is often prone to high inter- and intra-user variability. Automatic methods of vessel sizing have been developed for two-dimensional images but a fully three-dimensional (3D) method suitable for vessel sizing from volumetric X-ray computed tomography (CT) or magnetic resonance imaging has heretofore not been demonstrated and validated robustly. In this paper, we refined and objectively validated Gatortail, a method that creates a mathematical geometric 3D model of each branch in a vascular tree, simulates the appearance of the virtual vascular tree in a 3D CT image, and uses the similarity of the simulated image to a patient's CT scan to drive the optimization of the model parameters, including vessel size, to match that of the patient. The method was validated with a 2-dimensional virtual tree structure under deformation, and with a realistic 3D-printed vascular phantom in which the diameter of 64 branches were manually measured 3 times each. The phantom was then scanned on a conventional clinical CT imaging system and the images processed with the in-house software to automatically segment and mathematically model the vascular tree, label each branch, and perform the Gatortail optimization of branch size and trajectory. Previously proposed methods of vessel sizing using matched Gaussian filters and tubularity metrics were also tested. The Gatortail method was then demonstrated on the pulmonary arterial tree segmented from a human volunteer's CT scan. The standard deviation of the difference between the manually measured and Gatortail-based radii in the 3D physical phantom was 0.074 mm (0.087 in-plane pixel units for image voxels of dimension 0.85 × 0.85 × 1.0 mm) over the 64 branches, representing vessel diameters ranging from 1.2 to 7 mm. The linear regression fit gave a slope of 1.056 and an R 2 value of 0.989. These three metrics reflect superior agreement of the radii estimates relative to previously published results over all sizes tested. Sizing via matched Gaussian filters resulted in size underestimates of >33% over all three test vessels, while the tubularity-metric matching exhibited a sizing uncertainty of >50%. In the human chest CT data set, the vessel voxel intensity profiles with and without branch model optimization showed excellent agreement and improvement in the objective measure of image similarity. Gatortail has been demonstrated to be an automated, objective, accurate and robust method for sizing of vessels in 3D non-invasively from chest CT scans. We anticipate that Gatortail, an image-based approach to automatically compute estimates of blood vessel radii and trajectories from 3D medical images, will facilitate future quantitative evaluation of vascular response to disease and environmental insult and improve understanding of the biological mechanisms underlying vascular disease processes. © 2017 American Association of Physicists in Medicine.

SU-E-J-237: Image Feature Based DRR and Portal Image Registration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, X; Chang, J

Purpose: Two-dimensional (2D) matching of the kV X-ray and digitally reconstructed radiography (DRR) images is an important setup technique for image-guided radiotherapy (IGRT). In our clinics, mutual information based methods are used for this purpose on commercial linear accelerators, but with often needs for manual corrections. This work proved the feasibility that feature based image transform can be used to register kV and DRR images. Methods: The scale invariant feature transform (SIFT) method was implemented to detect the matching image details (or key points) between the kV and DRR images. These key points represent high image intensity gradients, and thusmore » the scale invariant features. Due to the poor image contrast from our kV image, direct application of the SIFT method yielded many detection errors. To assist the finding of key points, the center coordinates of the kV and DRR images were read from the DICOM header, and the two groups of key points with similar relative positions to their corresponding centers were paired up. Using these points, a rigid transform (with scaling, horizontal and vertical shifts) was estimated. We also artificially introduced vertical and horizontal shifts to test the accuracy of our registration method on anterior-posterior (AP) and lateral pelvic images. Results: The results provided a satisfactory overlay of the transformed kV onto the DRR image. The introduced vs. detected shifts were fit into a linear regression. In the AP image experiments, linear regression analysis showed a slope of 1.15 and 0.98 with an R2 of 0.89 and 0.99 for the horizontal and vertical shifts, respectively. The results are 1.2 and 1.3 with R2 of 0.72 and 0.82 for the lateral image shifts. Conclusion: This work provided an alternative technique for kV to DRR alignment. Further improvements in the estimation accuracy and image contrast tolerance are underway.« less

The non-parametric Parzen's window in stereo vision matching.

PubMed

Pajares, G; de la Cruz, J

2002-01-01

This paper presents an approach to the local stereovision matching problem using edge segments as features with four attributes. From these attributes we compute a matching probability between pairs of features of the stereo images. A correspondence is said true when such a probability is maximum. We introduce a nonparametric strategy based on Parzen's window (1962) to estimate a probability density function (PDF) which is used to obtain the matching probability. This is the main finding of the paper. A comparative analysis of other recent matching methods is included to show that this finding can be justified theoretically. A generalization of the proposed method is made in order to give guidelines about its use with the similarity constraint and also in different environments where other features and attributes are more suitable.

An automatic 2D–3D image matching method for reproducing spatial knee joint positions using single or dual fluoroscopic images

PubMed Central

Zhu, Zhonglin; Li, Guoan

2013-01-01

Fluoroscopic image technique, using either a single image or dual images, has been widely applied to measure in vivo human knee joint kinematics. However, few studies have compared the advantages of using single and dual fluoroscopic images. Furthermore, due to the size limitation of the image intensifiers, it is possible that only a portion of the knee joint could be captured by the fluoroscopy during dynamic knee joint motion. In this paper, we presented a systematic evaluation of an automatic 2D–3D image matching method in reproducing spatial knee joint positions using either single or dual fluoroscopic image techniques. The data indicated that for the femur and tibia, their spatial positions could be determined with an accuracy and precision less than 0.2 mm in translation and less than 0.4° in orientation when dual fluoroscopic images were used. Using single fluoroscopic images, the method could produce satisfactory accuracy in joint positions in the imaging plane (average up to 0.5 mm in translation and 1.3° in rotation), but large variations along the out-plane direction (in average up to 4.0 mm in translation and 2.28 in rotation). The precision of using single fluoroscopic images to determine the actual knee positions was worse than its accuracy obtained. The data also indicated that when using dual fluoroscopic image technique, if the knee joint outlines in one image were incomplete by 80%, the algorithm could still reproduce the joint positions with high precisions. PMID:21806411

Composite multi-lobe descriptor for cross spectral face recognition: matching active IR to visible light images

NASA Astrophysics Data System (ADS)

Cao, Zhicheng; Schmid, Natalia A.

2015-05-01

Matching facial images across electromagnetic spectrum presents a challenging problem in the field of biometrics and identity management. An example of this problem includes cross spectral matching of active infrared (IR) face images or thermal IR face images against a dataset of visible light images. This paper describes a new operator named Composite Multi-Lobe Descriptor (CMLD) for facial feature extraction in cross spectral matching of near-infrared (NIR) or short-wave infrared (SWIR) against visible light images. The new operator is inspired by the design of ordinal measures. The operator combines Gaussian-based multi-lobe kernel functions, Local Binary Pattern (LBP), generalized LBP (GLBP) and Weber Local Descriptor (WLD) and modifies them into multi-lobe functions with smoothed neighborhoods. The new operator encodes both the magnitude and phase responses of Gabor filters. The combining of LBP and WLD utilizes both the orientation and intensity information of edges. Introduction of multi-lobe functions with smoothed neighborhoods further makes the proposed operator robust against noise and poor image quality. Output templates are transformed into histograms and then compared by means of a symmetric Kullback-Leibler metric resulting in a matching score. The performance of the multi-lobe descriptor is compared with that of other operators such as LBP, Histogram of Oriented Gradients (HOG), ordinal measures, and their combinations. The experimental results show that in many cases the proposed method, CMLD, outperforms the other operators and their combinations. In addition to different infrared spectra, various standoff distances from close-up (1.5 m) to intermediate (50 m) and long (106 m) are also investigated in this paper. Performance of CMLD is evaluated for of each of the three cases of distances.

Inverse consistent non-rigid image registration based on robust point set matching

PubMed Central

2014-01-01

Background Robust point matching (RPM) has been extensively used in non-rigid registration of images to robustly register two sets of image points. However, except for the location at control points, RPM cannot estimate the consistent correspondence between two images because RPM is a unidirectional image matching approach. Therefore, it is an important issue to make an improvement in image registration based on RPM. Methods In our work, a consistent image registration approach based on the point sets matching is proposed to incorporate the property of inverse consistency and improve registration accuracy. Instead of only estimating the forward transformation between the source point sets and the target point sets in state-of-the-art RPM algorithms, the forward and backward transformations between two point sets are estimated concurrently in our algorithm. The inverse consistency constraints are introduced to the cost function of RPM and the fuzzy correspondences between two point sets are estimated based on both the forward and backward transformations simultaneously. A modified consistent landmark thin-plate spline registration is discussed in detail to find the forward and backward transformations during the optimization of RPM. The similarity of image content is also incorporated into point matching in order to improve image matching. Results Synthetic data sets, medical images are employed to demonstrate and validate the performance of our approach. The inverse consistent errors of our algorithm are smaller than RPM. Especially, the topology of transformations is preserved well for our algorithm for the large deformation between point sets. Moreover, the distance errors of our algorithm are similar to that of RPM, and they maintain a downward trend as whole, which demonstrates the convergence of our algorithm. The registration errors for image registrations are evaluated also. Again, our algorithm achieves the lower registration errors in same iteration number. The determinant of the Jacobian matrix of the deformation field is used to analyse the smoothness of the forward and backward transformations. The forward and backward transformations estimated by our algorithm are smooth for small deformation. For registration of lung slices and individual brain slices, large or small determinant of the Jacobian matrix of the deformation fields are observed. Conclusions Results indicate the improvement of the proposed algorithm in bi-directional image registration and the decrease of the inverse consistent errors of the forward and the reverse transformations between two images. PMID:25559889

SYNMAG PHOTOMETRY: A FAST TOOL FOR CATALOG-LEVEL MATCHED COLORS OF EXTENDED SOURCES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bundy, Kevin; Yasuda, Naoki; Hogg, David W.

2012-12-01

Obtaining reliable, matched photometry for galaxies imaged by different observatories represents a key challenge in the era of wide-field surveys spanning more than several hundred square degrees. Methods such as flux fitting, profile fitting, and PSF homogenization followed by matched-aperture photometry are all computationally expensive. We present an alternative solution called 'synthetic aperture photometry' that exploits galaxy profile fits in one band to efficiently model the observed, point-spread-function-convolved light profile in other bands and predict the flux in arbitrarily sized apertures. Because aperture magnitudes are the most widely tabulated flux measurements in survey catalogs, producing synthetic aperture magnitudes (SYNMAGs) enablesmore » very fast matched photometry at the catalog level, without reprocessing imaging data. We make our code public and apply it to obtain matched photometry between Sloan Digital Sky Survey ugriz and UKIDSS YJHK imaging, recovering red-sequence colors and photometric redshifts with a scatter and accuracy as good as if not better than FWHM-homogenized photometry from the GAMA Survey. Finally, we list some specific measurements that upcoming surveys could make available to facilitate and ease the use of SYNMAGs.« less

Deformed Palmprint Matching Based on Stable Regions.

PubMed

Wu, Xiangqian; Zhao, Qiushi

2015-12-01

Palmprint recognition (PR) is an effective technology for personal recognition. A main problem, which deteriorates the performance of PR, is the deformations of palmprint images. This problem becomes more severe on contactless occasions, in which images are acquired without any guiding mechanisms, and hence critically limits the applications of PR. To solve the deformation problems, in this paper, a model for non-linearly deformed palmprint matching is derived by approximating non-linear deformed palmprint images with piecewise-linear deformed stable regions. Based on this model, a novel approach for deformed palmprint matching, named key point-based block growing (KPBG), is proposed. In KPBG, an iterative M-estimator sample consensus algorithm based on scale invariant feature transform features is devised to compute piecewise-linear transformations to approximate the non-linear deformations of palmprints, and then, the stable regions complying with the linear transformations are decided using a block growing algorithm. Palmprint feature extraction and matching are performed over these stable regions to compute matching scores for decision. Experiments on several public palmprint databases show that the proposed models and the KPBG approach can effectively solve the deformation problem in palmprint verification and outperform the state-of-the-art methods.

Comparison of active-set method deconvolution and matched-filtering for derivation of an ultrasound transit time spectrum.

PubMed

Wille, M-L; Zapf, M; Ruiter, N V; Gemmeke, H; Langton, C M

2015-06-21

The quality of ultrasound computed tomography imaging is primarily determined by the accuracy of ultrasound transit time measurement. A major problem in analysis is the overlap of signals making it difficult to detect the correct transit time. The current standard is to apply a matched-filtering approach to the input and output signals. This study compares the matched-filtering technique with active set deconvolution to derive a transit time spectrum from a coded excitation chirp signal and the measured output signal. The ultrasound wave travels in a direct and a reflected path to the receiver, resulting in an overlap in the recorded output signal. The matched-filtering and deconvolution techniques were applied to determine the transit times associated with the two signal paths. Both techniques were able to detect the two different transit times; while matched-filtering has a better accuracy (0.13 μs versus 0.18 μs standard deviations), deconvolution has a 3.5 times improved side-lobe to main-lobe ratio. A higher side-lobe suppression is important to further improve image fidelity. These results suggest that a future combination of both techniques would provide improved signal detection and hence improved image fidelity.

Linking pesticides and human health: a geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure.

PubMed

VoPham, Trang; Wilson, John P; Ruddell, Darren; Rashed, Tarek; Brooks, Maria M; Yuan, Jian-Min; Talbott, Evelyn O; Chang, Chung-Chou H; Weissfeld, Joel L

2015-08-01

Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500 m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located.

Automated Geo/Co-Registration of Multi-Temporal Very-High-Resolution Imagery.

PubMed

Han, Youkyung; Oh, Jaehong

2018-05-17

For time-series analysis using very-high-resolution (VHR) multi-temporal satellite images, both accurate georegistration to the map coordinates and subpixel-level co-registration among the images should be conducted. However, applying well-known matching methods, such as scale-invariant feature transform and speeded up robust features for VHR multi-temporal images, has limitations. First, they cannot be used for matching an optical image to heterogeneous non-optical data for georegistration. Second, they produce a local misalignment induced by differences in acquisition conditions, such as acquisition platform stability, the sensor's off-nadir angle, and relief displacement of the considered scene. Therefore, this study addresses the problem by proposing an automated geo/co-registration framework for full-scene multi-temporal images acquired from a VHR optical satellite sensor. The proposed method comprises two primary steps: (1) a global georegistration process, followed by (2) a fine co-registration process. During the first step, two-dimensional multi-temporal satellite images are matched to three-dimensional topographic maps to assign the map coordinates. During the second step, a local analysis of registration noise pixels extracted between the multi-temporal images that have been mapped to the map coordinates is conducted to extract a large number of well-distributed corresponding points (CPs). The CPs are finally used to construct a non-rigid transformation function that enables minimization of the local misalignment existing among the images. Experiments conducted on five Kompsat-3 full scenes confirmed the effectiveness of the proposed framework, showing that the georegistration performance resulted in an approximately pixel-level accuracy for most of the scenes, and the co-registration performance further improved the results among all combinations of the georegistered Kompsat-3 image pairs by increasing the calculated cross-correlation values.

Lenses matching of compound eye for target positioning

NASA Astrophysics Data System (ADS)

Guo, Fang; Zheng, Yan Pei; Wang, Keyi

2012-10-01

Compound eye, as a new imaging method with multi-lens for a large field of view, could complete target positioning and detection fastly, especially at close range. Therefore it could be applicated in the fields of military and medical treatment and aviation with vast market potential and development prospect. Yet the compound eye imaging method designed use three layer construction of multiple lens array arranged in a curved surface and refractive lens and imaging sensor of CMOS. In order to simplify process structure and increase the imaging area of every sub-eye, the imaging area of every eye is coved with the whole CMOS. Therefore, for several imaging point of one target, the corresponding lens of every imaging point is unkonown, and thus to identify. So an algorithm was put forward. Firstly, according to the Regular Geometry relationship of several adjacent lenses, data organization of seven lenses with a main lens was built. Subsequently, by the data organization, when one target was caught by several unknown lenses, we search every combined type of the received lenses. And for every combined type, two lenses were selected to combine and were used to calculate one three-dimensional (3D) coordinate of the target. If the 3D coordinates are same to the some combine type of the lenses numbers, in theory, the lenses and the imaging points are matched. So according to error of the 3D coordinates is calculated by the different seven lenses numbers combines, the unknown lenses could be distinguished. The experimental results show that the presented algorithm is feasible and can complete matching task for imaging points and corresponding lenses.

Detection of anomaly in human retina using Laplacian Eigenmaps and vectorized matched filtering

NASA Astrophysics Data System (ADS)

Yacoubou Djima, Karamatou A.; Simonelli, Lucia D.; Cunningham, Denise; Czaja, Wojciech

2015-03-01

We present a novel method for automated anomaly detection on auto fluorescent data provided by the National Institute of Health (NIH). This is motivated by the need for new tools to improve the capability of diagnosing macular degeneration in its early stages, track the progression over time, and test the effectiveness of new treatment methods. In previous work, macular anomalies have been detected automatically through multiscale analysis procedures such as wavelet analysis or dimensionality reduction algorithms followed by a classification algorithm, e.g., Support Vector Machine. The method that we propose is a Vectorized Matched Filtering (VMF) algorithm combined with Laplacian Eigenmaps (LE), a nonlinear dimensionality reduction algorithm with locality preserving properties. By applying LE, we are able to represent the data in the form of eigenimages, some of which accentuate the visibility of anomalies. We pick significant eigenimages and proceed with the VMF algorithm that classifies anomalies across all of these eigenimages simultaneously. To evaluate our performance, we compare our method to two other schemes: a matched filtering algorithm based on anomaly detection on single images and a combination of PCA and VMF. LE combined with VMF algorithm performs best, yielding a high rate of accurate anomaly detection. This shows the advantage of using a nonlinear approach to represent the data and the effectiveness of VMF, which operates on the images as a data cube rather than individual images.

Configuration of automatic exposure control on mammography units for computed radiography to match patient dose of screen film systems

NASA Astrophysics Data System (ADS)

Yang, Chang-Ying Joseph; Huang, Weidong

2009-02-01

Computed radiography (CR) is considered a drop-in addition or replacement for traditional screen-film (SF) systems in digital mammography. Unlike other technologies, CR has the advantage of being compatible with existing mammography units. One of the challenges, however, is to properly configure the automatic exposure control (AEC) on existing mammography units for CR use. Unlike analogue systems, the capture and display of digital CR images is decoupled. The function of AEC is changed from ensuring proper and consistent optical density of the captured image on film to balancing image quality with patient dose needed for CR. One of the preferences when acquiring CR images under AEC is to use the same patient dose as SF systems. The challenge is whether the existing AEC design and calibration process-most of them proprietary from the X-ray systems manufacturers and tailored specifically for SF response properties-can be adapted for CR cassettes, in order to compensate for their response and attenuation differences. This paper describes the methods for configuring the AEC of three different mammography units models to match the patient dose used for CR with those that are used for a KODAK MIN-R 2000 SF System. Based on phantom test results, these methods provide the dose level under AEC for the CR systems to match with the dose of SF systems. These methods can be used in clinical environments that require the acquisition of CR images under AEC at the same dose levels as those used for SF systems.

Local coding based matching kernel method for image classification.

PubMed

Song, Yan; McLoughlin, Ian Vince; Dai, Li-Rong

2014-01-01

This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV) techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increased storage requirements. We show that a unified visual matching framework can be developed to encompass both BoV and kernel based metrics, in which local kernel plays an important role between feature pairs or between features and their reconstruction. Generally, local kernels are defined using Euclidean distance or its derivatives, based either explicitly or implicitly on an assumption of Gaussian noise. However, local features such as SIFT and HoG often follow a heavy-tailed distribution which tends to undermine the motivation behind Euclidean metrics. Motivated by recent advances in feature coding techniques, a novel efficient local coding based matching kernel (LCMK) method is proposed. This exploits the manifold structures in Hilbert space derived from local kernels. The proposed method combines advantages of both BoV and kernel based metrics, and achieves a linear computational complexity. This enables efficient and scalable visual matching to be performed on large scale image sets. To evaluate the effectiveness of the proposed LCMK method, we conduct extensive experiments with widely used benchmark datasets, including 15-Scenes, Caltech101/256, PASCAL VOC 2007 and 2011 datasets. Experimental results confirm the effectiveness of the relatively efficient LCMK method.

MatchGUI: A Graphical MATLAB-Based Tool for Automatic Image Co-Registration

NASA Technical Reports Server (NTRS)

Ansar, Adnan I.

2011-01-01

MatchGUI software, based on MATLAB, automatically matches two images and displays the match result by superimposing one image on the other. A slider bar allows focus to shift between the two images. There are tools for zoom, auto-crop to overlap region, and basic image markup. Given a pair of ortho-rectified images (focused primarily on Mars orbital imagery for now), this software automatically co-registers the imagery so that corresponding image pixels are aligned. MatchGUI requires minimal user input, and performs a registration over scale and inplane rotation fully automatically

Purification of Training Samples Based on Spectral Feature and Superpixel Segmentation

NASA Astrophysics Data System (ADS)

Guan, X.; Qi, W.; He, J.; Wen, Q.; Chen, T.; Wang, Z.

2018-04-01

Remote sensing image classification is an effective way to extract information from large volumes of high-spatial resolution remote sensing images. Generally, supervised image classification relies on abundant and high-precision training data, which is often manually interpreted by human experts to provide ground truth for training and evaluating the performance of the classifier. Remote sensing enterprises accumulated lots of manually interpreted products from early lower-spatial resolution remote sensing images by executing their routine research and business programs. However, these manually interpreted products may not match the very high resolution (VHR) image properly because of different dates or spatial resolution of both data, thus, hindering suitability of manually interpreted products in training classification models, or small coverage area of these manually interpreted products. We also face similar problems in our laboratory in 21st Century Aerospace Technology Co. Ltd (short for 21AT). In this work, we propose a method to purify the interpreted product to match newly available VHRI data and provide the best training data for supervised image classifiers in VHR image classification. And results indicate that our proposed method can efficiently purify the input data for future machine learning use.

Web image retrieval using an effective topic and content-based technique

NASA Astrophysics Data System (ADS)

Lee, Ching-Cheng; Prabhakara, Rashmi

2005-03-01

There has been an exponential growth in the amount of image data that is available on the World Wide Web since the early development of Internet. With such a large amount of information and image available and its usefulness, an effective image retrieval system is thus greatly needed. In this paper, we present an effective approach with both image matching and indexing techniques that improvise on existing integrated image retrieval methods. This technique follows a two-phase approach, integrating query by topic and query by example specification methods. In the first phase, The topic-based image retrieval is performed by using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. This technique consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. In the second phase, we use query by example specification to perform a low-level content-based image match in order to retrieve smaller and relatively closer results of the example image. From this, information related to the image feature is automatically extracted from the query image. The main objective of our approach is to develop a functional image search and indexing technique and to demonstrate that better retrieval results can be achieved.

Automatic Feature Detection, Description and Matching from Mobile Laser Scanning Data and Aerial Imagery

NASA Astrophysics Data System (ADS)

Hussnain, Zille; Oude Elberink, Sander; Vosselman, George

2016-06-01

In mobile laser scanning systems, the platform's position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of nadir aerial and MLSPC ortho images. In the data pre-processing step the MLSPC is patch-wise cropped and converted to ortho images. Furthermore, each aerial image patch covering the area of the corresponding MLSPC patch is also cropped from the aerial image. For feature detection, we implemented an adaptive variant of Harris-operator to automatically detect corner feature points on the vertices of road markings. In feature description phase, we used the LATCH binary descriptor, which is robust to data from different sensors. For descriptor matching, we developed an outlier filtering technique, which exploits the arrangements of relative Euclidean-distances and angles between corresponding sets of feature points. We found that the positioning accuracy of the computed correspondence has achieved the pixel level accuracy, where the image resolution is 12cm. Furthermore, the developed approach is reliable when enough road markings are available in the data sets. We conclude that, in urban areas, the developed approach can reliably extract features necessary to improve the MLSPC accuracy to pixel level.

Rotation invariants of vector fields from orthogonal moments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Bo; Kostková, Jitka; Flusser, Jan

Vector field images are a type of new multidimensional data that appear in many engineering areas. Although the vector fields can be visualized as images, they differ from graylevel and color images in several aspects. In order to analyze them, special methods and algorithms must be originally developed or substantially adapted from the traditional image processing area. Here, we propose a method for the description and matching of vector field patterns under an unknown rotation of the field. Rotation of a vector field is so-called total rotation, where the action is applied not only on the spatial coordinates but alsomore » on the field values. Invariants of vector fields with respect to total rotation constructed from orthogonal Gaussian–Hermite moments and Zernike moments are introduced. Their numerical stability is shown to be better than that of the invariants published so far. We demonstrate their usefulness in a real world template matching application of rotated vector fields.« less

Fly-through viewpoint video system for multi-view soccer movie using viewpoint interpolation

NASA Astrophysics Data System (ADS)

Inamoto, Naho; Saito, Hideo

2003-06-01

This paper presents a novel method for virtual view generation that allows viewers to fly through in a real soccer scene. A soccer match is captured by multiple cameras at a stadium and images of arbitrary viewpoints are synthesized by view-interpolation of two real camera images near the given viewpoint. In the proposed method, cameras do not need to be strongly calibrated, but epipolar geometry between the cameras is sufficient for the view-interpolation. Therefore, it can easily be applied to a dynamic event even in a large space, because the efforts for camera calibration can be reduced. A soccer scene is classified into several regions and virtual view images are generated based on the epipolar geometry in each region. Superimposition of the images completes virtual views for the whole soccer scene. An application for fly-through observation of a soccer match is introduced as well as the algorithm of the view-synthesis and experimental results..

Video see-through augmented reality for oral and maxillofacial surgery.

PubMed

Wang, Junchen; Suenaga, Hideyuki; Yang, Liangjing; Kobayashi, Etsuko; Sakuma, Ichiro

2017-06-01

Oral and maxillofacial surgery has not been benefitting from image guidance techniques owing to the limitations in image registration. A real-time markerless image registration method is proposed by integrating a shape matching method into a 2D tracking framework. The image registration is performed by matching the patient's teeth model with intraoperative video to obtain its pose. The resulting pose is used to overlay relevant models from the same CT space on the camera video for augmented reality. The proposed system was evaluated on mandible/maxilla phantoms, a volunteer and clinical data. Experimental results show that the target overlay error is about 1 mm, and the frame rate of registration update yields 3-5 frames per second with a 4 K camera. The significance of this work lies in its simplicity in clinical setting and the seamless integration into the current medical procedure with satisfactory response time and overlay accuracy. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Rotation invariants of vector fields from orthogonal moments

DOE PAGES

Yang, Bo; Kostková, Jitka; Flusser, Jan; ...

2017-09-11

Vector field images are a type of new multidimensional data that appear in many engineering areas. Although the vector fields can be visualized as images, they differ from graylevel and color images in several aspects. In order to analyze them, special methods and algorithms must be originally developed or substantially adapted from the traditional image processing area. Here, we propose a method for the description and matching of vector field patterns under an unknown rotation of the field. Rotation of a vector field is so-called total rotation, where the action is applied not only on the spatial coordinates but alsomore » on the field values. Invariants of vector fields with respect to total rotation constructed from orthogonal Gaussian–Hermite moments and Zernike moments are introduced. Their numerical stability is shown to be better than that of the invariants published so far. We demonstrate their usefulness in a real world template matching application of rotated vector fields.« less

Intrinsic feature-based pose measurement for imaging motion compensation

DOEpatents

Baba, Justin S.; Goddard, Jr., James Samuel

2014-08-19

Systems and methods for generating motion corrected tomographic images are provided. A method includes obtaining first images of a region of interest (ROI) to be imaged and associated with a first time, where the first images are associated with different positions and orientations with respect to the ROI. The method also includes defining an active region in the each of the first images and selecting intrinsic features in each of the first images based on the active region. Second, identifying a portion of the intrinsic features temporally and spatially matching intrinsic features in corresponding ones of second images of the ROI associated with a second time prior to the first time and computing three-dimensional (3D) coordinates for the portion of the intrinsic features. Finally, the method includes computing a relative pose for the first images based on the 3D coordinates.

SU-F-J-54: Towards Real-Time Volumetric Imaging Using the Treatment Beam and KV Beam

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, M; Rozario, T; Liu, A

Purpose: Existing real-time imaging uses dual (orthogonal) kV beam fluoroscopies and may result in significant amount of extra radiation to patients, especially for prolonged treatment cases. In addition, kV projections only provide 2D information, which is insufficient for in vivo dose reconstruction. We propose real-time volumetric imaging using prior knowledge of pre-treatment 4D images and real-time 2D transit data of treatment beam and kV beam. Methods: The pre-treatment multi-snapshot volumetric images are used to simulate 2D projections of both the treatment beam and kV beam, respectively, for each treatment field defined by the control point. During radiation delivery, the transitmore » signals acquired by the electronic portal image device (EPID) are processed for every projection and compared with pre-calculation by cross-correlation for phase matching and thus 3D snapshot identification or real-time volumetric imaging. The data processing involves taking logarithmic ratios of EPID signals with respect to the air scan to reduce modeling uncertainties in head scatter fluence and EPID response. Simulated 2D projections are also used to pre-calculate confidence levels in phase matching. Treatment beam projections that have a low confidence level either in pre-calculation or real-time acquisition will trigger kV beams so that complementary information can be exploited. In case both the treatment beam and kV beam return low confidence in phase matching, a predicted phase based on linear regression will be generated. Results: Simulation studies indicated treatment beams provide sufficient confidence in phase matching for most cases. At times of low confidence from treatment beams, kV imaging provides sufficient confidence in phase matching due to its complementary configuration. Conclusion: The proposed real-time volumetric imaging utilizes the treatment beam and triggers kV beams for complementary information when the treatment beam along does not provide sufficient confidence for phase matching. This strategy minimizes the use of extra radiation to patients. This project is partially supported by a Varian MRA grant.« less

TU-AB-202-06: Quantitative Evaluation of Deformable Image Registration in MRI-Guided Adaptive Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mooney, K; Zhao, T; Green, O

Purpose: To assess the performance of the deformable image registration algorithm used for MRI-guided adaptive radiation therapy using image feature analysis. Methods: MR images were collected from five patients treated on the MRIdian (ViewRay, Inc., Oakwood Village, OH), a three head Cobalt-60 therapy machine with an 0.35 T MR system. The images were acquired immediately prior to treatment with a uniform 1.5 mm resolution. Treatment sites were as follows: head/neck, lung, breast, stomach, and bladder. Deformable image registration was performed using the ViewRay software between the first fraction MRI and the final fraction MRI, and the DICE similarity coefficient (DSC)more » for the skin contours was reported. The SIFT and Harris feature detection and matching algorithms identified point features in each image separately, then found matching features in the other image. The target registration error (TRE) was defined as the vector distance between matched features on the two image sets. Each deformation was evaluated based on comparison of average TRE and DSC. Results: Image feature analysis produced between 2000–9500 points for evaluation on the patient images. The average (± standard deviation) TRE for all patients was 3.3 mm (±3.1 mm), and the passing rate of TRE<3 mm was 60% on the images. The head/neck patient had the best average TRE (1.9 mm±2.3 mm) and the best passing rate (80%). The lung patient had the worst average TRE (4.8 mm±3.3 mm) and the worst passing rate (37.2%). DSC was not significantly correlated with either TRE (p=0.63) or passing rate (p=0.55). Conclusions: Feature matching provides a quantitative assessment of deformable image registration, with a large number of data points for analysis. The TRE of matched features can be used to evaluate the registration of many objects throughout the volume, whereas DSC mainly provides a measure of gross overlap. We have a research agreement with ViewRay Inc.« less

Locality constrained joint dynamic sparse representation for local matching based face recognition.

PubMed

Wang, Jianzhong; Yi, Yugen; Zhou, Wei; Shi, Yanjiao; Qi, Miao; Zhang, Ming; Zhang, Baoxue; Kong, Jun

2014-01-01

Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.

An experimental comparison of standard stereo matching algorithms applied to cloud top height estimation from satellite IR images

NASA Astrophysics Data System (ADS)

Anzalone, Anna; Isgrò, Francesco

2016-10-01

The JEM-EUSO (Japanese Experiment Module-Extreme Universe Space Observatory) telescope will measure Ultra High Energy Cosmic Ray properties by detecting the UV fluorescent light generated in the interaction between cosmic rays and the atmosphere. Cloud information is crucial for a proper interpretation of these data. The problem of recovering the cloud-top height from satellite images in infrared has struck some attention over the last few decades, as a valuable tool for the atmospheric monitoring. A number of radiative methods do exist, like C02 slicing and Split Window algorithms, using one or more infrared bands. A different way to tackle the problem is, when possible, to exploit the availability of multiple views, and recover the cloud top height through stereo imaging and triangulation. A crucial step in the 3D reconstruction is the process that attempts to match a characteristic point or features selected in one image, with one of those detected in the second image. In this article the performance of a group matching algorithms that include both area-based and global techniques, has been tested. They are applied to stereo pairs of satellite IR images with the final aim of evaluating the cloud top height. Cloudy images from SEVIRI on the geostationary Meteosat Second Generation 9 and 10 (MSG-2, MSG-3) have been selected. After having applied to the cloudy scenes the algorithms for stereo matching, the outcoming maps of disparity are transformed in depth maps according to the geometry of the reference data system. As ground truth we have used the height maps provided by the database of MODIS (Moderate Resolution Imaging Spectroradiometer) on-board Terra/Aqua polar satellites, that contains images quasi-synchronous to the imaging provided by MSG.

The One to Multiple Automatic High Accuracy Registration of Terrestrial LIDAR and Optical Images

NASA Astrophysics Data System (ADS)

Wang, Y.; Hu, C.; Xia, G.; Xue, H.

2018-04-01

The registration of ground laser point cloud and close-range image is the key content of high-precision 3D reconstruction of cultural relic object. In view of the requirement of high texture resolution in the field of cultural relic at present, The registration of point cloud and image data in object reconstruction will result in the problem of point cloud to multiple images. In the current commercial software, the two pairs of registration of the two kinds of data are realized by manually dividing point cloud data, manual matching point cloud and image data, manually selecting a two - dimensional point of the same name of the image and the point cloud, and the process not only greatly reduces the working efficiency, but also affects the precision of the registration of the two, and causes the problem of the color point cloud texture joint. In order to solve the above problems, this paper takes the whole object image as the intermediate data, and uses the matching technology to realize the automatic one-to-one correspondence between the point cloud and multiple images. The matching of point cloud center projection reflection intensity image and optical image is applied to realize the automatic matching of the same name feature points, and the Rodrigo matrix spatial similarity transformation model and weight selection iteration are used to realize the automatic registration of the two kinds of data with high accuracy. This method is expected to serve for the high precision and high efficiency automatic 3D reconstruction of cultural relic objects, which has certain scientific research value and practical significance.

Volumetric Image Guidance Using Carina vs Spine as Registration Landmarks for Conventionally Fractionated Lung Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lavoie, Caroline; Higgins, Jane; Bissonnette, Jean-Pierre

2012-12-01

Purpose: To compare the relative accuracy of 2 image guided radiation therapy methods using carina vs spine as landmarks and then to identify which landmark is superior relative to tumor coverage. Methods and Materials: For 98 lung patients, 2596 daily image-guidance cone-beam computed tomography scans were analyzed. Tattoos were used for initial patient alignment; then, spine and carina registrations were performed independently. A separate analysis assessed the adequacy of gross tumor volume, internal target volume, and planning target volume coverage on cone-beam computed tomography using the initial, middle, and final fractions of radiation therapy. Coverage was recorded for primary tumormore » (T), nodes (N), and combined target (T+N). Three scenarios were compared: tattoos alignment, spine registration, and carina registration. Results: Spine and carina registrations identified setup errors {>=}5 mm in 35% and 46% of fractions, respectively. The mean vector difference between spine and carina matching had a magnitude of 3.3 mm. Spine and carina improved combined target coverage, compared with tattoos, in 50% and 34% (spine) to 54% and 46% (carina) of the first and final fractions, respectively. Carina matching showed greater combined target coverage in 17% and 23% of fractions for the first and final fractions, respectively; with spine matching, this was only observed in 4% (first) and 6% (final) of fractions. Carina matching provided superior nodes coverage at the end of radiation compared with spine matching (P=.0006), without compromising primary tumor coverage. Conclusion: Frequent patient setup errors occur in locally advanced lung cancer patients. Spine and carina registrations improved combined target coverage throughout the treatment course, but carina matching provided superior combined target coverage.« less

Distributed ISAR Subimage Fusion of Nonuniform Rotating Target Based on Matching Fourier Transform.

PubMed

Li, Yuanyuan; Fu, Yaowen; Zhang, Wenpeng

2018-06-04

In real applications, the image quality of the conventional monostatic Inverse Synthetic Aperture Radar (ISAR) for the maneuvering target is subject to the strong fluctuation of Radar Cross Section (RCS), as the target aspect varies enormously. Meanwhile, the maneuvering target introduces nonuniform rotation after translation motion compensation which degrades the imaging performance of the conventional Fourier Transform (FT)-based method in the cross-range dimension. In this paper, a method which combines the distributed ISAR technique and the Matching Fourier Transform (MFT) is proposed to overcome these problems. Firstly, according to the characteristics of the distributed ISAR, the multiple channel echoes of the nonuniform rotation target from different observation angles can be acquired. Then, by applying the MFT to the echo of each channel, the defocused problem of nonuniform rotation target which is inevitable by using the FT-based imaging method can be avoided. Finally, after preprocessing, scaling and rotation of all subimages, the noncoherent fusion image containing all the RCS information in all channels can be obtained. The accumulation coefficients of all subimages are calculated adaptively according to the their image qualities. Simulation and experimental data are used to validate the effectiveness of the proposed approach, and fusion image with improved recognizability can be obtained. Therefore, by using the distributed ISAR technique and MFT, subimages of high-maneuvering target from different observation angles can be obtained. Meanwhile, by employing the adaptive subimage fusion method, the RCS fluctuation can be alleviated and more recognizable final image can be obtained.

Research on the development of space target detecting system and three-dimensional reconstruction technology

NASA Astrophysics Data System (ADS)

Li, Dong; Wei, Zhen; Song, Dawei; Sun, Wenfeng; Fan, Xiaoyan

2016-11-01

With the development of space technology, the number of spacecrafts and debris are increasing year by year. The demand for detecting and identification of spacecraft is growing strongly, which provides support to the cataloguing, crash warning and protection of aerospace vehicles. The majority of existing approaches for three-dimensional reconstruction is scattering centres correlation, which is based on the radar high resolution range profile (HRRP). This paper proposes a novel method to reconstruct the threedimensional scattering centre structure of target from a sequence of radar ISAR images, which mainly consists of three steps. First is the azimuth scaling of consecutive ISAR images based on fractional Fourier transform (FrFT). The later is the extraction of scattering centres and matching between adjacent ISAR images using grid method. Finally, according to the coordinate matrix of scattering centres, the three-dimensional scattering centre structure is reconstructed using improved factorization method. The three-dimensional structure is featured with stable and intuitive characteristic, which provides a new way to improve the identification probability and reduce the complexity of the model matching library. A satellite model is reconstructed using the proposed method from four consecutive ISAR images. The simulation results prove that the method has gotten a satisfied consistency and accuracy.

A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images.

PubMed

Zhen, Xin; Chen, Haibin; Yan, Hao; Zhou, Linghong; Mell, Loren K; Yashar, Catheryn M; Jiang, Steve; Jia, Xun; Gu, Xuejun; Cervino, Laura

2015-04-07

Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based 'thin-plate-spline robust point matching' algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses.

SU-F-303-11: Implementation and Applications of Rapid, SIFT-Based Cine MR Image Binning and Region Tracking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mazur, T; Wang, Y; Fischer-Valuck, B

2015-06-15

Purpose: To develop a novel and rapid, SIFT-based algorithm for assessing feature motion on cine MR images acquired during MRI-guided radiotherapy treatments. In particular, we apply SIFT descriptors toward both partitioning cine images into respiratory states and tracking regions across frames. Methods: Among a training set of images acquired during a fraction, we densely assign SIFT descriptors to pixels within the images. We cluster these descriptors across all frames in order to produce a dictionary of trackable features. Associating the best-matching descriptors at every frame among the training images to these features, we construct motion traces for the features. Wemore » use these traces to define respiratory bins for sorting images in order to facilitate robust pixel-by-pixel tracking. Instead of applying conventional methods for identifying pixel correspondences across frames we utilize a recently-developed algorithm that derives correspondences via a matching objective for SIFT descriptors. Results: We apply these methods to a collection of lung, abdominal, and breast patients. We evaluate the procedure for respiratory binning using target sites exhibiting high-amplitude motion among 20 lung and abdominal patients. In particular, we investigate whether these methods yield minimal variation between images within a bin by perturbing the resulting image distributions among bins. Moreover, we compare the motion between averaged images across respiratory states to 4DCT data for these patients. We evaluate the algorithm for obtaining pixel correspondences between frames by tracking contours among a set of breast patients. As an initial case, we track easily-identifiable edges of lumpectomy cavities that show minimal motion over treatment. Conclusions: These SIFT-based methods reliably extract motion information from cine MR images acquired during patient treatments. While we performed our analysis retrospectively, the algorithm lends itself to prospective motion assessment. Applications of these methods include motion assessment, identifying treatment windows for gating, and determining optimal margins for treatment.« less

[Registration and 3D rendering of serial tissue section images].

PubMed

Liu, Zhexing; Jiang, Guiping; Dong, Wu; Zhang, Yu; Xie, Xiaomian; Hao, Liwei; Wang, Zhiyuan; Li, Shuxiang

2002-12-01

It is an important morphological research method to reconstruct the 3D imaging from serial section tissue images. Registration of serial images is a key step to 3D reconstruction. Firstly, an introduction to the segmentation-counting registration algorithm is presented, which is based on the joint histogram. After thresholding of the two images to be registered, the criterion function is defined as counting in a specific region of the joint histogram, which greatly speeds up the alignment process. Then, the method is used to conduct the serial tissue image matching task, and lies a solid foundation for 3D rendering. Finally, preliminary surface rendering results are presented.

Speeding up 3D speckle tracking using PatchMatch

NASA Astrophysics Data System (ADS)

Zontak, Maria; O'Donnell, Matthew

2016-03-01

Echocardiography provides valuable information to diagnose heart dysfunction. A typical exam records several minutes of real-time cardiac images. To enable complete analysis of 3D cardiac strains, 4-D (3-D+t) echocardiography is used. This results in a huge dataset and requires effective automated analysis. Ultrasound speckle tracking is an effective method for tissue motion analysis. It involves correlation of a 3D kernel (block) around a voxel with kernels in later frames. The search region is usually confined to a local neighborhood, due to biomechanical and computational constraints. For high strains and moderate frame-rates, however, this search region will remain large, leading to a considerable computational burden. Moreover, speckle decorrelation (due to high strains) leads to errors in tracking. To solve this, spatial motion coherency between adjacent voxels should be imposed, e.g., by averaging their correlation functions.1 This requires storing correlation functions for neighboring voxels, thus increasing memory demands. In this work, we propose an efficient search using PatchMatch, 2 a powerful method to find correspondences between images. Here we adopt PatchMatch for 3D volumes and radio-frequency signals. As opposed to an exact search, PatchMatch performs random sampling of the search region and propagates successive matches among neighboring voxels. We show that: 1) Inherently smooth offset propagation in PatchMatch contributes to spatial motion coherence without any additional processing or memory demand. 2) For typical scenarios, PatchMatch is at least 20 times faster than the exact search, while maintaining comparable tracking accuracy.

Texture based segmentation method to detect atherosclerotic plaque from optical tomography images

NASA Astrophysics Data System (ADS)

Prakash, Ammu; Hewko, Mark; Sowa, Michael; Sherif, Sherif

2013-06-01

Optical coherence tomography (OCT) imaging has been widely employed in assessing cardiovascular disease. Atherosclerosis is one of the major cause cardio vascular diseases. However visual detection of atherosclerotic plaque from OCT images is often limited and further complicated by high frame rates. We developed a texture based segmentation method to automatically detect plaque and non plaque regions from OCT images. To verify our results we compared them to photographs of the vascular tissue with atherosclerotic plaque that we used to generate the OCT images. Our results show a close match with photographs of vascular tissue with atherosclerotic plaque. Our texture based segmentation method for plaque detection could be potentially used in clinical cardiovascular OCT imaging for plaque detection.

Calibration-free quantification of interior properties of porous media with x-ray computed tomography.

PubMed

Hussein, Esam M A; Agbogun, H M D; Al, Tom A

2015-03-01

A method is presented for interpreting the values of x-ray attenuation coefficients reconstructed in computed tomography of porous media, while overcoming the ambiguity caused by the multichromatic nature of x-rays, dilution by void, and material heterogeneity. The method enables determination of porosity without relying on calibration or image segmentation or thresholding to discriminate pores from solid material. It distinguishes between solution-accessible and inaccessible pores, and provides the spatial and frequency distributions of solid-matrix material in a heterogeneous medium. This is accomplished by matching an image of a sample saturated with a contrast solution with that saturated with a transparent solution. Voxels occupied with solid-material and inaccessible pores are identified by the fact that they maintain the same location and image attributes in both images, with voxels containing inaccessible pores appearing empty in both images. Fully porous and accessible voxels exhibit the maximum contrast, while the rest are porous voxels containing mixtures of pore solutions and solid. This matching process is performed with an image registration computer code, and image processing software that requires only simple subtraction and multiplication (scaling) processes. The process is demonstrated in dolomite (non-uniform void distribution, homogeneous solid matrix) and sandstone (nearly uniform void distribution, heterogeneous solid matrix) samples, and its overall performance is shown to compare favorably with a method based on calibration and thresholding. Copyright © 2014 Elsevier Ltd. All rights reserved.

Depth estimation of features in video frames with improved feature matching technique using Kinect sensor

NASA Astrophysics Data System (ADS)

Sharma, Kajal; Moon, Inkyu; Kim, Sung Gaun

2012-10-01

Estimating depth has long been a major issue in the field of computer vision and robotics. The Kinect sensor's active sensing strategy provides high-frame-rate depth maps and can recognize user gestures and human pose. This paper presents a technique to estimate the depth of features extracted from video frames, along with an improved feature-matching method. In this paper, we used the Kinect camera developed by Microsoft, which captured color and depth images for further processing. Feature detection and selection is an important task for robot navigation. Many feature-matching techniques have been proposed earlier, and this paper proposes an improved feature matching between successive video frames with the use of neural network methodology in order to reduce the computation time of feature matching. The features extracted are invariant to image scale and rotation, and different experiments were conducted to evaluate the performance of feature matching between successive video frames. The extracted features are assigned distance based on the Kinect technology that can be used by the robot in order to determine the path of navigation, along with obstacle detection applications.

A graph theoretic approach to scene matching

NASA Technical Reports Server (NTRS)

Ranganath, Heggere S.; Chipman, Laure J.

1991-01-01

The ability to match two scenes is a fundamental requirement in a variety of computer vision tasks. A graph theoretic approach to inexact scene matching is presented which is useful in dealing with problems due to imperfect image segmentation. A scene is described by a set of graphs, with nodes representing objects and arcs representing relationships between objects. Each node has a set of values representing the relations between pairs of objects, such as angle, adjacency, or distance. With this method of scene representation, the task in scene matching is to match two sets of graphs. Because of segmentation errors, variations in camera angle, illumination, and other conditions, an exact match between the sets of observed and stored graphs is usually not possible. In the developed approach, the problem is represented as an association graph, in which each node represents a possible mapping of an observed region to a stored object, and each arc represents the compatibility of two mappings. Nodes and arcs have weights indicating the merit or a region-object mapping and the degree of compatibility between two mappings. A match between the two graphs corresponds to a clique, or fully connected subgraph, in the association graph. The task is to find the clique that represents the best match. Fuzzy relaxation is used to update the node weights using the contextual information contained in the arcs and neighboring nodes. This simplifies the evaluation of cliques. A method of handling oversegmentation and undersegmentation problems is also presented. The approach is tested with a set of realistic images which exhibit many types of sementation errors.

Patch-Based Super-Resolution of MR Spectroscopic Images: Application to Multiple Sclerosis

PubMed Central

Jain, Saurabh; Sima, Diana M.; Sanaei Nezhad, Faezeh; Hangel, Gilbert; Bogner, Wolfgang; Williams, Stephen; Van Huffel, Sabine; Maes, Frederik; Smeets, Dirk

2017-01-01

Purpose: Magnetic resonance spectroscopic imaging (MRSI) provides complementary information to conventional magnetic resonance imaging. Acquiring high resolution MRSI is time consuming and requires complex reconstruction techniques. Methods: In this paper, a patch-based super-resolution method is presented to increase the spatial resolution of metabolite maps computed from MRSI. The proposed method uses high resolution anatomical MR images (T1-weighted and Fluid-attenuated inversion recovery) to regularize the super-resolution process. The accuracy of the method is validated against conventional interpolation techniques using a phantom, as well as simulated and in vivo acquired human brain images of multiple sclerosis subjects. Results: The method preserves tissue contrast and structural information, and matches well with the trend of acquired high resolution MRSI. Conclusions: These results suggest that the method has potential for clinically relevant neuroimaging applications. PMID:28197066

Registration of opthalmic images using control points

NASA Astrophysics Data System (ADS)

Heneghan, Conor; Maguire, Paul

2003-03-01

A method for registering pairs of digital ophthalmic images of the retina is presented using anatomical features as control points present in both images. The anatomical features chosen are blood vessel crossings and bifurcations. These control points are identified by a combination of local contrast enhancement, and morphological processing. In general, the matching between control points is unknown, however, so an automated algorithm is used to determine the matching pairs of control points in the two images as follows. Using two control points from each image, rigid global transform (RGT) coefficients are calculated for all possible combinations of control point pairs, and the set of RGT coefficients is identified. Once control point pairs are established, registration of two images can be achieved by using linear regression to optimize an RGT, bilinear or second order polynomial global transform. An example of cross-modal image registration using an optical image and a fluorescein angiogram of an eye is presented to illustrate the technique.

Human body motion capture from multi-image video sequences

NASA Astrophysics Data System (ADS)

D'Apuzzo, Nicola

2003-01-01

In this paper is presented a method to capture the motion of the human body from multi image video sequences without using markers. The process is composed of five steps: acquisition of video sequences, calibration of the system, surface measurement of the human body for each frame, 3-D surface tracking and tracking of key points. The image acquisition system is currently composed of three synchronized progressive scan CCD cameras and a frame grabber which acquires a sequence of triplet images. Self calibration methods are applied to gain exterior orientation of the cameras, the parameters of internal orientation and the parameters modeling the lens distortion. From the video sequences, two kinds of 3-D information are extracted: a three-dimensional surface measurement of the visible parts of the body for each triplet and 3-D trajectories of points on the body. The approach for surface measurement is based on multi-image matching, using the adaptive least squares method. A full automatic matching process determines a dense set of corresponding points in the triplets. The 3-D coordinates of the matched points are then computed by forward ray intersection using the orientation and calibration data of the cameras. The tracking process is also based on least squares matching techniques. Its basic idea is to track triplets of corresponding points in the three images through the sequence and compute their 3-D trajectories. The spatial correspondences between the three images at the same time and the temporal correspondences between subsequent frames are determined with a least squares matching algorithm. The results of the tracking process are the coordinates of a point in the three images through the sequence, thus the 3-D trajectory is determined by computing the 3-D coordinates of the point at each time step by forward ray intersection. Velocities and accelerations are also computed. The advantage of this tracking process is twofold: it can track natural points, without using markers; and it can track local surfaces on the human body. In the last case, the tracking process is applied to all the points matched in the region of interest. The result can be seen as a vector field of trajectories (position, velocity and acceleration). The last step of the process is the definition of selected key points of the human body. A key point is a 3-D region defined in the vector field of trajectories, whose size can vary and whose position is defined by its center of gravity. The key points are tracked in a simple way: the position at the next time step is established by the mean value of the displacement of all the trajectories inside its region. The tracked key points lead to a final result comparable to the conventional motion capture systems: 3-D trajectories of key points which can be afterwards analyzed and used for animation or medical purposes.

Dense Matching Comparison Between Census and a Convolutional Neural Network Algorithm for Plant Reconstruction

NASA Astrophysics Data System (ADS)

Xia, Y.; Tian, J.; d'Angelo, P.; Reinartz, P.

2018-05-01

3D reconstruction of plants is hard to implement, as the complex leaf distribution highly increases the difficulty level in dense matching. Semi-Global Matching has been successfully applied to recover the depth information of a scene, but may perform variably when different matching cost algorithms are used. In this paper two matching cost computation algorithms, Census transform and an algorithm using a convolutional neural network, are tested for plant reconstruction based on Semi-Global Matching. High resolution close-range photogrammetric images from a handheld camera are used for the experiment. The disparity maps generated based on the two selected matching cost methods are comparable with acceptable quality, which shows the good performance of Census and the potential of neural networks to improve the dense matching.

Applications of matched field processing to damage detection in composite wind turbine blades

NASA Astrophysics Data System (ADS)

Tippmann, Jeffery D.; Lanza di Scalea, Francesco

2015-03-01

There are many structures serving vital infrastructure, energy, and national security purposes. Inspecting the components and areas of the structure most prone to failure during maintenance operations by using non- destructive evaluation methods has been essential in avoiding costly, but preventable, catastrophic failures. In many cases, the inspections are performed by introducing acoustic, ultrasonic, or even thermographic waves into the structure and then evaluating the response. Sometimes the structure, or a component, is not accessible for active inspection methods. Because of this, there is a growing interest to use passive methods, such as using ambient noise, or sources of opportunity, to produce a passive impulse response function similar to the active approach. Several matched field processing techniques most notably used in oceanography and seismology applications are examined in more detail. While sparse array imaging in structures has been studied for years, all methods studied previously have used an active interrogation approach. Here, structural damage detection is studied by use of the reconstructed impulse response functions in ambient noise within sparse array imaging techniques, such as matched-field processing. This has been studied in experiments on a 9-m wind turbine blade.

Motion compensation in digital subtraction angiography using graphics hardware.

PubMed

Deuerling-Zheng, Yu; Lell, Michael; Galant, Adam; Hornegger, Joachim

2006-07-01

An inherent disadvantage of digital subtraction angiography (DSA) is its sensitivity to patient motion which causes artifacts in the subtraction images. These artifacts could often reduce the diagnostic value of this technique. Automated, fast and accurate motion compensation is therefore required. To cope with this requirement, we first examine a method explicitly designed to detect local motions in DSA. Then, we implement a motion compensation algorithm by means of block matching on modern graphics hardware. Both methods search for maximal local similarity by evaluating a histogram-based measure. In this context, we are the first who have mapped an optimizing search strategy on graphics hardware while paralleling block matching. Moreover, we provide an innovative method for creating histograms on graphics hardware with vertex texturing and frame buffer blending. It turns out that both methods can effectively correct the artifacts in most case, as the hardware implementation of block matching performs much faster: the displacements of two 1024 x 1024 images can be calculated at 3 frames/s with integer precision or 2 frames/s with sub-pixel precision. Preliminary clinical evaluation indicates that the computation with integer precision could already be sufficient.

Automatic Detection and Evaluation of Solar Cell Micro-Cracks in Electroluminescence Images Using Matched Filters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Spataru, Sergiu; Hacke, Peter; Sera, Dezso

A method for detecting micro-cracks in solar cells using two dimensional matched filters was developed, derived from the electroluminescence intensity profile of typical micro-cracks. We describe the image processing steps to obtain a binary map with the location of the micro-cracks. Finally, we show how to automatically estimate the total length of each micro-crack from these maps, and propose a method to identify severe types of micro-cracks, such as parallel, dendritic, and cracks with multiple orientations. With an optimized threshold parameter, the technique detects over 90 % of cracks larger than 3 cm in length. The method shows great potentialmore » for quantifying micro-crack damage after manufacturing or module transportation for the determination of a module quality criterion for cell cracking in photovoltaic modules.« less

Deformably registering and annotating whole CLARITY brains to an atlas via masked LDDMM

NASA Astrophysics Data System (ADS)

Kutten, Kwame S.; Vogelstein, Joshua T.; Charon, Nicolas; Ye, Li; Deisseroth, Karl; Miller, Michael I.

2016-04-01

The CLARITY method renders brains optically transparent to enable high-resolution imaging in the structurally intact brain. Anatomically annotating CLARITY brains is necessary for discovering which regions contain signals of interest. Manually annotating whole-brain, terabyte CLARITY images is difficult, time-consuming, subjective, and error-prone. Automatically registering CLARITY images to a pre-annotated brain atlas offers a solution, but is difficult for several reasons. Removal of the brain from the skull and subsequent storage and processing cause variable non-rigid deformations, thus compounding inter-subject anatomical variability. Additionally, the signal in CLARITY images arises from various biochemical contrast agents which only sparsely label brain structures. This sparse labeling challenges the most commonly used registration algorithms that need to match image histogram statistics to the more densely labeled histological brain atlases. The standard method is a multiscale Mutual Information B-spline algorithm that dynamically generates an average template as an intermediate registration target. We determined that this method performs poorly when registering CLARITY brains to the Allen Institute's Mouse Reference Atlas (ARA), because the image histogram statistics are poorly matched. Therefore, we developed a method (Mask-LDDMM) for registering CLARITY images, that automatically finds the brain boundary and learns the optimal deformation between the brain and atlas masks. Using Mask-LDDMM without an average template provided better results than the standard approach when registering CLARITY brains to the ARA. The LDDMM pipelines developed here provide a fast automated way to anatomically annotate CLARITY images; our code is available as open source software at http://NeuroData.io.

Cone beam CT dose reduction in prostate radiotherapy using Likert scale methods.

PubMed

Langmack, Keith A; Newton, Louise A; Jordan, Suzanne; Smith, Ruth

2016-01-01

To use a Likert scale method to optimize image quality (IQ) for cone beam CT (CBCT) soft-tissue matching for image-guided radiotherapy of the prostate. 23 males with local/locally advanced prostate cancer had the CBCT IQ assessed using a 4-point Likert scale (4 = excellent, no artefacts; 3 = good, few artefacts; 2 = poor, just able to match; 1 = unsatisfactory, not able to match) at three levels of exposure. The lateral separations of the subjects were also measured. The Friedman test and Wilcoxon signed-rank tests were used to determine if the IQ was associated with the exposure level. We used the point-biserial correlation and a χ(2) test to investigate the relationship between the separation and IQ. The Friedman test showed that the IQ was related to exposure (p = 2 × 10(-7)) and the Wilcoxon signed-rank test demonstrated that the IQ decreased as exposure decreased (all p-values <0.005). We did not find a correlation between the IQ and the separation (correlation coefficient 0.045), but for separations <35 cm, it was possible to use the lowest exposure parameters studied. We can reduce exposure factors to 80% of those supplied with the system without hindering the matching process for all patients. For patients with lateral separations <35 cm, the exposure factors can be reduced further to 64% of the original values. Likert scales are a useful tool for measuring IQ in the optimization of CBCT IQ for soft-tissue matching in radiotherapy image guidance applications.

Feature Matching of Historical Images Based on Geometry of Quadrilaterals

NASA Astrophysics Data System (ADS)

Maiwald, F.; Schneider, D.; Henze, F.; Münster, S.; Niebling, F.

2018-05-01

This contribution shows an approach to match historical images from the photo library of the Saxon State and University Library Dresden (SLUB) in the context of a historical three-dimensional city model of Dresden. In comparison to recent images, historical photography provides diverse factors which make an automatical image analysis (feature detection, feature matching and relative orientation of images) difficult. Due to e.g. film grain, dust particles or the digitalization process, historical images are often covered by noise interfering with the image signal needed for a robust feature matching. The presented approach uses quadrilaterals in image space as these are commonly available in man-made structures and façade images (windows, stones, claddings). It is explained how to generally detect quadrilaterals in images. Consequently, the properties of the quadrilaterals as well as the relationship to neighbouring quadrilaterals are used for the description and matching of feature points. The results show that most of the matches are robust and correct but still small in numbers.

Crack Modelling for Radiography

NASA Astrophysics Data System (ADS)

Chady, T.; Napierała, L.

2010-02-01

In this paper, possibility of creation of three-dimensional crack models, both random type and based on real-life radiographic images is discussed. Method for storing cracks in a number of two-dimensional matrices, as well algorithm for their reconstruction into three-dimensional objects is presented. Also the possibility of using iterative algorithm for matching simulated images of cracks to real-life radiographic images is discussed.

Advanced Digital Forensic and Steganalysis Methods

DTIC Science & Technology

2009-02-01

investigation is simultaneously cropped, scaled, and processed, extending the technology when the digital image is printed, developing technology capable ...or other common processing operations). TECNOLOGY APPLICATIONS 1. Determining the origin of digital images 2. Matching an image to a camera...Technology Transfer and Innovation Partnerships Division of Research P.O. Box 6000 State University of New York Binghamton, NY 13902-6000 Phone: 607-777

Stereo matching using census cost over cross window and segmentation-based disparity refinement

NASA Astrophysics Data System (ADS)

Li, Qingwu; Ni, Jinyan; Ma, Yunpeng; Xu, Jinxin

2018-03-01

Stereo matching is a vital requirement for many applications, such as three-dimensional (3-D) reconstruction, robot navigation, object detection, and industrial measurement. To improve the practicability of stereo matching, a method using census cost over cross window and segmentation-based disparity refinement is proposed. First, a cross window is obtained using distance difference and intensity similarity in binocular images. Census cost over the cross window and color cost are combined as the matching cost, which is aggregated by the guided filter. Then, winner-takes-all strategy is used to calculate the initial disparities. Second, a graph-based segmentation method is combined with color and edge information to achieve moderate under-segmentation. The segmented regions are classified into reliable regions and unreliable regions by consistency checking. Finally, the two regions are optimized by plane fitting and propagation, respectively, to match the ambiguous pixels. The experimental results are on Middlebury Stereo Datasets, which show that the proposed method has good performance in occluded and discontinuous regions, and it obtains smoother disparity maps with a lower average matching error rate compared with other algorithms.

Global localization of 3D point clouds in building outline maps of urban outdoor environments.

PubMed

Landsiedel, Christian; Wollherr, Dirk

2017-01-01

This paper presents a method to localize a robot in a global coordinate frame based on a sparse 2D map containing outlines of building and road network information and no location prior information. Its input is a single 3D laser scan of the surroundings of the robot. The approach extends the generic chamfer matching template matching technique from image processing by including visibility analysis in the cost function. Thus, the observed building planes are matched to the expected view of the corresponding map section instead of to the entire map, which makes a more accurate matching possible. Since this formulation operates on generic edge maps from visual sensors, the matching formulation can be expected to generalize to other input data, e.g., from monocular or stereo cameras. The method is evaluated on two large datasets collected in different real-world urban settings and compared to a baseline method from literature and to the standard chamfer matching approach, where it shows considerable performance benefits, as well as the feasibility of global localization based on sparse building outline data.

A FPGA-based architecture for real-time image matching

NASA Astrophysics Data System (ADS)

Wang, Jianhui; Zhong, Sheng; Xu, Wenhui; Zhang, Weijun; Cao, Zhiguo

2013-10-01

Image matching is a fundamental task in computer vision. It is used to establish correspondence between two images taken at different viewpoint or different time from the same scene. However, its large computational complexity has been a challenge to most embedded systems. This paper proposes a single FPGA-based image matching system, which consists of SIFT feature detection, BRIEF descriptor extraction and BRIEF matching. It optimizes the FPGA architecture for the SIFT feature detection to reduce the FPGA resources utilization. Moreover, we implement BRIEF description and matching on FPGA also. The proposed system can implement image matching at 30fps (frame per second) for 1280x720 images. Its processing speed can meet the demand of most real-life computer vision applications.

SU-E-J-201: Position Verification in Breast Cancer Radiotherapy Using Tantalum Clips in the Lumpectomy Cavity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Santvoort, J van; Van der Drift, M; Kuipers, J

2014-06-01

Purpose: To find out whether tantalum surgical clips can be used for online position verification in treatment of the lumpectomy cavity (LC) in breast cancer patients. Tantalum is a high density metal that could be visible on Electronic Portal Images (EPIs) and be an affordable alternative to gold markers. Clips are considered more representative for the LC position than nearby bony structures. Methods: In twelve patients the surgeon had placed 2 to 5 tantalum clips in the LC. The AP and lateral fields used for portal imaging, were adapted. In doing so, both bony structures and tantalum clips were visiblemore » on EPIs. The following analyses were performed:1. Image degradation, with respect to delineating the CTV, of the axial CT slices by artefacts because of the tantalum clips was evaluated by a radiation oncologist;2. The visibility of the tantalum clips on the EPIs was evaluated by four radiation therapists (RTTs);3. Bony anatomy and tantalum clip matches were performed on the same images independently by two observers. Results: 1. Delineation of the CTV by the radiation oncologist was not hampered by CT image artefacts because of the clips.2. The mean score for visibility of the clips on the EPIs, analysed by the four RTTs, was 5.6 on a scale of 10 (range 3.9 – 8.0).3. In total 12 patients with 16 fractions each were analysed. The differences between clip match and bone match are significant with a mean vector length of 5.2 mm (SD 1.9 mm) for the difference. Conclusion: Results of matches on tantalum clips as compared to matches on bony structures differ substantially. Therefore clip matches can result in smaller CTV to PTV margins than bone matches. Visibility of the clips on EPIs is sufficient, so they can be an alternative to gold markers.« less

Survey of Technologies for the Airport Border of the Future

DTIC Science & Technology

2014-04-01

geometry Handwriting recognition ID cards Image classification Image enhancement Image fusion Image matching Image processing Image segmentation Iris...00 Tongue print Footstep recognition Odour recognition Retinal recognition Emotion recognition Periocular recognition Handwriting recognition Ear...recognition Palmprint recognition Hand geometry DNA matching Vein matching Ear recognition Handwriting recognition Periocular recognition Emotion

Multiple Semantic Matching on Augmented N-partite Graph for Object Co-segmentation.

PubMed

Wang, Chuan; Zhang, Hua; Yang, Liang; Cao, Xiaochun; Xiong, Hongkai

2017-09-08

Recent methods for object co-segmentation focus on discovering single co-occurring relation of candidate regions representing the foreground of multiple images. However, region extraction based only on low and middle level information often occupies a large area of background without the help of semantic context. In addition, seeking single matching solution very likely leads to discover local parts of common objects. To cope with these deficiencies, we present a new object cosegmentation framework, which takes advantages of semantic information and globally explores multiple co-occurring matching cliques based on an N-partite graph structure. To this end, we first propose to incorporate candidate generation with semantic context. Based on the regions extracted from semantic segmentation of each image, we design a merging mechanism to hierarchically generate candidates with high semantic responses. Secondly, all candidates are taken into consideration to globally formulate multiple maximum weighted matching cliques, which complements the discovery of part of the common objects induced by a single clique. To facilitate the discovery of multiple matching cliques, an N-partite graph, which inherently excludes intralinks between candidates from the same image, is constructed to separate multiple cliques without additional constraints. Further, we augment the graph with an additional virtual node in each part to handle irrelevant matches when the similarity between two candidates is too small. Finally, with the explored multiple cliques, we statistically compute pixel-wise co-occurrence map for each image. Experimental results on two benchmark datasets, i.e., iCoseg and MSRC datasets, achieve desirable performance and demonstrate the effectiveness of our proposed framework.

Image registration with uncertainty analysis

DOEpatents

Simonson, Katherine M [Cedar Crest, NM

2011-03-22

In an image registration method, edges are detected in a first image and a second image. A percentage of edge pixels in a subset of the second image that are also edges in the first image shifted by a translation is calculated. A best registration point is calculated based on a maximum percentage of edges matched. In a predefined search region, all registration points other than the best registration point are identified that are not significantly worse than the best registration point according to a predetermined statistical criterion.

The quantitative control and matching of an optical false color composite imaging system

NASA Astrophysics Data System (ADS)

Zhou, Chengxian; Dai, Zixin; Pan, Xizhe; Li, Yinxi

1993-10-01

Design of an imaging system for optical false color composite (OFCC) capable of high-precision density-exposure time control and color balance is presented. The system provides high quality FCC image data that can be analyzed using a quantitative calculation method. The quality requirement to each part of the image generation system is defined, and the distribution of satellite remote sensing image information is analyzed. The proposed technology makes it possible to present the remote sensing image data more effectively and accurately.

Enhanced speed in fluorescence imaging using beat frequency multiplexing

NASA Astrophysics Data System (ADS)

Mikami, Hideharu; Kobayashi, Hirofumi; Wang, Yisen; Hamad, Syed; Ozeki, Yasuyuki; Goda, Keisuke

2016-03-01

Fluorescence imaging using radiofrequency-tagged emission (FIRE) is an emerging technique that enables higher imaging speed (namely, temporal resolution) in fluorescence microscopy compared to conventional fluorescence imaging techniques such as confocal microscopy and wide-field microscopy. It works based on the principle that it uses multiple intensity-modulated fields in an interferometric setup as excitation fields and applies frequency-division multiplexing to fluorescence signals. Unfortunately, despite its high potential, FIRE has limited imaging speed due to two practical limitations: signal bandwidth and signal detection efficiency. The signal bandwidth is limited by that of an acousto-optic deflector (AOD) employed in the setup, which is typically 100-200 MHz for the spectral range of fluorescence excitation (400-600 nm). The signal detection efficiency is limited by poor spatial mode-matching between two interfering fields to produce a modulated excitation field. Here we present a method to overcome these limitations and thus to achieve higher imaging speed than the prior version of FIRE. Our method achieves an increase in signal bandwidth by a factor of two and nearly optimal mode matching, which enables the imaging speed limited by the lifetime of the target fluorophore rather than the imaging system itself. The higher bandwidth and better signal detection efficiency work synergistically because higher bandwidth requires higher signal levels to avoid the contribution of shot noise and amplifier noise to the fluorescence signal. Due to its unprecedentedly high-speed performance, our method has a wide variety of applications in cancer detection, drug discovery, and regenerative medicine.

Determination of Steering Wheel Angles during CAR Alignment by Image Analysis Methods

NASA Astrophysics Data System (ADS)

Mueller, M.; Voegtle, T.

2016-06-01

Optical systems for automatic visual inspections are of increasing importance in the field of automation in the industrial domain. A new application is the determination of steering wheel angles during wheel track setting of the final inspection of car manufacturing. The camera has to be positioned outside the car to avoid interruptions of the processes and therefore, oblique images of the steering wheel must be acquired. Three different approaches of computer vision are considered in this paper, i.e. a 2D shape-based matching (by means of a plane to plane rectification of the oblique images and detection of a shape model with a particular rotation), a 3D shape-based matching approach (by means of a series of different perspectives of the spatial shape of the steering wheel derived from a CAD design model) and a point-to-point matching (by means of the extraction of significant elements (e.g. multifunctional buttons) of a steering wheel and a pairwise connection of these points to straight lines). The HALCON system (HALCON, 2016) was used for all software developments and necessary adaptions. As reference a mechanical balance with an accuracy of 0.1° was used. The quality assessment was based on two different approaches, a laboratory test and a test during production process. In the laboratory a standard deviation of ±0.035° (2D shape-based matching), ±0.12° (3D approach) and ±0.029° (point-to-point matching) could be obtained. The field test of 291 measurements (27 cars with varying poses and angles of the steering wheel) results in a detection rate of 100% and ±0.48° (2D matching) and ±0.24° (point-to-point matching). Both methods also fulfil the request of real time processing (three measurements per second).

Measurement of left ventricular torsion using block-matching-based speckle tracking for two-dimensional echocardiography

NASA Astrophysics Data System (ADS)

Sun, Feng-Rong; Wang, Xiao-Jing; Wu, Qiang; Yao, Gui-Hua; Zhang, Yun

2013-01-01

Left ventricular (LV) torsion is a sensitive and global index of LV systolic and diastolic function, but how to noninvasively measure it is challenging. Two-dimensional echocardiography and the block-matching based speckle tracking method were used to measure LV torsion. Main advantages of the proposed method over the previous ones are summarized as follows: (1) The method is automatic, except for manually selecting some endocardium points on the end-diastolic frame in initialization step. (2) The diamond search strategy is applied, with a spatial smoothness constraint introduced into the sum of absolute differences matching criterion; and the reference frame during the search is determined adaptively. (3) The method is capable of removing abnormal measurement data automatically. The proposed method was validated against that using Doppler tissue imaging and some preliminary clinical experimental studies were presented to illustrate clinical values of the proposed method.

a Robust Method for Stereo Visual Odometry Based on Multiple Euclidean Distance Constraint and Ransac Algorithm

NASA Astrophysics Data System (ADS)

Zhou, Q.; Tong, X.; Liu, S.; Lu, X.; Liu, S.; Chen, P.; Jin, Y.; Xie, H.

2017-07-01

Visual Odometry (VO) is a critical component for planetary robot navigation and safety. It estimates the ego-motion using stereo images frame by frame. Feature points extraction and matching is one of the key steps for robotic motion estimation which largely influences the precision and robustness. In this work, we choose the Oriented FAST and Rotated BRIEF (ORB) features by considering both accuracy and speed issues. For more robustness in challenging environment e.g., rough terrain or planetary surface, this paper presents a robust outliers elimination method based on Euclidean Distance Constraint (EDC) and Random Sample Consensus (RANSAC) algorithm. In the matching process, a set of ORB feature points are extracted from the current left and right synchronous images and the Brute Force (BF) matcher is used to find the correspondences between the two images for the Space Intersection. Then the EDC and RANSAC algorithms are carried out to eliminate mismatches whose distances are beyond a predefined threshold. Similarly, when the left image of the next time matches the feature points with the current left images, the EDC and RANSAC are iteratively performed. After the above mentioned, there are exceptional remaining mismatched points in some cases, for which the third time RANSAC is applied to eliminate the effects of those outliers in the estimation of the ego-motion parameters (Interior Orientation and Exterior Orientation). The proposed approach has been tested on a real-world vehicle dataset and the result benefits from its high robustness.

A Multispectral Image Creating Method for a New Airborne Four-Camera System with Different Bandpass Filters

PubMed Central

Li, Hanlun; Zhang, Aiwu; Hu, Shaoxing

2015-01-01

This paper describes an airborne high resolution four-camera multispectral system which mainly consists of four identical monochrome cameras equipped with four interchangeable bandpass filters. For this multispectral system, an automatic multispectral data composing method was proposed. The homography registration model was chosen, and the scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) were used to generate matching points. For the difficult registration problem between visible band images and near-infrared band images in cases lacking manmade objects, we presented an effective method based on the structural characteristics of the system. Experiments show that our method can acquire high quality multispectral images and the band-to-band alignment error of the composed multiple spectral images is less than 2.5 pixels. PMID:26205264

WE-G-BRD-07: Automated MR Image Standardization and Auto-Contouring Strategy for MRI-Based Adaptive Brachytherapy for Cervix Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saleh, H Al; Erickson, B; Paulson, E

Purpose: MRI-based adaptive brachytherapy (ABT) is an emerging treatment modality for patients with gynecological tumors. However, MR image intensity non-uniformities (IINU) can vary from fraction to fraction, complicating image interpretation and auto-contouring accuracy. We demonstrate here an automated MR image standardization and auto-contouring strategy for MRI-based ABT of cervix cancer. Methods: MR image standardization consisted of: 1) IINU correction using the MNI N3 algorithm, 2) noise filtering using anisotropic diffusion, and 3) signal intensity normalization using the volumetric median. This post-processing chain was implemented as a series of custom Matlab and Java extensions in MIM (v6.4.5, MIM Software) and wasmore » applied to 3D T2 SPACE images of six patients undergoing MRI-based ABT at 3T. Coefficients of variation (CV=σ/µ) were calculated for both original and standardized images and compared using Mann-Whitney tests. Patient-specific cumulative MR atlases of bladder, rectum, and sigmoid contours were constructed throughout ABT, using original and standardized MR images from all previous ABT fractions. Auto-contouring was performed in MIM two ways: 1) best-match of one atlas image to the daily MR image, 2) multi-match of all previous fraction atlas images to the daily MR image. Dice’s Similarity Coefficients (DSCs) were calculated for auto-generated contours relative to reference contours for both original and standardized MR images and compared using Mann-Whitney tests. Results: Significant improvements in CV were detected following MR image standardization (p=0.0043), demonstrating an improvement in MR image uniformity. DSCs consistently increased for auto-contoured bladder, rectum, and sigmoid following MR image standardization, with the highest DSCs detected when the combination of MR image standardization and multi-match cumulative atlas-based auto-contouring was utilized. Conclusion: MR image standardization significantly improves MR image uniformity. The combination of MR image standardization and multi-match cumulative atlas-based auto-contouring produced the highest DSCs and is a promising strategy for MRI-based ABT for cervix cancer.« less

An efficient method for the computation of Legendre moments.

PubMed

Yap, Pew-Thian; Paramesran, Raveendran

2005-12-01

Legendre moments are continuous moments, hence, when applied to discrete-space images, numerical approximation is involved and error occurs. This paper proposes a method to compute the exact values of the moments by mathematically integrating the Legendre polynomials over the corresponding intervals of the image pixels. Experimental results show that the values obtained match those calculated theoretically, and the image reconstructed from these moments have lower error than that of the conventional methods for the same order. Although the same set of exact Legendre moments can be obtained indirectly from the set of geometric moments, the computation time taken is much longer than the proposed method.

Thermal-depth matching in dynamic scene based on affine projection and feature registration

NASA Astrophysics Data System (ADS)

Wang, Hongyu; Jia, Tong; Wu, Chengdong; Li, Yongqiang

2018-03-01

This paper aims to study the construction of 3D temperature distribution reconstruction system based on depth and thermal infrared information. Initially, a traditional calibration method cannot be directly used, because the depth and thermal infrared camera is not sensitive to the color calibration board. Therefore, this paper aims to design a depth and thermal infrared camera calibration board to complete the calibration of the depth and thermal infrared camera. Meanwhile a local feature descriptors in thermal and depth images is proposed. The belief propagation matching algorithm is also investigated based on the space affine transformation matching and local feature matching. The 3D temperature distribution model is built based on the matching of 3D point cloud and 2D thermal infrared information. Experimental results show that the method can accurately construct the 3D temperature distribution model, and has strong robustness.

3D reconstruction from multi-view VHR-satellite images in MicMac

NASA Astrophysics Data System (ADS)

Rupnik, Ewelina; Pierrot-Deseilligny, Marc; Delorme, Arthur

2018-05-01

This work addresses the generation of high quality digital surface models by fusing multiple depths maps calculated with the dense image matching method. The algorithm is adapted to very high resolution multi-view satellite images, and the main contributions of this work are in the multi-view fusion. The algorithm is insensitive to outliers, takes into account the matching quality indicators, handles non-correlated zones (e.g. occlusions), and is solved with a multi-directional dynamic programming approach. No geometric constraints (e.g. surface planarity) or auxiliary data in form of ground control points are required for its operation. Prior to the fusion procedures, the RPC geolocation parameters of all images are improved in a bundle block adjustment routine. The performance of the algorithm is evaluated on two VHR (Very High Resolution)-satellite image datasets (Pléiades, WorldView-3) revealing its good performance in reconstructing non-textured areas, repetitive patterns, and surface discontinuities.

LASER APPLICATIONS AND OTHER TOPICS IN QUANTUM ELECTRONICS: Characterisation of optically cleared paper by optical coherence tomography

NASA Astrophysics Data System (ADS)

Fabritius, T.; Alarousu, E.; Prykäri, T.; Hast, J.; Myllylä, Risto

2006-02-01

Due to the highly light scattering nature of paper, the imaging depth of optical methods such as optical coherence tomography (OCT) is limited. In this work, we study the effect of refractive index matching on improving the imaging depth of OCT in paper. To this end, four different refractive index matching liquids (ethanol, 1-pentanol, glycerol and benzyl alcohol) with a refraction index between 1.359 and 1.538 were used in experiments. Low coherent light transmission was studied in commercial copy paper sheets, and the results indicate that benzyl alcohol offers the best improvement in imaging depth, while also being sufficiently stable for the intended purpose. Constructed cross-sectional images demonstrate visually that the imaging depth of OCT is considerably improved by optical clearing. Both surfaces of paper sheets can be detected along with information about the sheet's inner structure.

Mixture-Tuned, Clutter Matched Filter for Remote Detection of Subpixel Spectral Signals

NASA Technical Reports Server (NTRS)

Thompson, David R.; Mandrake, Lukas; Green, Robert O.

2013-01-01

Mapping localized spectral features in large images demands sensitive and robust detection algorithms. Two aspects of large images that can harm matched-filter detection performance are addressed simultaneously. First, multimodal backgrounds may thwart the typical Gaussian model. Second, outlier features can trigger false detections from large projections onto the target vector. Two state-of-the-art approaches are combined that independently address outlier false positives and multimodal backgrounds. The background clustering models multimodal backgrounds, and the mixture tuned matched filter (MT-MF) addresses outliers. Combining the two methods captures significant additional performance benefits. The resulting mixture tuned clutter matched filter (MT-CMF) shows effective performance on simulated and airborne datasets. The classical MNF transform was applied, followed by k-means clustering. Then, each cluster s mean, covariance, and the corresponding eigenvalues were estimated. This yields a cluster-specific matched filter estimate as well as a cluster- specific feasibility score to flag outlier false positives. The technology described is a proof of concept that may be employed in future target detection and mapping applications for remote imaging spectrometers. It is of most direct relevance to JPL proposals for airborne and orbital hyperspectral instruments. Applications include subpixel target detection in hyperspectral scenes for military surveillance. Earth science applications include mineralogical mapping, species discrimination for ecosystem health monitoring, and land use classification.

A method of object recognition for single pixel imaging

NASA Astrophysics Data System (ADS)

Li, Boxuan; Zhang, Wenwen

2018-01-01

Computational ghost imaging(CGI), utilizing a single-pixel detector, has been extensively used in many fields. However, in order to achieve a high-quality reconstructed image, a large number of iterations are needed, which limits the flexibility of using CGI in practical situations, especially in the field of object recognition. In this paper, we purpose a method utilizing the feature matching to identify the number objects. In the given system, approximately 90% of accuracy of recognition rates can be achieved, which provides a new idea for the application of single pixel imaging in the field of object recognition

Roads Data Conflation Using Update High Resolution Satellite Images

NASA Astrophysics Data System (ADS)

Abdollahi, A.; Riyahi Bakhtiari, H. R.

2017-11-01

Urbanization, industrialization and modernization are rapidly growing in developing countries. New industrial cities, with all the problems brought on by rapid population growth, need infrastructure to support the growth. This has led to the expansion and development of the road network. A great deal of road network data has made by using traditional methods in the past years. Over time, a large amount of descriptive information has assigned to these map data, but their geometric accuracy and precision is not appropriate to today's need. In this regard, the improvement of the geometric accuracy of road network data by preserving the descriptive data attributed to them and updating of the existing geo databases is necessary. Due to the size and extent of the country, updating the road network maps using traditional methods is time consuming and costly. Conversely, using remote sensing technology and geographic information systems can reduce costs, save time and increase accuracy and speed. With increasing the availability of high resolution satellite imagery and geospatial datasets there is an urgent need to combine geographic information from overlapping sources to retain accurate data, minimize redundancy, and reconcile data conflicts. In this research, an innovative method for a vector-to-imagery conflation by integrating several image-based and vector-based algorithms presented. The SVM method for image classification and Level Set method used to extract the road the different types of road intersections extracted from imagery using morphological operators. For matching the extracted points and to find the corresponding points, matching function which uses the nearest neighborhood method was applied. Finally, after identifying the matching points rubber-sheeting method used to align two datasets. Two residual and RMSE criteria used to evaluate accuracy. The results demonstrated excellent performance. The average root-mean-square error decreased from 11.8 to 4.1 m.

An Efficient Seam Elimination Method for UAV Images Based on Wallis Dodging and Gaussian Distance Weight Enhancement

PubMed Central

Tian, Jinyan; Li, Xiaojuan; Duan, Fuzhou; Wang, Junqian; Ou, Yang

2016-01-01

The rapid development of Unmanned Aerial Vehicle (UAV) remote sensing conforms to the increasing demand for the low-altitude very high resolution (VHR) image data. However, high processing speed of massive UAV data has become an indispensable prerequisite for its applications in various industry sectors. In this paper, we developed an effective and efficient seam elimination approach for UAV images based on Wallis dodging and Gaussian distance weight enhancement (WD-GDWE). The method encompasses two major steps: first, Wallis dodging was introduced to adjust the difference of brightness between the two matched images, and the parameters in the algorithm were derived in this study. Second, a Gaussian distance weight distribution method was proposed to fuse the two matched images in the overlap region based on the theory of the First Law of Geography, which can share the partial dislocation in the seam to the whole overlap region with an effect of smooth transition. This method was validated at a study site located in Hanwang (Sichuan, China) which was a seriously damaged area in the 12 May 2008 enchuan Earthquake. Then, a performance comparison between WD-GDWE and the other five classical seam elimination algorithms in the aspect of efficiency and effectiveness was conducted. Results showed that WD-GDWE is not only efficient, but also has a satisfactory effectiveness. This method is promising in advancing the applications in UAV industry especially in emergency situations. PMID:27171091

An image retrieval framework for real-time endoscopic image retargeting.

PubMed

Ye, Menglong; Johns, Edward; Walter, Benjamin; Meining, Alexander; Yang, Guang-Zhong

2017-08-01

Serial endoscopic examinations of a patient are important for early diagnosis of malignancies in the gastrointestinal tract. However, retargeting for optical biopsy is challenging due to extensive tissue variations between examinations, requiring the method to be tolerant to these changes whilst enabling real-time retargeting. This work presents an image retrieval framework for inter-examination retargeting. We propose both a novel image descriptor tolerant of long-term tissue changes and a novel descriptor matching method in real time. The descriptor is based on histograms generated from regional intensity comparisons over multiple scales, offering stability over long-term appearance changes at the higher levels, whilst remaining discriminative at the lower levels. The matching method then learns a hashing function using random forests, to compress the string and allow for fast image comparison by a simple Hamming distance metric. A dataset that contains 13 in vivo gastrointestinal videos was collected from six patients, representing serial examinations of each patient, which includes videos captured with significant time intervals. Precision-recall for retargeting shows that our new descriptor outperforms a number of alternative descriptors, whilst our hashing method outperforms a number of alternative hashing approaches. We have proposed a novel framework for optical biopsy in serial endoscopic examinations. A new descriptor, combined with a novel hashing method, achieves state-of-the-art retargeting, with validation on in vivo videos from six patients. Real-time performance also allows for practical integration without disturbing the existing clinical workflow.

An Efficient Seam Elimination Method for UAV Images Based on Wallis Dodging and Gaussian Distance Weight Enhancement.

PubMed

Tian, Jinyan; Li, Xiaojuan; Duan, Fuzhou; Wang, Junqian; Ou, Yang

2016-05-10

The rapid development of Unmanned Aerial Vehicle (UAV) remote sensing conforms to the increasing demand for the low-altitude very high resolution (VHR) image data. However, high processing speed of massive UAV data has become an indispensable prerequisite for its applications in various industry sectors. In this paper, we developed an effective and efficient seam elimination approach for UAV images based on Wallis dodging and Gaussian distance weight enhancement (WD-GDWE). The method encompasses two major steps: first, Wallis dodging was introduced to adjust the difference of brightness between the two matched images, and the parameters in the algorithm were derived in this study. Second, a Gaussian distance weight distribution method was proposed to fuse the two matched images in the overlap region based on the theory of the First Law of Geography, which can share the partial dislocation in the seam to the whole overlap region with an effect of smooth transition. This method was validated at a study site located in Hanwang (Sichuan, China) which was a seriously damaged area in the 12 May 2008 enchuan Earthquake. Then, a performance comparison between WD-GDWE and the other five classical seam elimination algorithms in the aspect of efficiency and effectiveness was conducted. Results showed that WD-GDWE is not only efficient, but also has a satisfactory effectiveness. This method is promising in advancing the applications in UAV industry especially in emergency situations.

Using Trained Pixel Classifiers to Select Images of Interest

NASA Technical Reports Server (NTRS)

Mazzoni, D.; Wagstaff, K.; Castano, R.

2004-01-01

We present a machine-learning-based approach to ranking images based on learned priorities. Unlike previous methods for image evaluation, which typically assess the value of each image based on the presence of predetermined specific features, this method involves using two levels of machine-learning classifiers: one level is used to classify each pixel as belonging to one of a group of rather generic classes, and another level is used to rank the images based on these pixel classifications, given some example rankings from a scientist as a guide. Initial results indicate that the technique works well, producing new rankings that match the scientist's rankings significantly better than would be expected by chance. The method is demonstrated for a set of images collected by a Mars field-test rover.

Fuzzy Matching Based on Gray-scale Difference for Quantum Images

NASA Astrophysics Data System (ADS)

Luo, GaoFeng; Zhou, Ri-Gui; Liu, XingAo; Hu, WenWen; Luo, Jia

2018-05-01

Quantum image processing has recently emerged as an essential problem in practical tasks, e.g. real-time image matching. Previous studies have shown that the superposition and entanglement of quantum can greatly improve the efficiency of complex image processing. In this paper, a fuzzy quantum image matching scheme based on gray-scale difference is proposed to find out the target region in a reference image, which is very similar to the template image. Firstly, we employ the proposed enhanced quantum representation (NEQR) to store digital images. Then some certain quantum operations are used to evaluate the gray-scale difference between two quantum images by thresholding. If all of the obtained gray-scale differences are not greater than the threshold value, it indicates a successful fuzzy matching of quantum images. Theoretical analysis and experiments show that the proposed scheme performs fuzzy matching at a low cost and also enables exponentially significant speedup via quantum parallel computation.

Retinal biometrics based on Iterative Closest Point algorithm.

PubMed

Hatanaka, Yuji; Tajima, Mikiya; Kawasaki, Ryo; Saito, Koko; Ogohara, Kazunori; Muramatsu, Chisako; Sunayama, Wataru; Fujita, Hiroshi

2017-07-01

The pattern of blood vessels in the eye is unique to each person because it rarely changes over time. Therefore, it is well known that retinal blood vessels are useful for biometrics. This paper describes a biometrics method using the Jaccard similarity coefficient (JSC) based on blood vessel regions in retinal image pairs. The retinal image pairs were rough matched by the center of their optic discs. Moreover, the image pairs were aligned using the Iterative Closest Point algorithm based on detailed blood vessel skeletons. For registration, perspective transform was applied to the retinal images. Finally, the pairs were classified as either correct or incorrect using the JSC of the blood vessel region in the image pairs. The proposed method was applied to temporal retinal images, which were obtained in 2009 (695 images) and 2013 (87 images). The 87 images acquired in 2013 were all from persons already examined in 2009. The accuracy of the proposed method reached 100%.

Image denoising by exploring external and internal correlations.

PubMed

Yue, Huanjing; Sun, Xiaoyan; Yang, Jingyu; Wu, Feng

2015-06-01

Single image denoising suffers from limited data collection within a noisy image. In this paper, we propose a novel image denoising scheme, which explores both internal and external correlations with the help of web images. For each noisy patch, we build internal and external data cubes by finding similar patches from the noisy and web images, respectively. We then propose reducing noise by a two-stage strategy using different filtering approaches. In the first stage, since the noisy patch may lead to inaccurate patch selection, we propose a graph based optimization method to improve patch matching accuracy in external denoising. The internal denoising is frequency truncation on internal cubes. By combining the internal and external denoising patches, we obtain a preliminary denoising result. In the second stage, we propose reducing noise by filtering of external and internal cubes, respectively, on transform domain. In this stage, the preliminary denoising result not only enhances the patch matching accuracy but also provides reliable estimates of filtering parameters. The final denoising image is obtained by fusing the external and internal filtering results. Experimental results show that our method constantly outperforms state-of-the-art denoising schemes in both subjective and objective quality measurements, e.g., it achieves >2 dB gain compared with BM3D at a wide range of noise levels.

Bin Ratio-Based Histogram Distances and Their Application to Image Classification.

PubMed

Hu, Weiming; Xie, Nianhua; Hu, Ruiguang; Ling, Haibin; Chen, Qiang; Yan, Shuicheng; Maybank, Stephen

2014-12-01

Large variations in image background may cause partial matching and normalization problems for histogram-based representations, i.e., the histograms of the same category may have bins which are significantly different, and normalization may produce large changes in the differences between corresponding bins. In this paper, we deal with this problem by using the ratios between bin values of histograms, rather than bin values' differences which are used in the traditional histogram distances. We propose a bin ratio-based histogram distance (BRD), which is an intra-cross-bin distance, in contrast with previous bin-to-bin distances and cross-bin distances. The BRD is robust to partial matching and histogram normalization, and captures correlations between bins with only a linear computational complexity. We combine the BRD with the ℓ1 histogram distance and the χ(2) histogram distance to generate the ℓ1 BRD and the χ(2) BRD, respectively. These combinations exploit and benefit from the robustness of the BRD under partial matching and the robustness of the ℓ1 and χ(2) distances to small noise. We propose a method for assessing the robustness of histogram distances to partial matching. The BRDs and logistic regression-based histogram fusion are applied to image classification. The experimental results on synthetic data sets show the robustness of the BRDs to partial matching, and the experiments on seven benchmark data sets demonstrate promising results of the BRDs for image classification.

Automated facial recognition of manually generated clay facial approximations: Potential application in unidentified persons data repositories.

PubMed

Parks, Connie L; Monson, Keith L

2018-01-01

This research examined how accurately 2D images (i.e., photographs) of 3D clay facial approximations were matched to corresponding photographs of the approximated individuals using an objective automated facial recognition system. Irrespective of search filter (i.e., blind, sex, or ancestry) or rank class (R 1 , R 10 , R 25 , and R 50 ) employed, few operationally informative results were observed. In only a single instance of 48 potential match opportunities was a clay approximation matched to a corresponding life photograph within the top 50 images (R 50 ) of a candidate list, even with relatively small gallery sizes created from the application of search filters (e.g., sex or ancestry search restrictions). Increasing the candidate lists to include the top 100 images (R 100 ) resulted in only two additional instances of correct match. Although other untested variables (e.g., approximation method, 2D photographic process, and practitioner skill level) may have impacted the observed results, this study suggests that 2D images of manually generated clay approximations are not readily matched to life photos by automated facial recognition systems. Further investigation is necessary in order to identify the underlying cause(s), if any, of the poor recognition results observed in this study (e.g., potential inferior facial feature detection and extraction). Additional inquiry exploring prospective remedial measures (e.g., stronger feature differentiation) is also warranted, particularly given the prominent use of clay approximations in unidentified persons casework. Copyright © 2017. Published by Elsevier B.V.

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

PubMed Central

Guo, Yanrong; Gao, Yaozong

2016-01-01

Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods. PMID:26685226

Constructing a Database from Multiple 2D Images for Camera Pose Estimation and Robot Localization

NASA Technical Reports Server (NTRS)

Wolf, Michael; Ansar, Adnan I.; Brennan, Shane; Clouse, Daniel S.; Padgett, Curtis W.

2012-01-01

The LMDB (Landmark Database) Builder software identifies persistent image features (landmarks) in a scene viewed multiple times and precisely estimates the landmarks 3D world positions. The software receives as input multiple 2D images of approximately the same scene, along with an initial guess of the camera poses for each image, and a table of features matched pair-wise in each frame. LMDB Builder aggregates landmarks across an arbitrarily large collection of frames with matched features. Range data from stereo vision processing can also be passed to improve the initial guess of the 3D point estimates. The LMDB Builder aggregates feature lists across all frames, manages the process to promote selected features to landmarks, and iteratively calculates the 3D landmark positions using the current camera pose estimations (via an optimal ray projection method), and then improves the camera pose estimates using the 3D landmark positions. Finally, it extracts image patches for each landmark from auto-selected key frames and constructs the landmark database. The landmark database can then be used to estimate future camera poses (and therefore localize a robotic vehicle that may be carrying the cameras) by matching current imagery to landmark database image patches and using the known 3D landmark positions to estimate the current pose.

Low dose CT image restoration using a database of image patches

NASA Astrophysics Data System (ADS)

Ha, Sungsoo; Mueller, Klaus

2015-01-01

Reducing the radiation dose in CT imaging has become an active research topic and many solutions have been proposed to remove the significant noise and streak artifacts in the reconstructed images. Most of these methods operate within the domain of the image that is subject to restoration. This, however, poses limitations on the extent of filtering possible. We advocate to take into consideration the vast body of external knowledge that exists in the domain of already acquired medical CT images, since after all, this is what radiologists do when they examine these low quality images. We can incorporate this knowledge by creating a database of prior scans, either of the same patient or a diverse corpus of different patients, to assist in the restoration process. Our paper follows up on our previous work that used a database of images. Using images, however, is challenging since it requires tedious and error prone registration and alignment. Our new method eliminates these problems by storing a diverse set of small image patches in conjunction with a localized similarity matching scheme. We also empirically show that it is sufficient to store these patches without anatomical tags since their statistics are sufficiently strong to yield good similarity matches from the database and as a direct effect, produce image restorations of high quality. A final experiment demonstrates that our global database approach can recover image features that are difficult to preserve with conventional denoising approaches.

Infrared super-resolution imaging based on compressed sensing

NASA Astrophysics Data System (ADS)

Sui, Xiubao; Chen, Qian; Gu, Guohua; Shen, Xuewei

2014-03-01

The theoretical basis of traditional infrared super-resolution imaging method is Nyquist sampling theorem. The reconstruction premise is that the relative positions of the infrared objects in the low-resolution image sequences should keep fixed and the image restoration means is the inverse operation of ill-posed issues without fixed rules. The super-resolution reconstruction ability of the infrared image, algorithm's application area and stability of reconstruction algorithm are limited. To this end, we proposed super-resolution reconstruction method based on compressed sensing in this paper. In the method, we selected Toeplitz matrix as the measurement matrix and realized it by phase mask method. We researched complementary matching pursuit algorithm and selected it as the recovery algorithm. In order to adapt to the moving target and decrease imaging time, we take use of area infrared focal plane array to acquire multiple measurements at one time. Theoretically, the method breaks though Nyquist sampling theorem and can greatly improve the spatial resolution of the infrared image. The last image contrast and experiment data indicate that our method is effective in improving resolution of infrared images and is superior than some traditional super-resolution imaging method. The compressed sensing super-resolution method is expected to have a wide application prospect.

Quick probabilistic binary image matching: changing the rules of the game

NASA Astrophysics Data System (ADS)

Mustafa, Adnan A. Y.

2016-09-01

A Probabilistic Matching Model for Binary Images (PMMBI) is presented that predicts the probability of matching binary images with any level of similarity. The model relates the number of mappings, the amount of similarity between the images and the detection confidence. We show the advantage of using a probabilistic approach to matching in similarity space as opposed to a linear search in size space. With PMMBI a complete model is available to predict the quick detection of dissimilar binary images. Furthermore, the similarity between the images can be measured to a good degree if the images are highly similar. PMMBI shows that only a few pixels need to be compared to detect dissimilarity between images, as low as two pixels in some cases. PMMBI is image size invariant; images of any size can be matched at the same quick speed. Near-duplicate images can also be detected without much difficulty. We present tests on real images that show the prediction accuracy of the model.

Histogram Matching Extends Acceptable Signal Strength Range on Optical Coherence Tomography Images

PubMed Central

Chen, Chieh-Li; Ishikawa, Hiroshi; Wollstein, Gadi; Bilonick, Richard A.; Sigal, Ian A.; Kagemann, Larry; Schuman, Joel S.

2015-01-01

Purpose. We minimized the influence of image quality variability, as measured by signal strength (SS), on optical coherence tomography (OCT) thickness measurements using the histogram matching (HM) method. Methods. We scanned 12 eyes from 12 healthy subjects with the Cirrus HD-OCT device to obtain a series of OCT images with a wide range of SS (maximal range, 1–10) at the same visit. For each eye, the histogram of an image with the highest SS (best image quality) was set as the reference. We applied HM to the images with lower SS by shaping the input histogram into the reference histogram. Retinal nerve fiber layer (RNFL) thickness was automatically measured before and after HM processing (defined as original and HM measurements), and compared to the device output (device measurements). Nonlinear mixed effects models were used to analyze the relationship between RNFL thickness and SS. In addition, the lowest tolerable SSs, which gave the RNFL thickness within the variability margin of manufacturer recommended SS range (6–10), were determined for device, original, and HM measurements. Results. The HM measurements showed less variability across a wide range of image quality than the original and device measurements (slope = 1.17 vs. 4.89 and 1.72 μm/SS, respectively). The lowest tolerable SS was successfully reduced to 4.5 after HM processing. Conclusions. The HM method successfully extended the acceptable SS range on OCT images. This would qualify more OCT images with low SS for clinical assessment, broadening the OCT application to a wider range of subjects. PMID:26066749

A layered modulation method for pixel matching in online phase measuring profilometry

NASA Astrophysics Data System (ADS)

Li, Hongru; Feng, Guoying; Bourgade, Thomas; Yang, Peng; Zhou, Shouhuan; Asundi, Anand

2016-10-01

An online phase measuring profilometry with new layered modulation method for pixel matching is presented. In this method and in contrast with previous modulation matching methods, the captured images are enhanced by Retinex theory for better modulation distribution, and all different layer modulation masks are fully used to determine the displacement of a rectilinear moving object. High, medium and low modulation masks are obtained by performing binary segmentation with iterative Otsu method. The final shifting pixels are calculated based on centroid concept, and after that the aligned fringe patterns can be extracted from each frame. After performing Stoilov algorithm and a series of subsequent operations, the object profile on a translation stage is reconstructed. All procedures are carried out automatically, without setting specific parameters in advance. Numerical simulations are detailed and experimental results verify the validity and feasibility of the proposed approach.

Application of furniture images selection based on neural network

NASA Astrophysics Data System (ADS)

Wang, Yong; Gao, Wenwen; Wang, Ying

2018-05-01

In the construction of 2 million furniture image databases, aiming at the problem of low quality of database, a combination of CNN and Metric learning algorithm is proposed, which makes it possible to quickly and accurately remove duplicate and irrelevant samples in the furniture image database. Solve problems that images screening method is complex, the accuracy is not high, time-consuming is long. Deep learning algorithm achieve excellent image matching ability in actual furniture retrieval applications after improving data quality.

Architectures and algorithms for digital image processing; Proceedings of the Meeting, Cannes, France, December 5, 6, 1985

NASA Technical Reports Server (NTRS)

Duff, Michael J. B. (Editor); Siegel, Howard J. (Editor); Corbett, Francis J. (Editor)

1986-01-01

The conference presents papers on the architectures, algorithms, and applications of image processing. Particular attention is given to a very large scale integration system for image reconstruction from projections, a prebuffer algorithm for instant display of volume data, and an adaptive image sequence filtering scheme based on motion detection. Papers are also presented on a simple, direct practical method of sensing local motion and analyzing local optical flow, image matching techniques, and an automated biological dosimetry system.

Coherent imaging at the diffraction limit

PubMed Central

Thibault, Pierre; Guizar-Sicairos, Manuel; Menzel, Andreas

2014-01-01

X-ray ptychography, a scanning coherent diffractive imaging technique, holds promise for imaging with dose-limited resolution and sensitivity. If the foreseen increase of coherent flux by orders of magnitude can be matched by additional technological and analytical advances, ptychography may approach imaging speeds familiar from full-field methods while retaining its inherently quantitative nature and metrological versatility. Beyond promises of high throughput, spectroscopic applications in three dimensions become feasible, as do measurements of sample dynamics through time-resolved imaging or careful characterization of decoherence effects. PMID:25177990

Coherent imaging at the diffraction limit.

PubMed

Thibault, Pierre; Guizar-Sicairos, Manuel; Menzel, Andreas

2014-09-01

X-ray ptychography, a scanning coherent diffractive imaging technique, holds promise for imaging with dose-limited resolution and sensitivity. If the foreseen increase of coherent flux by orders of magnitude can be matched by additional technological and analytical advances, ptychography may approach imaging speeds familiar from full-field methods while retaining its inherently quantitative nature and metrological versatility. Beyond promises of high throughput, spectroscopic applications in three dimensions become feasible, as do measurements of sample dynamics through time-resolved imaging or careful characterization of decoherence effects.

Gaussian mixed model in support of semiglobal matching leveraged by ground control points

NASA Astrophysics Data System (ADS)

Ma, Hao; Zheng, Shunyi; Li, Chang; Li, Yingsong; Gui, Li

2017-04-01

Semiglobal matching (SGM) has been widely applied in large aerial images because of its good tradeoff between complexity and robustness. The concept of ground control points (GCPs) is adopted to make SGM more robust. We model the effect of GCPs as two data terms for stereo matching between high-resolution aerial epipolar images in an iterative scheme. One term based on GCPs is formulated by Gaussian mixture model, which strengths the relation between GCPs and the pixels to be estimated and encodes some degree of consistency between them with respect to disparity values. Another term depends on pixel-wise confidence, and we further design a confidence updating equation based on three rules. With this confidence-based term, the assignment of disparity can be heuristically selected among disparity search ranges during the iteration process. Several iterations are sufficient to bring out satisfactory results according to our experiments. Experimental results validate that the proposed method outperforms surface reconstruction, which is a representative variant of SGM and behaves excellently on aerial images.

The effect of image alterations on identification using palmar flexion creases.

PubMed

Cook, Tom; Sutton, Raul; Buckley, Kevan

2013-11-01

Palmprints are identified using matching of minutia points, which can be time consuming for fingerprint experts and in database searches. This article analyzes the operational characteristics of a palmar flexion crease (PFC) identification software tool, using a dataset of 10 replicates of 100 palms, where the user can label and match palmar line features. Results show that 100 palmprint images modified 10 times each using rotation, translation, and additive noise, mimicking some of the characteristics found in crime scene palmar marks, can be identified with a 99.2% genuine acceptance rate and 0% false acceptance rate when labeled within 3.5 mm of the PFC. Partial palmprint images can also be identified using the same method to filter the dataset prior to traditional matching, while maintaining an effective genuine acceptance rate. The work shows that identification using PFCs can improve palmprint identification through integration with existing systems, and through dedicated palmprint identification applications. © 2013 American Academy of Forensic Sciences.

Real-time vehicle matching for multi-camera tunnel surveillance

NASA Astrophysics Data System (ADS)

Jelača, Vedran; Niño Castañeda, Jorge Oswaldo; Frías-Velázquez, Andrés; Pižurica, Aleksandra; Philips, Wilfried

2011-03-01

Tracking multiple vehicles with multiple cameras is a challenging problem of great importance in tunnel surveillance. One of the main challenges is accurate vehicle matching across the cameras with non-overlapping fields of view. Since systems dedicated to this task can contain hundreds of cameras which observe dozens of vehicles each, for a real-time performance computational efficiency is essential. In this paper, we propose a low complexity, yet highly accurate method for vehicle matching using vehicle signatures composed of Radon transform like projection profiles of the vehicle image. The proposed signatures can be calculated by a simple scan-line algorithm, by the camera software itself and transmitted to the central server or to the other cameras in a smart camera environment. The amount of data is drastically reduced compared to the whole image, which relaxes the data link capacity requirements. Experiments on real vehicle images, extracted from video sequences recorded in a tunnel by two distant security cameras, validate our approach.

Accuracy analysis for DSM and orthoimages derived from SPOT HRS stereo data using direct georeferencing

NASA Astrophysics Data System (ADS)

Reinartz, Peter; Müller, Rupert; Lehner, Manfred; Schroeder, Manfred

During the HRS (High Resolution Stereo) Scientific Assessment Program the French space agency CNES delivered data sets from the HRS camera system with high precision ancillary data. Two test data sets from this program were evaluated: one is located in Germany, the other in Spain. The first goal was to derive orthoimages and digital surface models (DSM) from the along track stereo data by applying the rigorous model with direct georeferencing and without ground control points (GCPs). For the derivation of DSM, the stereo processing software, developed at DLR for the MOMS-2P three line stereo camera was used. As a first step, the interior and exterior orientation of the camera, delivered as ancillary data from positioning and attitude systems were extracted. A dense image matching, using nearly all pixels as kernel centers provided the parallaxes. The quality of the stereo tie points was controlled by forward and backward matching of the two stereo partners using the local least squares matching method. Forward intersection lead to points in object space which are subsequently interpolated to a DSM in a regular grid. DEM filtering methods were also applied and evaluations carried out differentiating between accuracies in forest and other areas. Additionally, orthoimages were generated from the images of the two stereo looking directions. The orthoimage and DSM accuracy was determined by using GCPs and available reference DEMs of superior accuracy (DEM derived from laser data and/or classical airborne photogrammetry). As expected the results obtained without using GCPs showed a bias in the order of 5-20 m to the reference data for all three coordinates. By image matching it could be shown that the two independently derived orthoimages exhibit a very constant shift behavior. In a second step few GCPs (3-4) were used to calculate boresight alignment angles, introduced into the direct georeferencing process of each image independently. This method improved the absolute accuracy of the resulting orthoimages and DSM significantly.

Implementation of sobel method to detect the seed rubber plant leaves

NASA Astrophysics Data System (ADS)

Suyanto; Munte, J.

2018-03-01

This research was conducted to develop a system that can identify and recognize the type of rubber tree based on the pattern of leaves of the plant. The steps research are started with the identification of the image data acquisition, image processing, image edge detection and identification method template matching. Edge detection is using Sobel edge detection. Pattern recognition would detect image as input and compared with other images in a database called templates. Experiments carried out in one phase, identification of the leaf edge, using a rubber plant leaf image 14 are superior and 5 for each type of test images (clones) of the plant. From the experimental results obtained by the recognition rate of 91.79%.

Constraint-based stereo matching

NASA Technical Reports Server (NTRS)

Kuan, D. T.

1987-01-01

The major difficulty in stereo vision is the correspondence problem that requires matching features in two stereo images. Researchers describe a constraint-based stereo matching technique using local geometric constraints among edge segments to limit the search space and to resolve matching ambiguity. Edge segments are used as image features for stereo matching. Epipolar constraint and individual edge properties are used to determine possible initial matches between edge segments in a stereo image pair. Local edge geometric attributes such as continuity, junction structure, and edge neighborhood relations are used as constraints to guide the stereo matching process. The result is a locally consistent set of edge segment correspondences between stereo images. These locally consistent matches are used to generate higher-level hypotheses on extended edge segments and junctions to form more global contexts to achieve global consistency.

Approximate Subgraph Isomorphism for Image Localization (Author’s Manuscript)

DTIC Science & Technology

2016-02-18

a working database for feature matching methods is nearly impossible to generate. In a proof of feasibility, Bansal et. al. [2] claim that overhead...of images in mountainous terrain. In Computer Vision–ECCV 2012, pages 517–530. Springer, 2012. 1 [2] M. Bansal , H. S. Sawhney, H. Cheng, and K

A comparative study between xerographic, computer-assisted overlay generation and animated-superimposition methods in bite mark analyses.

PubMed

Tai, Meng Wei; Chong, Zhen Feng; Asif, Muhammad Khan; Rahmat, Rabiah A; Nambiar, Phrabhakaran

2016-09-01

This study was to compare the suitability and precision of xerographic and computer-assisted methods for bite mark investigations. Eleven subjects were asked to bite on their forearm and the bite marks were photographically recorded. Alginate impressions of the subjects' dentition were taken and their casts were made using dental stone. The overlays generated by xerographic method were obtained by photocopying the subjects' casts and the incisal edge outlines were then transferred on a transparent sheet. The bite mark images were imported into Adobe Photoshop® software and printed to life-size. The bite mark analyses using xerographically generated overlays were done by comparing an overlay to the corresponding printed bite mark images manually. In computer-assisted method, the subjects' casts were scanned into Adobe Photoshop®. The bite mark analyses using computer-assisted overlay generation were done by matching an overlay and the corresponding bite mark images digitally using Adobe Photoshop®. Another comparison method was superimposing the cast images with corresponding bite mark images employing the Adobe Photoshop® CS6 and GIF-Animator©. A score with a range of 0-3 was given during analysis to each precision-determining criterion and the score was increased with better matching. The Kruskal Wallis H test showed significant difference between the three sets of data (H=18.761, p<0.05). In conclusion, bite mark analysis using the computer-assisted animated-superimposition method was the most accurate, followed by the computer-assisted overlay generation and lastly the xerographic method. The superior precision contributed by digital method is discernible despite the human skin being a poor recording medium of bite marks. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Earth elevation map production and high resolution sensing camera imaging analysis

NASA Astrophysics Data System (ADS)

Yang, Xiubin; Jin, Guang; Jiang, Li; Dai, Lu; Xu, Kai

2010-11-01

The Earth's digital elevation which impacts space camera imaging has prepared and imaging has analysed. Based on matching error that TDI CCD integral series request of the speed of image motion, statistical experimental methods-Monte Carlo method is used to calculate the distribution histogram of Earth's elevation in image motion compensated model which includes satellite attitude changes, orbital angular rate changes, latitude, longitude and the orbital inclination changes. And then, elevation information of the earth's surface from SRTM is read. Earth elevation map which produced for aerospace electronic cameras is compressed and spliced. It can get elevation data from flash according to the shooting point of latitude and longitude. If elevation data between two data, the ways of searching data uses linear interpolation. Linear interpolation can better meet the rugged mountains and hills changing requests. At last, the deviant framework and camera controller are used to test the character of deviant angle errors, TDI CCD camera simulation system with the material point corresponding to imaging point model is used to analyze the imaging's MTF and mutual correlation similarity measure, simulation system use adding cumulation which TDI CCD imaging exceeded the corresponding pixel horizontal and vertical offset to simulate camera imaging when stability of satellite attitude changes. This process is practicality. It can effectively control the camera memory space, and meet a very good precision TDI CCD camera in the request matches the speed of image motion and imaging.

Removal of bone in CT angiography by multiscale matched mask bone elimination.

PubMed

Gratama van Andel, H A F; Venema, H W; Streekstra, G J; van Straten, M; Majoie, C B L M; den Heeten, G J; Grimbergen, C A

2007-10-01

For clear visualization of vessels in CT angiography (CTA) images of the head and neck using maximum intensity projection (MIP) or volume rendering (VR) bone has to be removed. In the past we presented a fully automatic method to mask the bone [matched mask bone elimination (MMBE)] for this purpose. A drawback is that vessels adjacent to bone may be partly masked as well. We propose a modification, multiscale MMBE, which reduces this problem by using images at two scales: a higher resolution than usual for image processing and a lower resolution to which the processed images are transformed for use in the diagnostic process. A higher in-plane resolution is obtained by the use of a sharper reconstruction kernel. The out-of-plane resolution is improved by deconvolution or by scanning with narrower collimation. The quality of the mask that is used to remove bone is improved by using images at both scales. After masking, the desired resolution for the normal clinical use of the images is obtained by blurring with Gaussian kernels of appropriate widths. Both methods (multiscale and original) were compared in a phantom study and with clinical CTA data sets. With the multiscale approach the width of the strip of soft tissue adjacent to the bone that is masked can be reduced from 1.0 to 0.2 mm without reducing the quality of the bone removal. The clinical examples show that vessels adjacent to bone are less affected and therefore better visible. Images processed with multiscale MMBE have a slightly higher noise level or slightly reduced resolution compared with images processed by the original method and the reconstruction and processing time is also somewhat increased. Nevertheless, multiscale MMBE offers a way to remove bone automatically from CT angiography images without affecting the integrity of the blood vessels. The overall image quality of MIP or VR images is substantially improved relative to images processed with the original MMBE method.

Contactless and pose invariant biometric identification using hand surface.

PubMed

Kanhangad, Vivek; Kumar, Ajay; Zhang, David

2011-05-01

This paper presents a novel approach for hand matching that achieves significantly improved performance even in the presence of large hand pose variations. The proposed method utilizes a 3-D digitizer to simultaneously acquire intensity and range images of the user's hand presented to the system in an arbitrary pose. The approach involves determination of the orientation of the hand in 3-D space followed by pose normalization of the acquired 3-D and 2-D hand images. Multimodal (2-D as well as 3-D) palmprint and hand geometry features, which are simultaneously extracted from the user's pose normalized textured 3-D hand, are used for matching. Individual matching scores are then combined using a new dynamic fusion strategy. Our experimental results on the database of 114 subjects with significant pose variations yielded encouraging results. Consistent (across various hand features considered) performance improvement achieved with the pose correction demonstrates the usefulness of the proposed approach for hand based biometric systems with unconstrained and contact-free imaging. The experimental results also suggest that the dynamic fusion approach employed in this work helps to achieve performance improvement of 60% (in terms of EER) over the case when matching scores are combined using the weighted sum rule.

Effective image differencing with convolutional neural networks for real-time transient hunting

NASA Astrophysics Data System (ADS)

Sedaghat, Nima; Mahabal, Ashish

2018-06-01

Large sky surveys are increasingly relying on image subtraction pipelines for real-time (and archival) transient detection. In this process one has to contend with varying point-spread function (PSF) and small brightness variations in many sources, as well as artefacts resulting from saturated stars and, in general, matching errors. Very often the differencing is done with a reference image that is deeper than individual images and the attendant difference in noise characteristics can also lead to artefacts. We present here a deep-learning approach to transient detection that encapsulates all the steps of a traditional image-subtraction pipeline - image registration, background subtraction, noise removal, PSF matching and subtraction - in a single real-time convolutional network. Once trained, the method works lightening-fast and, given that it performs multiple steps in one go, the time saved and false positives eliminated for multi-CCD surveys like Zwicky Transient Facility and Large Synoptic Survey Telescope will be immense, as millions of subtractions will be needed per night.

Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

PubMed Central

Attique, Muhammad; Gilanie, Ghulam; Hafeez-Ullah; Mehmood, Malik S.; Naweed, Muhammad S.; Ikram, Masroor; Kamran, Javed A.; Vitkin, Alex

2012-01-01

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described. PMID:22479421

Smiles in face matching: Idiosyncratic information revealed through a smile improves unfamiliar face matching performance.

PubMed

Mileva, Mila; Burton, A Mike

2018-06-19

Unfamiliar face matching is a surprisingly difficult task, yet we often rely on people's matching decisions in applied settings (e.g., border control). Most attempts to improve accuracy (including training and image manipulation) have had very limited success. In a series of studies, we demonstrate that using smiling rather than neutral pairs of images brings about significant improvements in face matching accuracy. This is true for both match and mismatch trials, implying that the information provided through a smile helps us detect images of the same identity as well as distinguishing between images of different identities. Study 1 compares matching performance when images in the face pair display either an open-mouth smile or a neutral expression. In Study 2, we add an intermediate level, closed-mouth smile, to identify the effect of teeth being exposed, and Study 3 explores face matching accuracy when only information about the lower part of the face is available. Results demonstrate that an open-mouth smile changes the face in an idiosyncratic way which aids face matching decisions. Such findings have practical implications for matching in the applied context where we typically use neutral images to represent ourselves in official documents. © 2018 The British Psychological Society.

Hypothesis Support Mechanism for Mid-Level Visual Pattern Recognition

NASA Technical Reports Server (NTRS)

Amador, Jose J (Inventor)

2007-01-01

A method of mid-level pattern recognition provides for a pose invariant Hough Transform by parametrizing pairs of points in a pattern with respect to at least two reference points, thereby providing a parameter table that is scale- or rotation-invariant. A corresponding inverse transform may be applied to test hypothesized matches in an image and a distance transform utilized to quantify the level of match.

Automated detection and labeling of high-density EEG electrodes from structural MR images.

PubMed

Marino, Marco; Liu, Quanying; Brem, Silvia; Wenderoth, Nicole; Mantini, Dante

2016-10-01

Accurate knowledge about the positions of electrodes in electroencephalography (EEG) is very important for precise source localizations. Direct detection of electrodes from magnetic resonance (MR) images is particularly interesting, as it is possible to avoid errors of co-registration between electrode and head coordinate systems. In this study, we propose an automated MR-based method for electrode detection and labeling, particularly tailored to high-density montages. Anatomical MR images were processed to create an electrode-enhanced image in individual space. Image processing included intensity non-uniformity correction, background noise and goggles artifact removal. Next, we defined a search volume around the head where electrode positions were detected. Electrodes were identified as local maxima in the search volume and registered to the Montreal Neurological Institute standard space using an affine transformation. This allowed the matching of the detected points with the specific EEG montage template, as well as their labeling. Matching and labeling were performed by the coherent point drift method. Our method was assessed on 8 MR images collected in subjects wearing a 256-channel EEG net, using the displacement with respect to manually selected electrodes as performance metric. Average displacement achieved by our method was significantly lower compared to alternative techniques, such as the photogrammetry technique. The maximum displacement was for more than 99% of the electrodes lower than 1 cm, which is typically considered an acceptable upper limit for errors in electrode positioning. Our method showed robustness and reliability, even in suboptimal conditions, such as in the case of net rotation, imprecisely gathered wires, electrode detachment from the head, and MR image ghosting. We showed that our method provides objective, repeatable and precise estimates of EEG electrode coordinates. We hope our work will contribute to a more widespread use of high-density EEG as a brain-imaging tool.

Automated detection and labeling of high-density EEG electrodes from structural MR images

NASA Astrophysics Data System (ADS)

Marino, Marco; Liu, Quanying; Brem, Silvia; Wenderoth, Nicole; Mantini, Dante

2016-10-01

Objective. Accurate knowledge about the positions of electrodes in electroencephalography (EEG) is very important for precise source localizations. Direct detection of electrodes from magnetic resonance (MR) images is particularly interesting, as it is possible to avoid errors of co-registration between electrode and head coordinate systems. In this study, we propose an automated MR-based method for electrode detection and labeling, particularly tailored to high-density montages. Approach. Anatomical MR images were processed to create an electrode-enhanced image in individual space. Image processing included intensity non-uniformity correction, background noise and goggles artifact removal. Next, we defined a search volume around the head where electrode positions were detected. Electrodes were identified as local maxima in the search volume and registered to the Montreal Neurological Institute standard space using an affine transformation. This allowed the matching of the detected points with the specific EEG montage template, as well as their labeling. Matching and labeling were performed by the coherent point drift method. Our method was assessed on 8 MR images collected in subjects wearing a 256-channel EEG net, using the displacement with respect to manually selected electrodes as performance metric. Main results. Average displacement achieved by our method was significantly lower compared to alternative techniques, such as the photogrammetry technique. The maximum displacement was for more than 99% of the electrodes lower than 1 cm, which is typically considered an acceptable upper limit for errors in electrode positioning. Our method showed robustness and reliability, even in suboptimal conditions, such as in the case of net rotation, imprecisely gathered wires, electrode detachment from the head, and MR image ghosting. Significance. We showed that our method provides objective, repeatable and precise estimates of EEG electrode coordinates. We hope our work will contribute to a more widespread use of high-density EEG as a brain-imaging tool.

Signature detection and matching for document image retrieval.

PubMed

Zhu, Guangyu; Zheng, Yefeng; Doermann, David; Jaeger, Stefan

2009-11-01

As one of the most pervasive methods of individual identification and document authentication, signatures present convincing evidence and provide an important form of indexing for effective document image processing and retrieval in a broad range of applications. However, detection and segmentation of free-form objects such as signatures from clustered background is currently an open document analysis problem. In this paper, we focus on two fundamental problems in signature-based document image retrieval. First, we propose a novel multiscale approach to jointly detecting and segmenting signatures from document images. Rather than focusing on local features that typically have large variations, our approach captures the structural saliency using a signature production model and computes the dynamic curvature of 2D contour fragments over multiple scales. This detection framework is general and computationally tractable. Second, we treat the problem of signature retrieval in the unconstrained setting of translation, scale, and rotation invariant nonrigid shape matching. We propose two novel measures of shape dissimilarity based on anisotropic scaling and registration residual error and present a supervised learning framework for combining complementary shape information from different dissimilarity metrics using LDA. We quantitatively study state-of-the-art shape representations, shape matching algorithms, measures of dissimilarity, and the use of multiple instances as query in document image retrieval. We further demonstrate our matching techniques in offline signature verification. Extensive experiments using large real-world collections of English and Arabic machine-printed and handwritten documents demonstrate the excellent performance of our approaches.

A New Method for Computed Tomography Angiography (CTA) Imaging via Wavelet Decomposition-Dependented Edge Matching Interpolation.

PubMed

Li, Zeyu; Chen, Yimin; Zhao, Yan; Zhu, Lifeng; Lv, Shengqing; Lu, Jiahui

2016-08-01

The interpolation technique of computed tomography angiography (CTA) image provides the ability for 3D reconstruction, as well as reduces the detect cost and the amount of radiation. However, most of the image interpolation algorithms cannot take the automation and accuracy into account. This study provides a new edge matching interpolation algorithm based on wavelet decomposition of CTA. It includes mark, scale and calculation (MSC). Combining the real clinical image data, this study mainly introduces how to search for proportional factor and use the root mean square operator to find a mean value. Furthermore, we re- synthesize the high frequency and low frequency parts of the processed image by wavelet inverse operation, and get the final interpolation image. MSC can make up for the shortage of the conventional Computed Tomography (CT) and Magnetic Resonance Imaging(MRI) examination. The radiation absorption and the time to check through the proposed synthesized image were significantly reduced. In clinical application, it can help doctor to find hidden lesions in time. Simultaneously, the patients get less economic burden as well as less radiation exposure absorbed.

Development of 3D Image Measurement System and Stereo-matching Method, and Its Archeological Measurement

NASA Astrophysics Data System (ADS)

Kochi, Nobuo; Ito, Tadayuki; Kitamura, Kazuo; Kaneko, Syun'ichi

The three dimensional measurement & modeling system with digital cameras on PC is now making progress and its need and hope is increasingly felt in terrestrial (close-range) photogrammetry for such sectors as cultural heritage preservation, architecture, civil engineering, manufacturing, measurement etc. Therefore, we have developed a system to improve the accuracy of stereo-matching, which is the very core of 3D measurement. As for stereo-matching method, in order to minimize the mismatching and to be robust in geometric distortions, occlusion, as well as brightness change, we invented Coarse-to-Fine Strategy Method by integrating OCM (Orientation Code Matching) with LSM (Least Squares Matching). Thus this system could attain the accuracy of 0.26mm, when we experimented on a mannequin. And when we actually experimented on the archeological ruins in Greece and Turkey, the accuracy was within the range of 1cm, compared with their blue-print plan. Besides, formally workers used to take at least 1.5 month for this kind of survey operation with the existing method, but now workers need only 3 or 4 days. Thus, its practicality and efficiency was confirmed. This paper demonstrates our new system of 3D measurement and stereo-matching with some concrete examples as its practical application.

SKL algorithm based fabric image matching and retrieval

NASA Astrophysics Data System (ADS)

Cao, Yichen; Zhang, Xueqin; Ma, Guojian; Sun, Rongqing; Dong, Deping

2017-07-01

Intelligent computer image processing technology provides convenience and possibility for designers to carry out designs. Shape analysis can be achieved by extracting SURF feature. However, high dimension of SURF feature causes to lower matching speed. To solve this problem, this paper proposed a fast fabric image matching algorithm based on SURF K-means and LSH algorithm. By constructing the bag of visual words on K-Means algorithm, and forming feature histogram of each image, the dimension of SURF feature is reduced at the first step. Then with the help of LSH algorithm, the features are encoded and the dimension is further reduced. In addition, the indexes of each image and each class of image are created, and the number of matching images is decreased by LSH hash bucket. Experiments on fabric image database show that this algorithm can speed up the matching and retrieval process, the result can satisfy the requirement of dress designers with accuracy and speed.

Comparison of image segmentation of lungs using methods: connected threshold, neighborhood connected, and threshold level set segmentation

NASA Astrophysics Data System (ADS)

Amanda, A. R.; Widita, R.

2016-03-01

The aim of this research is to compare some image segmentation methods for lungs based on performance evaluation parameter (Mean Square Error (MSE) and Peak Signal Noise to Ratio (PSNR)). In this study, the methods compared were connected threshold, neighborhood connected, and the threshold level set segmentation on the image of the lungs. These three methods require one important parameter, i.e the threshold. The threshold interval was obtained from the histogram of the original image. The software used to segment the image here was InsightToolkit-4.7.0 (ITK). This research used 5 lung images to be analyzed. Then, the results were compared using the performance evaluation parameter determined by using MATLAB. The segmentation method is said to have a good quality if it has the smallest MSE value and the highest PSNR. The results show that four sample images match the criteria of connected threshold, while one sample refers to the threshold level set segmentation. Therefore, it can be concluded that connected threshold method is better than the other two methods for these cases.

Comparison of photo-matching algorithms commonly used for photographic capture-recapture studies.

PubMed

Matthé, Maximilian; Sannolo, Marco; Winiarski, Kristopher; Spitzen-van der Sluijs, Annemarieke; Goedbloed, Daniel; Steinfartz, Sebastian; Stachow, Ulrich

2017-08-01

Photographic capture-recapture is a valuable tool for obtaining demographic information on wildlife populations due to its noninvasive nature and cost-effectiveness. Recently, several computer-aided photo-matching algorithms have been developed to more efficiently match images of unique individuals in databases with thousands of images. However, the identification accuracy of these algorithms can severely bias estimates of vital rates and population size. Therefore, it is important to understand the performance and limitations of state-of-the-art photo-matching algorithms prior to implementation in capture-recapture studies involving possibly thousands of images. Here, we compared the performance of four photo-matching algorithms; Wild-ID, I3S Pattern+, APHIS, and AmphIdent using multiple amphibian databases of varying image quality. We measured the performance of each algorithm and evaluated the performance in relation to database size and the number of matching images in the database. We found that algorithm performance differed greatly by algorithm and image database, with recognition rates ranging from 100% to 22.6% when limiting the review to the 10 highest ranking images. We found that recognition rate degraded marginally with increased database size and could be improved considerably with a higher number of matching images in the database. In our study, the pixel-based algorithm of AmphIdent exhibited superior recognition rates compared to the other approaches. We recommend carefully evaluating algorithm performance prior to using it to match a complete database. By choosing a suitable matching algorithm, databases of sizes that are unfeasible to match "by eye" can be easily translated to accurate individual capture histories necessary for robust demographic estimates.

Measurement of the noise components in the medical x-ray intensity pattern due to overlaying nonrecognizable structures

NASA Astrophysics Data System (ADS)

Tischenko, Oleg; Hoeschen, Christoph; Effenberger, Olaf; Reissberg, Steffen; Buhr, Egbert; Doehring, Wilfried

2003-06-01

There are many aspects that influence and deteriorate the detection of pathologies in X-ray images. Some of those are due to effects taking place in the stage of forming the X-ray intensity pattern in front of the x-ray detector. These can be described as motion blurring, depth blurring, anatomical background, scatter noise and structural noise. Structural noise results from an overlapping of fine irrelevant anatomical structures. A method for measuring the combined effect of structural noise and scatter noise was developed and will be presented in this paper. This method is based on the consideration that within a pair of projections created after rotation of the object with a small angle (which is within the typical uncertainty in positioning the patient) both images would show the same relevant structures whereas the projection of the fine overlapping structures will appear quite differently in the two images. To demonstrate the method two X-ray radiographs of a lung phantom were produced. The second radiograph was achieved after rotating the lung by an angle of about 3. Dyadic wavelet representations of both images were regarded. For each value of the wavelet scale parameter the corresponding pair of approximations was matched using the cross correlation matching technique. The homologous regions of approximations were extracted. The image containing only those structures that appear in both images simultaneously was then reconstructed from the wavelet coefficients corresponding to the homologous regions. The difference between one of the original images and the noise-reduced image contains the structural noise and the scatter noise.

A change detection method for remote sensing image based on LBP and SURF feature

NASA Astrophysics Data System (ADS)

Hu, Lei; Yang, Hao; Li, Jin; Zhang, Yun

2018-04-01

Finding the change in multi-temporal remote sensing image is important in many the image application. Because of the infection of climate and illumination, the texture of the ground object is more stable relative to the gray in high-resolution remote sensing image. And the texture features of Local Binary Patterns (LBP) and Speeded Up Robust Features (SURF) are outstanding in extracting speed and illumination invariance. A method of change detection for matched remote sensing image pair is present, which compares the similarity by LBP and SURF to detect the change and unchanged of the block after blocking the image. And region growing is adopted to process the block edge zone. The experiment results show that the method can endure some illumination change and slight texture change of the ground object.

Analysis and improvement of the quantum image matching

NASA Astrophysics Data System (ADS)

Dang, Yijie; Jiang, Nan; Hu, Hao; Zhang, Wenyin

2017-11-01

We investigate the quantum image matching algorithm proposed by Jiang et al. (Quantum Inf Process 15(9):3543-3572, 2016). Although the complexity of this algorithm is much better than the classical exhaustive algorithm, there may be an error in it: After matching the area between two images, only the pixel at the upper left corner of the matched area played part in following steps. That is to say, the paper only matched one pixel, instead of an area. If more than one pixels in the big image are the same as the one at the upper left corner of the small image, the algorithm will randomly measure one of them, which causes the error. In this paper, an improved version is presented which takes full advantage of the whole matched area to locate a small image in a big image. The theoretical analysis indicates that the network complexity is higher than the previous algorithm, but it is still far lower than the classical algorithm. Hence, this algorithm is still efficient.

Multiscale Anomaly Detection and Image Registration Algorithms for Airborne Landmine Detection

DTIC Science & Technology

2008-05-01

with the sensed image. The two- dimensional correlation coefficient r for two matrices A and B both of size M ×N is given by r = ∑ m ∑ n (Amn...correlation based method by matching features in a high- dimensional feature- space . The current implementation of the SIFT algorithm uses a brute-force...by repeatedly convolving the image with a Guassian kernel. Each plane of the scale

Dictionary Approaches to Image Compression and Reconstruction

NASA Technical Reports Server (NTRS)

Ziyad, Nigel A.; Gilmore, Erwin T.; Chouikha, Mohamed F.

1998-01-01

This paper proposes using a collection of parameterized waveforms, known as a dictionary, for the purpose of medical image compression. These waveforms, denoted as phi(sub gamma), are discrete time signals, where gamma represents the dictionary index. A dictionary with a collection of these waveforms is typically complete or overcomplete. Given such a dictionary, the goal is to obtain a representation image based on the dictionary. We examine the effectiveness of applying Basis Pursuit (BP), Best Orthogonal Basis (BOB), Matching Pursuits (MP), and the Method of Frames (MOF) methods for the compression of digitized radiological images with a wavelet-packet dictionary. The performance of these algorithms is studied for medical images with and without additive noise.

Dictionary Approaches to Image Compression and Reconstruction

NASA Technical Reports Server (NTRS)

Ziyad, Nigel A.; Gilmore, Erwin T.; Chouikha, Mohamed F.

1998-01-01

This paper proposes using a collection of parameterized waveforms, known as a dictionary, for the purpose of medical image compression. These waveforms, denoted as lambda, are discrete time signals, where y represents the dictionary index. A dictionary with a collection of these waveforms Is typically complete or over complete. Given such a dictionary, the goal is to obtain a representation Image based on the dictionary. We examine the effectiveness of applying Basis Pursuit (BP), Best Orthogonal Basis (BOB), Matching Pursuits (MP), and the Method of Frames (MOF) methods for the compression of digitized radiological images with a wavelet-packet dictionary. The performance of these algorithms is studied for medical images with and without additive noise.

Poor textural image tie point matching via graph theory

NASA Astrophysics Data System (ADS)

Yuan, Xiuxiao; Chen, Shiyu; Yuan, Wei; Cai, Yang

2017-07-01

Feature matching aims to find corresponding points to serve as tie points between images. Robust matching is still a challenging task when input images are characterized by low contrast or contain repetitive patterns, occlusions, or homogeneous textures. In this paper, a novel feature matching algorithm based on graph theory is proposed. This algorithm integrates both geometric and radiometric constraints into an edge-weighted (EW) affinity tensor. Tie points are then obtained by high-order graph matching. Four pairs of poor textural images covering forests, deserts, bare lands, and urban areas are tested. For comparison, three state-of-the-art matching techniques, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), and features from accelerated segment test (FAST), are also used. The experimental results show that the matching recall obtained by SIFT, SURF, and FAST varies from 0 to 35% in different types of poor textures. However, through the integration of both geometry and radiometry and the EW strategy, the recall obtained by the proposed algorithm is better than 50% in all four image pairs. The better matching recall improves the number of correct matches, dispersion, and positional accuracy.

Joint image restoration and location in visual navigation system

NASA Astrophysics Data System (ADS)

Wu, Yuefeng; Sang, Nong; Lin, Wei; Shao, Yuanjie

2018-02-01

Image location methods are the key technologies of visual navigation, most previous image location methods simply assume the ideal inputs without taking into account the real-world degradations (e.g. low resolution and blur). In view of such degradations, the conventional image location methods first perform image restoration and then match the restored image on the reference image. However, the defective output of the image restoration can affect the result of localization, by dealing with the restoration and location separately. In this paper, we present a joint image restoration and location (JRL) method, which utilizes the sparse representation prior to handle the challenging problem of low-quality image location. The sparse representation prior states that the degraded input image, if correctly restored, will have a good sparse representation in terms of the dictionary constructed from the reference image. By iteratively solving the image restoration in pursuit of the sparest representation, our method can achieve simultaneous restoration and location. Based on such a sparse representation prior, we demonstrate that the image restoration task and the location task can benefit greatly from each other. Extensive experiments on real scene images with Gaussian blur are carried out and our joint model outperforms the conventional methods of treating the two tasks independently.

Artificial intelligence (AI)-based relational matching and multimodal medical image fusion: generalized 3D approaches

NASA Astrophysics Data System (ADS)

Vajdic, Stevan M.; Katz, Henry E.; Downing, Andrew R.; Brooks, Michael J.

1994-09-01

A 3D relational image matching/fusion algorithm is introduced. It is implemented in the domain of medical imaging and is based on Artificial Intelligence paradigms--in particular, knowledge base representation and tree search. The 2D reference and target images are selected from 3D sets and segmented into non-touching and non-overlapping regions, using iterative thresholding and/or knowledge about the anatomical shapes of human organs. Selected image region attributes are calculated. Region matches are obtained using a tree search, and the error is minimized by evaluating a `goodness' of matching function based on similarities of region attributes. Once the matched regions are found and the spline geometric transform is applied to regional centers of gravity, images are ready for fusion and visualization into a single 3D image of higher clarity.

Automatic Calibration of Stereo-Cameras Using Ordinary Chess-Board Patterns

NASA Astrophysics Data System (ADS)

Prokos, A.; Kalisperakis, I.; Petsa, E.; Karras, G.

2012-07-01

Automation of camera calibration is facilitated by recording coded 2D patterns. Our toolbox for automatic camera calibration using images of simple chess-board patterns is freely available on the Internet. But it is unsuitable for stereo-cameras whose calibration implies recovering camera geometry and their true-to-scale relative orientation. In contrast to all reported methods requiring additional specific coding to establish an object space coordinate system, a toolbox for automatic stereo-camera calibration relying on ordinary chess-board patterns is presented here. First, the camera calibration algorithm is applied to all image pairs of the pattern to extract nodes of known spacing, order them in rows and columns, and estimate two independent camera parameter sets. The actual node correspondences on stereo-pairs remain unknown. Image pairs of a textured 3D scene are exploited for finding the fundamental matrix of the stereo-camera by applying RANSAC to point matches established with the SIFT algorithm. A node is then selected near the centre of the left image; its match on the right image is assumed as the node closest to the corresponding epipolar line. This yields matches for all nodes (since these have already been ordered), which should also satisfy the 2D epipolar geometry. Measures for avoiding mismatching are taken. With automatically estimated initial orientation values, a bundle adjustment is performed constraining all pairs on a common (scaled) relative orientation. Ambiguities regarding the actual exterior orientations of the stereo-camera with respect to the pattern are irrelevant. Results from this automatic method show typical precisions not above 1/4 pixels for 640×480 web cameras.

Shadow Areas Robust Matching Among Image Sequence in Planetary Landing

NASA Astrophysics Data System (ADS)

Ruoyan, Wei; Xiaogang, Ruan; Naigong, Yu; Xiaoqing, Zhu; Jia, Lin

2017-01-01

In this paper, an approach for robust matching shadow areas in autonomous visual navigation and planetary landing is proposed. The approach begins with detecting shadow areas, which are extracted by Maximally Stable Extremal Regions (MSER). Then, an affine normalization algorithm is applied to normalize the areas. Thirdly, a descriptor called Multiple Angles-SIFT (MA-SIFT) that coming from SIFT is proposed, the descriptor can extract more features of an area. Finally, for eliminating the influence of outliers, a method of improved RANSAC based on Skinner Operation Condition is proposed to extract inliers. At last, series of experiments are conducted to test the performance of the approach this paper proposed, the results show that the approach can maintain the matching accuracy at a high level even the differences among the images are obvious with no attitude measurements supplied.

Comparison of image enhancement methods for the effective diagnosis in successive whole-body bone scans.

PubMed

Jeong, Chang Bu; Kim, Kwang Gi; Kim, Tae Sung; Kim, Seok Ki

2011-06-01

Whole-body bone scan is one of the most frequent diagnostic procedures in nuclear medicine. Especially, it plays a significant role in important procedures such as the diagnosis of osseous metastasis and evaluation of osseous tumor response to chemotherapy and radiation therapy. It can also be used to monitor the possibility of any recurrence of the tumor. However, it is a very time-consuming effort for radiologists to quantify subtle interval changes between successive whole-body bone scans because of many variations such as intensity, geometry, and morphology. In this paper, we present the most effective method of image enhancement based on histograms, which may assist radiologists in interpreting successive whole-body bone scans effectively. Forty-eight successive whole-body bone scans from 10 patients were obtained and evaluated using six methods of image enhancement based on histograms: histogram equalization, brightness-preserving bi-histogram equalization, contrast-limited adaptive histogram equalization, end-in search, histogram matching, and exact histogram matching (EHM). Comparison of the results of the different methods was made using three similarity measures peak signal-to-noise ratio, histogram intersection, and structural similarity. Image enhancement of successive bone scans using EHM showed the best results out of the six methods measured for all similarity measures. EHM is the best method of image enhancement based on histograms for diagnosing successive whole-body bone scans. The method for successive whole-body bone scans has the potential to greatly assist radiologists quantify interval changes more accurately and quickly by compensating for the variable nature of intensity information. Consequently, it can improve radiologists' diagnostic accuracy as well as reduce reading time for detecting interval changes.

Denoising imaging polarimetry by adapted BM3D method.

PubMed

Tibbs, Alexander B; Daly, Ilse M; Roberts, Nicholas W; Bull, David R

2018-04-01

In addition to the visual information contained in intensity and color, imaging polarimetry allows visual information to be extracted from the polarization of light. However, a major challenge of imaging polarimetry is image degradation due to noise. This paper investigates the mitigation of noise through denoising algorithms and compares existing denoising algorithms with a new method, based on BM3D (Block Matching 3D). This algorithm, Polarization-BM3D (PBM3D), gives visual quality superior to the state of the art across all images and noise standard deviations tested. We show that denoising polarization images using PBM3D allows the degree of polarization to be more accurately calculated by comparing it with spectral polarimetry measurements.

Atlas-based automatic measurements of the morphology of the tibiofemoral joint

NASA Astrophysics Data System (ADS)

Brehler, M.; Thawait, G.; Shyr, W.; Ramsay, J.; Siewerdsen, J. H.; Zbijewski, W.

2017-03-01

Purpose: Anatomical metrics of the tibiofemoral joint support assessment of joint stability and surgical planning. We propose an automated, atlas-based algorithm to streamline the measurements in 3D images of the joint and reduce userdependence of the metrics arising from manual identification of the anatomical landmarks. Methods: The method is initialized with coarse registrations of a set of atlas images to the fixed input image. The initial registrations are then refined separately for the tibia and femur and the best matching atlas is selected. Finally, the anatomical landmarks of the best matching atlas are transformed onto the input image by deforming a surface model of the atlas to fit the shape of the tibial plateau in the input image (a mesh-to-volume registration). We apply the method to weight-bearing volumetric images of the knee obtained from 23 subjects using an extremity cone-beam CT system. Results of the automated algorithm were compared to an expert radiologist for measurements of Static Alignment (SA), Medial Tibial Slope (MTS) and Lateral Tibial Slope (LTS). Results: Intra-reader variability as high as 10% for LTS and 7% for MTS (ratio of standard deviation to the mean in repeated measurements) was found for expert radiologist, illustrating the potential benefits of an automated approach in improving the precision of the metrics. The proposed method achieved excellent registration of the atlas mesh to the input volumes. The resulting automated measurements yielded high correlations with expert radiologist, as indicated by correlation coefficients of 0.72 for MTS, 0.8 for LTS, and 0.89 for SA. Conclusions: The automated method for measurement of anatomical metrics of the tibiofemoral joint achieves high correlation with expert radiologist without the need for time consuming and error prone manual selection of landmarks.

Spatial resolution properties of motion-compensated tomographic image reconstruction methods.

PubMed

Chun, Se Young; Fessler, Jeffrey A

2012-07-01

Many motion-compensated image reconstruction (MCIR) methods have been proposed to correct for subject motion in medical imaging. MCIR methods incorporate motion models to improve image quality by reducing motion artifacts and noise. This paper analyzes the spatial resolution properties of MCIR methods and shows that nonrigid local motion can lead to nonuniform and anisotropic spatial resolution for conventional quadratic regularizers. This undesirable property is akin to the known effects of interactions between heteroscedastic log-likelihoods (e.g., Poisson likelihood) and quadratic regularizers. This effect may lead to quantification errors in small or narrow structures (such as small lesions or rings) of reconstructed images. This paper proposes novel spatial regularization design methods for three different MCIR methods that account for known nonrigid motion. We develop MCIR regularization designs that provide approximately uniform and isotropic spatial resolution and that match a user-specified target spatial resolution. Two-dimensional PET simulations demonstrate the performance and benefits of the proposed spatial regularization design methods.

Multiscale Reconstruction for Magnetic Resonance Fingerprinting

PubMed Central

Pierre, Eric Y.; Ma, Dan; Chen, Yong; Badve, Chaitra; Griswold, Mark A.

2015-01-01

Purpose To reduce acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in-vivo data using the highly-undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. Results The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD) and B0 field variations in the brain was achieved in vivo for a 256×256 matrix for a total acquisition time of 10.2s, representing a 3-fold reduction in acquisition time. Conclusions The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. PMID:26132462

Aorta modeling with the element-based zero-stress state and isogeometric discretization

NASA Astrophysics Data System (ADS)

Takizawa, Kenji; Tezduyar, Tayfun E.; Sasaki, Takafumi

2017-02-01

Patient-specific arterial fluid-structure interaction computations, including aorta computations, require an estimation of the zero-stress state (ZSS), because the image-based arterial geometries do not come from a ZSS. We have earlier introduced a method for estimation of the element-based ZSS (EBZSS) in the context of finite element discretization of the arterial wall. The method has three main components. 1. An iterative method, which starts with a calculated initial guess, is used for computing the EBZSS such that when a given pressure load is applied, the image-based target shape is matched. 2. A method for straight-tube segments is used for computing the EBZSS so that we match the given diameter and longitudinal stretch in the target configuration and the "opening angle." 3. An element-based mapping between the artery and straight-tube is extracted from the mapping between the artery and straight-tube segments. This provides the mapping from the arterial configuration to the straight-tube configuration, and from the estimated EBZSS of the straight-tube configuration back to the arterial configuration, to be used as the initial guess for the iterative method that matches the image-based target shape. Here we present the version of the EBZSS estimation method with isogeometric wall discretization. With isogeometric discretization, we can obtain the element-based mapping directly, instead of extracting it from the mapping between the artery and straight-tube segments. That is because all we need for the element-based mapping, including the curvatures, can be obtained within an element. With NURBS basis functions, we may be able to achieve a similar level of accuracy as with the linear basis functions, but using larger-size and much fewer elements. Higher-order NURBS basis functions allow representation of more complex shapes within an element. To show how the new EBZSS estimation method performs, we first present 2D test computations with straight-tube configurations. Then we show how the method can be used in a 3D computation where the target geometry is coming from medical image of a human aorta.

High Density Aerial Image Matching: State-Of and Future Prospects

NASA Astrophysics Data System (ADS)

Haala, N.; Cavegn, S.

2016-06-01

Ongoing innovations in matching algorithms are continuously improving the quality of geometric surface representations generated automatically from aerial images. This development motivated the launch of the joint ISPRS/EuroSDR project "Benchmark on High Density Aerial Image Matching", which aims on the evaluation of photogrammetric 3D data capture in view of the current developments in dense multi-view stereo-image matching. Originally, the test aimed on image based DSM computation from conventional aerial image flights for different landuse and image block configurations. The second phase then put an additional focus on high quality, high resolution 3D geometric data capture in complex urban areas. This includes both the extension of the test scenario to oblique aerial image flights as well as the generation of filtered point clouds as additional output of the respective multi-view reconstruction. The paper uses the preliminary outcomes of the benchmark to demonstrate the state-of-the-art in airborne image matching with a special focus of high quality geometric data capture in urban scenarios.

An efficient mosaic algorithm considering seasonal variation: application to KOMPSAT-2 satellite images.

PubMed

Choi, Jaewon; Jung, Hyung-Sup; Yun, Sang-Ho

2015-03-09

As the aerospace industry grows, images obtained from Earth observation satellites have been successfully used in various fields. Specifically, the demand for a high-resolution (HR) optical images is gradually increasing, and hence the generation of a high-quality mosaic image is being magnified as an interesting issue. In this paper, we have proposed an efficient mosaic algorithm for HR optical images that are significantly different due to seasonal change. The algorithm includes main steps such as: (1) seamline extraction from gradient magnitude and seam images; (2) histogram matching; and (3) image feathering. Eleven Kompsat-2 images characterized by seasonal variations are used for the performance validation of the proposed method. The results of the performance test show that the proposed method effectively mosaics Kompsat-2 adjacent images including severe seasonal changes. Moreover, the results reveal that the proposed method is applicable to HR optic images such as GeoEye, IKONOS, QuickBird, RapidEye, SPOT, WorldView, etc.

Volumetric Two-photon Imaging of Neurons Using Stereoscopy (vTwINS)

PubMed Central

Song, Alexander; Charles, Adam S.; Koay, Sue Ann; Gauthier, Jeff L.; Thiberge, Stephan Y.; Pillow, Jonathan W.; Tank, David W.

2017-01-01

Two-photon laser scanning microscopy of calcium dynamics using fluorescent indicators is a widely used imaging method for large scale recording of neural activity in vivo. Here we introduce volumetric Two-photon Imaging of Neurons using Stereoscopy (vTwINS), a volumetric calcium imaging method that employs an elongated, V-shaped point spread function to image a 3D brain volume. Single neurons project to spatially displaced “image pairs” in the resulting 2D image, and the separation distance between images is proportional to depth in the volume. To demix the fluorescence time series of individual neurons, we introduce a novel orthogonal matching pursuit algorithm that also infers source locations within the 3D volume. We illustrate vTwINS by imaging neural population activity in mouse primary visual cortex and hippocampus. Our results demonstrate that vTwINS provides an effective method for volumetric two-photon calcium imaging that increases the number of neurons recorded while maintaining a high frame-rate. PMID:28319111

Mobile robots traversability awareness based on terrain visual sensory data fusion

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir

2007-04-01

In this paper, we have presented methods that significantly improve the robot awareness of its terrain traversability conditions. The terrain traversability awareness is achieved by association of terrain image appearances from different poses and fusion of extracted information from multimodality imaging and range sensor data for localization and clustering environment landmarks. Initially, we describe methods for extraction of salient features of the terrain for the purpose of landmarks registration from two or more images taken from different via points along the trajectory path of the robot. The method of image registration is applied as a means of overlaying (two or more) of the same terrain scene at different viewpoints. The registration geometrically aligns salient landmarks of two images (the reference and sensed images). A Similarity matching techniques is proposed for matching the terrain salient landmarks. Secondly, we present three terrain classifier models based on rule-based, supervised neural network, and fuzzy logic for classification of terrain condition under uncertainty and mapping the robot's terrain perception to apt traversability measures. This paper addresses the technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain spatial and textural cues.

Nonlocal Means Denoising of Self-Gated and k-Space Sorted 4-Dimensional Magnetic Resonance Imaging Using Block-Matching and 3-Dimensional Filtering: Implications for Pancreatic Tumor Registration and Segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Jun; McKenzie, Elizabeth; Fan, Zhaoyang

Purpose: To denoise self-gated k-space sorted 4-dimensional magnetic resonance imaging (SG-KS-4D-MRI) by applying a nonlocal means denoising filter, block-matching and 3-dimensional filtering (BM3D), to test its impact on the accuracy of 4D image deformable registration and automated tumor segmentation for pancreatic cancer patients. Methods and Materials: Nine patients with pancreatic cancer and abdominal SG-KS-4D-MRI were included in the study. Block-matching and 3D filtering was adapted to search in the axial slices/frames adjacent to the reference image patch in the spatial and temporal domains. The patches with high similarity to the reference patch were used to collectively denoise the 4D-MRI image. Themore » pancreas tumor was manually contoured on the first end-of-exhalation phase for both the raw and the denoised 4D-MRI. B-spline deformable registration was applied to the subsequent phases for contour propagation. The consistency of tumor volume defined by the standard deviation of gross tumor volumes from 10 breathing phases (σ-GTV), tumor motion trajectories in 3 cardinal motion planes, 4D-MRI imaging noise, and image contrast-to-noise ratio were compared between the raw and denoised groups. Results: Block-matching and 3D filtering visually and quantitatively reduced image noise by 52% and improved image contrast-to-noise ratio by 56%, without compromising soft tissue edge definitions. Automatic tumor segmentation is statistically more consistent on the denoised 4D-MRI (σ-GTV = 0.6 cm{sup 3}) than on the raw 4D-MRI (σ-GTV = 0.8 cm{sup 3}). Tumor end-of-exhalation location is also more reproducible on the denoised 4D-MRI than on the raw 4D-MRI in all 3 cardinal motion planes. Conclusions: Block-matching and 3D filtering can significantly reduce random image noise while maintaining structural features in the SG-KS-4D-MRI datasets. In this study of pancreatic tumor segmentation, automatic segmentation of GTV in the registered image sets is shown to be more consistent on the denoised 4D-MRI than on the raw 4D-MRI.« less

Background suppression of infrared small target image based on inter-frame registration

NASA Astrophysics Data System (ADS)

Ye, Xiubo; Xue, Bindang

2018-04-01

We propose a multi-frame background suppression method for remote infrared small target detection. Inter-frame information is necessary when the heavy background clutters make it difficult to distinguish real targets and false alarms. A registration procedure based on points matching in image patches is used to compensate the local deformation of background. Then the target can be separated by background subtraction. Experiments show our method serves as an effective preliminary of target detection.

Score-Level Fusion of Phase-Based and Feature-Based Fingerprint Matching Algorithms

NASA Astrophysics Data System (ADS)

Ito, Koichi; Morita, Ayumi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. In our previous work, we have already proposed an efficient fingerprint recognition algorithm using Phase-Only Correlation (POC), and developed commercial fingerprint verification units for access control applications. The use of Fourier phase information of fingerprint images makes it possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. This paper presents an idea of improving the performance of POC-based fingerprint matching by combining it with feature-based matching, where feature-based matching is introduced in order to improve recognition efficiency for images with nonlinear distortion. Experimental evaluation using two different types of fingerprint image databases demonstrates efficient recognition performance of the combination of the POC-based algorithm and the feature-based algorithm.

Visual Object Recognition and Tracking of Tools

NASA Technical Reports Server (NTRS)

English, James; Chang, Chu-Yin; Tardella, Neil

2011-01-01

A method has been created to automatically build an algorithm off-line, using computer-aided design (CAD) models, and to apply this at runtime. The object type is discriminated, and the position and orientation are identified. This system can work with a single image and can provide improved performance using multiple images provided from videos. The spatial processing unit uses three stages: (1) segmentation; (2) initial type, pose, and geometry (ITPG) estimation; and (3) refined type, pose, and geometry (RTPG) calculation. The image segmentation module files all the tools in an image and isolates them from the background. For this, the system uses edge-detection and thresholding to find the pixels that are part of a tool. After the pixels are identified, nearby pixels are grouped into blobs. These blobs represent the potential tools in the image and are the product of the segmentation algorithm. The second module uses matched filtering (or template matching). This approach is used for condensing synthetic images using an image subspace that captures key information. Three degrees of orientation, three degrees of position, and any number of degrees of freedom in geometry change are included. To do this, a template-matching framework is applied. This framework uses an off-line system for calculating template images, measurement images, and the measurements of the template images. These results are used online to match segmented tools against the templates. The final module is the RTPG processor. Its role is to find the exact states of the tools given initial conditions provided by the ITPG module. The requirement that the initial conditions exist allows this module to make use of a local search (whereas the ITPG module had global scope). To perform the local search, 3D model matching is used, where a synthetic image of the object is created and compared to the sensed data. The availability of low-cost PC graphics hardware allows rapid creation of synthetic images. In this approach, a function of orientation, distance, and articulation is defined as a metric on the difference between the captured image and a synthetic image with an object in the given orientation, distance, and articulation. The synthetic image is created using a model that is looked up in an object-model database. A composable software architecture is used for implementation. Video is first preprocessed to remove sensor anomalies (like dead pixels), and then is processed sequentially by a prioritized list of tracker-identifiers.

Reconstruction of measurable three-dimensional point cloud model based on large-scene archaeological excavation sites

NASA Astrophysics Data System (ADS)

Zhang, Chun-Sen; Zhang, Meng-Meng; Zhang, Wei-Xing

2017-01-01

This paper outlines a low-cost, user-friendly photogrammetric technique with nonmetric cameras to obtain excavation site digital sequence images, based on photogrammetry and computer vision. Digital camera calibration, automatic aerial triangulation, image feature extraction, image sequence matching, and dense digital differential rectification are used, combined with a certain number of global control points of the excavation site, to reconstruct the high precision of measured three-dimensional (3-D) models. Using the acrobatic figurines in the Qin Shi Huang mausoleum excavation as an example, our method solves the problems of little base-to-height ratio, high inclination, unstable altitudes, and significant ground elevation changes affecting image matching. Compared to 3-D laser scanning, the 3-D color point cloud obtained by this method can maintain the same visual result and has advantages of low project cost, simple data processing, and high accuracy. Structure-from-motion (SfM) is often used to reconstruct 3-D models of large scenes and has lower accuracy if it is a reconstructed 3-D model of a small scene at close range. Results indicate that this method quickly achieves 3-D reconstruction of large archaeological sites and produces heritage site distribution of orthophotos providing a scientific basis for accurate location of cultural relics, archaeological excavations, investigation, and site protection planning. This proposed method has a comprehensive application value.

Head rice rate measurement based on concave point matching

PubMed Central

Yao, Yuan; Wu, Wei; Yang, Tianle; Liu, Tao; Chen, Wen; Chen, Chen; Li, Rui; Zhou, Tong; Sun, Chengming; Zhou, Yue; Li, Xinlu

2017-01-01

Head rice rate is an important factor affecting rice quality. In this study, an inflection point detection-based technology was applied to measure the head rice rate by combining a vibrator and a conveyor belt for bulk grain image acquisition. The edge center mode proportion method (ECMP) was applied for concave points matching in which concave matching and separation was performed with collaborative constraint conditions followed by rice length calculation with a minimum enclosing rectangle (MER) to identify the head rice. Finally, the head rice rate was calculated using the sum area of head rice to the overall coverage of rice. Results showed that bulk grain image acquisition can be realized with test equipment, and the accuracy rate of separation of both indica rice and japonica rice exceeded 95%. An increase in the number of rice did not significantly affect ECMP and MER. High accuracy can be ensured with MER to calculate head rice rate by narrowing down its relative error between real values less than 3%. The test results show that the method is reliable as a reference for head rice rate calculation studies. PMID:28128315

Effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication.

PubMed

Dhir, L; Habib, N E; Monro, D M; Rakshit, S

2010-06-01

The purpose of this study was to investigate the effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication. Prospective non-comparative cohort study. Images of 15 subjects were captured before (enrolment), and 5, 10, and 15 min after instillation of mydriatics before routine cataract surgery. After cataract surgery, images were captured 2 weeks thereafter. Enrolled and test images (after pupillary dilation and after cataract surgery) were segmented to extract the iris. This was then unwrapped onto a rectangular format for normalization and a novel method using the Discrete Cosine Transform was applied to encode the image into binary bits. The numerical difference between two iris codes (Hamming distance, HD) was calculated. The HD between identification and enrolment codes was used as a score and was compared with a confidence threshold for specific equipment, giving a match or non-match result. The Correct Recognition Rate (CRR) and Equal Error Rates (EERs) were calculated to analyse overall system performance. After cataract surgery, perfect identification and verification was achieved, with zero false acceptance rate, zero false rejection rate, and zero EER. After pupillary dilation, non-elastic deformation occurs and a CRR of 86.67% and EER of 9.33% were obtained. Conventional circle-based localization methods are inadequate. Matching reliability decreases considerably with increase in pupillary dilation. Cataract surgery has no effect on iris pattern recognition, whereas pupil dilation may be used to defeat an iris-based authentication system.

Registration of 3D spectral OCT volumes using 3D SIFT feature point matching

NASA Astrophysics Data System (ADS)

Niemeijer, Meindert; Garvin, Mona K.; Lee, Kyungmoo; van Ginneken, Bram; Abràmoff, Michael D.; Sonka, Milan

2009-02-01

The recent introduction of next generation spectral OCT scanners has enabled routine acquisition of high resolution, 3D cross-sectional volumetric images of the retina. 3D OCT is used in the detection and management of serious eye diseases such as glaucoma and age-related macular degeneration. For follow-up studies, image registration is a vital tool to enable more precise, quantitative comparison of disease states. This work presents a registration method based on a recently introduced extension of the 2D Scale-Invariant Feature Transform (SIFT) framework1 to 3D.2 The SIFT feature extractor locates minima and maxima in the difference of Gaussian scale space to find salient feature points. It then uses histograms of the local gradient directions around each found extremum in 3D to characterize them in a 4096 element feature vector. Matching points are found by comparing the distance between feature vectors. We apply this method to the rigid registration of optic nerve head- (ONH) and macula-centered 3D OCT scans of the same patient that have only limited overlap. Three OCT data set pairs with known deformation were used for quantitative assessment of the method's robustness and accuracy when deformations of rotation and scaling were considered. Three-dimensional registration accuracy of 2.0+/-3.3 voxels was observed. The accuracy was assessed as average voxel distance error in N=1572 matched locations. The registration method was applied to 12 3D OCT scans (200 x 200 x 1024 voxels) of 6 normal eyes imaged in vivo to demonstrate the clinical utility and robustness of the method in a real-world environment.

Registration of 3D fetal neurosonography and MRI☆

PubMed Central

Kuklisova-Murgasova, Maria; Cifor, Amalia; Napolitano, Raffaele; Papageorghiou, Aris; Quaghebeur, Gerardine; Rutherford, Mary A.; Hajnal, Joseph V.; Noble, J. Alison; Schnabel, Julia A.

2013-01-01

We propose a method for registration of 3D fetal brain ultrasound with a reconstructed magnetic resonance fetal brain volume. This method, for the first time, allows the alignment of models of the fetal brain built from magnetic resonance images with 3D fetal brain ultrasound, opening possibilities to develop new, prior information based image analysis methods for 3D fetal neurosonography. The reconstructed magnetic resonance volume is first segmented using a probabilistic atlas and a pseudo ultrasound image volume is simulated from the segmentation. This pseudo ultrasound image is then affinely aligned with clinical ultrasound fetal brain volumes using a robust block-matching approach that can deal with intensity artefacts and missing features in the ultrasound images. A qualitative and quantitative evaluation demonstrates good performance of the method for our application, in comparison with other tested approaches. The intensity average of 27 ultrasound images co-aligned with the pseudo ultrasound template shows good correlation with anatomy of the fetal brain as seen in the reconstructed magnetic resonance image. PMID:23969169

An automated 3D reconstruction method of UAV images

NASA Astrophysics Data System (ADS)

Liu, Jun; Wang, He; Liu, Xiaoyang; Li, Feng; Sun, Guangtong; Song, Ping

2015-10-01

In this paper a novel fully automated 3D reconstruction approach based on low-altitude unmanned aerial vehicle system (UAVs) images will be presented, which does not require previous camera calibration or any other external prior knowledge. Dense 3D point clouds are generated by integrating orderly feature extraction, image matching, structure from motion (SfM) and multi-view stereo (MVS) algorithms, overcoming many of the cost, time limitations of rigorous photogrammetry techniques. An image topology analysis strategy is introduced to speed up large scene reconstruction by taking advantage of the flight-control data acquired by UAV. Image topology map can significantly reduce the running time of feature matching by limiting the combination of images. A high-resolution digital surface model of the study area is produced base on UAV point clouds by constructing the triangular irregular network. Experimental results show that the proposed approach is robust and feasible for automatic 3D reconstruction of low-altitude UAV images, and has great potential for the acquisition of spatial information at large scales mapping, especially suitable for rapid response and precise modelling in disaster emergency.

Co-Registration Between Multisource Remote-Sensing Images

NASA Astrophysics Data System (ADS)

Wu, J.; Chang, C.; Tsai, H.-Y.; Liu, M.-C.

2012-07-01

Image registration is essential for geospatial information systems analysis, which usually involves integrating multitemporal and multispectral datasets from remote optical and radar sensors. An algorithm that deals with feature extraction, keypoint matching, outlier detection and image warping is experimented in this study. The methods currently available in the literature rely on techniques, such as the scale-invariant feature transform, between-edge cost minimization, normalized cross correlation, leasts-quares image matching, random sample consensus, iterated data snooping and thin-plate splines. Their basics are highlighted and encoded into a computer program. The test images are excerpts from digital files created by the multispectral SPOT-5 and Formosat-2 sensors, and by the panchromatic IKONOS and QuickBird sensors. Suburban areas, housing rooftops, the countryside and hilly plantations are studied. The co-registered images are displayed with block subimages in a criss-cross pattern. Besides the imagery, the registration accuracy is expressed by the root mean square error. Toward the end, this paper also includes a few opinions on issues that are believed to hinder a correct correspondence between diverse images.

Detecting objects in radiographs for homeland security

NASA Astrophysics Data System (ADS)

Prasad, Lakshman; Snyder, Hans

2005-05-01

We present a general scheme for segmenting a radiographic image into polygons that correspond to visual features. This decomposition provides a vectorized representation that is a high-level description of the image. The polygons correspond to objects or object parts present in the image. This characterization of radiographs allows the direct application of several shape recognition algorithms to identify objects. In this paper we describe the use of constrained Delaunay triangulations as a uniform foundational tool to achieve multiple visual tasks, namely image segmentation, shape decomposition, and parts-based shape matching. Shape decomposition yields parts that serve as tokens representing local shape characteristics. Parts-based shape matching enables the recognition of objects in the presence of occlusions, which commonly occur in radiographs. The polygonal representation of image features affords the efficient design and application of sophisticated geometric filtering methods to detect large-scale structural properties of objects in images. Finally, the representation of radiographs via polygons results in significant reduction of image file sizes and permits the scalable graphical representation of images, along with annotations of detected objects, in the SVG (scalable vector graphics) format that is proposed by the world wide web consortium (W3C). This is a textual representation that can be compressed and encrypted for efficient and secure transmission of information over wireless channels and on the Internet. In particular, our methods described here provide an algorithmic framework for developing image analysis tools for screening cargo at ports of entry for homeland security.

Dynamic Transmit-Receive Beamforming by Spatial Matched Filtering for Ultrasound Imaging with Plane Wave Transmission.

PubMed

Chen, Yuling; Lou, Yang; Yen, Jesse

2017-07-01

During conventional ultrasound imaging, the need for multiple transmissions for one image and the time of flight for a desired imaging depth limit the frame rate of the system. Using a single plane wave pulse during each transmission followed by parallel receive processing allows for high frame rate imaging. However, image quality is degraded because of the lack of transmit focusing. Beamforming by spatial matched filtering (SMF) is a promising method which focuses ultrasonic energy using spatial filters constructed from the transmit-receive impulse response of the system. Studies by other researchers have shown that SMF beamforming can provide dynamic transmit-receive focusing throughout the field of view. In this paper, we apply SMF beamforming to plane wave transmissions (PWTs) to achieve both dynamic transmit-receive focusing at all imaging depths and high imaging frame rate (>5000 frames per second). We demonstrated the capability of the combined method (PWT + SMF) of achieving two-way focusing mathematically through analysis based on the narrowband Rayleigh-Sommerfeld diffraction theory. Moreover, the broadband performance of PWT + SMF was quantified in terms of lateral resolution and contrast from both computer simulations and experimental data. Results were compared between SMF beamforming and conventional delay-and-sum (DAS) beamforming in both simulations and experiments. At an imaging depth of 40 mm, simulation results showed a 29% lateral resolution improvement and a 160% contrast improvement with PWT + SMF. These improvements were 17% and 48% for experimental data with noise.

Mutual event observations of solar system objects by SRC on Mars Express. Analysis and release of observations

NASA Astrophysics Data System (ADS)

Ziese, R.; Willner, K.

2018-06-01

Context. Both Martian moons, Phobos and Deimos, have been observed during several imaging campaigns by the Super Resolution Channel (SRC) on the Mars Express probe. Several tens of images are obtained during mutual event observations - when the Martian moons are both observed or together with another solar system body. These observations provide new opportunities to determine the bodies' positions in their orbits. Aims: A method was sought to automate the observation of the positions of the imaged bodies. Within one image sequence a similarly accurate localization of the objects in all images should be possible. Methods: Shape models of Phobos and Deimos are applied to simulate the appearance of the bodies in the images. Matching the illuminated simulation against the observation provides a reliable determination of the bodies' location within the image. To enhance the matching confidence several corrections need to be applied to the simulation to closely reconstruct the observation. Results: A list of 884 relative positions between the different objects is provided through the Centre de Données astronomiques de Strasbourg (CDS). Tables A.1-A.4 are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/614/A15

Adaptive elastic segmentation of brain MRI via shape-model-guided evolutionary programming.

PubMed

Pitiot, Alain; Toga, Arthur W; Thompson, Paul M

2002-08-01

This paper presents a fully automated segmentation method for medical images. The goal is to localize and parameterize a variety of types of structure in these images for subsequent quantitative analysis. We propose a new hybrid strategy that combines a general elastic template matching approach and an evolutionary heuristic. The evolutionary algorithm uses prior statistical information about the shape of the target structure to control the behavior of a number of deformable templates. Each template, modeled in the form of a B-spline, is warped in a potential field which is itself dynamically adapted. Such a hybrid scheme proves to be promising: by maintaining a population of templates, we cover a large domain of the solution space under the global guidance of the evolutionary heuristic, and thoroughly explore interesting areas. We address key issues of automated image segmentation systems. The potential fields are initially designed based on the spatial features of the edges in the input image, and are subjected to spatially adaptive diffusion to guarantee the deformation of the template. This also improves its global consistency and convergence speed. The deformation algorithm can modify the internal structure of the templates to allow a better match. We investigate in detail the preprocessing phase that the images undergo before they can be used more effectively in the iterative elastic matching procedure: a texture classifier, trained via linear discriminant analysis of a learning set, is used to enhance the contrast of the target structure with respect to surrounding tissues. We show how these techniques interact within a statistically driven evolutionary scheme to achieve a better tradeoff between template flexibility and sensitivity to noise and outliers. We focus on understanding the features of template matching that are most beneficial in terms of the achieved match. Examples from simulated and real image data are discussed, with considerations of algorithmic efficiency.

Automatic generation of endocardial surface meshes with 1-to-1 correspondence from cine-MR images

NASA Astrophysics Data System (ADS)

Su, Yi; Teo, S.-K.; Lim, C. W.; Zhong, L.; Tan, R. S.

2015-03-01

In this work, we develop an automatic method to generate a set of 4D 1-to-1 corresponding surface meshes of the left ventricle (LV) endocardial surface which are motion registered over the whole cardiac cycle. These 4D meshes have 1- to-1 point correspondence over the entire set, and is suitable for advanced computational processing, such as shape analysis, motion analysis and finite element modelling. The inputs to the method are the set of 3D LV endocardial surface meshes of the different frames/phases of the cardiac cycle. Each of these meshes is reconstructed independently from border-delineated MR images and they have no correspondence in terms of number of vertices/points and mesh connectivity. To generate point correspondence, the first frame of the LV mesh model is used as a template to be matched to the shape of the meshes in the subsequent phases. There are two stages in the mesh correspondence process: (1) a coarse matching phase, and (2) a fine matching phase. In the coarse matching phase, an initial rough matching between the template and the target is achieved using a radial basis function (RBF) morphing process. The feature points on the template and target meshes are automatically identified using a 16-segment nomenclature of the LV. In the fine matching phase, a progressive mesh projection process is used to conform the rough estimate to fit the exact shape of the target. In addition, an optimization-based smoothing process is used to achieve superior mesh quality and continuous point motion.

Assigning Main Orientation to an EOH Descriptor on Multispectral Images.

PubMed

Li, Yong; Shi, Xiang; Wei, Lijun; Zou, Junwei; Chen, Fang

2015-07-01

This paper proposes an approach to compute an EOH (edge-oriented histogram) descriptor with main orientation. EOH has a better matching ability than SIFT (scale-invariant feature transform) on multispectral images, but does not assign a main orientation to keypoints. Alternatively, it tends to assign the same main orientation to every keypoint, e.g., zero degrees. This limits EOH to matching keypoints between images of translation misalignment only. Observing this limitation, we propose assigning to keypoints the main orientation that is computed with PIIFD (partial intensity invariant feature descriptor). In the proposed method, SIFT keypoints are detected from images as the extrema of difference of Gaussians, and every keypoint is assigned to the main orientation computed with PIIFD. Then, EOH is computed for every keypoint with respect to its main orientation. In addition, an implementation variant is proposed for fast computation of the EOH descriptor. Experimental results show that the proposed approach performs more robustly than the original EOH on image pairs that have a rotation misalignment.

A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation

NASA Astrophysics Data System (ADS)

Zhou, Xiran; Liu, Jun; Liu, Shuguang; Cao, Lei; Zhou, Qiming; Huang, Huawen

2014-02-01

High spatial resolution and spectral fidelity are basic standards for evaluating an image fusion algorithm. Numerous fusion methods for remote sensing images have been developed. Some of these methods are based on the intensity-hue-saturation (IHS) transform and the generalized IHS (GIHS), which may cause serious spectral distortion. Spectral distortion in the GIHS is proven to result from changes in saturation during fusion. Therefore, reducing such changes can achieve high spectral fidelity. A GIHS-based spectral preservation fusion method that can theoretically reduce spectral distortion is proposed in this study. The proposed algorithm consists of two steps. The first step is spectral modulation (SM), which uses the Gaussian function to extract spatial details and conduct SM of multispectral (MS) images. This method yields a desirable visual effect without requiring histogram matching between the panchromatic image and the intensity of the MS image. The second step uses the Gaussian convolution function to restore lost edge details during SM. The proposed method is proven effective and shown to provide better results compared with other GIHS-based methods.

Spacecraft angular velocity estimation algorithm for star tracker based on optical flow techniques

NASA Astrophysics Data System (ADS)

Tang, Yujie; Li, Jian; Wang, Gangyi

2018-02-01

An integrated navigation system often uses the traditional gyro and star tracker for high precision navigation with the shortcomings of large volume, heavy weight and high-cost. With the development of autonomous navigation for deep space and small spacecraft, star tracker has been gradually used for attitude calculation and angular velocity measurement directly. At the same time, with the dynamic imaging requirements of remote sensing satellites and other imaging satellites, how to measure the angular velocity in the dynamic situation to improve the accuracy of the star tracker is the hotspot of future research. We propose the approach to measure angular rate with a nongyro and improve the dynamic performance of the star tracker. First, the star extraction algorithm based on morphology is used to extract the star region, and the stars in the two images are matched according to the method of angular distance voting. The calculation of the displacement of the star image is measured by the improved optical flow method. Finally, the triaxial angular velocity of the star tracker is calculated by the star vector using the least squares method. The method has the advantages of fast matching speed, strong antinoise ability, and good dynamic performance. The triaxial angular velocity of star tracker can be obtained accurately with these methods. So, the star tracker can achieve better tracking performance and dynamic attitude positioning accuracy to lay a good foundation for the wide application of various satellites and complex space missions.

MR Measurement of Alloy Magnetic Susceptibility: Towards Developing Tissue-Susceptibility Matched Metals

PubMed Central

Astary, Garrett W.; Peprah, Marcus K.; Fisher, Charles R.; Stewart, Rachel L.; Carney, Paul R.; Sarntinoranont, Malisa; Meisel, Mark W.; Manuel, Michele V.; Mareci, Thomas H.

2013-01-01

Magnetic resonance imaging (MRI) can be used to relate structure to function mapped with high-temporal resolution electrophysiological recordings using metal electrodes. Additionally, MRI may be used to guide the placement of electrodes or conductive cannula in the brain. However, the magnetic susceptibility mismatch between implanted metals and surrounding brain tissue can severely distort MR images and spectra, particularly in high magnetic fields. In this study, we present a modified MR method of characterizing the magnetic susceptibility of materials that can be used to develop biocompatible, metal alloys that match the susceptibility of host tissue in order to eliminate MR distortions proximal to the implant. This method was applied at 4.7 T and 11.1 T to measure the susceptibility of a model solid-solution alloy of Cu and Sn, which is inexpensive but not biocompatible. MR-derived relative susceptibility values of four different compositions of Cu-Sn alloy deviated by less than 3.1% from SQUID magnetometry absolute susceptibility measurements performed up to 7 T. These results demonstrate that the magnetic susceptibility varies linearly with atomic percentage in these solid-solution alloys, but are not simply the weighted average of Cu and Sn magnetic susceptibilities. Therefore susceptibility measurements are necessary when developing susceptibility-matched, solid-solution alloys for the elimination of susceptibility artifacts in MR. This MR method does not require any specialized equipment and is free of geometrical constraints, such as sample shape requirements associated with SQUID magnetometry, so the method can be used at all stages of fabrication to guide the development of a susceptibility matched, biocompatible device. PMID:23727587

Real-time UAV trajectory generation using feature points matching between video image sequences

NASA Astrophysics Data System (ADS)

Byun, Younggi; Song, Jeongheon; Han, Dongyeob

2017-09-01

Unmanned aerial vehicles (UAVs), equipped with navigation systems and video capability, are currently being deployed for intelligence, reconnaissance and surveillance mission. In this paper, we present a systematic approach for the generation of UAV trajectory using a video image matching system based on SURF (Speeded up Robust Feature) and Preemptive RANSAC (Random Sample Consensus). Video image matching to find matching points is one of the most important steps for the accurate generation of UAV trajectory (sequence of poses in 3D space). We used the SURF algorithm to find the matching points between video image sequences, and removed mismatching by using the Preemptive RANSAC which divides all matching points to outliers and inliers. The inliers are only used to determine the epipolar geometry for estimating the relative pose (rotation and translation) between image sequences. Experimental results from simulated video image sequences showed that our approach has a good potential to be applied to the automatic geo-localization of the UAVs system