Edge detection and mathematic fitting for corneal surface with Matlab software.

PubMed

Di, Yue; Li, Mei-Yan; Qiao, Tong; Lu, Na

2017-01-01

To select the optimal edge detection methods to identify the corneal surface, and compare three fitting curve equations with Matlab software. Fifteen subjects were recruited. The corneal images from optical coherence tomography (OCT) were imported into Matlab software. Five edge detection methods (Canny, Log, Prewitt, Roberts, Sobel) were used to identify the corneal surface. Then two manual identifying methods (ginput and getpts) were applied to identify the edge coordinates respectively. The differences among these methods were compared. Binomial curve (y=Ax 2 +Bx+C), Polynomial curve [p(x)=p1x n +p2x n-1 +....+pnx+pn+1] and Conic section (Ax 2 +Bxy+Cy 2 +Dx+Ey+F=0) were used for curve fitting the corneal surface respectively. The relative merits among three fitting curves were analyzed. Finally, the eccentricity (e) obtained by corneal topography and conic section were compared with paired t -test. Five edge detection algorithms all had continuous coordinates which indicated the edge of the corneal surface. The ordinates of manual identifying were close to the inside of the actual edges. Binomial curve was greatly affected by tilt angle. Polynomial curve was lack of geometrical properties and unstable. Conic section could calculate the tilted symmetry axis, eccentricity, circle center, etc . There were no significant differences between 'e' values by corneal topography and conic section ( t =0.9143, P =0.3760 >0.05). It is feasible to simulate the corneal surface with mathematical curve with Matlab software. Edge detection has better repeatability and higher efficiency. The manual identifying approach is an indispensable complement for detection. Polynomial and conic section are both the alternative methods for corneal curve fitting. Conic curve was the optimal choice based on the specific geometrical properties.

A Semi-Automatic Method for Image Analysis of Edge Dynamics in Living Cells

PubMed Central

Huang, Lawrence; Helmke, Brian P.

2011-01-01

Spatial asymmetry of actin edge ruffling contributes to the process of cell polarization and directional migration, but mechanisms by which external cues control actin polymerization near cell edges remain unclear. We designed a quantitative image analysis strategy to measure the spatiotemporal distribution of actin edge ruffling. Time-lapse images of endothelial cells (ECs) expressing mRFP-actin were segmented using an active contour method. In intensity line profiles oriented normal to the cell edge, peak detection identified the angular distribution of polymerized actin within 1 µm of the cell edge, which was localized to lamellipodia and edge ruffles. Edge features associated with filopodia and peripheral stress fibers were removed. Circular statistical analysis enabled detection of cell polarity, indicated by a unimodal distribution of edge ruffles. To demonstrate the approach, we detected a rapid, nondirectional increase in edge ruffling in serum-stimulated ECs and a change in constitutive ruffling orientation in quiescent, nonpolarized ECs. Error analysis using simulated test images demonstrate robustness of the method to variations in image noise levels, edge ruffle arc length, and edge intensity gradient. These quantitative measurements of edge ruffling dynamics enable investigation at the cellular length scale of the underlying molecular mechanisms regulating actin assembly and cell polarization. PMID:21643526

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.

Detection and labeling ribs on expiration chest radiographs

NASA Astrophysics Data System (ADS)

Park, Mira; Jin, Jesse S.; Wilson, Laurence S.

2003-06-01

Typically, inspiration is preferred when xraying the lungs. The x-ray technologist will ask a patient to be still and to take a deep breath and to hold it. This not only reduces the possibility of a blurred image but also enhances the quality of the image since air-filled lungs are easier to see on x-ray film. However, inspiration causes low density in the inner part of lung field. That means that ribs in the inner part of lung field have lower density than the other parts nearer to the border of the lung field. That is why edge detection algorithms often fail to detect ribs. Therefore to make rib edges clear we try to produce an expiration lung field using a 'hemi-elliptical cavity.' Based on the expiration lung field, we extract the rib edges using canny edge detector and a new connectivity method, called '4 way with 10-neighbors connectivity' to detect clavicle and rib edge candidates. Once the edge candidates are formed, our system selects the best candidates using knowledge-based constraints such as a gradient, length and location. The edges can be paired and labeled as superior rib edge and inferior rib edge. Then the system uses the clavicle, which is obtained in a same method for the rib edge detection, as a landmark to label all detected ribs.

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%.

Edge grouping combining boundary and region information.

PubMed

Stahl, Joachim S; Wang, Song

2007-10-01

This paper introduces a new edge-grouping method to detect perceptually salient structures in noisy images. Specifically, we define a new grouping cost function in a ratio form, where the numerator measures the boundary proximity of the resulting structure and the denominator measures the area of the resulting structure. This area term introduces a preference towards detecting larger-size structures and, therefore, makes the resulting edge grouping more robust to image noise. To find the optimal edge grouping with the minimum grouping cost, we develop a special graph model with two different kinds of edges and then reduce the grouping problem to finding a special kind of cycle in this graph with a minimum cost in ratio form. This optimal cycle-finding problem can be solved in polynomial time by a previously developed graph algorithm. We implement this edge-grouping method, test it on both synthetic data and real images, and compare its performance against several available edge-grouping and edge-linking methods. Furthermore, we discuss several extensions of the proposed method, including the incorporation of the well-known grouping cues of continuity and intensity homogeneity, introducing a factor to balance the contributions from the boundary and region information, and the prevention of detecting self-intersecting boundaries.

Edge guided image reconstruction in linear scan CT by weighted alternating direction TV minimization.

PubMed

Cai, Ailong; Wang, Linyuan; Zhang, Hanming; Yan, Bin; Li, Lei; Xi, Xiaoqi; Li, Jianxin

2014-01-01

Linear scan computed tomography (CT) is a promising imaging configuration with high scanning efficiency while the data set is under-sampled and angularly limited for which high quality image reconstruction is challenging. In this work, an edge guided total variation minimization reconstruction (EGTVM) algorithm is developed in dealing with this problem. The proposed method is modeled on the combination of total variation (TV) regularization and iterative edge detection strategy. In the proposed method, the edge weights of intermediate reconstructions are incorporated into the TV objective function. The optimization is efficiently solved by applying alternating direction method of multipliers. A prudential and conservative edge detection strategy proposed in this paper can obtain the true edges while restricting the errors within an acceptable degree. Based on the comparison on both simulation studies and real CT data set reconstructions, EGTVM provides comparable or even better quality compared to the non-edge guided reconstruction and adaptive steepest descent-projection onto convex sets method. With the utilization of weighted alternating direction TV minimization and edge detection, EGTVM achieves fast and robust convergence and reconstructs high quality image when applied in linear scan CT with under-sampled data set.

High accuracy position method based on computer vision and error analysis

NASA Astrophysics Data System (ADS)

Chen, Shihao; Shi, Zhongke

2003-09-01

The study of high accuracy position system is becoming the hotspot in the field of autocontrol. And positioning is one of the most researched tasks in vision system. So we decide to solve the object locating by using the image processing method. This paper describes a new method of high accuracy positioning method through vision system. In the proposed method, an edge-detection filter is designed for a certain running condition. Here, the filter contains two mainly parts: one is image-processing module, this module is to implement edge detection, it contains of multi-level threshold self-adapting segmentation, edge-detection and edge filter; the other one is object-locating module, it is to point out the location of each object in high accurate, and it is made up of medium-filtering and curve-fitting. This paper gives some analysis error for the method to prove the feasibility of vision in position detecting. Finally, to verify the availability of the method, an example of positioning worktable, which is using the proposed method, is given at the end of the paper. Results show that the method can accurately detect the position of measured object and identify object attitude.

System and method for automated object detection in an image

DOEpatents

Kenyon, Garrett T.; Brumby, Steven P.; George, John S.; Paiton, Dylan M.; Schultz, Peter F.

2015-10-06

A contour/shape detection model may use relatively simple and efficient kernels to detect target edges in an object within an image or video. A co-occurrence probability may be calculated for two or more edge features in an image or video using an object definition. Edge features may be differentiated between in response to measured contextual support, and prominent edge features may be extracted based on the measured contextual support. The object may then be identified based on the extracted prominent edge features.

Intelligent identification of remnant ridge edges in region west of Yongxing Island, South China Sea

NASA Astrophysics Data System (ADS)

Wang, Weiwei; Guo, Jing; Cai, Guanqiang; Wang, Dawei

2018-02-01

Edge detection enables identification of geomorphologic unit boundaries and thus assists with geomorphical mapping. In this paper, an intelligent edge identification method is proposed and image processing techniques are applied to multi-beam bathymetry data. To accomplish this, a color image is generated by the bathymetry, and a weighted method is used to convert the color image to a gray image. As the quality of the image has a significant influence on edge detection, different filter methods are applied to the gray image for de-noising. The peak signal-to-noise ratio and mean square error are calculated to evaluate which filter method is most appropriate for depth image filtering and the edge is subsequently detected using an image binarization method. Traditional image binarization methods cannot manage the complicated uneven seafloor, and therefore a binarization method is proposed that is based on the difference between image pixel values; the appropriate threshold for image binarization is estimated according to the probability distribution of pixel value differences between two adjacent pixels in horizontal and vertical directions, respectively. Finally, an eight-neighborhood frame is adopted to thin the binary image, connect the intermittent edge, and implement contour extraction. Experimental results show that the method described here can recognize the main boundaries of geomorphologic units. In addition, the proposed automatic edge identification method avoids use of subjective judgment, and reduces time and labor costs.

Detecting the Edge of the Tongue: A Tutorial

ERIC Educational Resources Information Center

Iskarous, Khalil

2005-01-01

The goal of this paper is to provide a tutorial introduction to the topic of edge detection of the tongue from ultrasound scans for researchers in speech science and phonetics. The method introduced here is Active Contours (also called snakes), a method for searching for an edge, assuming that it is a smooth curve in the image data. The advantage…

Wear Detection of Drill Bit by Image-based Technique

NASA Astrophysics Data System (ADS)

Sukeri, Maziyah; Zulhilmi Paiz Ismadi, Mohd; Rahim Othman, Abdul; Kamaruddin, Shahrul

2018-03-01

Image processing for computer vision function plays an essential aspect in the manufacturing industries for the tool condition monitoring. This study proposes a dependable direct measurement method to measure the tool wear using image-based analysis. Segmentation and thresholding technique were used as the means to filter and convert the colour image to binary datasets. Then, the edge detection method was applied to characterize the edge of the drill bit. By using cross-correlation method, the edges of original and worn drill bits were correlated to each other. Cross-correlation graphs were able to detect the difference of the worn edge despite small difference between the graphs. Future development will focus on quantifying the worn profile as well as enhancing the sensitivity of the technique.

A novel cloning template designing method by using an artificial bee colony algorithm for edge detection of CNN based imaging sensors.

PubMed

Parmaksızoğlu, Selami; Alçı, Mustafa

2011-01-01

Cellular Neural Networks (CNNs) have been widely used recently in applications such as edge detection, noise reduction and object detection, which are among the main computer imaging processes. They can also be realized as hardware based imaging sensors. The fact that hardware CNN models produce robust and effective results has attracted the attention of researchers using these structures within image sensors. Realization of desired CNN behavior such as edge detection can be achieved by correctly setting a cloning template without changing the structure of the CNN. To achieve different behaviors effectively, designing a cloning template is one of the most important research topics in this field. In this study, the edge detecting process that is used as a preliminary process for segmentation, identification and coding applications is conducted by using CNN structures. In order to design the cloning template of goal-oriented CNN architecture, an Artificial Bee Colony (ABC) algorithm which is inspired from the foraging behavior of honeybees is used and the performance analysis of ABC for this application is examined with multiple runs. The CNN template generated by the ABC algorithm is tested by using artificial and real test images. The results are subjectively and quantitatively compared with well-known classical edge detection methods, and other CNN based edge detector cloning templates available in the imaging literature. The results show that the proposed method is more successful than other methods.

A Novel Cloning Template Designing Method by Using an Artificial Bee Colony Algorithm for Edge Detection of CNN Based Imaging Sensors

PubMed Central

Parmaksızoğlu, Selami; Alçı, Mustafa

2011-01-01

Cellular Neural Networks (CNNs) have been widely used recently in applications such as edge detection, noise reduction and object detection, which are among the main computer imaging processes. They can also be realized as hardware based imaging sensors. The fact that hardware CNN models produce robust and effective results has attracted the attention of researchers using these structures within image sensors. Realization of desired CNN behavior such as edge detection can be achieved by correctly setting a cloning template without changing the structure of the CNN. To achieve different behaviors effectively, designing a cloning template is one of the most important research topics in this field. In this study, the edge detecting process that is used as a preliminary process for segmentation, identification and coding applications is conducted by using CNN structures. In order to design the cloning template of goal-oriented CNN architecture, an Artificial Bee Colony (ABC) algorithm which is inspired from the foraging behavior of honeybees is used and the performance analysis of ABC for this application is examined with multiple runs. The CNN template generated by the ABC algorithm is tested by using artificial and real test images. The results are subjectively and quantitatively compared with well-known classical edge detection methods, and other CNN based edge detector cloning templates available in the imaging literature. The results show that the proposed method is more successful than other methods. PMID:22163903

Power spectrum weighted edge analysis for straight edge detection in images

NASA Astrophysics Data System (ADS)

Karvir, Hrishikesh V.; Skipper, Julie A.

2007-04-01

Most man-made objects provide characteristic straight line edges and, therefore, edge extraction is a commonly used target detection tool. However, noisy images often yield broken edges that lead to missed detections, and extraneous edges that may contribute to false target detections. We present a sliding-block approach for target detection using weighted power spectral analysis. In general, straight line edges appearing at a given frequency are represented as a peak in the Fourier domain at a radius corresponding to that frequency, and a direction corresponding to the orientation of the edges in the spatial domain. Knowing the edge width and spacing between the edges, a band-pass filter is designed to extract the Fourier peaks corresponding to the target edges and suppress image noise. These peaks are then detected by amplitude thresholding. The frequency band width and the subsequent spatial filter mask size are variable parameters to facilitate detection of target objects of different sizes under known imaging geometries. Many military objects, such as trucks, tanks and missile launchers, produce definite signatures with parallel lines and the algorithm proves to be ideal for detecting such objects. Moreover, shadow-casting objects generally provide sharp edges and are readily detected. The block operation procedure offers advantages of significant reduction in noise influence, improved edge detection, faster processing speed and versatility to detect diverse objects of different sizes in the image. With Scud missile launcher replicas as target objects, the method has been successfully tested on terrain board test images under different backgrounds, illumination and imaging geometries with cameras of differing spatial resolution and bit-depth.

Shearlet-based edge detection: flame fronts and tidal flats

NASA Astrophysics Data System (ADS)

King, Emily J.; Reisenhofer, Rafael; Kiefer, Johannes; Lim, Wang-Q.; Li, Zhen; Heygster, Georg

2015-09-01

Shearlets are wavelet-like systems which are better suited for handling geometric features in multi-dimensional data than traditional wavelets. A novel method for edge and line detection which is in the spirit of phase congruency but is based on a complex shearlet transform will be presented. This approach to detection yields an approximate tangent direction of detected discontinuities as a byproduct of the computation, which then yields local curvature estimates. Two applications of the edge detection method will be discussed. First, the tracking and classification of flame fronts is a critical component of research in technical thermodynamics. Quite often, the flame fronts are transient or weak and the images are noisy. The standard methods used in the field for the detection of flame fronts do not handle such data well. Fortunately, using the shearlet-based edge measure yields good results as well as an accurate approximation of local curvature. Furthermore, a modification of the method will yield line detection, which is important for certain imaging modalities. Second, the Wadden tidal flats are a biodiverse region along the North Sea coast. One approach to surveying the delicate region and tracking the topographical changes is to use pre-existing Synthetic Aperture Radar (SAR) images. Unfortunately, SAR data suffers from multiplicative noise as well as sensitivity to environmental factors. The first large-scale mapping project of that type showed good results but only with a tremendous amount of manual interaction because there are many edges in the data which are not boundaries of the tidal flats but are edges of features like fields or islands. Preliminary results will be presented.

Localization of tumors in various organs, using edge detection algorithms

NASA Astrophysics Data System (ADS)

López Vélez, Felipe

2015-09-01

The edge of an image is a set of points organized in a curved line, where in each of these points the brightness of the image changes abruptly, or has discontinuities, in order to find these edges there will be five different mathematical methods to be used and later on compared with its peers, this is with the aim of finding which of the methods is the one that can find the edges of any given image. In this paper these five methods will be used for medical purposes in order to find which one is capable of finding the edges of a scanned image more accurately than the others. The problem consists in analyzing the following two biomedicals images. One of them represents a brain tumor and the other one a liver tumor. These images will be analyzed with the help of the five methods described and the results will be compared in order to determine the best method to be used. It was decided to use different algorithms of edge detection in order to obtain the results shown below; Bessel algorithm, Morse algorithm, Hermite algorithm, Weibull algorithm and Sobel algorithm. After analyzing the appliance of each of the methods to both images it's impossible to determine the most accurate method for tumor detection due to the fact that in each case the best method changed, i.e., for the brain tumor image it can be noticed that the Morse method was the best at finding the edges of the image but for the liver tumor image it was the Hermite method. Making further observations it is found that Hermite and Morse have for these two cases the lowest standard deviations, concluding that these two are the most accurate method to find the edges in analysis of biomedical images.

A new edge detection algorithm based on Canny idea

NASA Astrophysics Data System (ADS)

Feng, Yingke; Zhang, Jinmin; Wang, Siming

2017-10-01

The traditional Canny algorithm has poor self-adaptability threshold, and it is more sensitive to noise. In order to overcome these drawbacks, this paper proposed a new edge detection method based on Canny algorithm. Firstly, the media filtering and filtering based on the method of Euclidean distance are adopted to process it; secondly using the Frei-chen algorithm to calculate gradient amplitude; finally, using the Otsu algorithm to calculate partial gradient amplitude operation to get images of thresholds value, then find the average of all thresholds that had been calculated, half of the average is high threshold value, and the half of the high threshold value is low threshold value. Experiment results show that this new method can effectively suppress noise disturbance, keep the edge information, and also improve the edge detection accuracy.

Edge-directed inference for microaneurysms detection in digital fundus images

NASA Astrophysics Data System (ADS)

Huang, Ke; Yan, Michelle; Aviyente, Selin

2007-03-01

Microaneurysms (MAs) detection is a critical step in diabetic retinopathy screening, since MAs are the earliest visible warning of potential future problems. A variety of algorithms have been proposed for MAs detection in mass screening. Different methods have been proposed for MAs detection. The core technology for most of existing methods is based on a directional mathematical morphological operation called "Top-Hat" filter that requires multiple filtering operations at each pixel. Background structure, uneven illumination and noise often cause confusion between MAs and some non-MA structures and limits the applicability of the filter. In this paper, a novel detection framework based on edge directed inference is proposed for MAs detection. The candidate MA regions are first delineated from the edge map of a fundus image. Features measuring shape, brightness and contrast are extracted for each candidate MA region to better exclude false detection from true MAs. Algorithmic analysis and empirical evaluation reveal that the proposed edge directed inference outperforms the "Top-Hat" based algorithm in both detection accuracy and computational speed.

Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform.

PubMed

Cao, Jianfang; Chen, Lichao; Wang, Min; Tian, Yun

2018-01-01

The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator's dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data. For the experiments, we constructed datasets of different scales from the Pascal VOC2012 image database. The proposed parallel Otsu-Canny edge detection algorithm performs better than other traditional edge detection algorithms. The parallel approach reduced the running time by approximately 67.2% on a Hadoop cluster architecture consisting of 5 nodes with a dataset of 60,000 images. Overall, our approach system speeds up the system by approximately 3.4 times when processing large-scale datasets, which demonstrates the obvious superiority of our method. The proposed algorithm in this study demonstrates both better edge detection performance and improved time performance.

First Order Statistics of Speckle around a Scatterer Volume Density Edge and Edge Detection in Ultrasound Images.

NASA Astrophysics Data System (ADS)

Li, Yue

1990-01-01

Ultrasonic imaging plays an important role in medical imaging. But the images exhibit a granular structure, commonly known as speckle. The speckle tends to mask the presence of low-contrast lesions and reduces the ability of a human observer to resolve fine details. Our interest in this research is to examine the problem of edge detection and come up with methods for improving the visualization of organ boundaries and tissue inhomogeneity edges. An edge in an image can be formed either by acoustic impedance change or by scatterer volume density change (or both). The echo produced from these two kinds of edges has different properties. In this work, it has been proved that the echo from a scatterer volume density edge is the Hilbert transform of the echo from a rough impedance boundary (except for a constant) under certain conditions. This result can be used for choosing the correct signal to transmit to optimize the performance of edge detectors and characterizing an edge. The signal to noise ratio of the echo produced by a scatterer volume density edge is also obtained. It is found that: (1) By transmitting a signal with high bandwidth ratio and low center frequency, one can obtain a higher signal to noise ratio. (2) For large area edges, the farther the transducer is from the edge, the larger is the signal to noise ratio. But for small area edges, the nearer the transducer is to the edge, the larger is the signal to noise ratio. These results enable us to maximize the signal to noise ratio by adjusting these parameters. (3) The signal to noise ratio is not only related to the ratio of scatterer volume densities at the edge, but also related to the absolute value of scatterer volume densities. Some of these results have been proved through simulation and experiment. Different edge detection methods have been used to detect simulated scatterer volume density edges to compare their performance. A so-called interlaced array method has been developed for speckle reduction in the images formed by synthetic aperture focussing technique, and experiments have been done to evaluate its performance.

Quality detection system and method of micro-accessory based on microscopic vision

NASA Astrophysics Data System (ADS)

Li, Dongjie; Wang, Shiwei; Fu, Yu

2017-10-01

Considering that the traditional manual detection of micro-accessory has some problems, such as heavy workload, low efficiency and large artificial error, a kind of quality inspection system of micro-accessory has been designed. Micro-vision technology has been used to inspect quality, which optimizes the structure of the detection system. The stepper motor is used to drive the rotating micro-platform to transfer quarantine device and the microscopic vision system is applied to get graphic information of micro-accessory. The methods of image processing and pattern matching, the variable scale Sobel differential edge detection algorithm and the improved Zernike moments sub-pixel edge detection algorithm are combined in the system in order to achieve a more detailed and accurate edge of the defect detection. The grade at the edge of the complex signal can be achieved accurately by extracting through the proposed system, and then it can distinguish the qualified products and unqualified products with high precision recognition.

Fpga based hardware synthesis for automatic segmentation of retinal blood vessels in diabetic retinopathy images.

PubMed

Sivakamasundari, J; Kavitha, G; Sujatha, C M; Ramakrishnan, S

2014-01-01

Diabetic Retinopathy (DR) is a disorder that affects the structure of retinal blood vessels due to long-standing diabetes mellitus. Real-Time mass screening system for DR is vital for timely diagnosis and periodic screening to prevent the patient from severe visual loss. Human retinal fundus images are widely used for an automated segmentation of blood vessel and diagnosis of various blood vessel disorders. In this work, an attempt has been made to perform hardware synthesis of Kirsch template based edge detection for segmentation of blood vessels. This method is implemented using LabVIEW software and is synthesized in field programmable gate array board to yield results in real-time application. The segmentation of blood vessels using Kirsch based edge detection is compared with other edge detection methods such as Sobel, Prewitt and Canny. The texture features such as energy, entropy, contrast, mean, homogeneity and structural feature namely ratio of vessel to vessel free area are obtained from the segmented images. The performance of segmentation is analysed in terms of sensitivity, specificity and accuracy. It is observed from the results that the Kirsch based edge detection technique segmented the edges of blood vessels better than other edge detection techniques. The ratio of vessel to vessel free area classified the normal and DR affected retinal images more significantly than other texture based features. FPGA based hardware synthesis of Kirsch edge detection method is able to differentiate normal and diseased images with high specificity (93%). This automated segmentation of retinal blood vessels system could be used in computer-assisted diagnosis for diabetic retinopathy screening in real-time application.

Sub-surface defects detection of by using active thermography and advanced image edge detection

NASA Astrophysics Data System (ADS)

Tse, Peter W.; Wang, Gaochao

2017-05-01

Active or pulsed thermography is a popular non-destructive testing (NDT) tool for inspecting the integrity and anomaly of industrial equipment. One of the recent research trends in using active thermography is to automate the process in detecting hidden defects. As of today, human effort has still been using to adjust the temperature intensity of the thermo camera in order to visually observe the difference in cooling rates caused by a normal target as compared to that by a sub-surface crack exists inside the target. To avoid the tedious human-visual inspection and minimize human induced error, this paper reports the design of an automatic method that is capable of detecting subsurface defects. The method used the technique of active thermography, edge detection in machine vision and smart algorithm. An infrared thermo-camera was used to capture a series of temporal pictures after slightly heating up the inspected target by flash lamps. Then the Canny edge detector was employed to automatically extract the defect related images from the captured pictures. The captured temporal pictures were preprocessed by a packet of Canny edge detector and then a smart algorithm was used to reconstruct the whole sequences of image signals. During the processes, noise and irrelevant backgrounds exist in the pictures were removed. Consequently, the contrast of the edges of defective areas had been highlighted. The designed automatic method was verified by real pipe specimens that contains sub-surface cracks. After applying such smart method, the edges of cracks can be revealed visually without the need of using manual adjustment on the setting of thermo-camera. With the help of this automatic method, the tedious process in manually adjusting the colour contract and the pixel intensity in order to reveal defects can be avoided.

Real-time line-width measurements: a new feature for reticle inspection systems

NASA Astrophysics Data System (ADS)

Eran, Yair; Greenberg, Gad; Joseph, Amnon; Lustig, Cornel; Mizrahi, Eyal

1997-07-01

The significance of line width control in mask production has become greater with the lessening of defect size. There are two conventional methods used for controlling line widths dimensions which employed in the manufacturing of masks for sub micron devices. These two methods are the critical dimensions (CD) measurement and the detection of edge defects. Achieving reliable and accurate control of line width errors is one of the most challenging tasks in mask production. Neither of the two methods cited above (namely CD measurement and the detection of edge defects) guarantees the detection of line width errors with good sensitivity over the whole mask area. This stems from the fact that CD measurement provides only statistical data on the mask features whereas applying edge defect detection method checks defects on each edge by itself, and does not supply information on the combined result of error detection on two adjacent edges. For example, a combination of a small edge defect together with a CD non- uniformity which are both within the allowed tolerance, may yield a significant line width error, which will not be detected using the conventional methods (see figure 1). A new approach for the detection of line width errors which overcomes this difficulty is presented. Based on this approach, a new sensitive line width error detector was developed and added to Orbot's RT-8000 die-to-database reticle inspection system. This innovative detector operates continuously during the mask inspection process and scans (inspects) the entire area of the reticle for line width errors. The detection is based on a comparison of measured line width that are taken on both the design database and the scanned image of the reticle. In section 2, the motivation for developing this new detector is presented. The section covers an analysis of various defect types, which are difficult to detect using conventional edge detection methods or, alternatively, CD measurements. In section 3, the basic concept of the new approach is introduced together with a description of the new detector and its characteristics. In section 4, the calibration process that took place in order to achieve reliable and repeatable line width measurements is presented. The description of an experiments conducted in order to evaluate the sensitivity of the new detector is given in section 5, followed by a report of the results of this evaluation. The conclusions are presented in section 6.

Single-view phase retrieval of an extended sample by exploiting edge detection and sparsity

DOE PAGES

Tripathi, Ashish; McNulty, Ian; Munson, Todd; ...

2016-10-14

We propose a new approach to robustly retrieve the exit wave of an extended sample from its coherent diffraction pattern by exploiting sparsity of the sample's edges. This approach enables imaging of an extended sample with a single view, without ptychography. We introduce nonlinear optimization methods that promote sparsity, and we derive update rules to robustly recover the sample's exit wave. We test these methods on simulated samples by varying the sparsity of the edge-detected representation of the exit wave. Finally, our tests illustrate the strengths and limitations of the proposed method in imaging extended samples.

The Superior-Edge-of-the-Knee Incision Method in Lymphaticovenular Anastomosis for Lower Extremity Lymphedema.

PubMed

Seki, Yukio; Yamamoto, Takumi; Yoshimatsu, Hidehiko; Hayashi, Akitatsu; Kurazono, Arito; Mori, Masanori; Kato, Yoichi; Koshima, Isao

2015-11-01

Lymphatic vessel diameter and lymph flow are important for accurate anastomosis and effective lymph-to-venous flow in lymphaticovenular anastomosis. The authors developed a reliable method, the superioredge-of-the-knee incision method, for detecting and making the best use of high-flow lymphatic vessels in the distal medial thigh between the deep and superficial fascia, where movement of the knee, combined with compression between these fascial layers, theoretically results in upward propulsion of lymphatic fluid. Intraoperative detection of large lymphatic vessels and of venous reflux and postoperative lymphedematous volume reduction were compared between 15 patients in whom lymphaticovenular anastomoses with the superior-edge-of-the-knee incision method were undergone and 15 in whom conventional lymphaticovenular anastomoses were undergone. Lymphaticovenular anastomosis at the thigh yielded 30 anastomoses in the superior-edge-of-the-knee incision group and 32 anastomoses in the non-superior-edge-of-the-knee incision group. Large lymphatic vessels were more frequently found in the superior-edge-of-the-knee incision group than in the non-superior-edge-of-the-knee incision group (60.0 percent versus 18.8 percent; p = 0.002). Venous reflux occurred less frequently in the superior-edge-of-the-knee incision group than in the non-superior-edge-of-the-knee incision group (10.0 percent versus 65.6 percent; p < 0.001). Reduction of the lower extremity lymphedema index was significantly greater in the superior-edge-of-the-knee incision group than in the non-superior-edge-of-the-knee incision group (24.427 ± 12.400 versus 0.032 ± 20.535; p < 0.001). The superior-edge-of-the-knee incision method facilitates detection and use of large, high-flow lymphatic vessels in the distal medial thigh, both of which are important for optimum therapeutic effects in patients with lower extremity lymphedema. Therapeutic, III.

Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform

PubMed Central

Wang, Min; Tian, Yun

2018-01-01

The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator's dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data. For the experiments, we constructed datasets of different scales from the Pascal VOC2012 image database. The proposed parallel Otsu-Canny edge detection algorithm performs better than other traditional edge detection algorithms. The parallel approach reduced the running time by approximately 67.2% on a Hadoop cluster architecture consisting of 5 nodes with a dataset of 60,000 images. Overall, our approach system speeds up the system by approximately 3.4 times when processing large-scale datasets, which demonstrates the obvious superiority of our method. The proposed algorithm in this study demonstrates both better edge detection performance and improved time performance. PMID:29861711

Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

USGS Publications Warehouse

Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.

2011-01-01

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.

Lunar-edge based on-orbit modulation transfer function (MTF) measurement

NASA Astrophysics Data System (ADS)

Cheng, Ying; Yi, Hongwei; Liu, Xinlong

2017-10-01

Modulation transfer function (MTF) is an important parameter for image quality evaluation of on-orbit optical image systems. Various methods have been proposed to determine the MTF of an imaging system which are based on images containing point, pulse and edge features. In this paper, the edge of the moon can be used as a high contrast target to measure on-orbit MTF of image systems based on knife-edge methods. The proposed method is an extension of the ISO 12233 Slanted-edge Spatial Frequency Response test, except that the shape of the edge is a circular arc instead of a straight line. In order to get more accurate edge locations and then obtain a more authentic edge spread function (ESF), we choose circular fitting method based on least square to fit lunar edge in sub-pixel edge detection process. At last, simulation results show that the MTF value at Nyquist frequency calculated using our lunar edge method is reliable and accurate with error less than 2% comparing with theoretical MTF value.

An Adaptive Immune Genetic Algorithm for Edge Detection

NASA Astrophysics Data System (ADS)

Li, Ying; Bai, Bendu; Zhang, Yanning

An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

Accurately estimating PSF with straight lines detected by Hough transform

NASA Astrophysics Data System (ADS)

Wang, Ruichen; Xu, Liangpeng; Fan, Chunxiao; Li, Yong

2018-04-01

This paper presents an approach to estimating point spread function (PSF) from low resolution (LR) images. Existing techniques usually rely on accurate detection of ending points of the profile normal to edges. In practice however, it is often a great challenge to accurately localize profiles of edges from a LR image, which hence leads to a poor PSF estimation of the lens taking the LR image. For precisely estimating the PSF, this paper proposes firstly estimating a 1-D PSF kernel with straight lines, and then robustly obtaining the 2-D PSF from the 1-D kernel by least squares techniques and random sample consensus. Canny operator is applied to the LR image for obtaining edges and then Hough transform is utilized to extract straight lines of all orientations. Estimating 1-D PSF kernel with straight lines effectively alleviates the influence of the inaccurate edge detection on PSF estimation. The proposed method is investigated on both natural and synthetic images for estimating PSF. Experimental results show that the proposed method outperforms the state-ofthe- art and does not rely on accurate edge detection.

Adaptive local thresholding for robust nucleus segmentation utilizing shape priors

NASA Astrophysics Data System (ADS)

Wang, Xiuzhong; Srinivas, Chukka

2016-03-01

This paper describes a novel local thresholding method for foreground detection. First, a Canny edge detection method is used for initial edge detection. Then, tensor voting is applied on the initial edge pixels, using a nonsymmetric tensor field tailored to encode prior information about nucleus size, shape, and intensity spatial distribution. Tensor analysis is then performed to generate the saliency image and, based on that, the refined edge. Next, the image domain is divided into blocks. In each block, at least one foreground and one background pixel are sampled for each refined edge pixel. The saliency weighted foreground histogram and background histogram are then created. These two histograms are used to calculate a threshold by minimizing the background and foreground pixel classification error. The block-wise thresholds are then used to generate the threshold for each pixel via interpolation. Finally, the foreground is obtained by comparing the original image with the threshold image. The effective use of prior information, combined with robust techniques, results in far more reliable foreground detection, which leads to robust nucleus segmentation.

An enhanced narrow-band imaging method for the microvessel detection

NASA Astrophysics Data System (ADS)

Yu, Feng; Song, Enmin; Liu, Hong; Wan, Youming; Zhu, Jun; Hung, Chih-Cheng

2018-02-01

A medical endoscope system combined with the narrow-band imaging (NBI), has been shown to be a superior diagnostic tool for early cancer detection. The NBI can reveal the morphologic changes of microvessels in the superficial cancer. In order to improve the conspicuousness of microvessel texture, we propose an enhanced NBI method to improve the conspicuousness of endoscopic images. To obtain the more conspicuous narrow-band images, we use the edge operator to extract the edge information of the narrow-band blue and green images, and give a weight to the extracted edges. Then, the weighted edges are fused with the narrow-band blue and green images. Finally, the displayed endoscopic images are reconstructed with the enhanced narrow-band images. In addition, we evaluate the performance of enhanced narrow-band images with different edge operators. Experimental results indicate that the Sobel and Canny operators achieve the best performance of all. Compared with traditional NBI method of Olympus company, our proposed method has more conspicuous texture of microvessel.

Edge detection for optical synthetic aperture based on deep neural network

NASA Astrophysics Data System (ADS)

Tan, Wenjie; Hui, Mei; Liu, Ming; Kong, Lingqin; Dong, Liquan; Zhao, Yuejin

2017-09-01

Synthetic aperture optics systems can meet the demands of the next-generation space telescopes being lighter, larger and foldable. However, the boundaries of segmented aperture systems are much more complex than that of the whole aperture. More edge regions mean more imaging edge pixels, which are often mixed and discretized. In order to achieve high-resolution imaging, it is necessary to identify the gaps between the sub-apertures and the edges of the projected fringes. In this work, we introduced the algorithm of Deep Neural Network into the edge detection of optical synthetic aperture imaging. According to the detection needs, we constructed image sets by experiments and simulations. Based on MatConvNet, a toolbox of MATLAB, we ran the neural network, trained it on training image set and tested its performance on validation set. The training was stopped when the test error on validation set stopped declining. As an input image is given, each intra-neighbor area around the pixel is taken into the network, and scanned pixel by pixel with the trained multi-hidden layers. The network outputs make a judgment on whether the center of the input block is on edge of fringes. We experimented with various pre-processing and post-processing techniques to reveal their influence on edge detection performance. Compared with the traditional algorithms or their improvements, our method makes decision on a much larger intra-neighbor, and is more global and comprehensive. Experiments on more than 2,000 images are also given to prove that our method outperforms classical algorithms in optical images-based edge detection.

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

Automatic airline baggage counting using 3D image segmentation

NASA Astrophysics Data System (ADS)

Yin, Deyu; Gao, Qingji; Luo, Qijun

2017-06-01

The baggage number needs to be checked automatically during baggage self-check-in. A fast airline baggage counting method is proposed in this paper using image segmentation based on height map which is projected by scanned baggage 3D point cloud. There is height drop in actual edge of baggage so that it can be detected by the edge detection operator. And then closed edge chains are formed from edge lines that is linked by morphological processing. Finally, the number of connected regions segmented by closed chains is taken as the baggage number. Multi-bag experiment that is performed on the condition of different placement modes proves the validity of the method.

Content-based unconstrained color logo and trademark retrieval with color edge gradient co-occurrence histograms

NASA Astrophysics Data System (ADS)

Phan, Raymond; Androutsos, Dimitrios

2008-01-01

In this paper, we present a logo and trademark retrieval system for unconstrained color image databases that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, in comparison to the simple color pixel difference classification of edges as seen in the CECH. Our proposed method is thus reliant on edge gradient information, and as such, we call this the Color Edge Gradient Co-occurrence Histogram (CEGCH). We use this as the main mechanism for our unconstrained color logo and trademark retrieval scheme. Results illustrate that the proposed retrieval system retrieves logos and trademarks with good accuracy, and outperforms the CECH object detection scheme with higher precision and recall.

Method and apparatus for detecting flaws and defects in heat seals

NASA Technical Reports Server (NTRS)

Rai, Kula R. (Inventor); Lew, Thomas M. (Inventor); Sinclair, Robert B. (Inventor)

1993-01-01

Flaws and defects in heat seals formed between sheets of translucent film are identified by optically examining consecutive lateral sections of the seal along the seal length. Each lateral seal section is illuminated and an optical sensor array detects the intensity of light transmitted through the seal section for the purpose of detecting and locating edges in the heat seal. A line profile for each consecutive seal section is derived having an amplitude proportional to the change in light intensity across the seal section. Instances in the derived line profile where the amplitude is greater than a threshold level indicate the detection of a seal edge. The detected edges in each derived line profile are then compared to a preset profile edge standard to identify the existence of a flaw or defect.

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.

High Precision Edge Detection Algorithm for Mechanical Parts

NASA Astrophysics Data System (ADS)

Duan, Zhenyun; Wang, Ning; Fu, Jingshun; Zhao, Wenhui; Duan, Boqiang; Zhao, Jungui

2018-04-01

High precision and high efficiency measurement is becoming an imperative requirement for a lot of mechanical parts. So in this study, a subpixel-level edge detection algorithm based on the Gaussian integral model is proposed. For this purpose, the step edge normal section line Gaussian integral model of the backlight image is constructed, combined with the point spread function and the single step model. Then gray value of discrete points on the normal section line of pixel edge is calculated by surface interpolation, and the coordinate as well as gray information affected by noise is fitted in accordance with the Gaussian integral model. Therefore, a precise location of a subpixel edge was determined by searching the mean point. Finally, a gear tooth was measured by M&M3525 gear measurement center to verify the proposed algorithm. The theoretical analysis and experimental results show that the local edge fluctuation is reduced effectively by the proposed method in comparison with the existing subpixel edge detection algorithms. The subpixel edge location accuracy and computation speed are improved. And the maximum error of gear tooth profile total deviation is 1.9 μm compared with measurement result with gear measurement center. It indicates that the method has high reliability to meet the requirement of high precision measurement.

Edge detection, cosmic strings and the south pole telescope

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

Stewart, Andrew; Brandenberger, Robert, E-mail: stewarta@physics.mcgill.ca, E-mail: rhb@physics.mcgill.ca

2009-02-15

We develop a method of constraining the cosmic string tension G{mu} which uses the Canny edge detection algorithm as a means of searching CMB temperature maps for the signature of the Kaiser-Stebbins effect. We test the potential of this method using high resolution, simulated CMB temperature maps. By modeling the future output from the South Pole Telescope project (including anticipated instrumental noise), we find that cosmic strings with G{mu} > 5.5 Multiplication-Sign 10{sup -8} could be detected.

Image steganography based on 2k correction and coherent bit length

NASA Astrophysics Data System (ADS)

Sun, Shuliang; Guo, Yongning

2014-10-01

In this paper, a novel algorithm is proposed. Firstly, the edge of cover image is detected with Canny operator and secret data is embedded in edge pixels. Sorting method is used to randomize the edge pixels in order to enhance security. Coherent bit length L is determined by relevant edge pixels. Finally, the method of 2k correction is applied to achieve better imperceptibility in stego image. The experiment shows that the proposed method is better than LSB-3 and Jae-Gil Yu's in PSNR and capacity.

Edge detection based on adaptive threshold b-spline wavelet for optical sub-aperture measuring

NASA Astrophysics Data System (ADS)

Zhang, Shiqi; Hui, Mei; Liu, Ming; Zhao, Zhu; Dong, Liquan; Liu, Xiaohua; Zhao, Yuejin

2015-08-01

In the research of optical synthetic aperture imaging system, phase congruency is the main problem and it is necessary to detect sub-aperture phase. The edge of the sub-aperture system is more complex than that in the traditional optical imaging system. And with the existence of steep slope for large-aperture optical component, interference fringe may be quite dense when interference imaging. Deep phase gradient may cause a loss of phase information. Therefore, it's urgent to search for an efficient edge detection method. Wavelet analysis as a powerful tool is widely used in the fields of image processing. Based on its properties of multi-scale transform, edge region is detected with high precision in small scale. Longing with the increase of scale, noise is reduced in contrary. So it has a certain suppression effect on noise. Otherwise, adaptive threshold method which sets different thresholds in various regions can detect edge points from noise. Firstly, fringe pattern is obtained and cubic b-spline wavelet is adopted as the smoothing function. After the multi-scale wavelet decomposition of the whole image, we figure out the local modulus maxima in gradient directions. However, it also contains noise, and thus adaptive threshold method is used to select the modulus maxima. The point which greater than threshold value is boundary point. Finally, we use corrosion and expansion deal with the resulting image to get the consecutive boundary of image.

Improved imaging algorithm for bridge crack detection

NASA Astrophysics Data System (ADS)

Lu, Jingxiao; Song, Pingli; Han, Kaihong

2012-04-01

This paper present an improved imaging algorithm for bridge crack detection, through optimizing the eight-direction Sobel edge detection operator, making the positioning of edge points more accurate than without the optimization, and effectively reducing the false edges information, so as to facilitate follow-up treatment. In calculating the crack geometry characteristics, we use the method of extracting skeleton on single crack length. In order to calculate crack area, we construct the template of area by making logical bitwise AND operation of the crack image. After experiment, the results show errors of the crack detection method and actual manual measurement are within an acceptable range, meet the needs of engineering applications. This algorithm is high-speed and effective for automated crack measurement, it can provide more valid data for proper planning and appropriate performance of the maintenance and rehabilitation processes of bridge.

Ultrasound-Guided Bar Edge Labeling in the Perioperative Assessment of Nuss Bar Removal.

PubMed

Incerti, Filippo; Bertocchini, Alessia; Ghionzoli, Marco; Messineo, Antonio

2017-12-01

Nuss bar removal after minimally invasive repair of pectus excavatum in patients where bar ends are not palpable, can be a challenging procedure for the surgeon; a blind dissection toward the bar edges may lead to intercostal vessels or deep intercostal muscle injuries. In this article, we describe a fast, repeatable, low-cost technique to detect bar edge and stabilizers. A perioperative scan is performed by means of a portable ultrasonograph a few minutes before the operation. The bar edge stabilizer is detected as a hyperechogenic image with a concentric crescent while the bar edge is detected as a hyperechogenic dashed line with net edges. The scan is performed, and the actual projection on the skin of the metal plaque bulk is then labeled on the patient's chest by an ink marker. We believe that this method may improve morbidity, operative time, and consequently, hospitalization length and costs.

Automated macromolecular crystal detection system and method

DOEpatents

Christian, Allen T [Tracy, CA; Segelke, Brent [San Ramon, CA; Rupp, Bernard [Livermore, CA; Toppani, Dominique [Fontainebleau, FR

2007-06-05

An automated macromolecular method and system for detecting crystals in two-dimensional images, such as light microscopy images obtained from an array of crystallization screens. Edges are detected from the images by identifying local maxima of a phase congruency-based function associated with each image. The detected edges are segmented into discrete line segments, which are subsequently geometrically evaluated with respect to each other to identify any crystal-like qualities such as, for example, parallel lines, facing each other, similarity in length, and relative proximity. And from the evaluation a determination is made as to whether crystals are present in each image.

Development of a fully automatic scheme for detection of masses in whole breast ultrasound images.

PubMed

Ikedo, Yuji; Fukuoka, Daisuke; Hara, Takeshi; Fujita, Hiroshi; Takada, Etsuo; Endo, Tokiko; Morita, Takako

2007-11-01

Ultrasonography has been used for breast cancer screening in Japan. Screening using a conventional hand-held probe is operator dependent and thus it is possible that some areas of the breast may not be scanned. To overcome such problems, a mechanical whole breast ultrasound (US) scanner has been proposed and developed for screening purposes. However, another issue is that radiologists might tire while interpreting all images in a large-volume screening; this increases the likelihood that masses may remain undetected. Therefore, the aim of this study is to develop a fully automatic scheme for the detection of masses in whole breast US images in order to assist the interpretations of radiologists and potentially improve the screening accuracy. The authors database comprised 109 whole breast US imagoes, which include 36 masses (16 malignant masses, 5 fibroadenomas, and 15 cysts). A whole breast US image with 84 slice images (interval between two slice images: 2 mm) was obtained by the ASU-1004 US scanner (ALOKA Co., Ltd., Japan). The feature based on the edge directions in each slice and a method for subtracting between the slice images were used for the detection of masses in the authors proposed scheme. The Canny edge detector was applied to detect edges in US images; these edges were classified as near-vertical edges or near-horizontal edges using a morphological method. The positions of mass candidates were located using the near-vertical edges as a cue. Then, the located positions were segmented by the watershed algorithm and mass candidate regions were detected using the segmented regions and the low-density regions extracted by the slice subtraction method. For the removal of false positives (FPs), rule-based schemes and a quadratic discriminant analysis were applied for the distribution between masses and FPs. As a result, the sensitivity of the authors scheme for the detection of masses was 80.6% (29/36) with 3.8 FPs per whole breast image. The authors scheme for a computer-aided detection may be useful in improving the screening performance and efficiency.

Feature extraction algorithm for space targets based on fractal theory

NASA Astrophysics Data System (ADS)

Tian, Balin; Yuan, Jianping; Yue, Xiaokui; Ning, Xin

2007-11-01

In order to offer a potential for extending the life of satellites and reducing the launch and operating costs, satellite servicing including conducting repairs, upgrading and refueling spacecraft on-orbit become much more frequently. Future space operations can be more economically and reliably executed using machine vision systems, which can meet real time and tracking reliability requirements for image tracking of space surveillance system. Machine vision was applied to the research of relative pose for spacecrafts, the feature extraction algorithm was the basis of relative pose. In this paper fractal geometry based edge extraction algorithm which can be used in determining and tracking the relative pose of an observed satellite during proximity operations in machine vision system was presented. The method gets the gray-level image distributed by fractal dimension used the Differential Box-Counting (DBC) approach of the fractal theory to restrain the noise. After this, we detect the consecutive edge using Mathematical Morphology. The validity of the proposed method is examined by processing and analyzing images of space targets. The edge extraction method not only extracts the outline of the target, but also keeps the inner details. Meanwhile, edge extraction is only processed in moving area to reduce computation greatly. Simulation results compared edge detection using the method which presented by us with other detection methods. The results indicate that the presented algorithm is a valid method to solve the problems of relative pose for spacecrafts.

Information theoretic analysis of edge detection in visual communication

NASA Astrophysics Data System (ADS)

Jiang, Bo; Rahman, Zia-ur

2010-08-01

Generally, the designs of digital image processing algorithms and image gathering devices remain separate. Consequently, the performance of digital image processing algorithms is evaluated without taking into account the artifacts introduced into the process by the image gathering process. However, experiments show that the image gathering process profoundly impacts the performance of digital image processing and the quality of the resulting images. Huck et al. proposed one definitive theoretic analysis of visual communication channels, where the different parts, such as image gathering, processing, and display, are assessed in an integrated manner using Shannon's information theory. In this paper, we perform an end-to-end information theory based system analysis to assess edge detection methods. We evaluate the performance of the different algorithms as a function of the characteristics of the scene, and the parameters, such as sampling, additive noise etc., that define the image gathering system. The edge detection algorithm is regarded to have high performance only if the information rate from the scene to the edge approaches the maximum possible. This goal can be achieved only by jointly optimizing all processes. People generally use subjective judgment to compare different edge detection methods. There is not a common tool that can be used to evaluate the performance of the different algorithms, and to give people a guide for selecting the best algorithm for a given system or scene. Our information-theoretic assessment becomes this new tool to which allows us to compare the different edge detection operators in a common environment.

Generation algorithm of craniofacial structure contour in cephalometric images

NASA Astrophysics Data System (ADS)

Mondal, Tanmoy; Jain, Ashish; Sardana, H. K.

2010-02-01

Anatomical structure tracing on cephalograms is a significant way to obtain cephalometric analysis. Computerized cephalometric analysis involves both manual and automatic approaches. The manual approach is limited in accuracy and repeatability. In this paper we have attempted to develop and test a novel method for automatic localization of craniofacial structure based on the detected edges on the region of interest. According to the grey scale feature at the different region of the cephalometric images, an algorithm for obtaining tissue contour is put forward. Using edge detection with specific threshold an improved bidirectional contour tracing approach is proposed by an interactive selection of the starting edge pixels, the tracking process searches repetitively for an edge pixel at the neighborhood of previously searched edge pixel to segment images, and then craniofacial structures are obtained. The effectiveness of the algorithm is demonstrated by the preliminary experimental results obtained with the proposed method.

A study on obstacle detection method of the frontal view using a camera on highway

NASA Astrophysics Data System (ADS)

Nguyen, Van-Quang; Park, Jeonghyeon; Seo, Changjun; Kim, Heungseob; Boo, Kwangsuck

2018-03-01

In this work, we introduce an approach to detect vehicles for driver assistance, or warning system. For driver assistance system, it must detect both lanes (left and right side lane), and discover vehicles ahead of the test vehicle. Therefore, in this study, we use a camera, it is installed on the windscreen of the test vehicle. Images from the camera are used to detect three lanes, and detect multiple vehicles. In lane detection, line detection and vanishing point estimation are used. For the vehicle detection, we combine the horizontal and vertical edge detection, the horizontal edge is used to detect the vehicle candidates, and then the vertical edge detection is used to verify the vehicle candidates. The proposed algorithm works with of 480 × 640 image frame resolution. The system was tested on the highway in Korea.

Segmentation of blurred objects using wavelet transform: application to x-ray images

NASA Astrophysics Data System (ADS)

Barat, Cecile S.; Ducottet, Christophe; Bilgot, Anne; Desbat, Laurent

2004-02-01

First, we present a wavelet-based algorithm for edge detection and characterization, which is an adaptation of Mallat and Hwang"s method. This algorithm relies on a modelization of contours as smoothed singularities of three particular types (transitions, peaks and lines). On the one hand, it allows to detect and locate edges at an adapted scale. On the other hand, it is able to identify the type of each detected edge point and to measure its amplitude and smoothing size. The latter parameters represent respectively the contrast and the smoothness level of the edge point. Second, we explain that this method has been integrated in a 3D bone surface reconstruction algorithm designed for computer-assisted and minimal invasive orthopaedic surgery. In order to decrease the dose to the patient and to obtain rapidly a 3D image, we propose to identify a bone shape from few X-ray projections by using statistical shape models registered to segmented X-ray projections. We apply this approach to pedicle screw insertion (scoliosis, fractures...) where ten to forty percent of the screws are known to be misplaced. In this context, the proposed edge detection algorithm allows to overcome the major problem of vertebrae segmentation in the X-ray images.

Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks.

PubMed

He, Jieyue; Li, Chaojun; Ye, Baoliu; Zhong, Wei

2012-06-25

Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures. In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules. The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms. Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.

Image processing system and method for recognizing and removing shadows from the image of a monitored scene

DOEpatents

Osbourn, Gordon C.

1996-01-01

The shadow contrast sensitivity of the human vision system is simulated by configuring information obtained from an image sensor so that the information may be evaluated with multiple pixel widths in order to produce a machine vision system able to distinguish between shadow edges and abrupt object edges. A second difference of the image intensity for each line of the image is developed and this second difference is used to screen out high frequency noise contributions from the final edge detection signals. These edge detection signals are constructed from first differences of the image intensity where the screening conditions are satisfied. The positional coincidence of oppositely signed maxima in the first difference signal taken from the right and the second difference signal taken from the left is used to detect the presence of an object edge. Alternatively, the effective number of responding operators (ENRO) may be utilized to determine the presence of object edges.

Direct Detection Doppler Lidar for Spaceborne Wind Measurement

NASA Technical Reports Server (NTRS)

Korb, C. Laurence; Flesia, Cristina

1999-01-01

Aerosol and molecular based versions of the double-edge technique can be used for direct detection Doppler lidar spaceborne wind measurement. The edge technique utilizes the edge of a high spectral resolution filter for high accuracy wind measurement using direct detection lidar. The signal is split between an edge filter channel and a broadband energy monitor channel. The energy monitor channel is used for signal normalization. The edge measurement is made as a differential frequency measurement between the outgoing laser signal and the atmospheric backscattered return for each pulse. As a result the measurement is insensitive to laser and edge filter frequency jitter and drift at a level less than a few parts in 10(exp 10). We have developed double edge versions of the edge technique for aerosol and molecular-based lidar measurement of the wind. Aerosol-based wind measurements have been made at Goddard Space Flight Center and molecular-based wind measurements at the University of Geneva. We have demonstrated atmospheric measurements using these techniques for altitudes from 1 to more than 10 km. Measurement accuracies of better than 1.25 m/s have been obtained with integration times from 5 to 30 seconds. The measurements can be scaled to space and agree, within a factor of two, with satellite-based simulations of performance based on Poisson statistics. The theory of the double edge aerosol technique is described by a generalized formulation which substantially extends the capabilities of the edge technique. It uses two edges with opposite slopes located about the laser frequency at approximately the half-width of each edge filter. This doubles the signal change for a given Doppler shift and yields a factor of 1.6 improvement in the measurement accuracy compared to the single edge technique. The use of two high resolution edge filters substantially reduces the effects of Rayleigh scattering on the measurement, as much as order of magnitude, and allows the signal to noise ratio to be substantially improved in areas of low aerosol backscatter. We describe a method that allows the Rayleigh and aerosol components of the signal to be independently determined using the two edge channels and an energy monitor channel. The effects of Rayleigh scattering may then subtracted from the measurement and we show that the correction process does not significantly increase the measurement noise for Rayleigh to aerosol ratios up to 10. We show that for small Doppler shifts a measurement accuracy of 0.4 m/s can be obtained for 5000 detected photon, 1.2 m/s for 1000 detected photons, and 3.7 m/s for 50 detected photons for a Rayleigh to aerosol ratio of 5. Methods for increasing the dynamic range of the aerosol-based system to more than +/- 100 m/s are given.

Threshold-adaptive canny operator based on cross-zero points

NASA Astrophysics Data System (ADS)

Liu, Boqi; Zhang, Xiuhua; Hong, Hanyu

2018-03-01

Canny edge detection[1] is a technique to extract useful structural information from different vision objects and dramatically reduce the amount of data to be processed. It has been widely applied in various computer vision systems. There are two thresholds have to be settled before the edge is segregated from background. Usually, by the experience of developers, two static values are set as the thresholds[2]. In this paper, a novel automatic thresholding method is proposed. The relation between the thresholds and Cross-zero Points is analyzed, and an interpolation function is deduced to determine the thresholds. Comprehensive experimental results demonstrate the effectiveness of proposed method and advantageous for stable edge detection at changing illumination.

Slot angle detecting method for fiber fixed chip

NASA Astrophysics Data System (ADS)

Zhang, Jiaquan; Wang, Jiliang; Zhou, Chaochao

2018-04-01

The slot angle of fiber fixed chip has a significant impact on performance of photoelectric devices. In order to solve the actual engineering problem, this paper put forward a detecting method based on imaging processing. Because the images have very low contrast that is hardly segmented, so this paper proposes imaging segment methods based on edge character. Then get fixed chip edge line slope k2 and calculate the fiber fixed slot line slope k1, which can be used calculating the slot angle. Lastly, test the repeatability and accuracy of system, which show that this method has very fast operation speed and good robustness. Clearly, it is also satisfied to the actual demand of fiber fixed chip slot angle detection.

Reflection symmetry detection using locally affine invariant edge correspondence.

PubMed

Wang, Zhaozhong; Tang, Zesheng; Zhang, Xiao

2015-04-01

Reflection symmetry detection receives increasing attentions in recent years. The state-of-the-art algorithms mainly use the matching of intensity-based features (such as the SIFT) within a single image to find symmetry axes. This paper proposes a novel approach by establishing the correspondence of locally affine invariant edge-based features, which are superior to the intensity based in the aspects that it is insensitive to illumination variations, and applicable to textureless objects. The locally affine invariance is achieved by simple linear algebra for efficient and robust computations, making the algorithm suitable for detections under object distortions like perspective projection. Commonly used edge detectors and a voting process are, respectively, used before and after the edge description and matching steps to form a complete reflection detection pipeline. Experiments are performed using synthetic and real-world images with both multiple and single reflection symmetry axis. The test results are compared with existing algorithms to validate the proposed method.

Salient man-made structure detection in infrared images

NASA Astrophysics Data System (ADS)

Li, Dong-jie; Zhou, Fu-gen; Jin, Ting

2013-09-01

Target detection, segmentation and recognition is a hot research topic in the field of image processing and pattern recognition nowadays, among which salient area or object detection is one of core technologies of precision guided weapon. Many theories have been raised in this paper; we detect salient objects in a series of input infrared images by using the classical feature integration theory and Itti's visual attention system. In order to find the salient object in an image accurately, we present a new method to solve the edge blur problem by calculating and using the edge mask. We also greatly improve the computing speed by improving the center-surround differences method. Unlike the traditional algorithm, we calculate the center-surround differences through rows and columns separately. Experimental results show that our method is effective in detecting salient object accurately and rapidly.

Glue detection based on teaching points constraint and tracking model of pixel convolution

NASA Astrophysics Data System (ADS)

Geng, Lei; Ma, Xiao; Xiao, Zhitao; Wang, Wen

2018-01-01

On-line glue detection based on machine version is significant for rust protection and strengthening in car production. Shadow stripes caused by reflect light and unevenness of inside front cover of car reduce the accuracy of glue detection. In this paper, we propose an effective algorithm to distinguish the edges of the glue and shadow stripes. Teaching points are utilized to calculate slope between the two adjacent points. Then a tracking model based on pixel convolution along motion direction is designed to segment several local rectangular regions using distance. The distance is the height of rectangular region. The pixel convolution along the motion direction is proposed to extract edges of gules in local rectangular region. A dataset with different illumination and complexity shape stripes are used to evaluate proposed method, which include 500 thousand images captured from the camera of glue gun machine. Experimental results demonstrate that the proposed method can detect the edges of glue accurately. The shadow stripes are distinguished and removed effectively. Our method achieves the 99.9% accuracies for the image dataset.

Fast Edge Detection and Segmentation of Terrestrial Laser Scans Through Normal Variation Analysis

NASA Astrophysics Data System (ADS)

Che, E.; Olsen, M. J.

2017-09-01

Terrestrial Laser Scanning (TLS) utilizes light detection and ranging (lidar) to effectively and efficiently acquire point cloud data for a wide variety of applications. Segmentation is a common procedure of post-processing to group the point cloud into a number of clusters to simplify the data for the sequential modelling and analysis needed for most applications. This paper presents a novel method to rapidly segment TLS data based on edge detection and region growing. First, by computing the projected incidence angles and performing the normal variation analysis, the silhouette edges and intersection edges are separated from the smooth surfaces. Then a modified region growing algorithm groups the points lying on the same smooth surface. The proposed method efficiently exploits the gridded scan pattern utilized during acquisition of TLS data from most sensors and takes advantage of parallel programming to process approximately 1 million points per second. Moreover, the proposed segmentation does not require estimation of the normal at each point, which limits the errors in normal estimation propagating to segmentation. Both an indoor and outdoor scene are used for an experiment to demonstrate and discuss the effectiveness and robustness of the proposed segmentation method.

Enhanced Component Performance Study: Emergency Diesel Generators 1998–2014

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

Schroeder, John Alton

2015-11-01

This report presents an enhanced performance evaluation of emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using (1) Institute of Nuclear Power Operations (INPO) Consolidated Events Database (ICES) data from 1998 through 2014 and (2) maintenance unavailability (UA) performance data from Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2014. The objective is to show estimates of current failure probabilities and rates related to EDGs, trend these data on an annual basis, determine if the current data are consistent with the probability distributions currently recommended for use inmore » NRC probabilistic risk assessments, show how the reliability data differ for different EDG manufacturers and for EDGs with different ratings; and summarize the subcomponents, causes, detection methods, and recovery associated with each EDG failure mode. Engineering analyses were performed with respect to time period and failure mode without regard to the actual number of EDGs at each plant. The factors analyzed are: sub-component, failure cause, detection method, recovery, manufacturer, and EDG rating. Six trends with varying degrees of statistical significance were identified in the data.« less

Real-time biscuit tile image segmentation method based on edge detection.

PubMed

Matić, Tomislav; Aleksi, Ivan; Hocenski, Željko; Kraus, Dieter

2018-05-01

In this paper we propose a novel real-time Biscuit Tile Segmentation (BTS) method for images from ceramic tile production line. BTS method is based on signal change detection and contour tracing with a main goal of separating tile pixels from background in images captured on the production line. Usually, human operators are visually inspecting and classifying produced ceramic tiles. Computer vision and image processing techniques can automate visual inspection process if they fulfill real-time requirements. Important step in this process is a real-time tile pixels segmentation. BTS method is implemented for parallel execution on a GPU device to satisfy the real-time constraints of tile production line. BTS method outperforms 2D threshold-based methods, 1D edge detection methods and contour-based methods. Proposed BTS method is in use in the biscuit tile production line. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Contrast-guided image interpolation.

PubMed

Wei, Zhe; Ma, Kai-Kuang

2013-11-01

In this paper a contrast-guided image interpolation method is proposed that incorporates contrast information into the image interpolation process. Given the image under interpolation, four binary contrast-guided decision maps (CDMs) are generated and used to guide the interpolation filtering through two sequential stages: 1) the 45(°) and 135(°) CDMs for interpolating the diagonal pixels and 2) the 0(°) and 90(°) CDMs for interpolating the row and column pixels. After applying edge detection to the input image, the generation of a CDM lies in evaluating those nearby non-edge pixels of each detected edge for re-classifying them possibly as edge pixels. This decision is realized by solving two generalized diffusion equations over the computed directional variation (DV) fields using a derived numerical approach to diffuse or spread the contrast boundaries or edges, respectively. The amount of diffusion or spreading is proportional to the amount of local contrast measured at each detected edge. The diffused DV fields are then thresholded for yielding the binary CDMs, respectively. Therefore, the decision bands with variable widths will be created on each CDM. The two CDMs generated in each stage will be exploited as the guidance maps to conduct the interpolation process: for each declared edge pixel on the CDM, a 1-D directional filtering will be applied to estimate its associated to-be-interpolated pixel along the direction as indicated by the respective CDM; otherwise, a 2-D directionless or isotropic filtering will be used instead to estimate the associated missing pixels for each declared non-edge pixel. Extensive simulation results have clearly shown that the proposed contrast-guided image interpolation is superior to other state-of-the-art edge-guided image interpolation methods. In addition, the computational complexity is relatively low when compared with existing methods; hence, it is fairly attractive for real-time image applications.

Using fuzzy fractal features of digital images for the material surface analisys

NASA Astrophysics Data System (ADS)

Privezentsev, D. G.; Zhiznyakov, A. L.; Astafiev, A. V.; Pugin, E. V.

2018-01-01

Edge detection is an important task in image processing. There are a lot of approaches in this area: Sobel, Canny operators and others. One of the perspective techniques in image processing is the use of fuzzy logic and fuzzy sets theory. They allow us to increase processing quality by representing information in its fuzzy form. Most of the existing fuzzy image processing methods switch to fuzzy sets on very late stages, so this leads to some useful information loss. In this paper, a novel method of edge detection based on fuzzy image representation and fuzzy pixels is proposed. With this approach, we convert the image to fuzzy form on the first step. Different approaches to this conversion are described. Several membership functions for fuzzy pixel description and requirements for their form and view are given. A novel approach to edge detection based on Sobel operator and fuzzy image representation is proposed. Experimental testing of developed method was performed on remote sensing images.

An Iris Segmentation Algorithm based on Edge Orientation for Off-angle Iris Recognition

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

Karakaya, Mahmut; Barstow, Del R; Santos-Villalobos, Hector J

Iris recognition is known as one of the most accurate and reliable biometrics. However, the accuracy of iris recognition systems depends on the quality of data capture and is negatively affected by several factors such as angle, occlusion, and dilation. In this paper, we present a segmentation algorithm for off-angle iris images that uses edge detection, edge elimination, edge classification, and ellipse fitting techniques. In our approach, we first detect all candidate edges in the iris image by using the canny edge detector; this collection contains edges from the iris and pupil boundaries as well as eyelash, eyelids, iris texturemore » etc. Edge orientation is used to eliminate the edges that cannot be part of the iris or pupil. Then, we classify the remaining edge points into two sets as pupil edges and iris edges. Finally, we randomly generate subsets of iris and pupil edge points, fit ellipses for each subset, select ellipses with similar parameters, and average to form the resultant ellipses. Based on the results from real experiments, the proposed method shows effectiveness in segmentation for off-angle iris images.« less

An improved algorithm of laser spot center detection in strong noise background

NASA Astrophysics Data System (ADS)

Zhang, Le; Wang, Qianqian; Cui, Xutai; Zhao, Yu; Peng, Zhong

2018-01-01

Laser spot center detection is demanded in many applications. The common algorithms for laser spot center detection such as centroid and Hough transform method have poor anti-interference ability and low detection accuracy in the condition of strong background noise. In this paper, firstly, the median filtering was used to remove the noise while preserving the edge details of the image. Secondly, the binarization of the laser facula image was carried out to extract target image from background. Then the morphological filtering was performed to eliminate the noise points inside and outside the spot. At last, the edge of pretreated facula image was extracted and the laser spot center was obtained by using the circle fitting method. In the foundation of the circle fitting algorithm, the improved algorithm added median filtering, morphological filtering and other processing methods. This method could effectively filter background noise through theoretical analysis and experimental verification, which enhanced the anti-interference ability of laser spot center detection and also improved the detection accuracy.

In silico simulation of liver crack detection using ultrasonic shear wave imaging.

PubMed

Nie, Erwei; Yu, Jiao; Dutta, Debaditya; Zhu, Yanying

2018-05-16

Liver trauma is an important source of morbidity and mortality worldwide. A timely detection and precise evaluation of traumatic liver injury and the bleeding site is necessary. There is a need to develop better imaging modalities of hepatic injuries to increase the sensitivity of ultrasonic imaging techniques for sites of hemorrhage caused by cracks. In this study, we conduct an in silico simulation of liver crack detection and delineation using an ultrasonic shear wave imaging (USWI) based method. We simulate the generation and propagation of the shear wave in a liver tissue medium having a crack using COMSOL. Ultrasound radio frequency (RF) signal synthesis and the two-dimensional speckle tracking algorithm are applied to simulate USWI in a medium with randomly distributed scatterers. Crack detection is performed using the directional filter and the edge detection algorithm rather than the conventional inversion algorithm. Cracks with varied sizes and locations are studied with our method and the crack localization results are compared with the given crack. Our pilot simulation study shows that, by using USWI combined with a directional filter cum edge detection technique, the near-end edge of the crack can be detected in all the three cracks that we studied. The detection errors are within 5%. For a crack of 1.6 mm thickness, little shear wave can pass through it and the far-end edge of the crack cannot be detected. The detected crack lengths using USWI are all slightly shorter than the actual crack length. The robustness of our method in detecting a straight crack, a curved crack and a subtle crack of 0.5 mm thickness is demonstrated. In this paper, we simulate the use of a USWI based method for the detection and delineation of the crack in liver. The in silico simulation helps to improve understanding and interpretation of USWI measurements in a physical scattered liver medium with a crack. This pilot study provides a basis for improved insights in future crack detection studies in a tissue phantom or liver.

Axial segmentation of lungs CT scan images using canny method and morphological operation

NASA Astrophysics Data System (ADS)

Noviana, Rina; Febriani, Rasal, Isram; Lubis, Eva Utari Cintamurni

2017-08-01

Segmentation is a very important topic in digital image process. It is found simply in varied fields of image analysis, particularly within the medical imaging field. Axial segmentation of lungs CT scan is beneficial in designation of abnormalities and surgery planning. It will do to ascertain every section within the lungs. The results of the segmentation are accustomed discover the presence of nodules. The method which utilized in this analysis are image cropping, image binarization, Canny edge detection and morphological operation. Image cropping is done so as to separate the lungs areas, that is the region of interest. Binarization method generates a binary image that has 2 values with grey level, that is black and white (ROI), from another space of lungs CT scan image. Canny method used for the edge detection. Morphological operation is applied to smoothing the lungs edge. The segmentation methodology shows an honest result. It obtains an awfully smooth edge. Moreover, the image background can also be removed in order to get the main focus, the lungs.

[Road Extraction in Remote Sensing Images Based on Spectral and Edge Analysis].

PubMed

Zhao, Wen-zhi; Luo, Li-qun; Guo, Zhou; Yue, Jun; Yu, Xue-ying; Liu, Hui; Wei, Jing

2015-10-01

Roads are typically man-made objects in urban areas. Road extraction from high-resolution images has important applications for urban planning and transportation development. However, due to the confusion of spectral characteristic, it is difficult to distinguish roads from other objects by merely using traditional classification methods that mainly depend on spectral information. Edge is an important feature for the identification of linear objects (e. g. , roads). The distribution patterns of edges vary greatly among different objects. It is crucial to merge edge statistical information into spectral ones. In this study, a new method that combines spectral information and edge statistical features has been proposed. First, edge detection is conducted by using self-adaptive mean-shift algorithm on the panchromatic band, which can greatly reduce pseudo-edges and noise effects. Then, edge statistical features are obtained from the edge statistical model, which measures the length and angle distribution of edges. Finally, by integrating the spectral and edge statistical features, SVM algorithm is used to classify the image and roads are ultimately extracted. A series of experiments are conducted and the results show that the overall accuracy of proposed method is 93% comparing with only 78% overall accuracy of the traditional. The results demonstrate that the proposed method is efficient and valuable for road extraction, especially on high-resolution images.

A new method of inshore ship detection in high-resolution optical remote sensing images

NASA Astrophysics Data System (ADS)

Hu, Qifeng; Du, Yaling; Jiang, Yunqiu; Ming, Delie

2015-10-01

Ship as an important military target and water transportation, of which the detection has great significance. In the military field, the automatic detection of ships can be used to monitor ship dynamic in the harbor and maritime of enemy, and then analyze the enemy naval power. In civilian field, the automatic detection of ships can be used in monitoring transportation of harbor and illegal behaviors such as illegal fishing, smuggling and pirates, etc. In recent years, research of ship detection is mainly concentrated in three categories: forward-looking infrared images, downward-looking SAR image, and optical remote sensing images with sea background. Little research has been done into ship detection of optical remote sensing images with harbor background, as the gray-scale and texture features of ships are similar to the coast in high-resolution optical remote sensing images. In this paper, we put forward an effective harbor ship target detection method. First of all, in order to overcome the shortage of the traditional difference method in obtaining histogram valley as the segmentation threshold, we propose an iterative histogram valley segmentation method which separates the harbor and ships from the water quite well. Secondly, as landing ships in optical remote sensing images usually lead to discontinuous harbor edges, we use Hough Transform method to extract harbor edges. First, lines are detected by Hough Transform. Then, lines that have similar slope are connected into a new line, thus we access continuous harbor edges. Secondary segmentation on the result of the land-and-sea separation, we eventually get the ships. At last, we calculate the aspect ratio of the ROIs, thereby remove those targets which are not ship. The experiment results show that our method has good robustness and can tolerate a certain degree of noise and occlusion.

Local extinction and turnover rates at the edge and interior of species' ranges

USGS Publications Warehouse

Doherty, P.F.; Boulinier, T.; James., D.

2003-01-01

One hypothesis for the maintenance of the edge of a species' range suggests that more central (and abundant) populations are relatively stable and edge populations are less stable with increased local extinction and turnover rates. To date, estimates of such metrics are equivocal due to design and analysis flaws. Apparent increased estimates of extinction and turnover rates at the edge of range, versus the interior, could be a function of decreased detection probabilities alone, and not of a biological process. We estimated extinction and turnover rates for species at the interiors and edges of their ranges using an approach which incorporates potential heterogeneity in species detection probabilities. Extinction rates were higher at the edges (0.17 ?? 0.03 []) than in the interiors (0.04 ?? 0.01), as was turnover. Without taking the probability of detection into account these differences would be artificially magnified. Knowledge of extinction and turnover rates is essential in furthering our understanding of range dynamics, and in directing conservation efforts. This study further illustrates the practical application of methods proposed recently for estimating extinction rates and other community dynamic parameters.

Local extinction and turnover rates at the edge and interior of species' ranges

USGS Publications Warehouse

Doherty, P.F.; Boulinier, T.; Nichols, J.D.

2003-01-01

One hypothesis for the maintenance of the edge of a species' range suggests that more central (and abundant) populations are relatively stable and edge populations are less stable with increased local extinction and turnover rates. To date, estimates of such metrics are equivocal due to design and analysis flaws. Apparent increased estimates of extinction and turnover rates at the edge of range, versus the interior, could be a function of decreased detection probabilities alone, and not of a biological process. We estimated extinction and turnover rates for species at the interiors and edges of their ranges using an approach which incorporates potential heterogeneity in species detection probabilities. Extinction rates were higher at the edges (0.17 ' 0.03 [SE]) than in the interiors (0.04 ' 0.01), as was turnover. Without taking the probability of detection into account these differences would be artificially magnified. Knowledge of extinction and turnover rates is essential in furthering our understanding of range dynamics, and in directing conservation efforts. This study further illustrates the practical application of methods proposed recently for estimating extinction rates and other community dynamic parameters.

Brain's tumor image processing using shearlet transform

NASA Astrophysics Data System (ADS)

Cadena, Luis; Espinosa, Nikolai; Cadena, Franklin; Korneeva, Anna; Kruglyakov, Alexey; Legalov, Alexander; Romanenko, Alexey; Zotin, Alexander

2017-09-01

Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, and classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.

Multi person detection and tracking based on hierarchical level-set method

NASA Astrophysics Data System (ADS)

Khraief, Chadia; Benzarti, Faouzi; Amiri, Hamid

2018-04-01

In this paper, we propose an efficient unsupervised method for mutli-person tracking based on hierarchical level-set approach. The proposed method uses both edge and region information in order to effectively detect objects. The persons are tracked on each frame of the sequence by minimizing an energy functional that combines color, texture and shape information. These features are enrolled in covariance matrix as region descriptor. The present method is fully automated without the need to manually specify the initial contour of Level-set. It is based on combined person detection and background subtraction methods. The edge-based is employed to maintain a stable evolution, guide the segmentation towards apparent boundaries and inhibit regions fusion. The computational cost of level-set is reduced by using narrow band technique. Many experimental results are performed on challenging video sequences and show the effectiveness of the proposed method.

Reliable clarity automatic-evaluation method for optical remote sensing images

NASA Astrophysics Data System (ADS)

Qin, Bangyong; Shang, Ren; Li, Shengyang; Hei, Baoqin; Liu, Zhiwen

2015-10-01

Image clarity, which reflects the sharpness degree at the edge of objects in images, is an important quality evaluate index for optical remote sensing images. Scholars at home and abroad have done a lot of work on estimation of image clarity. At present, common clarity-estimation methods for digital images mainly include frequency-domain function methods, statistical parametric methods, gradient function methods and edge acutance methods. Frequency-domain function method is an accurate clarity-measure approach. However, its calculation process is complicate and cannot be carried out automatically. Statistical parametric methods and gradient function methods are both sensitive to clarity of images, while their results are easy to be affected by the complex degree of images. Edge acutance method is an effective approach for clarity estimate, while it needs picking out the edges manually. Due to the limits in accuracy, consistent or automation, these existing methods are not applicable to quality evaluation of optical remote sensing images. In this article, a new clarity-evaluation method, which is based on the principle of edge acutance algorithm, is proposed. In the new method, edge detection algorithm and gradient search algorithm are adopted to automatically search the object edges in images. Moreover, The calculation algorithm for edge sharpness has been improved. The new method has been tested with several groups of optical remote sensing images. Compared with the existing automatic evaluation methods, the new method perform better both in accuracy and consistency. Thus, the new method is an effective clarity evaluation method for optical remote sensing images.

CNNEDGEPOT: CNN based edge detection of 2D near surface potential field data

NASA Astrophysics Data System (ADS)

Aydogan, D.

2012-09-01

All anomalies are important in the interpretation of gravity and magnetic data because they indicate some important structural features. One of the advantages of using gravity or magnetic data for searching contacts is to be detected buried structures whose signs could not be seen on the surface. In this paper, a general view of the cellular neural network (CNN) method with a large scale nonlinear circuit is presented focusing on its image processing applications. The proposed CNN model is used consecutively in order to extract body and body edges. The algorithm is a stochastic image processing method based on close neighborhood relationship of the cells and optimization of A, B and I matrices entitled as cloning template operators. Setting up a CNN (continues time cellular neural network (CTCNN) or discrete time cellular neural network (DTCNN)) for a particular task needs a proper selection of cloning templates which determine the dynamics of the method. The proposed algorithm is used for image enhancement and edge detection. The proposed method is applied on synthetic and field data generated for edge detection of near-surface geological bodies that mask each other in various depths and dimensions. The program named as CNNEDGEPOT is a set of functions written in MATLAB software. The GUI helps the user to easily change all the required CNN model parameters. A visual evaluation of the outputs due to DTCNN and CTCNN are carried out and the results are compared with each other. These examples demonstrate that in detecting the geological features the CNN model can be used for visual interpretation of near surface gravity or magnetic anomaly maps.

Image change detection systems, methods, and articles of manufacture

DOEpatents

Jones, James L.; Lassahn, Gordon D.; Lancaster, Gregory D.

2010-01-05

Aspects of the invention relate to image change detection systems, methods, and articles of manufacture. According to one aspect, a method of identifying differences between a plurality of images is described. The method includes loading a source image and a target image into memory of a computer, constructing source and target edge images from the source and target images to enable processing of multiband images, displaying the source and target images on a display device of the computer, aligning the source and target edge images, switching displaying of the source image and the target image on the display device, to enable identification of differences between the source image and the target image.

Range image segmentation using Zernike moment-based generalized edge detector

NASA Technical Reports Server (NTRS)

Ghosal, S.; Mehrotra, R.

1992-01-01

The authors proposed a novel Zernike moment-based generalized step edge detection method which can be used for segmenting range and intensity images. A generalized step edge detector is developed to identify different kinds of edges in range images. These edge maps are thinned and linked to provide final segmentation. A generalized edge is modeled in terms of five parameters: orientation, two slopes, one step jump at the location of the edge, and the background gray level. Two complex and two real Zernike moment-based masks are required to determine all these parameters of the edge model. Theoretical noise analysis is performed to show that these operators are quite noise tolerant. Experimental results are included to demonstrate edge-based segmentation technique.

Optic disc detection using ant colony optimization

NASA Astrophysics Data System (ADS)

Dias, Marcy A.; Monteiro, Fernando C.

2012-09-01

The retinal fundus images are used in the treatment and diagnosis of several eye diseases, such as diabetic retinopathy and glaucoma. This paper proposes a new method to detect the optic disc (OD) automatically, due to the fact that the knowledge of the OD location is essential to the automatic analysis of retinal images. Ant Colony Optimization (ACO) is an optimization algorithm inspired by the foraging behaviour of some ant species that has been applied in image processing for edge detection. Recently, the ACO was used in fundus images to detect edges, and therefore, to segment the OD and other anatomical retinal structures. We present an algorithm for the detection of OD in the retina which takes advantage of the Gabor wavelet transform, entropy and ACO algorithm. Forty images of the retina from DRIVE database were used to evaluate the performance of our method.

MuSCoWERT: multi-scale consistence of weighted edge Radon transform for horizon detection in maritime images.

PubMed

Prasad, Dilip K; Rajan, Deepu; Rachmawati, Lily; Rajabally, Eshan; Quek, Chai

2016-12-01

This paper addresses the problem of horizon detection, a fundamental process in numerous object detection algorithms, in a maritime environment. The maritime environment is characterized by the absence of fixed features, the presence of numerous linear features in dynamically changing objects and background and constantly varying illumination, rendering the typically simple problem of detecting the horizon a challenging one. We present a novel method called multi-scale consistence of weighted edge Radon transform, abbreviated as MuSCoWERT. It detects the long linear features consistent over multiple scales using multi-scale median filtering of the image followed by Radon transform on a weighted edge map and computing the histogram of the detected linear features. We show that MuSCoWERT has excellent performance, better than seven other contemporary methods, for 84 challenging maritime videos, containing over 33,000 frames, and captured using visible range and near-infrared range sensors mounted onboard, onshore, or on floating buoys. It has a median error of about 2 pixels (less than 0.2%) from the center of the actual horizon and a median angular error of less than 0.4 deg. We are also sharing a new challenging horizon detection dataset of 65 videos of visible, infrared cameras for onshore and onboard ship camera placement.

Robust approach to ocular fundus image analysis

NASA Astrophysics Data System (ADS)

Tascini, Guido; Passerini, Giorgio; Puliti, Paolo; Zingaretti, Primo

1993-07-01

The analysis of morphological and structural modifications of retinal blood vessels plays an important role both to establish the presence of some systemic diseases as hypertension and diabetes and to study their course. The paper describes a robust set of techniques developed to quantitatively evaluate morphometric aspects of the ocular fundus vascular and micro vascular network. They are defined: (1) the concept of 'Local Direction of a vessel' (LD); (2) a special form of edge detection, named Signed Edge Detection (SED), which uses LD to choose the convolution kernel in the edge detection process and is able to distinguish between the left or the right vessel edge; (3) an iterative tracking (IT) method. The developed techniques use intensively both LD and SED in: (a) the automatic detection of number, position and size of blood vessels departing from the optical papilla; (b) the tracking of body and edges of the vessels; (c) the recognition of vessel branches and crossings; (d) the extraction of a set of features as blood vessel length and average diameter, arteries and arterioles tortuosity, crossing position and angle between two vessels. The algorithms, implemented in C language, have an execution time depending on the complexity of the currently processed vascular network.

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.

An edge-directed interpolation method for fetal spine MR images.

PubMed

Yu, Shaode; Zhang, Rui; Wu, Shibin; Hu, Jiani; Xie, Yaoqin

2013-10-10

Fetal spinal magnetic resonance imaging (MRI) is a prenatal routine for proper assessment of fetus development, especially when suspected spinal malformations occur while ultrasound fails to provide details. Limited by hardware, fetal spine MR images suffer from its low resolution.High-resolution MR images can directly enhance readability and improve diagnosis accuracy. Image interpolation for higher resolution is required in clinical situations, while many methods fail to preserve edge structures. Edge carries heavy structural messages of objects in visual scenes for doctors to detect suspicions, classify malformations and make correct diagnosis. Effective interpolation with well-preserved edge structures is still challenging. In this paper, we propose an edge-directed interpolation (EDI) method and apply it on a group of fetal spine MR images to evaluate its feasibility and performance. This method takes edge messages from Canny edge detector to guide further pixel modification. First, low-resolution (LR) images of fetal spine are interpolated into high-resolution (HR) images with targeted factor by bi-linear method. Then edge information from LR and HR images is put into a twofold strategy to sharpen or soften edge structures. Finally a HR image with well-preserved edge structures is generated. The HR images obtained from proposed method are validated and compared with that from other four EDI methods. Performances are evaluated from six metrics, and subjective analysis of visual quality is based on regions of interest (ROI). All these five EDI methods are able to generate HR images with enriched details. From quantitative analysis of six metrics, the proposed method outperforms the other four from signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM) and mutual information (MI) with seconds-level time consumptions (TC). Visual analysis of ROI shows that the proposed method maintains better consistency in edge structures with the original images. The proposed method classifies edge orientations into four categories and well preserves structures. It generates convincing HR images with fine details and is suitable in real-time situations. Iterative curvature-based interpolation (ICBI) method may result in crisper edges, while the other three methods are sensitive to noise and artifacts.

Three-dimensional contour edge detection algorithm

NASA Astrophysics Data System (ADS)

Wang, Yizhou; Ong, Sim Heng; Kassim, Ashraf A.; Foong, Kelvin W. C.

2000-06-01

This paper presents a novel algorithm for automatically extracting 3D contour edges, which are points of maximum surface curvature in a surface range image. The 3D image data are represented as a surface polygon mesh. The algorithm transforms the range data, obtained by scanning a dental plaster cast, into a 2D gray scale image by linearly converting the z-value of each vertex to a gray value. The Canny operator is applied to the median-filtered image to obtain the edge pixels and their orientations. A vertex in the 3D object corresponding to the detected edge pixel and its neighbors in the direction of the edge gradient are further analyzed with respect to their n-curvatures to extract the real 3D contour edges. This algorithm provides a fast method of reducing and sorting the unwieldy data inherent in the surface mesh representation. It employs powerful 2D algorithms to extract features from the transformed 3D models and refers to the 3D model for further analysis of selected data. This approach substantially reduces the computational burden without losing accuracy. It is also easily extended to detect 3D landmarks and other geometrical features, thus making it applicable to a wide range of applications.

Image Edge Tracking via Ant Colony Optimization

NASA Astrophysics Data System (ADS)

Li, Ruowei; Wu, Hongkun; Liu, Shilong; Rahman, M. A.; Liu, Sanchi; Kwok, Ngai Ming

2018-04-01

A good edge plot should use continuous thin lines to describe the complete contour of the captured object. However, the detection of weak edges is a challenging task because of the associated low pixel intensities. Ant Colony Optimization (ACO) has been employed by many researchers to address this problem. The algorithm is a meta-heuristic method developed by mimicking the natural behaviour of ants. It uses iterative searches to find the optimal solution that cannot be found via traditional optimization approaches. In this work, ACO is employed to track and repair broken edges obtained via conventional Sobel edge detector to produced a result with more connected edges.

Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata.

PubMed

Ghanizadeh, Afshin; Abarghouei, Amir Atapour; Sinaie, Saman; Saad, Puteh; Shamsuddin, Siti Mariyam

2011-07-01

Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.

Detecting Edges in Images by Use of Fuzzy Reasoning

NASA Technical Reports Server (NTRS)

Dominguez, Jesus A.; Klinko, Steve

2003-01-01

A method of processing digital image data to detect edges includes the use of fuzzy reasoning. The method is completely adaptive and does not require any advance knowledge of an image. During initial processing of image data at a low level of abstraction, the nature of the data is indeterminate. Fuzzy reasoning is used in the present method because it affords an ability to construct useful abstractions from approximate, incomplete, and otherwise imperfect sets of data. Humans are able to make some sense of even unfamiliar objects that have imperfect high-level representations. It appears that to perceive unfamiliar objects or to perceive familiar objects in imperfect images, humans apply heuristic algorithms to understand the images

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

Mihailescu, Lucian

This disclosure provides systems, methods, and apparatus related to ion beam therapy. In one aspect, a system includes a position sensitive detector and a collimator. The position sensitive detector configured to detect gamma rays generated by an ion beam interacting with a target. The collimator is positioned between the target and the position sensitive detector. The collimator includes a plurality of knife-edge slits, with a first knife-edge slit intersecting with a second knife-edge slit.

Three-dimensional profile extraction from CD-SEM image and top/bottom CD measurement by line-edge roughness analysis

NASA Astrophysics Data System (ADS)

Yamaguchi, Atsuko; Ohashi, Takeyoshi; Kawasaki, Takahiro; Inoue, Osamu; Kawada, Hiroki

2013-04-01

A new method for calculating critical dimension (CDs) at the top and bottom of three-dimensional (3D) pattern profiles from a critical-dimension scanning electron microscope (CD-SEM) image, called as "T-sigma method", is proposed and evaluated. Without preparing a library of database in advance, T-sigma can estimate a feature of a pattern sidewall. Furthermore, it supplies the optimum edge-definition (i.e., threshold level for determining edge position from a CDSEM signal) to detect the top and bottom of the pattern. This method consists of three steps. First, two components of line-edge roughness (LER); noise-induced bias (i.e., LER bias) and unbiased component (i.e., bias-free LER) are calculated with set threshold level. Second, these components are calculated with various threshold values, and the threshold-dependence of these two components, "T-sigma graph", is obtained. Finally, the optimum threshold value for the top and the bottom edge detection are given by the analysis of T-sigma graph. T-sigma was applied to CD-SEM images of three kinds of resist-pattern samples. In addition, reference metrology was performed with atomic force microscope (AFM) and scanning transmission electron microscope (STEM). Sensitivity of CD measured by T-sigma to the reference CD was higher than or equal to that measured by the conventional edge definition. Regarding the absolute measurement accuracy, T-sigma showed better results than the conventional definition. Furthermore, T-sigma graphs were calculated from CD-SEM images of two kinds of resist samples and compared with corresponding STEM observation results. Both bias-free LER and LER bias increased as the detected edge point moved from the bottom to the top of the pattern in the case that the pattern had a straight sidewall and a round top. On the other hand, they were almost constant in the case that the pattern had a re-entrant profile. T-sigma will be able to reveal a re-entrant feature. From these results, it is found that T-sigma method can provide rough cross-sectional pattern features and achieve quick, easy and accurate measurements of top and bottom CD.

Real-Time Lane Region Detection Using a Combination of Geometrical and Image Features

PubMed Central

Cáceres Hernández, Danilo; Kurnianggoro, Laksono; Filonenko, Alexander; Jo, Kang Hyun

2016-01-01

Over the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performance. PMID:27869657

Segmentation of neuroanatomy in magnetic resonance images

NASA Astrophysics Data System (ADS)

Simmons, Andrew; Arridge, Simon R.; Barker, G. J.; Tofts, Paul S.

1992-06-01

Segmentation in neurological magnetic resonance imaging (MRI) is necessary for feature extraction, volume measurement and for the three-dimensional display of neuroanatomy. Automated and semi-automated methods offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. We have used dual echo multi-slice spin-echo data sets which take advantage of the intrinsically multispectral nature of MRI. As a pre-processing step, a rf non-uniformity correction is applied and if the data is noisy the images are smoothed using a non-isotropic blurring method. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing has been used to significantly simplify edge images and thus allow simple postprocessing to pick out the brain contour in each slice of the data set. Edge- focusing is a technique which locates significant edges using a high degree of smoothing at a coarse level and tracks these edges to a fine level where the edges can be determined with high positional accuracy. Both 2-D and 3-D edge-detection methods have been compared. Once isolated, the brain is further processed to identify CSF, and, depending upon the MR pulse sequence used, the brain itself may be sub-divided into gray matter and white matter using semi-automatic contrast enhancement and clustering methods.

Identification of jasmine flower (Jasminum sp.) based on the shape of the flower using sobel edge and k-nearest neighbour

NASA Astrophysics Data System (ADS)

Qur’ania, A.; Sarinah, I.

2018-03-01

People often wrong in knowing the type of jasmine by just looking at the white color of the jasmine, while not all white flowers including jasmine and not all jasmine flowers have white. There is a jasmine that is yellow and there is a jasmine that is white and purple.The aim of this research is to identify Jasmine flower (Jasminum sp.) based on the shape of the flower image-based using Sobel edge detection and k-Nearest Neighbor. Edge detection is used to detect the type of flower from the flower shape. Edge detection aims to improve the appearance of the border of a digital image. While k-Nearest Neighbor method is used to classify the classification of test objects into classes that have neighbouring properties closest to the object of training. The data used in this study are three types of jasmine namely jasmine white (Jasminum sambac), jasmine gambir (Jasminum pubescens), and jasmine japan (Pseuderanthemum reticulatum). Testing of jasmine flower image resized 50 × 50 pixels, 100 × 100 pixels, 150 × 150 pixels yields an accuracy of 84%. Tests on distance values of the k-NN method with spacing 5, 10 and 15 resulted in different accuracy rates for 5 and 10 closest distances yielding the same accuracy rate of 84%, for the 15 shortest distance resulted in a small accuracy of 65.2%.

Using gradient-based ray and candidate shadow maps for environmental illumination distribution estimation

NASA Astrophysics Data System (ADS)

Eem, Changkyoung; Kim, Iksu; Hong, Hyunki

2015-07-01

A method to estimate the environmental illumination distribution of a scene with gradient-based ray and candidate shadow maps is presented. In the shadow segmentation stage, we apply a Canny edge detector to the shadowed image by using a three-dimensional (3-D) augmented reality (AR) marker of a known size and shape. Then the hierarchical tree of the connected edge components representing the topological relation is constructed, and the connected components are merged, taking their hierarchical structures into consideration. A gradient-based ray that is perpendicular to the gradient of the edge pixel in the shadow image can be used to extract the shadow regions. In the light source detection stage, shadow regions with both a 3-D AR marker and the light sources are partitioned into candidate shadow maps. A simple logic operation between each candidate shadow map and the segmented shadow is used to efficiently compute the area ratio between them. The proposed method successively extracts the main light sources according to their relative contributions on the segmented shadows. The proposed method can reduce unwanted effects due to the sampling positions in the shadow region and the threshold values in the shadow edge detection.

Extensions of algebraic image operators: An approach to model-based vision

NASA Technical Reports Server (NTRS)

Lerner, Bao-Ting; Morelli, Michael V.

1990-01-01

Researchers extend their previous research on a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets. Addition and multiplication are defined for the set of all grey-level images, which can then be described as polynomials of two variables. Utilizing this new algebraic structure, researchers devised an innovative, efficient edge detection scheme. An accurate method for deriving gradient component information from this edge detector is presented. Based upon this new edge detection system researchers developed a robust method for linear feature extraction by combining the techniques of a Hough transform and a line follower. The major advantage of this feature extractor is its general, object-independent nature. Target attributes, such as line segment lengths, intersections, angles of intersection, and endpoints are derived by the feature extraction algorithm and employed during model matching. The algebraic operators are global operations which are easily reconfigured to operate on any size or shape region. This provides a natural platform from which to pursue dynamic scene analysis. A method for optimizing the linear feature extractor which capitalizes on the spatially reconfiguration nature of the edge detector/gradient component operator is discussed.

Low dose CBCT reconstruction via prior contour based total variation (PCTV) regularization: a feasibility study

NASA Astrophysics Data System (ADS)

Chen, Yingxuan; Yin, Fang-Fang; Zhang, Yawei; Zhang, You; Ren, Lei

2018-04-01

Purpose: compressed sensing reconstruction using total variation (TV) tends to over-smooth the edge information by uniformly penalizing the image gradient. The goal of this study is to develop a novel prior contour based TV (PCTV) method to enhance the edge information in compressed sensing reconstruction for CBCT. Methods: the edge information is extracted from prior planning-CT via edge detection. Prior CT is first registered with on-board CBCT reconstructed with TV method through rigid or deformable registration. The edge contours in prior-CT is then mapped to CBCT and used as the weight map for TV regularization to enhance edge information in CBCT reconstruction. The PCTV method was evaluated using extended-cardiac-torso (XCAT) phantom, physical CatPhan phantom and brain patient data. Results were compared with both TV and edge preserving TV (EPTV) methods which are commonly used for limited projection CBCT reconstruction. Relative error was used to calculate pixel value difference and edge cross correlation was defined as the similarity of edge information between reconstructed images and ground truth in the quantitative evaluation. Results: compared to TV and EPTV, PCTV enhanced the edge information of bone, lung vessels and tumor in XCAT reconstruction and complex bony structures in brain patient CBCT. In XCAT study using 45 half-fan CBCT projections, compared with ground truth, relative errors were 1.5%, 0.7% and 0.3% and edge cross correlations were 0.66, 0.72 and 0.78 for TV, EPTV and PCTV, respectively. PCTV is more robust to the projection number reduction. Edge enhancement was reduced slightly with noisy projections but PCTV was still superior to other methods. PCTV can maintain resolution while reducing the noise in the low mAs CatPhan reconstruction. Low contrast edges were preserved better with PCTV compared with TV and EPTV. Conclusion: PCTV preserved edge information as well as reduced streak artifacts and noise in low dose CBCT reconstruction. PCTV is superior to TV and EPTV methods in edge enhancement, which can potentially improve the localization accuracy in radiation therapy.

Manhole Cover Detection Using Vehicle-Based Multi-Sensor Data

NASA Astrophysics Data System (ADS)

Ji, S.; Shi, Y.; Shi, Z.

2012-07-01

A new method combined wit multi-view matching and feature extraction technique is developed to detect manhole covers on the streets using close-range images combined with GPS/IMU and LINDAR data. The covers are an important target on the road traffic as same as transport signs, traffic lights and zebra crossing but with more unified shapes. However, the different shoot angle and distance, ground material, complex street scene especially its shadow, and cars in the road have a great impact on the cover detection rate. The paper introduces a new method in edge detection and feature extraction in order to overcome these difficulties and greatly improve the detection rate. The LIDAR data are used to do scene segmentation and the street scene and cars are excluded from the roads. And edge detection method base on canny which sensitive to arcs and ellipses is applied on the segmented road scene and the interesting areas contain arcs are extracted and fitted to ellipse. The ellipse are then resampled for invariance to shooting angle and distance and then are matched to adjacent images for further checking if covers and . More than 1000 images with different scenes are used in our tests and the detection rate is analyzed. The results verified our method have its advantages in correct covers detection in the complex street scene.

Vertical cup-to-disc ratio measurement for diagnosis of glaucoma on fundus images

NASA Astrophysics Data System (ADS)

Hatanaka, Yuji; Noudo, Atsushi; Muramatsu, Chisako; Sawada, Akira; Hara, Takeshi; Yamamoto, Tetsuya; Fujita, Hiroshi

2010-03-01

Glaucoma is a leading cause of permanent blindness. Retinal fundus image examination is useful for early detection of glaucoma. In order to evaluate the presence of glaucoma, the ophthalmologists determine the cup and disc areas and they diagnose glaucoma using a vertical cup-to-disc ratio. However, determination of the cup area is very difficult, thus we propose a method to measure the cup-to-disc ratio using a vertical profile on the optic disc. First, the blood vessels were erased from the image and then the edge of optic disc was then detected by use of a canny edge detection filter. Twenty profiles were then obtained around the center of the optic disc in the vertical direction on blue channel of the color image, and the profile was smoothed by averaging these profiles. After that, the edge of the cup area on the vertical profile was determined by thresholding technique. Lastly, the vertical cup-to-disc ratio was calculated. Using seventy nine images, including twenty five glaucoma images, the sensitivity of 80% and a specificity of 85% were achieved with this method. These results indicated that this method can be useful for the analysis of the optic disc in glaucoma examinations.

Practical Study for the Properties of Hueckel Edge Detection Operator

NASA Astrophysics Data System (ADS)

Jabbar, Hameed M. Abdul; Hatem, Amal J.; Ameer, Inbethaq M. A. Abdul

2018-05-01

The first practical study for the Hueckel edge detection operator was presented in this research, where it is tested on standard step edge set images. A number of criteria were adopted to evaluate its practical performance, which is the accuracy in detecting the edges direction, the error in the edges location (dislocation), edges width, the calculated edge goodness criterion and the consumed execution time. These criteria were studied with the edge direction and the used disk radius of the Hueckel edge detection operator. Important notes were recorded for the performance of this operator depending on the direction of the edge and/or with the radius of the used disk. There is a variation in the performance of the operator in terms of precision in detecting of the edges direction and position. A discussion was presented for the all criteria adopted in the research.

A thesis on the Development of an Automated SWIFT Edge Detection Algorithm

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

Trujillo, Christopher J.

Throughout the world, scientists and engineers such as those at Los Alamos National Laboratory, perform research and testing unique only to applications aimed towards advancing technology, and understanding the nature of materials. With this testing, comes a need for advanced methods of data acquisition and most importantly, a means of analyzing and extracting the necessary information from such acquired data. In this thesis, I aim to produce an automated method implementing advanced image processing techniques and tools to analyze SWIFT image datasets for Detonator Technology at Los Alamos National Laboratory. Such an effective method for edge detection and point extractionmore » can prove to be advantageous in analyzing such unique datasets and provide for consistency in producing results.« less

Research on improved edge extraction algorithm of rectangular piece

NASA Astrophysics Data System (ADS)

He, Yi-Bin; Zeng, Ya-Jun; Chen, Han-Xin; Xiao, San-Xia; Wang, Yan-Wei; Huang, Si-Yu

Traditional edge detection operators such as Prewitt operator, LOG operator and Canny operator, etc. cannot meet the requirements of the modern industrial measurement. This paper proposes a kind of image edge detection algorithm based on improved morphological gradient. It can be detect the image using structural elements, which deals with the characteristic information of the image directly. Choosing different shapes and sizes of structural elements to use together, the ideal image edge information can be detected. The experimental result shows that the algorithm can well extract image edge with noise, which is clearer, and has more detailed edges compared with the previous edge detection algorithm.

Spatial characterization of the edge barrier in wide superconducting films

NASA Astrophysics Data System (ADS)

Sivakov, A. G.; Turutanov, O. G.; Kolinko, A. E.; Pokhila, A. S.

2018-03-01

The current-induced destruction of superconductivity is discussed in wide superconducting thin films, whose width is greater than the magnetic field penetration depth, in weak magnetic fields. Particular attention is paid to the role of the boundary potential barrier (the Bin-Livingston barrier) in critical state formation and detection of the edge responsible for this critical state with different mutual orientations of external perpendicular magnetic field and transport current. Critical and resistive states of the film were visualized using the space-resolving low-temperature laser scanning microscopy (LTLSM) method, which enables detection of critical current-determining areas on the film edges. Based on these observations, a simple technique was developed for investigation of the critical state separately at each film edge, and for the estimation of residual magnetic fields in cryostats. The proposed method only requires recording of the current-voltage characteristics of the film in a weak magnetic field, thus circumventing the need for complex LTLSM techniques. Information thus obtained is particularly important for interpretation of studies of superconducting film single-photon light emission detectors.

New scheme for image edge detection using the switching mechanism of nonlinear optical material

NASA Astrophysics Data System (ADS)

Pahari, Nirmalya; Mukhopadhyay, Sourangshu

2006-03-01

The limitations of electronics in conducting parallel arithmetic, algebraic, and logic processing are well known. Very high-speed (terahertz) performance cannot be expected in conventional electronic mechanisms. To achieve such performance we can introduce optics instead of electronics for information processing, computing, and data handling. Nonlinear optical material (NOM) is a successful candidate in this regard to play a major role in the domain of optically controlled switching systems. The character of some NOMs is such as to reflect the probe beam in the presence of two read beams (or pump beams) exciting the material from opposite directions, using the principle of four-wave mixing. In image processing, edge extraction from an image is an important and essential task. Several optical methods of digital image processing are used for properly evaluating the image edges. We propose here a new method of image edge detection, extraction, and enhancement by use of AND-based switching operations with NOM. In this process we have used the optically inverted image of a supplied image. This can be obtained by the EXOR switching operation of the NOM.

Effects of Edge Directions on the Structural Controllability of Complex Networks

PubMed Central

Xiao, Yandong; Lao, Songyang; Hou, Lvlin; Small, Michael; Bai, Liang

2015-01-01

Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain “inappropriate” edge directions. However, the existence of multiple sets of “inappropriate” edge directions suggests that different edges have different effects on optimal controllability—that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: critical, redundant and intermittent. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions—utilizing only local information—which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks. PMID:26281042

Effects of Edge Directions on the Structural Controllability of Complex Networks.

PubMed

Xiao, Yandong; Lao, Songyang; Hou, Lvlin; Small, Michael; Bai, Liang

2015-01-01

Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain "inappropriate" edge directions. However, the existence of multiple sets of "inappropriate" edge directions suggests that different edges have different effects on optimal controllability-that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: critical, redundant and intermittent. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions-utilizing only local information-which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks.

Detection of pulmonary nodules in CT images based on fuzzy integrated active contour model and hybrid parametric mixture model.

PubMed

Li, Bin; Chen, Kan; Tian, Lianfang; Yeboah, Yao; Ou, Shanxing

2013-01-01

The segmentation and detection of various types of nodules in a Computer-aided detection (CAD) system present various challenges, especially when (1) the nodule is connected to a vessel and they have very similar intensities; (2) the nodule with ground-glass opacity (GGO) characteristic possesses typical weak edges and intensity inhomogeneity, and hence it is difficult to define the boundaries. Traditional segmentation methods may cause problems of boundary leakage and "weak" local minima. This paper deals with the above mentioned problems. An improved detection method which combines a fuzzy integrated active contour model (FIACM)-based segmentation method, a segmentation refinement method based on Parametric Mixture Model (PMM) of juxta-vascular nodules, and a knowledge-based C-SVM (Cost-sensitive Support Vector Machines) classifier, is proposed for detecting various types of pulmonary nodules in computerized tomography (CT) images. Our approach has several novel aspects: (1) In the proposed FIACM model, edge and local region information is incorporated. The fuzzy energy is used as the motivation power for the evolution of the active contour. (2) A hybrid PMM Model of juxta-vascular nodules combining appearance and geometric information is constructed for segmentation refinement of juxta-vascular nodules. Experimental results of detection for pulmonary nodules show desirable performances of the proposed method.

Eb&D: A new clustering approach for signed social networks based on both edge-betweenness centrality and density of subgraphs

NASA Astrophysics Data System (ADS)

Qi, Xingqin; Song, Huimin; Wu, Jianliang; Fuller, Edgar; Luo, Rong; Zhang, Cun-Quan

2017-09-01

Clustering algorithms for unsigned social networks which have only positive edges have been studied intensively. However, when a network has like/dislike, love/hate, respect/disrespect, or trust/distrust relationships, unsigned social networks with only positive edges are inadequate. Thus we model such kind of networks as signed networks which can have both negative and positive edges. Detecting the cluster structures of signed networks is much harder than for unsigned networks, because it not only requires that positive edges within clusters are as many as possible, but also requires that negative edges between clusters are as many as possible. Currently, we have few clustering algorithms for signed networks, and most of them requires the number of final clusters as an input while it is actually hard to predict beforehand. In this paper, we will propose a novel clustering algorithm called Eb &D for signed networks, where both the betweenness of edges and the density of subgraphs are used to detect cluster structures. A hierarchically nested system will be constructed to illustrate the inclusion relationships of clusters. To show the validity and efficiency of Eb &D, we test it on several classical social networks and also hundreds of synthetic data sets, and all obtain better results compared with other methods. The biggest advantage of Eb &D compared with other methods is that the number of clusters do not need to be known prior.

The edge detection method of the infrared imagery of the laser spot

NASA Astrophysics Data System (ADS)

Che, Jinxi; Zhang, Jinchun; Li, Zhongmin

2016-01-01

In the jamming effectiveness experiments, in which the thermal infrared imager was interfered by the CO2 Laser, in order to evaluate the jamming effect of the thermal infrared imager by the CO2 Laser, it was needed to analyses the obtained infrared imagery of laser spot. Because the laser spot pictures obtained from the thermal infrared imager are irregular, the edge detection is an important process. The image edge is one of the most basic characteristics of the image, and it contains most of the information of the image. Generally, because of the thermal balance effect, the partly temperature of objective is no quite difference; therefore the infrared imagery's ability of reflecting the local detail of object is obvious week. At the same time, when the information of heat distribution of the thermal imagery was combined with the basic information of target, such as the object size, the relative position of field of view, shape and outline, and so on, the information just has more value. Hence, it is an important step for making image processing to extract the objective edge of the infrared imagery. Meanwhile it is an important part of image processing procedure and it is the premise of many subsequent processing. So as to extract outline information of the target from the original thermal imagery, and overcome the disadvantage, such as the low image contrast of the image and serious noise interference, and so on, the edge of thermal imagery needs detecting and processing. The principles of the Roberts, Sobel, Prewitt and Canny operator were analyzed, and then they were used to making edge detection on the thermal imageries of laser spot, which were obtained from the jamming effect experiments of CO2 laser jamming the thermal infrared imager. On the basis of the detection result, their performances were compared. At the end, the characteristics of the operators were summarized, which provide reference for the choice of edge detection operators in thermal imagery processing in future.

Visual information processing II; Proceedings of the Meeting, Orlando, FL, Apr. 14-16, 1993

NASA Technical Reports Server (NTRS)

Huck, Friedrich O. (Editor); Juday, Richard D. (Editor)

1993-01-01

Various papers on visual information processing are presented. Individual topics addressed include: aliasing as noise, satellite image processing using a hammering neural network, edge-detetion method using visual perception, adaptive vector median filters, design of a reading test for low-vision image warping, spatial transformation architectures, automatic image-enhancement method, redundancy reduction in image coding, lossless gray-scale image compression by predictive GDF, information efficiency in visual communication, optimizing JPEG quantization matrices for different applications, use of forward error correction to maintain image fidelity, effect of peanoscanning on image compression. Also discussed are: computer vision for autonomous robotics in space, optical processor for zero-crossing edge detection, fractal-based image edge detection, simulation of the neon spreading effect by bandpass filtering, wavelet transform (WT) on parallel SIMD architectures, nonseparable 2D wavelet image representation, adaptive image halftoning based on WT, wavelet analysis of global warming, use of the WT for signal detection, perfect reconstruction two-channel rational filter banks, N-wavelet coding for pattern classification, simulation of image of natural objects, number-theoretic coding for iconic systems.

Computer assisted diagnostic system in tumor radiography.

PubMed

Faisal, Ahmed; Parveen, Sharmin; Badsha, Shahriar; Sarwar, Hasan; Reza, Ahmed Wasif

2013-06-01

An improved and efficient method is presented in this paper to achieve a better trade-off between noise removal and edge preservation, thereby detecting the tumor region of MRI brain images automatically. Compass operator has been used in the fourth order Partial Differential Equation (PDE) based denoising technique to preserve the anatomically significant information at the edges. A new morphological technique is also introduced for stripping skull region from the brain images, which consequently leading to the process of detecting tumor accurately. Finally, automatic seeded region growing segmentation based on an improved single seed point selection algorithm is applied to detect the tumor. The method is tested on publicly available MRI brain images and it gives an average PSNR (Peak Signal to Noise Ratio) of 36.49. The obtained results also show detection accuracy of 99.46%, which is a significant improvement than that of the existing results.

Computer vision, camouflage breaking and countershading

PubMed Central

Tankus, Ariel; Yeshurun, Yehezkel

2008-01-01

Camouflage is frequently used in the animal kingdom in order to conceal oneself from visual detection or surveillance. Many camouflage techniques are based on masking the familiar contours and texture of the subject by superposition of multiple edges on top of it. This work presents an operator, Darg, for the detection of three-dimensional smooth convex (or, equivalently, concave) objects. It can be used to detect curved objects on a relatively flat background, regardless of image edges, contours and texture. We show that a typical camouflage found in some animal species seems to be a ‘countermeasure’ taken against detection that might be based on our method. Detection by Darg is shown to be very robust, from both theoretical considerations and practical examples of real-life images. PMID:18990669

Two-Dimensional Edge Detection by Guided Mode Resonant Metasurface

NASA Astrophysics Data System (ADS)

Saba, Amirhossein; Tavakol, Mohammad Reza; Karimi-Khoozani, Parisa; Khavasi, Amin

2018-05-01

In this letter, a new approach to perform edge detection is presented using an all-dielectric CMOS-compatible metasurface. The design is based on guided-mode resonance which provides a high quality factor resonance to make the edge detection experimentally realizable. The proposed structure that is easy to fabricate, can be exploited for detection of edges in two dimensions due to its symmetry. Also, the trade-off between gain and resolution of edge detection is discussed which can be adjusted by appropriate design parameters. The proposed edge detector has also the potential to be used in ultrafast analog computing and image processing.

Human recognition based on head-shoulder contour extraction and BP neural network

NASA Astrophysics Data System (ADS)

Kong, Xiao-fang; Wang, Xiu-qin; Gu, Guohua; Chen, Qian; Qian, Wei-xian

2014-11-01

In practical application scenarios like video surveillance and human-computer interaction, human body movements are uncertain because the human body is a non-rigid object. Based on the fact that the head-shoulder part of human body can be less affected by the movement, and will seldom be obscured by other objects, in human detection and recognition, a head-shoulder model with its stable characteristics can be applied as a detection feature to describe the human body. In order to extract the head-shoulder contour accurately, a head-shoulder model establish method with combination of edge detection and the mean-shift algorithm in image clustering has been proposed in this paper. First, an adaptive method of mixture Gaussian background update has been used to extract targets from the video sequence. Second, edge detection has been used to extract the contour of moving objects, and the mean-shift algorithm has been combined to cluster parts of target's contour. Third, the head-shoulder model can be established, according to the width and height ratio of human head-shoulder combined with the projection histogram of the binary image, and the eigenvectors of the head-shoulder contour can be acquired. Finally, the relationship between head-shoulder contour eigenvectors and the moving objects will be formed by the training of back-propagation (BP) neural network classifier, and the human head-shoulder model can be clustered for human detection and recognition. Experiments have shown that the method combined with edge detection and mean-shift algorithm proposed in this paper can extract the complete head-shoulder contour, with low calculating complexity and high efficiency.

Edge map analysis in chest X-rays for automatic pulmonary abnormality screening.

PubMed

Santosh, K C; Vajda, Szilárd; Antani, Sameer; Thoma, George R

2016-09-01

Our particular motivator is the need for screening HIV+ populations in resource-constrained regions for the evidence of tuberculosis, using posteroanterior chest radiographs (CXRs). The proposed method is motivated by the observation that abnormal CXRs tend to exhibit corrupted and/or deformed thoracic edge maps. We study histograms of thoracic edges for all possible orientations of gradients in the range [Formula: see text] at different numbers of bins and different pyramid levels, using five different regions-of-interest selection. We have used two CXR benchmark collections made available by the U.S. National Library of Medicine and have achieved a maximum abnormality detection accuracy (ACC) of 86.36 % and area under the ROC curve (AUC) of 0.93 at 1 s per image, on average. We have presented an automatic method for screening pulmonary abnormalities using thoracic edge map in CXR images. The proposed method outperforms previously reported state-of-the-art results.

A graph signal filtering-based approach for detection of different edge types on airborne lidar data

NASA Astrophysics Data System (ADS)

Bayram, Eda; Vural, Elif; Alatan, Aydin

2017-10-01

Airborne Laser Scanning is a well-known remote sensing technology, which provides a dense and highly accurate, yet unorganized point cloud of earth surface. During the last decade, extracting information from the data generated by airborne LiDAR systems has been addressed by many studies in geo-spatial analysis and urban monitoring applications. However, the processing of LiDAR point clouds is challenging due to their irregular structure and 3D geometry. In this study, we propose a novel framework for the detection of the boundaries of an object or scene captured by LiDAR. Our approach is motivated by edge detection techniques in vision research and it is established on graph signal filtering which is an exciting and promising field of signal processing for irregular data types. Due to the convenient applicability of graph signal processing tools on unstructured point clouds, we achieve the detection of the edge points directly on 3D data by using a graph representation that is constructed exclusively to answer the requirements of the application. Moreover, considering the elevation data as the (graph) signal, we leverage aerial characteristic of the airborne LiDAR data. The proposed method can be employed both for discovering the jump edges on a segmentation problem and for exploring the crease edges on a LiDAR object on a reconstruction/modeling problem, by only adjusting the filter characteristics.

Optimal frequency domain textural edge detection filter

NASA Technical Reports Server (NTRS)

Townsend, J. K.; Shanmugan, K. S.; Frost, V. S.

1985-01-01

An optimal frequency domain textural edge detection filter is developed and its performance evaluated. For the given model and filter bandwidth, the filter maximizes the amount of output image energy placed within a specified resolution interval centered on the textural edge. Filter derivation is based on relating textural edge detection to tonal edge detection via the complex low-pass equivalent representation of narrowband bandpass signals and systems. The filter is specified in terms of the prolate spheroidal wave functions translated in frequency. Performance is evaluated using the asymptotic approximation version of the filter. This evaluation demonstrates satisfactory filter performance for ideal and nonideal textures. In addition, the filter can be adjusted to detect textural edges in noisy images at the expense of edge resolution.

Scene-based method for spatial misregistration detection in hyperspectral imagery.

PubMed

Dell'Endice, Francesco; Nieke, Jens; Schläpfer, Daniel; Itten, Klaus I

2007-05-20

Hyperspectral imaging (HSI) sensors suffer from spatial misregistration, an artifact that prevents the accurate acquisition of the spectra. Physical considerations let us assume that the influence of the spatial misregistration on the acquired data depends both on the wavelength and on the across-track position. A scene-based method, based on edge detection, is therefore proposed. Such a procedure measures the variation on the spatial location of an edge between its various monochromatic projections, giving an estimation for spatial misregistration, and also allowing identification of misalignments. The method has been applied to several hyperspectral sensors, either prism, or grating-based designs. The results confirm the dependence assumptions on lambda and theta, spectral wavelength and across-track pixel, respectively. Suggestions are also given to correct for spatial misregistration.

A threshold-based fixed predictor for JPEG-LS image compression

NASA Astrophysics Data System (ADS)

Deng, Lihua; Huang, Zhenghua; Yao, Shoukui

2018-03-01

In JPEG-LS, fixed predictor based on median edge detector (MED) only detect horizontal and vertical edges, and thus produces large prediction errors in the locality of diagonal edges. In this paper, we propose a threshold-based edge detection scheme for the fixed predictor. The proposed scheme can detect not only the horizontal and vertical edges, but also diagonal edges. For some certain thresholds, the proposed scheme can be simplified to other existing schemes. So, it can also be regarded as the integration of these existing schemes. For a suitable threshold, the accuracy of horizontal and vertical edges detection is higher than the existing median edge detection in JPEG-LS. Thus, the proposed fixed predictor outperforms the existing JPEG-LS predictors for all images tested, while the complexity of the overall algorithm is maintained at a similar level.

Doppler lidar wind measurement with the edge technique

NASA Technical Reports Server (NTRS)

Korb, C. Laurence; Gentry, Bruce M.

1992-01-01

The edge technique is a new and powerful method for measuring small frequency shifts. Range resolved lidar measurements of winds can be made with high accuracy and high vertical resolution using the edge technique to measure the Doppler shift of an atmospheric backscattered signal from a pulsed laser. The edge technique can be used at near-infrared or visible wavelengths using well developed solid state lasers and detectors with various edge filters. In the edge technique, the laser frequency is located on the steep slope of the spectral response function of a high resolution optical filter. Due to the steep slope of the edge, very small frequency shifts cause large changes in measured signal. The frequency of the outgoing laser pulse is determined by measuring its location on the edge of the filter. This is accomplished by sending a small portion of the beam to the edge detection setup where the incoming light is split into two channels - an edge filter and an energy monitor channel. The energy monitor signal is used to normalize the edge filter signal for magnitude. The laser return backscattered from the atmosphere is collected by a telescope and directed through the edge detection setup to determine its frequency (location on the edge) in a similar manner for each range element. The Doppler shift, and thus the wind, is determined from a differential measurement of the frequency of the outgoing laser pulse and the frequency of the laser return backscattered from the atmosphere. We have conducted simulations of the performance of an edge lidar system using an injection seeded pulsed Nd:YAG laser at 1.06 microns. The central fringe of a Fabry-Perot etalon is used as a high resolution edge filter to measure the shift of the aerosol return.

High-Throughput Method for Automated Colony and Cell Counting by Digital Image Analysis Based on Edge Detection

PubMed Central

Choudhry, Priya

2016-01-01

Counting cells and colonies is an integral part of high-throughput screens and quantitative cellular assays. Due to its subjective and time-intensive nature, manual counting has hindered the adoption of cellular assays such as tumor spheroid formation in high-throughput screens. The objective of this study was to develop an automated method for quick and reliable counting of cells and colonies from digital images. For this purpose, I developed an ImageJ macro Cell Colony Edge and a CellProfiler Pipeline Cell Colony Counting, and compared them to other open-source digital methods and manual counts. The ImageJ macro Cell Colony Edge is valuable in counting cells and colonies, and measuring their area, volume, morphology, and intensity. In this study, I demonstrate that Cell Colony Edge is superior to other open-source methods, in speed, accuracy and applicability to diverse cellular assays. It can fulfill the need to automate colony/cell counting in high-throughput screens, colony forming assays, and cellular assays. PMID:26848849

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

Tripathi, Ashish; McNulty, Ian; Munson, Todd

We propose a new approach to robustly retrieve the exit wave of an extended sample from its coherent diffraction pattern by exploiting sparsity of the sample's edges. This approach enables imaging of an extended sample with a single view, without ptychography. We introduce nonlinear optimization methods that promote sparsity, and we derive update rules to robustly recover the sample's exit wave. We test these methods on simulated samples by varying the sparsity of the edge-detected representation of the exit wave. Finally, our tests illustrate the strengths and limitations of the proposed method in imaging extended samples.

Hypervelocity Impact (HVI). Volume 7; WLE High Fidelity Specimen RCC16R

NASA Technical Reports Server (NTRS)

Gorman, Michael R.; Ziola, Steven M.

2007-01-01

During 2003 and 2004, the Johnson Space Center's White Sands Testing Facility in Las Cruces, New Mexico conducted hypervelocity impact tests on the space shuttle wing leading edge. Hypervelocity impact tests were conducted to determine if Micro-Meteoroid/Orbital Debris impacts could be reliably detected and located using simple passive ultrasonic methods. The objective of Target RCC16R was to study hypervelocity impacts through the reinforced carbon-carbon (RCC) panels of the Wing Leading Edge. Impact damage was detected using lightweight, low power instrumentation capable of being used in flight.

Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction

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

Tsantis, Stavros; Spiliopoulos, Stavros; Karnabatidis, Dimitrios

Purpose: Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. Methods: The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. Results: A totalmore » of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists’ qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. Conclusions: A new wavelet-based EFCM clustering model was introduced toward noise reduction and detail preservation. The proposed method improves the overall US image quality, which in turn could affect the decision-making on whether additional imaging and/or intervention is needed.« less

Spatial heterogeneity of type I error for local cluster detection tests

PubMed Central

2014-01-01

Background Just as power, type I error of cluster detection tests (CDTs) should be spatially assessed. Indeed, CDTs’ type I error and power have both a spatial component as CDTs both detect and locate clusters. In the case of type I error, the spatial distribution of wrongly detected clusters (WDCs) can be particularly affected by edge effect. This simulation study aims to describe the spatial distribution of WDCs and to confirm and quantify the presence of edge effect. Methods A simulation of 40 000 datasets has been performed under the null hypothesis of risk homogeneity. The simulation design used realistic parameters from survey data on birth defects, and in particular, two baseline risks. The simulated datasets were analyzed using the Kulldorff’s spatial scan as a commonly used test whose behavior is otherwise well known. To describe the spatial distribution of type I error, we defined the participation rate for each spatial unit of the region. We used this indicator in a new statistical test proposed to confirm, as well as quantify, the edge effect. Results The predefined type I error of 5% was respected for both baseline risks. Results showed strong edge effect in participation rates, with a descending gradient from center to edge, and WDCs more often centrally situated. Conclusions In routine analysis of real data, clusters on the edge of the region should be carefully considered as they rarely occur when there is no cluster. Further work is needed to combine results from power studies with this work in order to optimize CDTs performance. PMID:24885343

The SKED: speckle knife edge detector

NASA Astrophysics Data System (ADS)

Sharpies, S. D.; Light, R. A.; Achamfuo-Yeboah, S. O.; Clark, M.; Somekh, M. G.

2014-06-01

The knife edge detector—also known as optical beam deflection—is a simple and robust method of detecting ultrasonic waves using a laser. It is particularly suitable for detection of high frequency surface acoustic waves as the response is proportional to variation of the local tilt of the surface. In the case of a specular reflection of the incident laser beam from a smooth surface, any lateral movement of the reflected beam caused by the ultrasonic waves is easily detected by a pair of photodiodes. The major disadvantage of the knife edge detector is that it does not cope well with optically rough surfaces, those that give a speckled reflection. The optical speckles from a rough surface adversely affect the efficiency of the knife edge detector, because 'dark' speckles move synchronously with 'bright' speckles, and their contributions to the ultrasonic signal cancel each other out. We have developed a new self-adapting sensor which can cope with the optical speckles reflected from a rough surface. It is inelegantly called the SKED—speckle knife edge detector—and like its smooth surface namesake it is simple, cheap, compact, and robust. We describe the theory of its operation, and present preliminary experimental results validating the overall concept and the operation of the prototype device.

Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction.

PubMed

Tsantis, Stavros; Spiliopoulos, Stavros; Skouroliakou, Aikaterini; Karnabatidis, Dimitrios; Hazle, John D; Kagadis, George C

2014-07-01

Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. A total of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists' qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. A new wavelet-based EFCM clustering model was introduced toward noise reduction and detail preservation. The proposed method improves the overall US image quality, which in turn could affect the decision-making on whether additional imaging and/or intervention is needed.

Design of measuring system for wire diameter based on sub-pixel edge detection algorithm

NASA Astrophysics Data System (ADS)

Chen, Yudong; Zhou, Wang

2016-09-01

Light projection method is often used in measuring system for wire diameter, which is relatively simpler structure and lower cost, and the measuring accuracy is limited by the pixel size of CCD. Using a CCD with small pixel size can improve the measuring accuracy, but will increase the cost and difficulty of making. In this paper, through the comparative analysis of a variety of sub-pixel edge detection algorithms, polynomial fitting method is applied for data processing in measuring system for wire diameter, to improve the measuring accuracy and enhance the ability of anti-noise. In the design of system structure, light projection method with orthogonal structure is used for the detection optical part, which can effectively reduce the error caused by line jitter in the measuring process. For the electrical part, ARM Cortex-M4 microprocessor is used as the core of the circuit module, which can not only drive double channel linear CCD but also complete the sampling, processing and storage of the CCD video signal. In addition, ARM microprocessor can complete the high speed operation of the whole measuring system for wire diameter in the case of no additional chip. The experimental results show that sub-pixel edge detection algorithm based on polynomial fitting can make up for the lack of single pixel size and improve the precision of measuring system for wire diameter significantly, without increasing hardware complexity of the entire system.

Contour Detection and Completion for Inpainting and Segmentation Based on Topological Gradient and Fast Marching Algorithms

PubMed Central

Auroux, Didier; Cohen, Laurent D.; Masmoudi, Mohamed

2011-01-01

We combine in this paper the topological gradient, which is a powerful method for edge detection in image processing, and a variant of the minimal path method in order to find connected contours. The topological gradient provides a more global analysis of the image than the standard gradient and identifies the main edges of an image. Several image processing problems (e.g., inpainting and segmentation) require continuous contours. For this purpose, we consider the fast marching algorithm in order to find minimal paths in the topological gradient image. This coupled algorithm quickly provides accurate and connected contours. We present then two numerical applications, to image inpainting and segmentation, of this hybrid algorithm. PMID:22194734

Edges in CNC polishing: from mirror-segments towards semiconductors, paper 1: edges on processing the global surface.

PubMed

Walker, David; Yu, Guoyu; Li, Hongyu; Messelink, Wilhelmus; Evans, Rob; Beaucamp, Anthony

2012-08-27

Segment-edges for extremely large telescopes are critical for observations requiring high contrast and SNR, e.g. detecting exo-planets. In parallel, industrial requirements for edge-control are emerging in several applications. This paper reports on a new approach, where edges are controlled throughout polishing of the entire surface of a part, which has been pre-machined to its final external dimensions. The method deploys compliant bonnets delivering influence functions of variable diameter, complemented by small pitch tools sized to accommodate aspheric mis-fit. We describe results on witness hexagons in preparation for full size prototype segments for the European Extremely Large Telescope, and comment on wider applications of the technology.

Autonomous navigation method for substation inspection robot based on travelling deviation

NASA Astrophysics Data System (ADS)

Yang, Guoqing; Xu, Wei; Li, Jian; Fu, Chongguang; Zhou, Hao; Zhang, Chuanyou; Shao, Guangting

2017-06-01

A new method of edge detection is proposed in substation environment, which can realize the autonomous navigation of the substation inspection robot. First of all, the road image and information are obtained by using an image acquisition device. Secondly, the noise in the region of interest which is selected in the road image, is removed with the digital image processing algorithm, the road edge is extracted by Canny operator, and the road boundaries are extracted by Hough transform. Finally, the distance between the robot and the left and the right boundaries is calculated, and the travelling distance is obtained. The robot's walking route is controlled according to the travel deviation and the preset threshold. Experimental results show that the proposed method can detect the road area in real time, and the algorithm has high accuracy and stable performance.

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms

PubMed Central

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts’ Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2–100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms. PMID:28487831

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms.

PubMed

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts' Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2-100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms.

Extraction of linear features on SAR imagery

NASA Astrophysics Data System (ADS)

Liu, Junyi; Li, Deren; Mei, Xin

2006-10-01

Linear features are usually extracted from SAR imagery by a few edge detectors derived from the contrast ratio edge detector with a constant probability of false alarm. On the other hand, the Hough Transform is an elegant way of extracting global features like curve segments from binary edge images. Randomized Hough Transform can reduce the computation time and memory usage of the HT drastically. While Randomized Hough Transform will bring about a great deal of cells invalid during the randomized sample. In this paper, we propose a new approach to extract linear features on SAR imagery, which is an almost automatic algorithm based on edge detection and Randomized Hough Transform. The presented improved method makes full use of the directional information of each edge candidate points so as to solve invalid cumulate problems. Applied result is in good agreement with the theoretical study, and the main linear features on SAR imagery have been extracted automatically. The method saves storage space and computational time, which shows its effectiveness and applicability.

Android malware detection based on evolutionary super-network

NASA Astrophysics Data System (ADS)

Yan, Haisheng; Peng, Lingling

2018-04-01

In the paper, an android malware detection method based on evolutionary super-network is proposed in order to improve the precision of android malware detection. Chi square statistics method is used for selecting characteristics on the basis of analyzing android authority. Boolean weighting is utilized for calculating characteristic weight. Processed characteristic vector is regarded as the system training set and test set; hyper edge alternative strategy is used for training super-network classification model, thereby classifying test set characteristic vectors, and it is compared with traditional classification algorithm. The results show that the detection method proposed in the paper is close to or better than traditional classification algorithm. The proposed method belongs to an effective Android malware detection means.

Information theoretic analysis of canny edge detection in visual communication

NASA Astrophysics Data System (ADS)

Jiang, Bo; Rahman, Zia-ur

2011-06-01

In general edge detection evaluation, the edge detectors are examined, analyzed, and compared either visually or with a metric for specific an application. This analysis is usually independent of the characteristics of the image-gathering, transmission and display processes that do impact the quality of the acquired image and thus, the resulting edge image. We propose a new information theoretic analysis of edge detection that unites the different components of the visual communication channel and assesses edge detection algorithms in an integrated manner based on Shannon's information theory. The edge detection algorithm here is considered to achieve high performance only if the information rate from the scene to the edge approaches the maximum possible. Thus, by setting initial conditions of the visual communication system as constant, different edge detection algorithms could be evaluated. This analysis is normally limited to linear shift-invariant filters so in order to examine the Canny edge operator in our proposed system, we need to estimate its "power spectral density" (PSD). Since the Canny operator is non-linear and shift variant, we perform the estimation for a set of different system environment conditions using simulations. In our paper we will first introduce the PSD of the Canny operator for a range of system parameters. Then, using the estimated PSD, we will assess the Canny operator using information theoretic analysis. The information-theoretic metric is also used to compare the performance of the Canny operator with other edge-detection operators. This also provides a simple tool for selecting appropriate edgedetection algorithms based on system parameters, and for adjusting their parameters to maximize information throughput.

Quantitative Ultrasound Assessment of Duchenne Muscular Dystrophy Using Edge Detection Analysis.

PubMed

Koppaka, Sisir; Shklyar, Irina; Rutkove, Seward B; Darras, Basil T; Anthony, Brian W; Zaidman, Craig M; Wu, Jim S

2016-09-01

The purpose of this study was to investigate the ability of quantitative ultrasound (US) using edge detection analysis to assess patients with Duchenne muscular dystrophy (DMD). After Institutional Review Board approval, US examinations with fixed technical parameters were performed unilaterally in 6 muscles (biceps, deltoid, wrist flexors, quadriceps, medial gastrocnemius, and tibialis anterior) in 19 boys with DMD and 21 age-matched control participants. The muscles of interest were outlined by a tracing tool, and the upper third of the muscle was used for analysis. Edge detection values for each muscle were quantified by the Canny edge detection algorithm and then normalized to the number of edge pixels in the muscle region. The edge detection values were extracted at multiple sensitivity thresholds (0.01-0.99) to determine the optimal threshold for distinguishing DMD from normal. Area under the receiver operating curve values were generated for each muscle and averaged across the 6 muscles. The average age in the DMD group was 8.8 years (range, 3.0-14.3 years), and the average age in the control group was 8.7 years (range, 3.4-13.5 years). For edge detection, a Canny threshold of 0.05 provided the best discrimination between DMD and normal (area under the curve, 0.96; 95% confidence interval, 0.84-1.00). According to a Mann-Whitney test, edge detection values were significantly different between DMD and controls (P < .0001). Quantitative US imaging using edge detection can distinguish patients with DMD from healthy controls at low Canny thresholds, at which discrimination of small structures is best. Edge detection by itself or in combination with other tests can potentially serve as a useful biomarker of disease progression and effectiveness of therapy in muscle disorders.

BP fusion model for the detection of oil spills on the sea by remote sensing

NASA Astrophysics Data System (ADS)

Chen, Weiwei; An, Jubai; Zhang, Hande; Lin, Bin

2003-06-01

Oil spills are very serious marine pollution in many countries. In order to detect and identify the oil-spilled on the sea by remote sensor, scientists have to conduct a research work on the remote sensing image. As to the detection of oil spills on the sea, edge detection is an important technology in image processing. There are many algorithms of edge detection developed for image processing. These edge detection algorithms always have their own advantages and disadvantages in the image processing. Based on the primary requirements of edge detection of the oil spills" image on the sea, computation time and detection accuracy, we developed a fusion model. The model employed a BP neural net to fuse the detection results of simple operators. The reason we selected BP neural net as the fusion technology is that the relation between simple operators" result of edge gray level and the image"s true edge gray level is nonlinear, while BP neural net is good at solving the nonlinear identification problem. Therefore in this paper we trained a BP neural net by some oil spill images, then applied the BP fusion model on the edge detection of other oil spill images and obtained a good result. In this paper the detection result of some gradient operators and Laplacian operator are also compared with the result of BP fusion model to analysis the fusion effect. At last the paper pointed out that the fusion model has higher accuracy and higher speed in the processing oil spill image"s edge detection.

Universal phase transition in community detectability under a stochastic block model.

PubMed

Chen, Pin-Yu; Hero, Alfred O

2015-03-01

We prove the existence of an asymptotic phase-transition threshold on community detectability for the spectral modularity method [M. E. J. Newman, Phys. Rev. E 74, 036104 (2006) and Proc. Natl. Acad. Sci. (USA) 103, 8577 (2006)] under a stochastic block model. The phase transition on community detectability occurs as the intercommunity edge connection probability p grows. This phase transition separates a subcritical regime of small p, where modularity-based community detection successfully identifies the communities, from a supercritical regime of large p where successful community detection is impossible. We show that, as the community sizes become large, the asymptotic phase-transition threshold p* is equal to √[p1p2], where pi(i=1,2) is the within-community edge connection probability. Thus the phase-transition threshold is universal in the sense that it does not depend on the ratio of community sizes. The universal phase-transition phenomenon is validated by simulations for moderately sized communities. Using the derived expression for the phase-transition threshold, we propose an empirical method for estimating this threshold from real-world data.

Anomaly Detection in Dynamic Networks

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

Turcotte, Melissa

2014-10-14

Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. Amore » second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the communication counts. In a sequential analysis, anomalous behavior is then identified from outlying behavior with respect to the fitted predictive probability models. Seasonality is again incorporated into the model and is treated as a changepoint model on the transition probabilities of a discrete time Markov process. Second stage analytics are then developed which combine anomalous edges to identify anomalous substructures in the network.« less

A Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-seq Data.

PubMed

Choi, Yoonha; Coram, Marc; Peng, Jie; Tang, Hua

2017-07-01

Constructing expression networks using transcriptomic data is an effective approach for studying gene regulation. A popular approach for constructing such a network is based on the Gaussian graphical model (GGM), in which an edge between a pair of genes indicates that the expression levels of these two genes are conditionally dependent, given the expression levels of all other genes. However, GGMs are not appropriate for non-Gaussian data, such as those generated in RNA-seq experiments. We propose a novel statistical framework that maximizes a penalized likelihood, in which the observed count data follow a Poisson log-normal distribution. To overcome the computational challenges, we use Laplace's method to approximate the likelihood and its gradients, and apply the alternating directions method of multipliers to find the penalized maximum likelihood estimates. The proposed method is evaluated and compared with GGMs using both simulated and real RNA-seq data. The proposed method shows improved performance in detecting edges that represent covarying pairs of genes, particularly for edges connecting low-abundant genes and edges around regulatory hubs.

Weighted small subdomain filtering technology

NASA Astrophysics Data System (ADS)

Tai, Zhenhua; Zhang, Fengxu; Zhang, Fengqin; Zhang, Xingzhou; Hao, Mengcheng

2017-09-01

A high-resolution method to define the horizontal edges of gravity sources is presented by improving the three-directional small subdomain filtering (TDSSF). This proposed method is the weighted small subdomain filtering (WSSF). The WSSF uses a numerical difference instead of the phase conversion in the TDSSF to reduce the computational complexity. To make the WSSF more insensitive to noise, the numerical difference is combined with the average algorithm. Unlike the TDSSF, the WSSF uses a weighted sum to integrate the numerical difference results along four directions into one contour, for making its interpretation more convenient and accurate. The locations of tightened gradient belts are used to define the edges of sources in the WSSF result. This proposed method is tested on synthetic data. The test results show that the WSSF provides the horizontal edges of sources more clearly and correctly, even if the sources are interfered with one another and the data is corrupted with random noise. Finally, the WSSF and two other known methods are applied to a real data respectively. The detected edges by the WSSF are sharper and more accurate.

Edge detection

NASA Astrophysics Data System (ADS)

Hildreth, E. C.

1985-09-01

For both biological systems and machines, vision begins with a large and unwieldly array of measurements of the amount of light reflected from surfaces in the environment. The goal of vision is to recover physical properties of objects in the scene such as the location of object boundaries and the structure, color and texture of object surfaces, from the two-dimensional image that is projected onto the eye or camera. This goal is not achieved in a single step: vision proceeds in stages, with each stage producing increasingly more useful descriptions of the image and then the scene. The first clues about the physical properties of the scene are provided by the changes of intensity in the image. The importance of intensity changes and edges in early visual processing has led to extensive research on their detection, description and use, both in computer and biological vision systems. This article reviews some of the theory that underlies the detection of edges, and the methods used to carry out this analysis.

Automated detection of kinks from blood vessels for optic cup segmentation in retinal images

NASA Astrophysics Data System (ADS)

Wong, D. W. K.; Liu, J.; Lim, J. H.; Li, H.; Wong, T. Y.

2009-02-01

The accurate localization of the optic cup in retinal images is important to assess the cup to disc ratio (CDR) for glaucoma screening and management. Glaucoma is physiologically assessed by the increased excavation of the optic cup within the optic nerve head, also known as the optic disc. The CDR is thus an important indicator of risk and severity of glaucoma. In this paper, we propose a method of determining the cup boundary using non-stereographic retinal images by the automatic detection of a morphological feature within the optic disc known as kinks. Kinks are defined as the bendings of small vessels as they traverse from the disc to the cup, providing physiological validation for the cup boundary. To detect kinks, localized patches are first generated from a preliminary cup boundary obtained via level set. Features obtained using edge detection and wavelet transform are combined using a statistical approach rule to identify likely vessel edges. The kinks are then obtained automatically by analyzing the detected vessel edges for angular changes, and these kinks are subsequently used to obtain the cup boundary. A set of retinal images from the Singapore Eye Research Institute was obtained to assess the performance of the method, with each image being clinically graded for the CDR. From experiments, when kinks were used, the error on the CDR was reduced to less than 0.1 CDR units relative to the clinical CDR, which is within the intra-observer variability of 0.2 CDR units.

NTilt as an improved enhanced tilt derivative filter for edge detection of potential field anomalies

NASA Astrophysics Data System (ADS)

Nasuti, Yasin; Nasuti, Aziz

2018-07-01

We develop a new phase-based filter to enhance the edges of geological sources from potential-field data called NTilt, which utilizes the vertical derivative of the analytical signal in different orders to the tilt derivative equation. This will equalize signals from sources buried at different depths. In order to evaluate the designed filter, we compared the results obtained from our filter with those from recently applied methods, testing against both synthetic data, and measured data from the Finnmark region of North Norway were used. The results demonstrate that the new filter permits better definition of the edges of causative anomalies, as well as better highlighting several anomalies that either are not shown in tilt derivative and other methods or not very well defined. The proposed technique also shows improvements in delineation of the actual edges of deep-seated anomalies compared to tilt derivative and other methods. The NTilt filter provides more accurate and sharper edges and makes the nearby anomalies more distinguishable, and also can avoid bringing some additional false edges reducing the ambiguity in potential field interpretations. This filter, thus, appears to be promising in providing a better qualitative interpretation of the gravity and magnetic data in comparison with the more commonly used filters.

Toward accurate and fast iris segmentation for iris biometrics.

PubMed

He, Zhaofeng; Tan, Tieniu; Sun, Zhenan; Qiu, Xianchao

2009-09-01

Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first built to extract a rough position of the iris center. Edge points of iris boundaries are then detected, and an elastic model named pulling and pushing is established. Under this model, the center and radius of the circular iris boundaries are iteratively refined in a way driven by the restoring forces of Hooke's law. Furthermore, a smoothing spline-based edge fitting scheme is presented to deal with noncircular iris boundaries. After that, eyelids are localized via edge detection followed by curve fitting. The novelty here is the adoption of a rank filter for noise elimination and a histogram filter for tackling the shape irregularity of eyelids. Finally, eyelashes and shadows are detected via a learned prediction model. This model provides an adaptive threshold for eyelash and shadow detection by analyzing the intensity distributions of different iris regions. Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed.

Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

PubMed

Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

2017-03-01

Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Enhancement of laser-induced breakdown spectroscopy (LIBS) Detection limit using a low-pressure and short-pulse laser-induced plasma process.

PubMed

Wang, Zhen Zhen; Deguchi, Yoshihiro; Kuwahara, Masakazu; Yan, Jun Jie; Liu, Ji Ping

2013-11-01

Laser-induced breakdown spectroscopy (LIBS) technology is an appealing technique compared with many other types of elemental analysis because of the fast response, high sensitivity, real-time, and noncontact features. One of the challenging targets of LIBS is the enhancement of the detection limit. In this study, the detection limit of gas-phase LIBS analysis has been improved by controlling the pressure and laser pulse width. In order to verify this method, low-pressure gas plasma was induced using nanosecond and picosecond lasers. The method was applied to the detection of Hg. The emission intensity ratio of the Hg atom to NO (IHg/INO) was analyzed to evaluate the LIBS detection limit because the NO emission (interference signal) was formed during the plasma generation and cooling process of N2 and O2 in the air. It was demonstrated that the enhancement of IHg/INO arose by decreasing the pressure to a few kilopascals, and the IHg/INO of the picosecond breakdown was always much higher than that of the nanosecond breakdown at low buffer gas pressure. Enhancement of IHg/INO increased more than 10 times at 700 Pa using picosecond laser with 35 ps pulse width. The detection limit was enhanced to 0.03 ppm (parts per million). We also saw that the spectra from the center and edge parts of plasma showed different features. Comparing the central spectra with the edge spectra, IHg/INO of the edge spectra was higher than that of the central spectra using the picosecond laser breakdown process.

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.

Restoration of out-of-focus images based on circle of confusion estimate

NASA Astrophysics Data System (ADS)

Vivirito, Paolo; Battiato, Sebastiano; Curti, Salvatore; La Cascia, M.; Pirrone, Roberto

2002-11-01

In this paper a new method for a fast out-of-focus blur estimation and restoration is proposed. It is suitable for CFA (Color Filter Array) images acquired by typical CCD/CMOS sensor. The method is based on the analysis of a single image and consists of two steps: 1) out-of-focus blur estimation via Bayer pattern analysis; 2) image restoration. Blur estimation is based on a block-wise edge detection technique. This edge detection is carried out on the green pixels of the CFA sensor image also called Bayer pattern. Once the blur level has been estimated the image is restored through the application of a new inverse filtering technique. This algorithm gives sharp images reducing ringing and crisping artifact, involving wider region of frequency. Experimental results show the effectiveness of the method, both in subjective and numerical way, by comparison with other techniques found in literature.

Salient object detection method based on multiple semantic features

NASA Astrophysics Data System (ADS)

Wang, Chunyang; Yu, Chunyan; Song, Meiping; Wang, Yulei

2018-04-01

The existing salient object detection model can only detect the approximate location of salient object, or highlight the background, to resolve the above problem, a salient object detection method was proposed based on image semantic features. First of all, three novel salient features were presented in this paper, including object edge density feature (EF), object semantic feature based on the convex hull (CF) and object lightness contrast feature (LF). Secondly, the multiple salient features were trained with random detection windows. Thirdly, Naive Bayesian model was used for combine these features for salient detection. The results on public datasets showed that our method performed well, the location of salient object can be fixed and the salient object can be accurately detected and marked by the specific window.

Integration of adaptive guided filtering, deep feature learning, and edge-detection techniques for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Wan, Xiaoqing; Zhao, Chunhui; Gao, Bing

2017-11-01

The integration of an edge-preserving filtering technique in the classification of a hyperspectral image (HSI) has been proven effective in enhancing classification performance. This paper proposes an ensemble strategy for HSI classification using an edge-preserving filter along with a deep learning model and edge detection. First, an adaptive guided filter is applied to the original HSI to reduce the noise in degraded images and to extract powerful spectral-spatial features. Second, the extracted features are fed as input to a stacked sparse autoencoder to adaptively exploit more invariant and deep feature representations; then, a random forest classifier is applied to fine-tune the entire pretrained network and determine the classification output. Third, a Prewitt compass operator is further performed on the HSI to extract the edges of the first principal component after dimension reduction. Moreover, the regional growth rule is applied to the resulting edge logical image to determine the local region for each unlabeled pixel. Finally, the categories of the corresponding neighborhood samples are determined in the original classification map; then, the major voting mechanism is implemented to generate the final output. Extensive experiments proved that the proposed method achieves competitive performance compared with several traditional approaches.

Edge Principal Components and Squash Clustering: Using the Special Structure of Phylogenetic Placement Data for Sample Comparison

PubMed Central

Matsen IV, Frederick A.; Evans, Steven N.

2013-01-01

Principal components analysis (PCA) and hierarchical clustering are two of the most heavily used techniques for analyzing the differences between nucleic acid sequence samples taken from a given environment. They have led to many insights regarding the structure of microbial communities. We have developed two new complementary methods that leverage how this microbial community data sits on a phylogenetic tree. Edge principal components analysis enables the detection of important differences between samples that contain closely related taxa. Each principal component axis is a collection of signed weights on the edges of the phylogenetic tree, and these weights are easily visualized by a suitable thickening and coloring of the edges. Squash clustering outputs a (rooted) clustering tree in which each internal node corresponds to an appropriate “average” of the original samples at the leaves below the node. Moreover, the length of an edge is a suitably defined distance between the averaged samples associated with the two incident nodes, rather than the less interpretable average of distances produced by UPGMA, the most widely used hierarchical clustering method in this context. We present these methods and illustrate their use with data from the human microbiome. PMID:23505415

Automatic Detection of Frontal Face Midline by Chain-coded Merlin-Farber Hough Trasform

NASA Astrophysics Data System (ADS)

Okamoto, Daichi; Ohyama, Wataru; Wakabayashi, Tetsushi; Kimura, Fumitaka

We propose a novel approach for detection of the facial midline (facial symmetry axis) from a frontal face image. The facial midline has several applications, for instance reducing computational cost required for facial feature extraction (FFE) and postoperative assessment for cosmetic or dental surgery. The proposed method detects the facial midline of a frontal face from an edge image as the symmetry axis using the Merlin-Faber Hough transformation. And a new performance improvement scheme for midline detection by MFHT is present. The main concept of the proposed scheme is suppression of redundant vote on the Hough parameter space by introducing chain code representation for the binary edge image. Experimental results on the image dataset containing 2409 images from FERET database indicate that the proposed algorithm can improve the accuracy of midline detection from 89.9% to 95.1 % for face images with different scales and rotation.

Vehicle detection in aerial surveillance using dynamic Bayesian networks.

PubMed

Cheng, Hsu-Yung; Weng, Chih-Chia; Chen, Yi-Ying

2012-04-01

We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixelwise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixelwise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and nonvehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of the Canny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixelwise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles.

Quantifying camouflage: how to predict detectability from appearance.

PubMed

Troscianko, Jolyon; Skelhorn, John; Stevens, Martin

2017-01-06

Quantifying the conspicuousness of objects against particular backgrounds is key to understanding the evolution and adaptive value of animal coloration, and in designing effective camouflage. Quantifying detectability can reveal how colour patterns affect survival, how animals' appearances influence habitat preferences, and how receiver visual systems work. Advances in calibrated digital imaging are enabling the capture of objective visual information, but it remains unclear which methods are best for measuring detectability. Numerous descriptions and models of appearance have been used to infer the detectability of animals, but these models are rarely empirically validated or directly compared to one another. We compared the performance of human 'predators' to a bank of contemporary methods for quantifying the appearance of camouflaged prey. Background matching was assessed using several established methods, including sophisticated feature-based pattern analysis, granularity approaches and a range of luminance and contrast difference measures. Disruptive coloration is a further camouflage strategy where high contrast patterns disrupt they prey's tell-tale outline, making it more difficult to detect. Disruptive camouflage has been studied intensely over the past decade, yet defining and measuring it have proven far more problematic. We assessed how well existing disruptive coloration measures predicted capture times. Additionally, we developed a new method for measuring edge disruption based on an understanding of sensory processing and the way in which false edges are thought to interfere with animal outlines. Our novel measure of disruptive coloration was the best predictor of capture times overall, highlighting the importance of false edges in concealment over and above pattern or luminance matching. The efficacy of our new method for measuring disruptive camouflage together with its biological plausibility and computational efficiency represents a substantial advance in our understanding of the measurement, mechanism and definition of disruptive camouflage. Our study also provides the first test of the efficacy of many established methods for quantifying how conspicuous animals are against particular backgrounds. The validation of these methods opens up new lines of investigation surrounding the form and function of different types of camouflage, and may apply more broadly to the evolution of any visual signal.

Text, photo, and line extraction in scanned documents

NASA Astrophysics Data System (ADS)

Erkilinc, M. Sezer; Jaber, Mustafa; Saber, Eli; Bauer, Peter; Depalov, Dejan

2012-07-01

We propose a page layout analysis algorithm to classify a scanned document into different regions such as text, photo, or strong lines. The proposed scheme consists of five modules. The first module performs several image preprocessing techniques such as image scaling, filtering, color space conversion, and gamma correction to enhance the scanned image quality and reduce the computation time in later stages. Text detection is applied in the second module wherein wavelet transform and run-length encoding are employed to generate and validate text regions, respectively. The third module uses a Markov random field based block-wise segmentation that employs a basis vector projection technique with maximum a posteriori probability optimization to detect photo regions. In the fourth module, methods for edge detection, edge linking, line-segment fitting, and Hough transform are utilized to detect strong edges and lines. In the last module, the resultant text, photo, and edge maps are combined to generate a page layout map using K-Means clustering. The proposed algorithm has been tested on several hundred documents that contain simple and complex page layout structures and contents such as articles, magazines, business cards, dictionaries, and newsletters, and compared against state-of-the-art page-segmentation techniques with benchmark performance. The results indicate that our methodology achieves an average of ˜89% classification accuracy in text, photo, and background regions.

Enhanced change detection index for disaster response, recovery assessment and monitoring of buildings and critical facilities-A case study for Muzzaffarabad, Pakistan

NASA Astrophysics Data System (ADS)

de Alwis Pitts, Dilkushi A.; So, Emily

2017-12-01

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects such as buildings and critical facilities. The change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. More emphasis is put on the building edges to capture the structural damage in quantifying change after disaster. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in potentially large areas. Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management and recovery practices. The recovery and monitoring can be analyzed using the index in zones extending from to epicentre of disaster or administrative boundaries over time.

Image edge detection based tool condition monitoring with morphological component analysis.

PubMed

Yu, Xiaolong; Lin, Xin; Dai, Yiquan; Zhu, Kunpeng

2017-07-01

The measurement and monitoring of tool condition are keys to the product precision in the automated manufacturing. To meet the need, this study proposes a novel tool wear monitoring approach based on the monitored image edge detection. Image edge detection has been a fundamental tool to obtain features of images. This approach extracts the tool edge with morphological component analysis. Through the decomposition of original tool wear image, the approach reduces the influence of texture and noise for edge measurement. Based on the target image sparse representation and edge detection, the approach could accurately extract the tool wear edge with continuous and complete contour, and is convenient in charactering tool conditions. Compared to the celebrated algorithms developed in the literature, this approach improves the integrity and connectivity of edges, and the results have shown that it achieves better geometry accuracy and lower error rate in the estimation of tool conditions. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Hypervelocity Impact (HVI). Volume 6; WLE High Fidelity Specimen Fg(RCC)-2

NASA Technical Reports Server (NTRS)

Gorman, Michael R.; Ziola, Steven M.

2007-01-01

During 2003 and 2004, the Johnson Space Center's White Sands Testing Facility in Las Cruces, New Mexico conducted hypervelocity impact tests on the space shuttle wing leading edge. Hypervelocity impact tests were conducted to determine if Micro-Meteoroid/Orbital Debris impacts could be reliably detected and located using simple passive ultrasonic methods. The objective of Target Fg(RCC)-2 was to study hypervelocity impacts through the reinforced carbon-carbon (RCC) panels of the Wing Leading Edge. Fiberglass was used in place of RCC in the initial tests. Impact damage was detected using lightweight, low power instrumentation capable of being used in flight.

Hypervelocity Impact (HVI). Volume 4; WLE Small-Scale Fiberglass Panel Flat Target C-2

NASA Technical Reports Server (NTRS)

Gorman, Michael R.; Ziola, Steven M.

2007-01-01

During 2003 and 2004, the Johnson Space Center's White Sands Testing Facility in Las Cruces, New Mexico conducted hypervelocity impact tests on the space shuttle wing leading edge. Hypervelocity impact tests were conducted to determine if Micro-Meteoroid/Orbital Debris impacts could be reliably detected and located using simple passive ultrasonic methods. The objective of Target C-2 was to study impacts through the reinforced carboncarbon (RCC) panels of the Wing Leading Edge. Fiberglass was used in place of RCC in the initial tests. Impact damage was detected using lightweight, low power instrumentation capable of being used in flight.

Hypervelocity Impact (HVI). Volume 5; WLE High Fidelity Specimen Fg(RCC)-1

NASA Technical Reports Server (NTRS)

Gorman, Michael R.; Ziola, Steven M.

2007-01-01

During 2003 and 2004, the Johnson Space Center's White Sands Testing Facility in Las Cruces, New Mexico conducted hypervelocity impact tests on the space shuttle wing leading edge. Hypervelocity impact tests were conducted to determine if Micro-Meteoroid/Orbital Debris impacts could be reliably detected and located using simple passive ultrasonic methods. The objective of Target Fg(RCC)-1 was to study hypervelocity impacts through the reinforced carbon-carbon (RCC) panels of the Wing Leading Edge. Fiberglass was used in place of RCC in the initial tests. Impact damage was detected using lightweight, low power instrumentation capable of being used in flight.

Hypervelocity Impact (HVI). Volume 3; WLE Small-Scale Fiberglass Panel Flat Target C-1

NASA Technical Reports Server (NTRS)

Gorman, Michael R.; Ziola, Steven M.

2007-01-01

During 2003 and 2004, the Johnson Space Center's White Sands Testing Facility in Las Cruces, New Mexico conducted hypervelocity impact tests on the space shuttle wing leading edge. Hypervelocity impact tests were conducted to determine if Micro-Meteoroid/Orbital Debris impacts could be reliably detected and located using simple passive ultrasonic methods. The objective of Target C-1 was to study hypervelocity impacts on the reinforced carbon-carbon (RCC) panels of the Wing Leading Edge. Fiberglass was used in place of RCC in the initial tests. Impact damage was detected using lightweight, low power instrumentation capable of being used in flight.

Real time automated inspection

DOEpatents

Fant, Karl M.; Fundakowski, Richard A.; Levitt, Tod S.; Overland, John E.; Suresh, Bindinganavle R.; Ulrich, Franz W.

1985-01-01

A method and apparatus relating to the real time automatic detection and classification of characteristic type surface imperfections occurring on the surfaces of material of interest such as moving hot metal slabs produced by a continuous steel caster. A data camera transversely scans continuous lines of such a surface to sense light intensities of scanned pixels and generates corresponding voltage values. The voltage values are converted to corresponding digital values to form a digital image of the surface which is subsequently processed to form an edge-enhanced image having scan lines characterized by intervals corresponding to the edges of the image. The edge-enhanced image is thresholded to segment out the edges and objects formed by the edges are segmented out by interval matching and bin tracking. Features of the objects are derived and such features are utilized to classify the objects into characteristic type surface imperfections.

Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing

PubMed Central

Sarrafzadeh, Omid; Dehnavi, Alireza Mehri

2015-01-01

Background: Segmentation of leukocytes acts as the foundation for all automated image-based hematological disease recognition systems. Most of the time, hematologists are interested in evaluation of white blood cells only. Digital image processing techniques can help them in their analysis and diagnosis. Materials and Methods: The main objective of this paper is to detect leukocytes from a blood smear microscopic image and segment them into their two dominant elements, nucleus and cytoplasm. The segmentation is conducted using two stages of applying K-means clustering. First, the nuclei are segmented using K-means clustering. Then, a proposed method based on region growing is applied to separate the connected nuclei. Next, the nuclei are subtracted from the original image. Finally, the cytoplasm is segmented using the second stage of K-means clustering. Results: The results indicate that the proposed method is able to extract the nucleus and cytoplasm regions accurately and works well even though there is no significant contrast between the components in the image. Conclusions: In this paper, a method based on K-means clustering and region growing is proposed in order to detect leukocytes from a blood smear microscopic image and segment its components, the nucleus and the cytoplasm. As region growing step of the algorithm relies on the information of edges, it will not able to separate the connected nuclei more accurately in poor edges and it requires at least a weak edge to exist between the nuclei. The nucleus and cytoplasm segments of a leukocyte can be used for feature extraction and classification which leads to automated leukemia detection. PMID:26605213

Hair segmentation using adaptive threshold from edge and branch length measures.

PubMed

Lee, Ian; Du, Xian; Anthony, Brian

2017-10-01

Non-invasive imaging techniques allow the monitoring of skin structure and diagnosis of skin diseases in clinical applications. However, hair in skin images hampers the imaging and classification of the skin structure of interest. Although many hair segmentation methods have been proposed for digital hair removal, a major challenge in hair segmentation remains in detecting hairs that are thin, overlapping, of similar contrast or color to underlying skin, or overlaid on highly-textured skin structure. To solve the problem, we present an automatic hair segmentation method that uses edge density (ED) and mean branch length (MBL) to measure hair. First, hair is detected by the integration of top-hat transform and modified second-order Gaussian filter. Second, we employ a robust adaptive threshold of ED and MBL to generate a hair mask. Third, the hair mask is refined by k-NN classification of hair and skin pixels. The proposed algorithm was tested using two datasets of healthy skin images and lesion images respectively. These datasets were taken from different imaging platforms in various illumination levels and varying skin colors. We compared the hair detection and segmentation results from our algorithm and six other hair segmentation methods of state of the art. Our method exhibits high value of sensitivity: 75% and specificity: 95%, which indicates significantly higher accuracy and better balance between true positive and false positive detection than the other methods. Published by Elsevier Ltd.

Cest Analysis: Automated Change Detection from Very-High Remote Sensing Images

NASA Astrophysics Data System (ADS)

Ehlers, M.; Klonus, S.; Jarmer, T.; Sofina, N.; Michel, U.; Reinartz, P.; Sirmacek, B.

2012-08-01

A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye) new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST) analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT) and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment) with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST) of the change algorithms is applied to calculate the probability of change for a particular location. CEST was tested with high-resolution satellite images of the crisis areas of Darfur (Sudan). CEST results are compared with a number of standard algorithms for automated change detection such as image difference, image ratioe, principal component analysis, delta cue technique and post classification change detection. The new combined method shows superior results averaging between 45% and 15% improvement in accuracy.

Patellar segmentation from 3D magnetic resonance images using guided recursive ray-tracing for edge pattern detection

NASA Astrophysics Data System (ADS)

Cheng, Ruida; Jackson, Jennifer N.; McCreedy, Evan S.; Gandler, William; Eijkenboom, J. J. F. A.; van Middelkoop, M.; McAuliffe, Matthew J.; Sheehan, Frances T.

2016-03-01

The paper presents an automatic segmentation methodology for the patellar bone, based on 3D gradient recalled echo and gradient recalled echo with fat suppression magnetic resonance images. Constricted search space outlines are incorporated into recursive ray-tracing to segment the outer cortical bone. A statistical analysis based on the dependence of information in adjacent slices is used to limit the search in each image to between an outer and inner search region. A section based recursive ray-tracing mechanism is used to skip inner noise regions and detect the edge boundary. The proposed method achieves higher segmentation accuracy (0.23mm) than the current state-of-the-art methods with the average dice similarity coefficient of 96.0% (SD 1.3%) agreement between the auto-segmentation and ground truth surfaces.

Feature Detection in SAR Interferograms With Missing Data Displays Fault Slip Near El Mayor-Cucapah and South Napa Earthquakes

NASA Astrophysics Data System (ADS)

Parker, J. W.; Donnellan, A.; Glasscoe, M. T.; Stough, T.

2015-12-01

Edge detection identifies seismic or aseismic fault motion, as demonstrated in repeat-pass inteferograms obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) program. But this identification, demonstrated in 2010, was not robust: for best results, it requires a flattened background image, interpolation into missing data (holes) and outliers, and background noise that is either sufficiently small or roughly white Gaussian. Proper treatment of missing data, bursting noise patches, and tiny noise differences at short distances apart from bursts are essential to creating an acceptably reliable method sensitive to small near-surface fractures. Clearly a robust method is needed for machine scanning of the thousands of UAVSAR repeat-pass interferograms for evidence of fault slip, landslides, and other local features: hand-crafted intervention will not do. Effective methods of identifying, removing and filling in bad pixels reveal significant features of surface fractures. A rich network of edges (probably fractures and subsidence) in difference images spanning the South Napa earthquake give way to a simple set of postseismically slipping faults. Coseismic El Mayor-Cucapah interferograms compared to post-seismic difference images show nearly disjoint patterns of surface fractures in California's Sonoran Desert; the combined pattern reveals a network of near-perpendicular, probably conjugate faults not mapped before the earthquake. The current algorithms for UAVSAR interferogram edge detections are shown to be effective in difficult environments, including agricultural (Napa, Imperial Valley) and difficult urban areas (Orange County.).

Detecting Faults in Southern California using Computer-Vision Techniques and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Interferometry

NASA Astrophysics Data System (ADS)

Barba, M.; Rains, C.; von Dassow, W.; Parker, J. W.; Glasscoe, M. T.

2013-12-01

Knowing the location and behavior of active faults is essential for earthquake hazard assessment and disaster response. In Interferometric Synthetic Aperture Radar (InSAR) images, faults are revealed as linear discontinuities. Currently, interferograms are manually inspected to locate faults. During the summer of 2013, the NASA-JPL DEVELOP California Disasters team contributed to the development of a method to expedite fault detection in California using remote-sensing technology. The team utilized InSAR images created from polarimetric L-band data from NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) project. A computer-vision technique known as 'edge-detection' was used to automate the fault-identification process. We tested and refined an edge-detection algorithm under development through NASA's Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) project. To optimize the algorithm we used both UAVSAR interferograms and synthetic interferograms generated through Disloc, a web-based modeling program available through NASA's QuakeSim project. The edge-detection algorithm detected seismic, aseismic, and co-seismic slip along faults that were identified and compared with databases of known fault systems. Our optimization process was the first step toward integration of the edge-detection code into E-DECIDER to provide decision support for earthquake preparation and disaster management. E-DECIDER partners that will use the edge-detection code include the California Earthquake Clearinghouse and the US Department of Homeland Security through delivery of products using the Unified Incident Command and Decision Support (UICDS) service. Through these partnerships, researchers, earthquake disaster response teams, and policy-makers will be able to use this new methodology to examine the details of ground and fault motions for moderate to large earthquakes. Following an earthquake, the newly discovered faults can be paired with infrastructure overlays, allowing emergency response teams to identify sites that may have been exposed to damage. The faults will also be incorporated into a database for future integration into fault models and earthquake simulations, improving future earthquake hazard assessment. As new faults are mapped, they will further understanding of the complex fault systems and earthquake hazards within the seismically dynamic state of California.

Detecting trends in academic research from a citation network using network representation learning

PubMed Central

Mori, Junichiro; Ochi, Masanao; Sakata, Ichiro

2018-01-01

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth. PMID:29782521

Synthetic Minority Oversampling Technique and Fractal Dimension for Identifying Multiple Sclerosis

NASA Astrophysics Data System (ADS)

Zhang, Yu-Dong; Zhang, Yin; Phillips, Preetha; Dong, Zhengchao; Wang, Shuihua

Multiple sclerosis (MS) is a severe brain disease. Early detection can provide timely treatment. Fractal dimension can provide statistical index of pattern changes with scale at a given brain image. In this study, our team used susceptibility weighted imaging technique to obtain 676 MS slices and 880 healthy slices. We used synthetic minority oversampling technique to process the unbalanced dataset. Then, we used Canny edge detector to extract distinguishing edges. The Minkowski-Bouligand dimension was a fractal dimension estimation method and used to extract features from edges. Single hidden layer neural network was used as the classifier. Finally, we proposed a three-segment representation biogeography-based optimization to train the classifier. Our method achieved a sensitivity of 97.78±1.29%, a specificity of 97.82±1.60% and an accuracy of 97.80±1.40%. The proposed method is superior to seven state-of-the-art methods in terms of sensitivity and accuracy.

Edge Detection Based On the Characteristic of Primary Visual Cortex Cells

NASA Astrophysics Data System (ADS)

Zhu, M. M.; Xu, Y. L.; Ma, H. Q.

2018-01-01

Aiming at the problem that it is difficult to balance the accuracy of edge detection and anti-noise performance, and referring to the dynamic and static perceptions of the primary visual cortex (V1) cells, a V1 cell model is established to perform edge detection. A spatiotemporal filter is adopted to simulate the receptive field of V1 simple cells, the model V1 cell is obtained after integrating the responses of simple cells by half-wave rectification and normalization, Then the natural image edge is detected by using static perception of V1 cells. The simulation results show that, the V1 model can basically fit the biological data and has the universality of biology. What’s more, compared with other edge detection operators, the proposed model is more effective and has better robustness

Using new edges for anomaly detection in computer networks

DOEpatents

Neil, Joshua Charles

2017-07-04

Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

Using new edges for anomaly detection in computer networks

DOEpatents

Neil, Joshua Charles

2015-05-19

Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

Deep convolutional networks for automated detection of posterior-element fractures on spine CT

NASA Astrophysics Data System (ADS)

Roth, Holger R.; Wang, Yinong; Yao, Jianhua; Lu, Le; Burns, Joseph E.; Summers, Ronald M.

2016-03-01

Injuries of the spine, and its posterior elements in particular, are a common occurrence in trauma patients, with potentially devastating consequences. Computer-aided detection (CADe) could assist in the detection and classification of spine fractures. Furthermore, CAD could help assess the stability and chronicity of fractures, as well as facilitate research into optimization of treatment paradigms. In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine. First, the vertebra bodies of the spine with its posterior elements are segmented in spine CT using multi-atlas label fusion. Then, edge maps of the posterior elements are computed. These edge maps serve as candidate regions for predicting a set of probabilities for fractures along the image edges using ConvNets in a 2.5D fashion (three orthogonal patches in axial, coronal and sagittal planes). We explore three different methods for training the ConvNet using 2.5D patches along the edge maps of `positive', i.e. fractured posterior-elements and `negative', i.e. non-fractured elements. An experienced radiologist retrospectively marked the location of 55 displaced posterior-element fractures in 18 trauma patients. We randomly split the data into training and testing cases. In testing, we achieve an area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at 5 or 10 false-positives per patient, respectively. Analysis of our set of trauma patients demonstrates the feasibility of detecting posterior-element fractures in spine CT images using computer vision techniques such as deep convolutional networks.

Anti-impulse-noise Edge Detection via Anisotropic Morphological Directional Derivatives.

PubMed

Shui, Peng-Lang; Wang, Fu-Ping

2017-07-13

Traditional differential-based edge detection suffers from abrupt degradation in performance when images are corrupted by impulse noises. The morphological operators such as the median filters and weighted median filters possess the intrinsic ability to counteract impulse noise. In this paper, by combining the biwindow configuration with weighted median filters, anisotropic morphological directional derivatives (AMDD) robust to impulse noise are proposed to measure the local grayscale variation around a pixel. For ideal step edges, the AMDD spatial response and directional representation are derived. The characteristics and edge resolution of two kinds of typical biwindows are analyzed thoroughly. In terms of the AMDD spatial response and directional representation of ideal step edges, the spatial matched filter is used to extract the edge strength map (ESM) from the AMDDs of an image. The spatial and directional matched filters are used to extract the edge direction map (EDM). Embedding the extracted ESM and EDM into the standard route of the differential-based edge detection, an anti-impulse-noise AMDD-based edge detector is constructed. It is compared with the existing state-of-the-art detectors on a recognized image dataset for edge detection evaluation. The results show that it attains competitive performance in noise-free and Gaussian noise cases and the best performance in impulse noise cases.

Core level electron energy-loss spectra of minerals: pre-edge fine structures at the oxygen K-edge . Comment on ``Water in minerals detectable by electron energy-loss spectroscopy EELS'' by R. Wirth, Phys Chem Minerals (1997) 24:561-568

NASA Astrophysics Data System (ADS)

van Aken, P. A.; Liebscher, B.; Styrsa, V. J.

In a recent paper entitled ``Water in minerals detectable by electron energy-loss spectroscopy EELS'' by R. Wirth, it has been claimed that OH-- and H2O-bearing minerals exhibit a characteristic peak in the ELNES spectra at about 528 eV prior to the onset of the O K-edge at 532 eV, which could be used for (semi-)quantitative determination of water- or OH-contents on a nanometer scale. It is shown here by parallel electron energy-loss spectroscopy (PEELS) recorded in a transmission electron microscope (TEM) that O K-pre-edge peaks with very high intensities may also exist in water-free compounds and minerals, in particular when they contain transition metals. These spectral features arise from covalent mixing of the metal and oxygen states, which introduces oxygen p character in unoccupied states of mainly metal character. The point is illustrated by the comparison of hematite (α-Fe2O3) and lepidocrocite (γ-FeOOH) O K-edge PEELS spectra which exhibit similar intensities of the pre-edge peak, despite of their grossly different OH- contents. As a consequence, the general validity of the method proposed by Wirth is questioned.

Correlation of phenotypic tests with the presence of the blaZ gene for detection of beta-lactamase.

PubMed

Ferreira, Adriano Martison; Martins, Katheryne Benini; Silva, Vanessa Rocha da; Mondelli, Alessandro Lia; Cunha, Maria de Lourdes Ribeiro de Souza da

Staphylococcus aureus and Staphylococcus saprophyticus are the most common and most important staphylococcal species associated with urinary tract infections. The objective of the present study was to compare and to evaluate the accuracy of four phenotypic methods for the detection of beta-lactamase production in Staphylococcus spp. Seventy-three strains produced a halo with a diameter ≤28mm (penicillin resistant) and all of them were positive for the blaZ gene. Among the 28 susceptible strain (halo ≥29mm), 23 carried the blaZ gene and five did not. The zone edge test was the most sensitive (90.3%), followed by MIC determination (85.5%), but the specificity of the former was low (40.0%). The nitrocefin test was the least sensitive (28.9%). However, the nitrocefin test together with the disk diffusion method showed the highest specificity (100%). The present results demonstrated that the zone edge test was the most sensitive phenotypic test for detection of beta-lactamase, although it is still not an ideal test to detect this type of resistance since its specificity was low. However, the inhibition halo diameter of the penicillin disk can be used together with the zone edge test since the same disk is employed in the two tests. Combined analysis of the two tests shows a sensitivity of 90.3% and specificity of 100%, proving better sensitivity, especially for S. saprophyticus. This is a low-cost test of easy application and interpretation that can be used in small and medium-sized laboratories where susceptibility testing is usually performed by the disk diffusion method. Copyright © 2016 Sociedade Brasileira de Microbiologia. Published by Elsevier Editora Ltda. All rights reserved.

Evaluation of moisture ingress from the perimeter of photovoltaic modules: Evaluation of moisture ingress

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

Kempe, Michael D.; Dameron, Arrelaine A.; Reese, Matthew O.

2013-05-14

Many thin film photovoltaic (PV) technologies can be sensitive to corrosion induced by the presence of water vapor in the packaging materials. Typically impermeable front and backsheets are used in conjunction with an edge-seal around the perimeter to prevent water vapor ingress. These edge-seal materials are often made of a polyisobutylene resin filled with desiccant, which dramatically increases the time for moisture to reach sensitive module components. While edge-seals can prevent moisture ingress, even the lowest diffusivity transparent encapsulant materials are insufficient for the lifetime of a module. To evaluate the performance of edge-seal and encapsulant materials in a mannermore » that simulates their function in a PV module, an optical method was devised where ingress is detected by reaction of a Ca film with water. Using this method, we have exposed test samples to heat and humidity allowing quantitative comparison of different edge-seal and encapsulant materials. Next, we use measurements of polymer diffusivity and solubility to evaluate the ability to model this moisture ingress. Here, we find good agreement between these two methods highlighting the much greater ability of polyisobutylene materials to keep moisture out as compared with typical encapsulant materials used in the PV industry.« less

Image enhancement of real-time television to benefit the visually impaired.

PubMed

Wolffsohn, James S; Mukhopadhyay, Ditipriya; Rubinstein, Martin

2007-09-01

To examine the use of real-time, generic edge detection, image processing techniques to enhance the television viewing of the visually impaired. Prospective, clinical experimental study. One hundred and two sequential visually impaired (average age 73.8 +/- 14.8 years; 59% female) in a single center optimized a dynamic television image with respect to edge detection filter (Prewitt, Sobel, or the two combined), color (red, green, blue, or white), and intensity (one to 15 times) of the overlaid edges. They then rated the original television footage compared with a black-and-white image displaying the edges detected and the original television image with the detected edges overlaid in the chosen color and at the intensity selected. Footage of news, an advertisement, and the end of program credits were subjectively assessed in a random order. A Prewitt filter was preferred (44%) compared with the Sobel filter (27%) or a combination of the two (28%). Green and white were equally popular for displaying the detected edges (32%), with blue (22%) and red (14%) less so. The average preferred edge intensity was 3.5 +/- 1.7 times. The image-enhanced television was significantly preferred to the original (P < .001), which in turn was preferred to viewing the detected edges alone (P < .001) for each of the footage clips. Preference was not dependent on the condition causing visual impairment. Seventy percent were definitely willing to buy a set-top box that could achieve these effects for a reasonable price. Simple generic edge detection image enhancement options can be performed on television in real-time and significantly enhance the viewing of the visually impaired.

3CCD image segmentation and edge detection based on MATLAB

NASA Astrophysics Data System (ADS)

He, Yong; Pan, Jiazhi; Zhang, Yun

2006-09-01

This research aimed to identify weeds from crops in early stage in the field operation by using image-processing technology. As 3CCD images offer greater binary value difference between weed and crop section than ordinary digital images taken by common cameras. It has 3 channels (green, red, ifred) which takes a snap-photo of the same area, and the three images can be composed into one image, which facilitates the segmentation of different areas. By the application of image-processing toolkit on MATLAB, the different areas in the image can be segmented clearly. As edge detection technique is the first and very important step in image processing, The different result of different processing method was compared. Especially, by using the wavelet packet transform toolkit on MATLAB, An image was preprocessed and then the edge was extracted, and getting more clearly cut image of edge. The segmentation methods include operations as erosion, dilation and other algorithms to preprocess the images. It is of great importance to segment different areas in digital images in field real time, so as to be applied in precision farming, to saving energy and herbicide and many other materials. At present time Large scale software as MATLAB on PC was used, but the computation can be reduced and integrated into a small embed system, which means that the application of this technique in agricultural engineering is feasible and of great economical value.

Effects of boundary-layer separation controllers on a desktop fume hood.

PubMed

Huang, Rong Fung; Chen, Jia-Kun; Hsu, Ching Min; Hung, Shuo-Fu

2016-10-02

A desktop fume hood installed with an innovative design of flow boundary-layer separation controllers on the leading edges of the side plates, work surface, and corners was developed and characterized for its flow and containment leakage characteristics. The geometric features of the developed desktop fume hood included a rearward offset suction slot, two side plates, two side-plate boundary-layer separation controllers on the leading edges of the side plates, a slanted surface on the leading edge of the work surface, and two small triangular plates on the upper left and right corners of the hood face. The flow characteristics were examined using the laser-assisted smoke flow visualization technique. The containment leakages were measured by the tracer gas (sulphur hexafluoride) detection method on the hood face plane with a mannequin installed in front of the hood. The results of flow visualization showed that the smoke dispersions induced by the boundary-layer separations on the leading edges of the side plates and work surface, as well as the three-dimensional complex flows on the upper-left and -right corners of the hood face, were effectively alleviated by the boundary-layer separation controllers. The results of the tracer gas detection method with a mannequin standing in front of the hood showed that the leakage levels were negligibly small (≤0.003 ppm) at low face velocities (≥0.19 m/s).

Real time automated inspection

DOEpatents

Fant, K.M.; Fundakowski, R.A.; Levitt, T.S.; Overland, J.E.; Suresh, B.R.; Ulrich, F.W.

1985-05-21

A method and apparatus are described relating to the real time automatic detection and classification of characteristic type surface imperfections occurring on the surfaces of material of interest such as moving hot metal slabs produced by a continuous steel caster. A data camera transversely scans continuous lines of such a surface to sense light intensities of scanned pixels and generates corresponding voltage values. The voltage values are converted to corresponding digital values to form a digital image of the surface which is subsequently processed to form an edge-enhanced image having scan lines characterized by intervals corresponding to the edges of the image. The edge-enhanced image is thresholded to segment out the edges and objects formed by the edges by interval matching and bin tracking. Features of the objects are derived and such features are utilized to classify the objects into characteristic type surface imperfections. 43 figs.

Edge analyzing properties of center/surround response functions in cybernetic vision

NASA Technical Reports Server (NTRS)

Jobson, D. J.

1984-01-01

The ability of center/surround response functions to make explicit high resolution spatial information in optical images was investigated by performing convolutions of two dimensional response functions and image intensity functions (mainly edges). The center/surround function was found to have the unique property of separating edge contrast from shape variations and of providing a direct basis for determining contrast and subsequently shape of edges in images. Computationally simple measures of contrast and shape were constructed for potential use in cybernetic vision systems. For one class of response functions these measures were found to be reasonably resilient for a range of scan direction and displacements of the response functions relative to shaped edges. A pathological range of scan directions was also defined and methods for detecting and handling these cases were developed. The relationship of these results to biological vision is discussed speculatively.

Role of chiral quantum Hall edge states in nuclear spin polarization.

PubMed

Yang, Kaifeng; Nagase, Katsumi; Hirayama, Yoshiro; Mishima, Tetsuya D; Santos, Michael B; Liu, Hongwu

2017-04-20

Resistively detected NMR (RDNMR) based on dynamic nuclear polarization (DNP) in a quantum Hall ferromagnet (QHF) is a highly sensitive method for the discovery of fascinating quantum Hall phases; however, the mechanism of this DNP and, in particular, the role of quantum Hall edge states in it are unclear. Here we demonstrate the important but previously unrecognized effect of chiral edge modes on the nuclear spin polarization. A side-by-side comparison of the RDNMR signals from Hall bar and Corbino disk configurations allows us to distinguish the contributions of bulk and edge states to DNP in QHF. The unidirectional current flow along chiral edge states makes the polarization robust to thermal fluctuations at high temperatures and makes it possible to observe a reciprocity principle of the RDNMR response. These findings help us better understand complex NMR responses in QHF, which has important implications for the development of RDNMR techniques.

Discovering protein complexes in protein interaction networks via exploring the weak ties effect

PubMed Central

2012-01-01

Background Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges. Results To provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes. Conclusions We proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting that the roles of edges are critical in discovering protein complexes. PMID:23046740

Improvement and implementation for Canny edge detection algorithm

NASA Astrophysics Data System (ADS)

Yang, Tao; Qiu, Yue-hong

2015-07-01

Edge detection is necessary for image segmentation and pattern recognition. In this paper, an improved Canny edge detection approach is proposed due to the defect of traditional algorithm. A modified bilateral filter with a compensation function based on pixel intensity similarity judgment was used to smooth image instead of Gaussian filter, which could preserve edge feature and remove noise effectively. In order to solve the problems of sensitivity to the noise in gradient calculating, the algorithm used 4 directions gradient templates. Finally, Otsu algorithm adaptively obtain the dual-threshold. All of the algorithm simulated with OpenCV 2.4.0 library in the environments of vs2010, and through the experimental analysis, the improved algorithm has been proved to detect edge details more effectively and with more adaptability.

A Novel Method for Mining SaaS Software Tag via Community Detection in Software Services Network

NASA Astrophysics Data System (ADS)

Qin, Li; Li, Bing; Pan, Wei-Feng; Peng, Tao

The number of online software services based on SaaS paradigm is increasing. However, users usually find it hard to get the exact software services they need. At present, tags are widely used to annotate specific software services and also to facilitate the searching of them. Currently these tags are arbitrary and ambiguous since mostly of them are generated manually by service developers. This paper proposes a method for mining tags from the help documents of software services. By extracting terms from the help documents and calculating the similarity between the terms, we construct a software similarity network where nodes represent software services, edges denote the similarity relationship between software services, and the weights of the edges are the similarity degrees. The hierarchical clustering algorithm is used for community detection in this software similarity network. At the final stage, tags are mined for each of the communities and stored as ontology.

Model-based registration of multi-rigid-body for augmented reality

NASA Astrophysics Data System (ADS)

Ikeda, Sei; Hori, Hajime; Imura, Masataka; Manabe, Yoshitsugu; Chihara, Kunihiro

2009-02-01

Geometric registration between a virtual object and the real space is the most basic problem in augmented reality. Model-based tracking methods allow us to estimate three-dimensional (3-D) position and orientation of a real object by using a textured 3-D model instead of visual marker. However, it is difficult to apply existing model-based tracking methods to the objects that have movable parts such as a display of a mobile phone, because these methods suppose a single, rigid-body model. In this research, we propose a novel model-based registration method for multi rigid-body objects. For each frame, the 3-D models of each rigid part of the object are first rendered according to estimated motion and transformation from the previous frame. Second, control points are determined by detecting the edges of the rendered image and sampling pixels on these edges. Motion and transformation are then simultaneously calculated from distances between the edges and the control points. The validity of the proposed method is demonstrated through experiments using synthetic videos.

Visual navigation using edge curve matching for pinpoint planetary landing

NASA Astrophysics Data System (ADS)

Cui, Pingyuan; Gao, Xizhen; Zhu, Shengying; Shao, Wei

2018-05-01

Pinpoint landing is challenging for future Mars and asteroid exploration missions. Vision-based navigation scheme based on feature detection and matching is practical and can achieve the required precision. However, existing algorithms are computationally prohibitive and utilize poor-performance measurements, which pose great challenges for the application of visual navigation. This paper proposes an innovative visual navigation scheme using crater edge curves during descent and landing phase. In the algorithm, the edge curves of the craters tracked from two sequential images are utilized to determine the relative attitude and position of the lander through a normalized method. Then, considering error accumulation of relative navigation, a method is developed. That is to integrate the crater-based relative navigation method with crater-based absolute navigation method that identifies craters using a georeferenced database for continuous estimation of absolute states. In addition, expressions of the relative state estimate bias are derived. Novel necessary and sufficient observability criteria based on error analysis are provided to improve the navigation performance, which hold true for similar navigation systems. Simulation results demonstrate the effectiveness and high accuracy of the proposed navigation method.

Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder.

PubMed

Chen, Shuoyang; Xu, Tingfa; Li, Daqun; Zhang, Jizhou; Jiang, Shenwang

2016-10-21

During the process of moving object detection in an intelligent visual surveillance system, a scenario with complex background is sure to appear. The traditional methods, such as "frame difference" and "optical flow", may not able to deal with the problem very well. In such scenarios, we use a modified algorithm to do the background modeling work. In this paper, we use edge detection to get an edge difference image just to enhance the ability of resistance illumination variation. Then we use a "multi-block temporal-analyzing LBP (Local Binary Pattern)" algorithm to do the segmentation. In the end, a connected component is used to locate the object. We also produce a hardware platform, the core of which consists of the DSP (Digital Signal Processor) and FPGA (Field Programmable Gate Array) platforms and the high-precision intelligent holder.

A deblocking algorithm based on color psychology for display quality enhancement

NASA Astrophysics Data System (ADS)

Yeh, Chia-Hung; Tseng, Wen-Yu; Huang, Kai-Lin

2012-12-01

This article proposes a post-processing deblocking filter to reduce blocking effects. The proposed algorithm detects blocking effects by fusing the results of Sobel edge detector and wavelet-based edge detector. The filtering stage provides four filter modes to eliminate blocking effects at different color regions according to human color vision and color psychology analysis. Experimental results show that the proposed algorithm has better subjective and objective qualities for H.264/AVC reconstructed videos when compared to several existing methods.

Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing.

PubMed

Sarrafzadeh, Omid; Dehnavi, Alireza Mehri

2015-01-01

Segmentation of leukocytes acts as the foundation for all automated image-based hematological disease recognition systems. Most of the time, hematologists are interested in evaluation of white blood cells only. Digital image processing techniques can help them in their analysis and diagnosis. The main objective of this paper is to detect leukocytes from a blood smear microscopic image and segment them into their two dominant elements, nucleus and cytoplasm. The segmentation is conducted using two stages of applying K-means clustering. First, the nuclei are segmented using K-means clustering. Then, a proposed method based on region growing is applied to separate the connected nuclei. Next, the nuclei are subtracted from the original image. Finally, the cytoplasm is segmented using the second stage of K-means clustering. The results indicate that the proposed method is able to extract the nucleus and cytoplasm regions accurately and works well even though there is no significant contrast between the components in the image. In this paper, a method based on K-means clustering and region growing is proposed in order to detect leukocytes from a blood smear microscopic image and segment its components, the nucleus and the cytoplasm. As region growing step of the algorithm relies on the information of edges, it will not able to separate the connected nuclei more accurately in poor edges and it requires at least a weak edge to exist between the nuclei. The nucleus and cytoplasm segments of a leukocyte can be used for feature extraction and classification which leads to automated leukemia detection.

[An object-oriented remote sensing image segmentation approach based on edge detection].

PubMed

Tan, Yu-Min; Huai, Jian-Zhu; Tang, Zhong-Shi

2010-06-01

Satellite sensor technology endorsed better discrimination of various landscape objects. Image segmentation approaches to extracting conceptual objects and patterns hence have been explored and a wide variety of such algorithms abound. To this end, in order to effectively utilize edge and topological information in high resolution remote sensing imagery, an object-oriented algorithm combining edge detection and region merging is proposed. Susan edge filter is firstly applied to the panchromatic band of Quickbird imagery with spatial resolution of 0.61 m to obtain the edge map. Thanks to the resulting edge map, a two-phrase region-based segmentation method operates on the fusion image from panchromatic and multispectral Quickbird images to get the final partition result. In the first phase, a quad tree grid consisting of squares with sides parallel to the image left and top borders agglomerates the square subsets recursively where the uniform measure is satisfied to derive image object primitives. Before the merger of the second phrase, the contextual and spatial information, (e. g., neighbor relationship, boundary coding) of the resulting squares are retrieved efficiently by means of the quad tree structure. Then a region merging operation is performed with those primitives, during which the criterion for region merging integrates edge map and region-based features. This approach has been tested on the QuickBird images of some site in Sanxia area and the result is compared with those of ENVI Zoom Definiens. In addition, quantitative evaluation of the quality of segmentation results is also presented. Experiment results demonstrate stable convergence and efficiency.

Flow Liner Slot Edge Replication Feasibility Study

NASA Technical Reports Server (NTRS)

Newman, John A.; Willard, Scott A.; Smith, Stephen W.; Piascik, Robert S.

2006-01-01

Surface replication has been proposed as a method for crack detection in space shuttle main engine flowliner slots. The results of a feasibility study show that examination of surface replicas with a scanning electron microscope can result in the detection of cracks as small as 0.005 inch, and surface flaws as small as 0.001 inch, for the flowliner material.

An onboard data analysis method to track the seasonal polar caps on Mars

USGS Publications Warehouse

Wagstaff, K.L.; Castano, R.; Chien, S.; Ivanov, A.B.; Pounders, E.; Titus, T.N.; ,

2005-01-01

The Martian seasonal CO2 ice caps advance and retreat each year. They are currently studied using instruments such as the THermal EMission Imaging System (THEMIS), a visible and infra-red camera on the Mars Odyssey spacecraft [1]. However, each image must be downlinked to Earth prior to analysis. In contrast, we have developed the Bimodal Image Temperature (BIT) histogram analysis method for onboard detection of the cap edge, before transmission. In downlink-limited scenarios when the entire image cannot be transmitted, the location of the cap edge can still be identified and sent to Earth. In this paper, we evaluate our method on uncalibrated THEMIS data and find 1) agreement with manual cap edge identifications to within 28.2 km, and 2) high accuracy even with a smaller analysis window, yielding large reductions in memory requirements. This algorithm is currently being considered as a capability enhancement for the Odyssey second extended mission, beginning in fall 2006.

Parallel Hough Transform-Based Straight Line Detection and Its FPGA Implementation in Embedded Vision

PubMed Central

Lu, Xiaofeng; Song, Li; Shen, Sumin; He, Kang; Yu, Songyu; Ling, Nam

2013-01-01

Hough Transform has been widely used for straight line detection in low-definition and still images, but it suffers from execution time and resource requirements. Field Programmable Gate Arrays (FPGA) provide a competitive alternative for hardware acceleration to reap tremendous computing performance. In this paper, we propose a novel parallel Hough Transform (PHT) and FPGA architecture-associated framework for real-time straight line detection in high-definition videos. A resource-optimized Canny edge detection method with enhanced non-maximum suppression conditions is presented to suppress most possible false edges and obtain more accurate candidate edge pixels for subsequent accelerated computation. Then, a novel PHT algorithm exploiting spatial angle-level parallelism is proposed to upgrade computational accuracy by improving the minimum computational step. Moreover, the FPGA based multi-level pipelined PHT architecture optimized by spatial parallelism ensures real-time computation for 1,024 × 768 resolution videos without any off-chip memory consumption. This framework is evaluated on ALTERA DE2-115 FPGA evaluation platform at a maximum frequency of 200 MHz, and it can calculate straight line parameters in 15.59 ms on the average for one frame. Qualitative and quantitative evaluation results have validated the system performance regarding data throughput, memory bandwidth, resource, speed and robustness. PMID:23867746

Parallel Hough Transform-based straight line detection and its FPGA implementation in embedded vision.

PubMed

Lu, Xiaofeng; Song, Li; Shen, Sumin; He, Kang; Yu, Songyu; Ling, Nam

2013-07-17

Hough Transform has been widely used for straight line detection in low-definition and still images, but it suffers from execution time and resource requirements. Field Programmable Gate Arrays (FPGA) provide a competitive alternative for hardware acceleration to reap tremendous computing performance. In this paper, we propose a novel parallel Hough Transform (PHT) and FPGA architecture-associated framework for real-time straight line detection in high-definition videos. A resource-optimized Canny edge detection method with enhanced non-maximum suppression conditions is presented to suppress most possible false edges and obtain more accurate candidate edge pixels for subsequent accelerated computation. Then, a novel PHT algorithm exploiting spatial angle-level parallelism is proposed to upgrade computational accuracy by improving the minimum computational step. Moreover, the FPGA based multi-level pipelined PHT architecture optimized by spatial parallelism ensures real-time computation for 1,024 × 768 resolution videos without any off-chip memory consumption. This framework is evaluated on ALTERA DE2-115 FPGA evaluation platform at a maximum frequency of 200 MHz, and it can calculate straight line parameters in 15.59 ms on the average for one frame. Qualitative and quantitative evaluation results have validated the system performance regarding data throughput, memory bandwidth, resource, speed and robustness.

GPU accelerated edge-region based level set evolution constrained by 2D gray-scale histogram.

PubMed

Balla-Arabé, Souleymane; Gao, Xinbo; Wang, Bin

2013-07-01

Due to its intrinsic nature which allows to easily handle complex shapes and topological changes, the level set method (LSM) has been widely used in image segmentation. Nevertheless, LSM is computationally expensive, which limits its applications in real-time systems. For this purpose, we propose a new level set algorithm, which uses simultaneously edge, region, and 2D histogram information in order to efficiently segment objects of interest in a given scene. The computational complexity of the proposed LSM is greatly reduced by using the highly parallelizable lattice Boltzmann method (LBM) with a body force to solve the level set equation (LSE). The body force is the link with image data and is defined from the proposed LSE. The proposed LSM is then implemented using an NVIDIA graphics processing units to fully take advantage of the LBM local nature. The new algorithm is effective, robust against noise, independent to the initial contour, fast, and highly parallelizable. The edge and region information enable to detect objects with and without edges, and the 2D histogram information enable the effectiveness of the method in a noisy environment. Experimental results on synthetic and real images demonstrate subjectively and objectively the performance of the proposed method.

Contour Tracking with a Spatio-Temporal Intensity Moment.

PubMed

Demi, Marcello

2016-06-01

Standard edge detection operators such as the Laplacian of Gaussian and the gradient of Gaussian can be used to track contours in image sequences. When using edge operators, a contour, which is determined on a frame of the sequence, is simply used as a starting contour to locate the nearest contour on the subsequent frame. However, the strategy used to look for the nearest edge points may not work when tracking contours of non isolated gray level discontinuities. In these cases, strategies derived from the optical flow equation, which look for similar gray level distributions, appear to be more appropriate since these can work with a lower frame rate than that needed for strategies based on pure edge detection operators. However, an optical flow strategy tends to propagate the localization errors through the sequence and an additional edge detection procedure is essential to compensate for such a drawback. In this paper a spatio-temporal intensity moment is proposed which integrates the two basic functions of edge detection and tracking.

Preliminary study of detection of buried landmines using a programmable hyperspectral imager

NASA Astrophysics Data System (ADS)

McFee, John E.; Ripley, Herb T.; Buxton, Roger; Thriscutt, Andrew M.

1996-05-01

Experiments were conducted to determine if buried mines could be detected by measuring the change in reflectance spectra of vegetation above mine burial sites. Mines were laid using hand methods and simulated mechanical methods and spectral images were obtained over a three month period using a casi hyperspectral imager scanned from a personnel lift. Mines were not detectable by measurement of the shift of the red edge of vegetative spectra. By calculating the linear correlation coefficient image, some mines in light vegetative cover (grass, grass/blueberries) were apparently detected, but mines buried in heavy vegetation cover (deep ferns) were not detectable. Due to problems with ground truthing, accurate probabilities of detection and false alarm rates were not obtained.

Verifying the Presence of Low Levels of Neptunium in a Uranium Matrix with Electron Energy-Loss Spectroscopy

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

Buck, Edgar C.; Douglas, Matthew; Wittman, Richard S.

2010-01-01

This paper examines the problems associated with the analysis of low levels of neptunium (Np) in a uranium (U) matrix with electron energy-loss spectroscopy (EELS) on the transmission electron microscope (TEM). The detection of Np in a matrix of uranium (U) can be impeded by the occurrence of a plural scattering event from U (U-M5 + U-O4,5) that results in severe overlap on the Np-M5 edge at 3665 eV. Low levels (1600 - 6300 ppm) of Np can be detected in U solids by confirming the energy gap between the Np-M5 and Np-M4 edges is at 184 eV and showingmore » that the M4/M5 ratio for the Np is smaller than that for U. The Richardson-Lucy deconvolution method was applied to energy-loss spectral images and was shown to increase the signal to noise. This method also improves the limits of detection for Np in a U matrix.« less

LIDAR Point Cloud Data Extraction and Establishment of 3D Modeling of Buildings

NASA Astrophysics Data System (ADS)

Zhang, Yujuan; Li, Xiuhai; Wang, Qiang; Liu, Jiang; Liang, Xin; Li, Dan; Ni, Chundi; Liu, Yan

2018-01-01

This paper takes the method of Shepard’s to deal with the original LIDAR point clouds data, and generate regular grid data DSM, filters the ground point cloud and non ground point cloud through double least square method, and obtains the rules of DSM. By using region growing method for the segmentation of DSM rules, the removal of non building point cloud, obtaining the building point cloud information. Uses the Canny operator to extract the image segmentation is needed after the edges of the building, uses Hough transform line detection to extract the edges of buildings rules of operation based on the smooth and uniform. At last, uses E3De3 software to establish the 3D model of buildings.

Segmentation of prostate from ultrasound images using level sets on active band and intensity variation across edges.

PubMed

Li, Xu; Li, Chunming; Fedorov, Andriy; Kapur, Tina; Yang, Xiaoping

2016-06-01

In this paper, the authors propose a novel efficient method to segment ultrasound images of the prostate with weak boundaries. Segmentation of the prostate from ultrasound images with weak boundaries widely exists in clinical applications. One of the most typical examples is the diagnosis and treatment of prostate cancer. Accurate segmentation of the prostate boundaries from ultrasound images plays an important role in many prostate-related applications such as the accurate placement of the biopsy needles, the assignment of the appropriate therapy in cancer treatment, and the measurement of the prostate volume. Ultrasound images of the prostate are usually corrupted with intensity inhomogeneities, weak boundaries, and unwanted edges, which make the segmentation of the prostate an inherently difficult task. Regarding to these difficulties, the authors introduce an active band term and an edge descriptor term in the modified level set energy functional. The active band term is to deal with intensity inhomogeneities and the edge descriptor term is to capture the weak boundaries or to rule out unwanted boundaries. The level set function of the proposed model is updated in a band region around the zero level set which the authors call it an active band. The active band restricts the authors' method to utilize the local image information in a banded region around the prostate contour. Compared to traditional level set methods, the average intensities inside∖outside the zero level set are only computed in this banded region. Thus, only pixels in the active band have influence on the evolution of the level set. For weak boundaries, they are hard to be distinguished by human eyes, but in local patches in the band region around prostate boundaries, they are easier to be detected. The authors incorporate an edge descriptor to calculate the total intensity variation in a local patch paralleled to the normal direction of the zero level set, which can detect weak boundaries and avoid unwanted edges in the ultrasound images. The efficiency of the proposed model is demonstrated by experiments on real 3D volume images and 2D ultrasound images and comparisons with other approaches. Validation results on real 3D TRUS prostate images show that the authors' model can obtain a Dice similarity coefficient (DSC) of 94.03% ± 1.50% and a sensitivity of 93.16% ± 2.30%. Experiments on 100 typical 2D ultrasound images show that the authors' method can obtain a sensitivity of 94.87% ± 1.85% and a DSC of 95.82% ± 2.23%. A reproducibility experiment is done to evaluate the robustness of the proposed model. As far as the authors know, prostate segmentation from ultrasound images with weak boundaries and unwanted edges is a difficult task. A novel method using level sets with active band and the intensity variation across edges is proposed in this paper. Extensive experimental results demonstrate that the proposed method is more efficient and accurate.

Bone mass density estimation: Archimede’s principle versus automatic X-ray histogram and edge detection technique in ovariectomized rats treated with germinated brown rice bioactives

PubMed Central

Muhammad, Sani Ismaila; Maznah, Ismail; Mahmud, Rozi Binti; Esmaile, Maher Faik; Zuki, Abu Bakar Zakaria

2013-01-01

Background Bone mass density is an important parameter used in the estimation of the severity and depth of lesions in osteoporosis. Estimation of bone density using existing methods in experimental models has its advantages as well as drawbacks. Materials and methods In this study, the X-ray histogram edge detection technique was used to estimate the bone mass density in ovariectomized rats treated orally with germinated brown rice (GBR) bioactives, and the results were compared with estimated results obtained using Archimede’s principle. New bone cell proliferation was assessed by histology and immunohistochemical reaction using polyclonal nuclear antigen. Additionally, serum alkaline phosphatase activity, serum and bone calcium and zinc concentrations were detected using a chemistry analyzer and atomic absorption spectroscopy. Rats were divided into groups of six as follows: sham (nonovariectomized, nontreated); ovariectomized, nontreated; and ovariectomized and treated with estrogen, or Remifemin®, GBR-phenolics, acylated steryl glucosides, gamma oryzanol, and gamma amino-butyric acid extracted from GBR at different doses. Results Our results indicate a significant increase in alkaline phosphatase activity, serum and bone calcium, and zinc and ash content in the treated groups compared with the ovariectomized nontreated group (P < 0.05). Bone density increased significantly (P < 0.05) in groups treated with estrogen, GBR, Remifemin®, and gamma oryzanol compared to the ovariectomized nontreated group. Histological sections revealed more osteoblasts in the treated groups when compared with the untreated groups. A polyclonal nuclear antigen reaction showing proliferating new cells was observed in groups treated with estrogen, Remifemin®, GBR, acylated steryl glucosides, and gamma oryzanol. There was a good correlation between bone mass densities estimated using Archimede’s principle and the edge detection technique between the treated groups (r2 = 0.737, P = 0.004). Conclusion Our study shows that GBR bioactives increase bone density, which might be via the activation of zinc formation and increased calcium content, and that X-ray edge detection technique is effective in the measurement of bone density and can be employed effectively in this respect. PMID:24187491

An Improved Text Localization Method for Natural Scene Images

NASA Astrophysics Data System (ADS)

Jiang, Mengdi; Cheng, Jianghua; Chen, Minghui; Ku, Xishu

2018-01-01

In order to extract text information effectively from natural scene image with complex background, multi-orientation perspective and multilingual languages, we present a new method based on the improved Stroke Feature Transform (SWT). Firstly, The Maximally Stable Extremal Region (MSER) method is used to detect text candidate regions. Secondly, the SWT algorithm is used in the candidate regions, which can improve the edge detection compared with tradition SWT method. Finally, the Frequency-tuned (FT) visual saliency is introduced to remove non-text candidate regions. The experiment results show that, the method can achieve good robustness for complex background with multi-orientation perspective, various characters and font sizes.

Implementation of Canny and Isotropic Operator with Power Law Transformation to Identify Cervical Cancer

NASA Astrophysics Data System (ADS)

Amalia, A.; Rachmawati, D.; Lestari, I. A.; Mourisa, C.

2018-03-01

Colposcopy has been used primarily to diagnose pre-cancer and cancerous lesions because this procedure gives an exaggerated view of the tissues of the vagina and the cervix. But, the poor quality of colposcopy image sometimes makes physician challenging to recognize and analyze it. Generally, Implementation of image processing to identify cervical cancer have to implement a complex classification or clustering method. In this study, we wanted to prove that by only applying the identification of edge detection in the colposcopy image, identification of cervical cancer can be determined. In this study, we implement and comparing two edge detection operator which are isotropic and canny operator. Research methodology in this paper composed by image processing, training, and testing stages. In the image processing step, colposcopy image transformed by nth root power transformation to get better detection result and continued with edge detection process. Training is a process of labelling all dataset image with cervical cancer stage. This process involved pathology doctor as an expert in diagnosing the colposcopy image as a reference. Testing is a process of deciding cancer stage classification by comparing the similarity image of colposcopy results in the testing stage with the image of the results of the training process. We used 30 images as a dataset. The result gets same accuracy which is 80% for both Canny or Isotropic operator. Average running time for Canny operator implementation is 0.3619206 ms while Isotropic get 1.49136262 ms. The result showed that Canny operator is better than isotropic operator because Canny operator generates a more precise edge with a fast time instead.

Measurement of marine picoplankton cell size by using a cooled, charge-coupled device camera with image-analyzed fluorescence microscopy.

PubMed Central

Viles, C L; Sieracki, M E

1992-01-01

Accurate measurement of the biomass and size distribution of picoplankton cells (0.2 to 2.0 microns) is paramount in characterizing their contribution to the oceanic food web and global biogeochemical cycling. Image-analyzed fluorescence microscopy, usually based on video camera technology, allows detailed measurements of individual cells to be taken. The application of an imaging system employing a cooled, slow-scan charge-coupled device (CCD) camera to automated counting and sizing of individual picoplankton cells from natural marine samples is described. A slow-scan CCD-based camera was compared to a video camera and was superior for detecting and sizing very small, dim particles such as fluorochrome-stained bacteria. Several edge detection methods for accurately measuring picoplankton cells were evaluated. Standard fluorescent microspheres and a Sargasso Sea surface water picoplankton population were used in the evaluation. Global thresholding was inappropriate for these samples. Methods used previously in image analysis of nanoplankton cells (2 to 20 microns) also did not work well with the smaller picoplankton cells. A method combining an edge detector and an adaptive edge strength operator worked best for rapidly generating accurate cell sizes. A complete sample analysis of more than 1,000 cells averages about 50 min and yields size, shape, and fluorescence data for each cell. With this system, the entire size range of picoplankton can be counted and measured. Images PMID:1610183

Processing Images of Craters for Spacecraft Navigation

NASA Technical Reports Server (NTRS)

Cheng, Yang; Johnson, Andrew E.; Matthies, Larry H.

2009-01-01

A crater-detection algorithm has been conceived to enable automation of what, heretofore, have been manual processes for utilizing images of craters on a celestial body as landmarks for navigating a spacecraft flying near or landing on that body. The images are acquired by an electronic camera aboard the spacecraft, then digitized, then processed by the algorithm, which consists mainly of the following steps: 1. Edges in an image detected and placed in a database. 2. Crater rim edges are selected from the edge database. 3. Edges that belong to the same crater are grouped together. 4. An ellipse is fitted to each group of crater edges. 5. Ellipses are refined directly in the image domain to reduce errors introduced in the detection of edges and fitting of ellipses. 6. The quality of each detected crater is evaluated. It is planned to utilize this algorithm as the basis of a computer program for automated, real-time, onboard processing of crater-image data. Experimental studies have led to the conclusion that this algorithm is capable of a detection rate >93 percent, a false-alarm rate <5 percent, a geometric error <0.5 pixel, and a position error <0.3 pixel.

A cascade method for TFT-LCD defect detection

NASA Astrophysics Data System (ADS)

Yi, Songsong; Wu, Xiaojun; Yu, Zhiyang; Mo, Zhuoya

2017-07-01

In this paper, we propose a novel cascade detection algorithm which focuses on point and line defects on TFT-LCD. At the first step of the algorithm, we use the gray level difference of su-bimage to segment the abnormal area. The second step is based on phase only transform (POT) which corresponds to the Discrete Fourier Transform (DFT), normalized by the magnitude. It can remove regularities like texture and noise. After that, we improve the method of setting regions of interest (ROI) with the method of edge segmentation and polar transformation. The algorithm has outstanding performance in both computation speed and accuracy. It can solve most of the defect detections including dark point, light point, dark line, etc.

Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder

PubMed Central

Chen, Shuoyang; Xu, Tingfa; Li, Daqun; Zhang, Jizhou; Jiang, Shenwang

2016-01-01

During the process of moving object detection in an intelligent visual surveillance system, a scenario with complex background is sure to appear. The traditional methods, such as “frame difference” and “optical flow”, may not able to deal with the problem very well. In such scenarios, we use a modified algorithm to do the background modeling work. In this paper, we use edge detection to get an edge difference image just to enhance the ability of resistance illumination variation. Then we use a “multi-block temporal-analyzing LBP (Local Binary Pattern)” algorithm to do the segmentation. In the end, a connected component is used to locate the object. We also produce a hardware platform, the core of which consists of the DSP (Digital Signal Processor) and FPGA (Field Programmable Gate Array) platforms and the high-precision intelligent holder. PMID:27775671

Application of Reflectance Transformation Imaging Technique to Improve Automated Edge Detection in a Fossilized Oyster Reef

NASA Astrophysics Data System (ADS)

Djuricic, Ana; Puttonen, Eetu; Harzhauser, Mathias; Dorninger, Peter; Székely, Balázs; Mandic, Oleg; Nothegger, Clemens; Molnár, Gábor; Pfeifer, Norbert

2016-04-01

The world's largest fossilized oyster reef is located in Stetten, Lower Austria excavated during field campaigns of the Natural History Museum Vienna between 2005 and 2008. It is studied in paleontology to learn about change in climate from past events. In order to support this study, a laser scanning and photogrammetric campaign was organized in 2014 for 3D documentation of the large and complex site. The 3D point clouds and high resolution images from this field campaign are visualized by photogrammetric methods in form of digital surface models (DSM, 1 mm resolution) and orthophoto (0.5 mm resolution) to help paleontological interpretation of data. Due to size of the reef, automated analysis techniques are needed to interpret all digital data obtained from the field. One of the key components in successful automation is detection of oyster shell edges. We have tested Reflectance Transformation Imaging (RTI) to visualize the reef data sets for end-users through a cultural heritage viewing interface (RTIViewer). The implementation includes a Lambert shading method to visualize DSMs derived from terrestrial laser scanning using scientific software OPALS. In contrast to shaded RTI no devices consisting of a hardware system with LED lights, or a body to rotate the light source around the object are needed. The gray value for a given shaded pixel is related to the angle between light source and the normal at that position. Brighter values correspond to the slope surfaces facing the light source. Increasing of zenith angle results in internal shading all over the reef surface. In total, oyster reef surface contains 81 DSMs with 3 m x 2 m each. Their surface was illuminated by moving the virtual sun every 30 degrees (12 azimuth angles from 20-350) and every 20 degrees (4 zenith angles from 20-80). This technique provides paleontologists an interactive approach to virtually inspect the oyster reef, and to interpret the shell surface by changing the light source direction. One source of light for shading does show all morphologic features needed for description. Additionally, more details such as fault lines, overlaps and characteristic edges of complex shell structures are clearly detected by simply changing the illumination on the shaded digital surface model. In a further study, the potential of edge detection of the individual shells will be analyzed based on statistical analysis by keeping track of the local accumulative shading gradient. The results are compared to manually identified edges. In a following study phase, the detected edges will be improved by graph cut segmentation. We assume that this technique can lead to automatically extracted teaching set for object segmentation on a complex environment. The project is supported by the Austrian Science Fund (FWF P 25883-N29).

Alpha-ray detection with a MgB 2 transition edge sensor

NASA Astrophysics Data System (ADS)

Okayasu, S.; Katagiri, M.; Hojou, K.; Morii, Y.; Miki, S.; Shimakage, H.; Wang, Z.; Ishida, T.

2008-09-01

We have been investigating for neutron detection with the MgB 2 transition edge sensor (TES). For the purpose, we have been developing a low noise measurement system for the detection. To confirm the performance of the detecting sensor, alpha ray detection from an americium-241 ( 241Am) alpha-ray source was achieved. A short microfabricated sample with 10 μm length and 1 μm width is used to improve the S/N ratio. The detection is achieved under a constant current condition in the range between 1 and 6 μA bias current, and the resistivity changes at the sample due to the alpha ray irradiation is detected just on the transition edge.

Optical Production and Detection of Ultrasonic Waves in Metals for Nondestructive Testing

NASA Technical Reports Server (NTRS)

Morrison, R. A.

1972-01-01

Ultrasonic waves were produced by striking the surface of a metal with the focused one-joule pulse of a Q-switched ruby laser. Rayleigh (surface) waves and longitudinal waves were detected with conventional transducers. Optical methods of detection were tested and developed. Rayleigh waves were produced with an oscillator and transducer. They were optically detected on curved polished surfaces, and on unpolished surfaces. The technique uses a knife edge to detect small angle changes of the surface as the wave pulse passes the illuminated spot. Optical flaw detection using pulse echo and attenuation is demonstrated.

LROC assessment of non-linear filtering methods in Ga-67 SPECT imaging

NASA Astrophysics Data System (ADS)

De Clercq, Stijn; Staelens, Steven; De Beenhouwer, Jan; D'Asseler, Yves; Lemahieu, Ignace

2006-03-01

In emission tomography, iterative reconstruction is usually followed by a linear smoothing filter to make such images more appropriate for visual inspection and diagnosis by a physician. This will result in a global blurring of the images, smoothing across edges and possibly discarding valuable image information for detection tasks. The purpose of this study is to investigate which possible advantages a non-linear, edge-preserving postfilter could have on lesion detection in Ga-67 SPECT imaging. Image quality can be defined based on the task that has to be performed on the image. This study used LROC observer studies based on a dataset created by CPU-intensive Gate Monte Carlo simulations of a voxelized digital phantom. The filters considered in this study were a linear Gaussian filter, a bilateral filter, the Perona-Malik anisotropic diffusion filter and the Catte filtering scheme. The 3D MCAT software phantom was used to simulate the distribution of Ga-67 citrate in the abdomen. Tumor-present cases had a 1-cm diameter tumor randomly placed near the edges of the anatomical boundaries of the kidneys, bone, liver and spleen. Our data set was generated out of a single noisy background simulation using the bootstrap method, to significantly reduce the simulation time and to allow for a larger observer data set. Lesions were simulated separately and added to the background afterwards. These were then reconstructed with an iterative approach, using a sufficiently large number of MLEM iterations to establish convergence. The output of a numerical observer was used in a simplex optimization method to estimate an optimal set of parameters for each postfilter. No significant improvement was found for using edge-preserving filtering techniques over standard linear Gaussian filtering.

Digital Enhancement Of Pneumothoraces

NASA Astrophysics Data System (ADS)

Cocklin, M.; Kaye, G.; Kerr, I.; Lams, P.

1982-11-01

If a patient presents with symptoms indicative of a pneumothorax it is improbable that it would not be detected in a chest radiograph. However, detection on the radiograph can be difficult and a small pneumothorax may be missed when there is no clinical suspicion of its presence. This report presents some methods by which the characteristic pneumothorax edge may be enhanced by digital image processing. Various examples are given.

A median filter approach for correcting errors in a vector field

NASA Technical Reports Server (NTRS)

Schultz, H.

1985-01-01

Techniques are presented for detecting and correcting errors in a vector field. These methods employ median filters which are frequently used in image processing to enhance edges and remove noise. A detailed example is given for wind field maps produced by a spaceborne scatterometer. The error detection and replacement algorithm was tested with simulation data from the NASA Scatterometer (NSCAT) project.

An effective hair detection algorithm for dermoscopic melanoma images of skin lesions

NASA Astrophysics Data System (ADS)

Chakraborti, Damayanti; Kaur, Ravneet; Umbaugh, Scott; LeAnder, Robert

2016-09-01

Dermoscopic images are obtained using the method of skin surface microscopy. Pigmented skin lesions are evaluated in terms of texture features such as color and structure. Artifacts, such as hairs, bubbles, black frames, ruler-marks, etc., create obstacles that prevent accurate detection of skin lesions by both clinicians and computer-aided diagnosis. In this article, we propose a new algorithm for the automated detection of hairs, using an adaptive, Canny edge-detection method, followed by morphological filtering and an arithmetic addition operation. The algorithm was applied to 50 dermoscopic melanoma images. In order to ascertain this method's relative detection accuracy, it was compared to the Razmjooy hair-detection method [1], using segmentation error (SE), true detection rate (TDR) and false positioning rate (FPR). The new method produced 6.57% SE, 96.28% TDR and 3.47% FPR, compared to 15.751% SE, 86.29% TDR and 11.74% FPR produced by the Razmjooy method [1]. Because of the 7.27-9.99% improvement in those parameters, we conclude that the new algorithm produces much better results for detecting thick, thin, dark and light hairs. The new method proposed here, shows an appreciable difference in the rate of detecting bubbles, as well.

Comparative dynamics of avian communities across edges and interiors of North American ecoregions

USGS Publications Warehouse

Karanth, K.K.; Nichols, J.D.; Sauer, J.R.; Hines, J.E.

2006-01-01

Aim Based on a priori hypotheses, we developed predictions about how avian communities might differ at the edges vs. interiors of ecoregions. Specifically, we predicted lower species richness and greater local turnover and extinction probabilities for regional edges. We tested these predictions using North American Breeding Bird Survey (BBS) data across nine ecoregions over a 20-year time period. Location Data from 2238 BBS routes within nine ecoregions of the United States were used. Methods The estimation methods used accounted for species detection probabilities < 1. Parameter estimates for species richness, local turnover and extinction probabilities were obtained using the program COMDYN. We examined the difference in community-level parameters estimated from within exterior edges (the habitat interface between ecoregions), interior edges (the habitat interface between two bird conservation regions within the same ecoregion) and interior (habitat excluding interfaces). General linear models were constructed to examine sources of variation in community parameters for five ecoregions (containing all three habitat types) and all nine ecoregions (containing two habitat types). Results Analyses provided evidence that interior habitats and interior edges had on average higher bird species richness than exterior edges, providing some evidence of reduced species richness near habitat edges. Lower average extinction probabilities and turnover rates in interior habitats (five-region analysis) provided some support for our predictions about these quantities. However, analyses directed at all three response variables, i.e. species richness, local turnover, and local extinction probability, provided evidence of an interaction between habitat and region, indicating that the relationships did not hold in all regions. Main conclusions The overall predictions of lower species richness, higher local turnover and extinction probabilities in regional edge habitats, as opposed to interior habitats, were generally supported. However, these predicted tendencies did not hold in all regions.

A powerful nonparametric method for detecting differentially co-expressed genes: distance correlation screening and edge-count test.

PubMed

Zhang, Qingyang

2018-05-16

Differential co-expression analysis, as a complement of differential expression analysis, offers significant insights into the changes in molecular mechanism of different phenotypes. A prevailing approach to detecting differentially co-expressed genes is to compare Pearson's correlation coefficients in two phenotypes. However, due to the limitations of Pearson's correlation measure, this approach lacks the power to detect nonlinear changes in gene co-expression which is common in gene regulatory networks. In this work, a new nonparametric procedure is proposed to search differentially co-expressed gene pairs in different phenotypes from large-scale data. Our computational pipeline consisted of two main steps, a screening step and a testing step. The screening step is to reduce the search space by filtering out all the independent gene pairs using distance correlation measure. In the testing step, we compare the gene co-expression patterns in different phenotypes by a recently developed edge-count test. Both steps are distribution-free and targeting nonlinear relations. We illustrate the promise of the new approach by analyzing the Cancer Genome Atlas data and the METABRIC data for breast cancer subtypes. Compared with some existing methods, the new method is more powerful in detecting nonlinear type of differential co-expressions. The distance correlation screening can greatly improve computational efficiency, facilitating its application to large data sets.

Possible realization of interacting symmetry-protected topological phases in topological crystalline insulators

NASA Astrophysics Data System (ADS)

Isobe, Hiroki; Fu, Liang

2015-03-01

The effects of electron-electron interaction in edge states of mirror-symmetry protected topological crystalline insulators (TCI's) are discussed. The analysis is performed by using bosonized Hamiltonian following the Tomonaga-Luttinger liquid theory. When two pairs of helical edge states exist, electron-electron interaction could gap out one edge mode, which is a possible realization of interacting symmetry-protected topological (SPT) phases. This type of SPT phase is closely related to a Luther-Emery liquid in spinful 1D system. We also propose a method of detecting the SPT phases by STM. The other focus of the study is the classification of SPT phases in mirror-symmetry protected TCI's. By adopting the Chern-Simons theory, we find that electron-electron interaction reduces the classification from Z to Z4. It means that the edge states can be gapped out when four pairs of edge states exist. In other cases, the edge modes cannot be fully gapped. Each of these states corresponds to a different SPT phase depending on the relevant interaction process.

Detecting wood surface defects with fusion algorithm of visual saliency and local threshold segmentation

NASA Astrophysics Data System (ADS)

Wang, Xuejuan; Wu, Shuhang; Liu, Yunpeng

2018-04-01

This paper presents a new method for wood defect detection. It can solve the over-segmentation problem existing in local threshold segmentation methods. This method effectively takes advantages of visual saliency and local threshold segmentation. Firstly, defect areas are coarsely located by using spectral residual method to calculate global visual saliency of them. Then, the threshold segmentation of maximum inter-class variance method is adopted for positioning and segmenting the wood surface defects precisely around the coarse located areas. Lastly, we use mathematical morphology to process the binary images after segmentation, which reduces the noise and small false objects. Experiments on test images of insect hole, dead knot and sound knot show that the method we proposed obtains ideal segmentation results and is superior to the existing segmentation methods based on edge detection, OSTU and threshold segmentation.

A novel line segment detection algorithm based on graph search

NASA Astrophysics Data System (ADS)

Zhao, Hong-dan; Liu, Guo-ying; Song, Xu

2018-02-01

To overcome the problem of extracting line segment from an image, a method of line segment detection was proposed based on the graph search algorithm. After obtaining the edge detection result of the image, the candidate straight line segments are obtained in four directions. For the candidate straight line segments, their adjacency relationships are depicted by a graph model, based on which the depth-first search algorithm is employed to determine how many adjacent line segments need to be merged. Finally we use the least squares method to fit the detected straight lines. The comparative experimental results verify that the proposed algorithm has achieved better results than the line segment detector (LSD).

Edge detection techniques for iris recognition system

NASA Astrophysics Data System (ADS)

Tania, U. T.; Motakabber, S. M. A.; Ibrahimy, M. I.

2013-12-01

Nowadays security and authentication are the major parts of our daily life. Iris is one of the most reliable organ or part of human body which can be used for identification and authentication purpose. To develop an iris authentication algorithm for personal identification, this paper examines two edge detection techniques for iris recognition system. Between the Sobel and the Canny edge detection techniques, the experimental result shows that the Canny's technique has better ability to detect points in a digital image where image gray level changes even at slow rate.

Quality optimized medical image information hiding algorithm that employs edge detection and data coding.

PubMed

Al-Dmour, Hayat; Al-Ani, Ahmed

2016-04-01

The present work has the goal of developing a secure medical imaging information system based on a combined steganography and cryptography technique. It attempts to securely embed patient's confidential information into his/her medical images. The proposed information security scheme conceals coded Electronic Patient Records (EPRs) into medical images in order to protect the EPRs' confidentiality without affecting the image quality and particularly the Region of Interest (ROI), which is essential for diagnosis. The secret EPR data is converted into ciphertext using private symmetric encryption method. Since the Human Visual System (HVS) is less sensitive to alterations in sharp regions compared to uniform regions, a simple edge detection method has been introduced to identify and embed in edge pixels, which will lead to an improved stego image quality. In order to increase the embedding capacity, the algorithm embeds variable number of bits (up to 3) in edge pixels based on the strength of edges. Moreover, to increase the efficiency, two message coding mechanisms have been utilized to enhance the ±1 steganography. The first one, which is based on Hamming code, is simple and fast, while the other which is known as the Syndrome Trellis Code (STC), is more sophisticated as it attempts to find a stego image that is close to the cover image through minimizing the embedding impact. The proposed steganography algorithm embeds the secret data bits into the Region of Non Interest (RONI), where due to its importance; the ROI is preserved from modifications. The experimental results demonstrate that the proposed method can embed large amount of secret data without leaving a noticeable distortion in the output image. The effectiveness of the proposed algorithm is also proven using one of the efficient steganalysis techniques. The proposed medical imaging information system proved to be capable of concealing EPR data and producing imperceptible stego images with minimal embedding distortions compared to other existing methods. In order to refrain from introducing any modifications to the ROI, the proposed system only utilizes the Region of Non Interest (RONI) in embedding the EPR data. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

An Automated Method of Scanning Probe Microscopy (SPM) Data Analysis and Reactive Site Tracking for Mineral-Water Interface Reactions Observed at the Nanometer Scale

NASA Astrophysics Data System (ADS)

Campbell, B. D.; Higgins, S. R.

2008-12-01

Developing a method for bridging the gap between macroscopic and microscopic measurements of reaction kinetics at the mineral-water interface has important implications in geological and chemical fields. Investigating these reactions on the nanometer scale with SPM is often limited by image analysis and data extraction due to the large quantity of data usually obtained in SPM experiments. Here we present a computer algorithm for automated analysis of mineral-water interface reactions. This algorithm automates the analysis of sequential SPM images by identifying the kinetically active surface sites (i.e., step edges), and by tracking the displacement of these sites from image to image. The step edge positions in each image are readily identified and tracked through time by a standard edge detection algorithm followed by statistical analysis on the Hough Transform of the edge-mapped image. By quantifying this displacement as a function of time, the rate of step edge displacement is determined. Furthermore, the total edge length, also determined from analysis of the Hough Transform, combined with the computed step speed, yields the surface area normalized rate of the reaction. The algorithm was applied to a study of the spiral growth of the calcite(104) surface from supersaturated solutions, yielding results almost 20 times faster than performing this analysis by hand, with results being statistically similar for both analysis methods. This advance in analysis of kinetic data from SPM images will facilitate the building of experimental databases on the microscopic kinetics of mineral-water interface reactions.

Technical Note: Range verification system using edge detection method for a scintillator and a CCD camera system

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

Saotome, Naoya, E-mail: naosao@nirs.go.jp; Furukawa, Takuji; Hara, Yousuke

Purpose: Three-dimensional irradiation with a scanned carbon-ion beam has been performed from 2011 at the authors’ facility. The authors have developed the rotating-gantry equipped with the scanning irradiation system. The number of combinations of beam properties to measure for the commissioning is more than 7200, i.e., 201 energy steps, 3 intensities, and 12 gantry angles. To compress the commissioning time, quick and simple range verification system is required. In this work, the authors develop a quick range verification system using scintillator and charge-coupled device (CCD) camera and estimate the accuracy of the range verification. Methods: A cylindrical plastic scintillator blockmore » and a CCD camera were installed on the black box. The optical spatial resolution of the system is 0.2 mm/pixel. The camera control system was connected and communicates with the measurement system that is part of the scanning system. The range was determined by image processing. Reference range for each energy beam was determined by a difference of Gaussian (DOG) method and the 80% of distal dose of the depth-dose distribution that were measured by a large parallel-plate ionization chamber. The authors compared a threshold method and a DOG method. Results: The authors found that the edge detection method (i.e., the DOG method) is best for the range detection. The accuracy of range detection using this system is within 0.2 mm, and the reproducibility of the same energy measurement is within 0.1 mm without setup error. Conclusions: The results of this study demonstrate that the authors’ range check system is capable of quick and easy range verification with sufficient accuracy.« less

Biological object recognition in μ-radiography images

NASA Astrophysics Data System (ADS)

Prochazka, A.; Dammer, J.; Weyda, F.; Sopko, V.; Benes, J.; Zeman, J.; Jandejsek, I.

2015-03-01

This study presents an applicability of real-time microradiography to biological objects, namely to horse chestnut leafminer, Cameraria ohridella (Insecta: Lepidoptera, Gracillariidae) and following image processing focusing on image segmentation and object recognition. The microradiography of insects (such as horse chestnut leafminer) provides a non-invasive imaging that leaves the organisms alive. The imaging requires a high spatial resolution (micrometer scale) radiographic system. Our radiographic system consists of a micro-focus X-ray tube and two types of detectors. The first is a charge integrating detector (Hamamatsu flat panel), the second is a pixel semiconductor detector (Medipix2 detector). The latter allows detection of single quantum photon of ionizing radiation. We obtained numerous horse chestnuts leafminer pupae in several microradiography images easy recognizable in automatic mode using the image processing methods. We implemented an algorithm that is able to count a number of dead and alive pupae in images. The algorithm was based on two methods: 1) noise reduction using mathematical morphology filters, 2) Canny edge detection. The accuracy of the algorithm is higher for the Medipix2 (average recall for detection of alive pupae =0.99, average recall for detection of dead pupae =0.83), than for the flat panel (average recall for detection of alive pupae =0.99, average recall for detection of dead pupae =0.77). Therefore, we conclude that Medipix2 has lower noise and better displays contours (edges) of biological objects. Our method allows automatic selection and calculation of dead and alive chestnut leafminer pupae. It leads to faster monitoring of the population of one of the world's important insect pest.

Wavelet Fusion for Concealed Object Detection Using Passive Millimeter Wave Sequence Images

NASA Astrophysics Data System (ADS)

Chen, Y.; Pang, L.; Liu, H.; Xu, X.

2018-04-01

PMMW imaging system can create interpretable imagery on the objects concealed under clothing, which gives the great advantage to the security check system. Paper addresses wavelet fusion to detect concealed objects using passive millimeter wave (PMMW) sequence images. According to PMMW real-time imager acquired image characteristics and storage methods firstly, using the sum of squared difference (SSD) as the image-related parameters to screen the sequence images. Secondly, the selected images are optimized using wavelet fusion algorithm. Finally, the concealed objects are detected by mean filter, threshold segmentation and edge detection. The experimental results show that this method improves the detection effect of concealed objects by selecting the most relevant images from PMMW sequence images and using wavelet fusion to enhance the information of the concealed objects. The method can be effectively applied to human body concealed object detection in millimeter wave video.

Breast mass segmentation in mammography using plane fitting and dynamic programming.

PubMed

Song, Enmin; Jiang, Luan; Jin, Renchao; Zhang, Lin; Yuan, Yuan; Li, Qiang

2009-07-01

Segmentation is an important and challenging task in a computer-aided diagnosis (CAD) system. Accurate segmentation could improve the accuracy in lesion detection and characterization. The objective of this study is to develop and test a new segmentation method that aims at improving the performance level of breast mass segmentation in mammography, which could be used to provide accurate features for classification. This automated segmentation method consists of two main steps and combines the edge gradient, the pixel intensity, as well as the shape characteristics of the lesions to achieve good segmentation results. First, a plane fitting method was applied to a background-trend corrected region-of-interest (ROI) of a mass to obtain the edge candidate points. Second, dynamic programming technique was used to find the "optimal" contour of the mass from the edge candidate points. Area-based similarity measures based on the radiologist's manually marked annotation and the segmented region were employed as criteria to evaluate the performance level of the segmentation method. With the evaluation criteria, the new method was compared with 1) the dynamic programming method developed by Timp and Karssemeijer, and 2) the normalized cut segmentation method, based on 337 ROIs extracted from a publicly available image database. The experimental results indicate that our segmentation method can achieve a higher performance level than the other two methods, and the improvements in segmentation performance level were statistically significant. For instance, the mean overlap percentage for the new algorithm was 0.71, whereas those for Timp's dynamic programming method and the normalized cut segmentation method were 0.63 (P < .001) and 0.61 (P < .001), respectively. We developed a new segmentation method by use of plane fitting and dynamic programming, which achieved a relatively high performance level. The new segmentation method would be useful for improving the accuracy of computerized detection and classification of breast cancer in mammography.

Segmentation of prostate from ultrasound images using level sets on active band and intensity variation across edges

PubMed Central

Li, Xu; Li, Chunming; Fedorov, Andriy; Kapur, Tina; Yang, Xiaoping

2016-01-01

Purpose: In this paper, the authors propose a novel efficient method to segment ultrasound images of the prostate with weak boundaries. Segmentation of the prostate from ultrasound images with weak boundaries widely exists in clinical applications. One of the most typical examples is the diagnosis and treatment of prostate cancer. Accurate segmentation of the prostate boundaries from ultrasound images plays an important role in many prostate-related applications such as the accurate placement of the biopsy needles, the assignment of the appropriate therapy in cancer treatment, and the measurement of the prostate volume. Methods: Ultrasound images of the prostate are usually corrupted with intensity inhomogeneities, weak boundaries, and unwanted edges, which make the segmentation of the prostate an inherently difficult task. Regarding to these difficulties, the authors introduce an active band term and an edge descriptor term in the modified level set energy functional. The active band term is to deal with intensity inhomogeneities and the edge descriptor term is to capture the weak boundaries or to rule out unwanted boundaries. The level set function of the proposed model is updated in a band region around the zero level set which the authors call it an active band. The active band restricts the authors’ method to utilize the local image information in a banded region around the prostate contour. Compared to traditional level set methods, the average intensities inside∖outside the zero level set are only computed in this banded region. Thus, only pixels in the active band have influence on the evolution of the level set. For weak boundaries, they are hard to be distinguished by human eyes, but in local patches in the band region around prostate boundaries, they are easier to be detected. The authors incorporate an edge descriptor to calculate the total intensity variation in a local patch paralleled to the normal direction of the zero level set, which can detect weak boundaries and avoid unwanted edges in the ultrasound images. Results: The efficiency of the proposed model is demonstrated by experiments on real 3D volume images and 2D ultrasound images and comparisons with other approaches. Validation results on real 3D TRUS prostate images show that the authors’ model can obtain a Dice similarity coefficient (DSC) of 94.03% ± 1.50% and a sensitivity of 93.16% ± 2.30%. Experiments on 100 typical 2D ultrasound images show that the authors’ method can obtain a sensitivity of 94.87% ± 1.85% and a DSC of 95.82% ± 2.23%. A reproducibility experiment is done to evaluate the robustness of the proposed model. Conclusions: As far as the authors know, prostate segmentation from ultrasound images with weak boundaries and unwanted edges is a difficult task. A novel method using level sets with active band and the intensity variation across edges is proposed in this paper. Extensive experimental results demonstrate that the proposed method is more efficient and accurate. PMID:27277056

Vegetation's red edge: a possible spectroscopic biosignature of extraterrestrial plants.

PubMed

Seager, S; Turner, E L; Schafer, J; Ford, E B

2005-06-01

Earth's deciduous plants have a sharp order-of-magnitude increase in leaf reflectance between approximately 700 and 750 nm wavelength. This strong reflectance of Earth's vegetation suggests that surface biosignatures with sharp spectral features might be detectable in the spectrum of scattered light from a spatially unresolved extrasolar terrestrial planet. We assess the potential of Earth's step-function-like spectroscopic feature, referred to as the "red edge," as a tool for astrobiology. We review the basic characteristics and physical origin of the red edge and summarize its use in astronomy: early spectroscopic efforts to search for vegetation on Mars and recent reports of detection of the red edge in the spectrum of Earthshine (i.e., the spatially integrated scattered light spectrum of Earth). We present Earthshine observations from Apache Point Observatory (New Mexico) to emphasize that time variability is key to detecting weak surface biosignatures such as the vegetation red edge. We briefly discuss the evolutionary advantages of vegetation's red edge reflectance, and speculate that while extraterrestrial "light-harvesting organisms" have no compelling reason to display the exact same red edge feature as terrestrial vegetation, they might have similar spectroscopic features at different wavelengths than terrestrial vegetation. This implies that future terrestrial-planet-characterizing space missions should obtain data that allow time-varying, sharp spectral features at unknown wavelengths to be identified. We caution that some mineral reflectance edges are similar in slope and strength to vegetation's red edge (albeit at different wavelengths); if an extrasolar planet reflectance edge is detected care must be taken with its interpretation.

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

Gislason-Lee, Amber J., E-mail: A.J.Gislason@leeds.ac.uk; Tunstall, Clare M.; Kengyelics, Stephen K.

Purpose: Cardiac x-ray detectors are used to acquire moving images in real-time for angiography and interventional procedures. Detective quantum efficiency (DQE) is not generally measured on these dynamic detectors; the required “for processing” image data and control of x-ray settings have not been accessible. By 2016, USA hospital physicists will have the ability to measure DQE and will likely utilize the International Electrotechnical Commission (IEC) standard for measuring DQE of dynamic x-ray imaging devices. The current IEC standard requires an image of a tilted tungsten edge test object to obtain modulation transfer function (MTF) for DQE calculation. It specifies themore » range of edge angles to use; however, it does not specify a preferred method to determine this angle for image analysis. The study aimed to answer the question “will my choice in method impact my results?” Four different established edge angle determination methods were compared to investigate the impact on DQE. Methods: Following the IEC standard, edge and flat field images were acquired on a cardiac flat-panel detector to calculate MTF and noise power spectrum, respectively, to determine DQE. Accuracy of the methods in determining the correct angle was ascertained using a simulated edge image with known angulations. Precision of the methods was ascertained using variability of MTF and DQE, calculated via bootstrapping. Results: Three methods provided near equal angles and the same MTF while the fourth, with an angular difference of 6%, had a MTF lower by 3% at 1.5 mm{sup −1} spatial frequency and 8% at 2.5 mm{sup −1}; corresponding DQE differences were 6% at 1.5 mm{sup −1} and 17% at 2.5 mm{sup −1}; differences were greater than standard deviations in the measurements. Conclusions: DQE measurements may vary by a significant amount, depending on the method used to determine the edge angle when following the IEC standard methodology for a cardiac x-ray detector. The most accurate and precise methods are recommended for absolute assessments and reproducible measurements, respectively.« less

A passive infrared ice detection technique for helicopter applications

NASA Technical Reports Server (NTRS)

Dershowitz, Adam L.; Hansman, R. John, Jr.

1991-01-01

A technique has been developed, and successfully tested, to detect icing remotely on helicopter rotor blades. Using passive infrared (IR) thermometry it is possible to detect the warming caused by latent heat released as supercooled water freezes. During icing, the ice accretion region on the leading edge of the blade is found to be warmer than the uniced trailing edge resulting in a chordwise temperature profile characteristic of icing. Preliminary tests, using an IR Thermal video system, were conducted on a static model in the NASA Icing Research Tunnel (IRT) for a variety of wet (glaze) and dry (rime) ice conditions. A prototype detector system was built consisting of a single point IR pyrometer, and experiments were run on a small scale rotor model. Using this prototype detector, the characteristic chordwise temperature profiles were again observed for a range of icing conditions. Several signal processing methods were investigated, to allow automatic recognition of the icing signature. Additionally, several implementation issues were considered. Based on both the static and subscale rotor tests, where ice was successfully detected, the passive IR technique appears to be promising for rotor ice detection.

Classification with an edge: Improving semantic image segmentation with boundary detection

NASA Astrophysics Data System (ADS)

Marmanis, D.; Schindler, K.; Wegner, J. D.; Galliani, S.; Datcu, M.; Stilla, U.

2018-01-01

We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state-of-the-art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large receptive fields. However, this success comes at a cost, since the associated loss of effective spatial resolution washes out high-frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge detection, thus making class boundaries explicit in the model. First, we construct a comparatively simple, memory-efficient model by adding boundary detection to the SEGNET encoder-decoder architecture. Second, we also include boundary detection in FCN-type models and set up a high-end classifier ensemble. We show that boundary detection significantly improves semantic segmentation with CNNs in an end-to-end training scheme. Our best model achieves >90% overall accuracy on the ISPRS Vaihingen benchmark.

Mathematical models used in segmentation and fractal methods of 2-D ultrasound images

NASA Astrophysics Data System (ADS)

Moldovanu, Simona; Moraru, Luminita; Bibicu, Dorin

2012-11-01

Mathematical models are widely used in biomedical computing. The extracted data from images using the mathematical techniques are the "pillar" achieving scientific progress in experimental, clinical, biomedical, and behavioural researches. This article deals with the representation of 2-D images and highlights the mathematical support for the segmentation operation and fractal analysis in ultrasound images. A large number of mathematical techniques are suitable to be applied during the image processing stage. The addressed topics cover the edge-based segmentation, more precisely the gradient-based edge detection and active contour model, and the region-based segmentation namely Otsu method. Another interesting mathematical approach consists of analyzing the images using the Box Counting Method (BCM) to compute the fractal dimension. The results of the paper provide explicit samples performed by various combination of methods.

CH PLIF and PIV implementation using C-X (0,0) and intra-vibrational band filtered detection

NASA Astrophysics Data System (ADS)

Hammack, Stephen D.; Skiba, Aaron W.; Lee, Tonghun; Carter, Campbell D.

2018-02-01

This study demonstrates advancement in a low-pulse energy methylidyne (CH) planar laser-induced fluorescence (PLIF) method that facilitates its application alongside flows seeded for particle image velocimetry (PIV) or other particle scattering based methods, as well as in high scattering environments. The C-X (0,0) R-branch excitation and filtered detection are carefully selected such that the laser line frequency is heavily attenuated by an edge filter while allowing transmission of most of the (0,0) band fluorescence. There are strong OH A-X (0,0) lines in the vicinity, but they can be avoided or utilized through dye laser tuning. As a demonstration of efficacy, PIV is performed simultaneously with the PLIF imaging. Using the edge filter, particle scattering signal is reduced to sub-fluorescence levels, allowing for flame-front analysis. This achievement enables flame-front tracking at high repetition rates (due to the low-pulse energy required) in combination with a scattering method such as PIV or use in high scattering environments such as enclosed combustors or near burner surfaces.

To cut or not to cut? Assessing the modular structure of brain networks.

PubMed

Chang, Yu-Teng; Pantazis, Dimitrios; Leahy, Richard M

2014-05-01

A wealth of methods has been developed to identify natural divisions of brain networks into groups or modules, with one of the most prominent being modularity. Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. It is well known that even random networks can exhibit high modularity because of incidental concentration of edges, even though they have no underlying organizational structure. Consequently, interpretation of community structure is confounded by the lack of principled and computationally tractable approaches to statistically control for spurious modules. In this paper we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. We compute parametric formulas for the distribution of modularity for random networks as a function of network size and edge variance, and show that we can efficiently control for false positives in brain and other real-world networks. Copyright © 2014 Elsevier Inc. All rights reserved.

Blurred image restoration using knife-edge function and optimal window Wiener filtering.

PubMed

Wang, Min; Zhou, Shudao; Yan, Wei

2018-01-01

Motion blur in images is usually modeled as the convolution of a point spread function (PSF) and the original image represented as pixel intensities. The knife-edge function can be used to model various types of motion-blurs, and hence it allows for the construction of a PSF and accurate estimation of the degradation function without knowledge of the specific degradation model. This paper addresses the problem of image restoration using a knife-edge function and optimal window Wiener filtering. In the proposed method, we first calculate the motion-blur parameters and construct the optimal window. Then, we use the detected knife-edge function to obtain the system degradation function. Finally, we perform Wiener filtering to obtain the restored image. Experiments show that the restored image has improved resolution and contrast parameters with clear details and no discernible ringing effects.

Blurred image restoration using knife-edge function and optimal window Wiener filtering

PubMed Central

Zhou, Shudao; Yan, Wei

2018-01-01

Motion blur in images is usually modeled as the convolution of a point spread function (PSF) and the original image represented as pixel intensities. The knife-edge function can be used to model various types of motion-blurs, and hence it allows for the construction of a PSF and accurate estimation of the degradation function without knowledge of the specific degradation model. This paper addresses the problem of image restoration using a knife-edge function and optimal window Wiener filtering. In the proposed method, we first calculate the motion-blur parameters and construct the optimal window. Then, we use the detected knife-edge function to obtain the system degradation function. Finally, we perform Wiener filtering to obtain the restored image. Experiments show that the restored image has improved resolution and contrast parameters with clear details and no discernible ringing effects. PMID:29377950

Image flows and one-liner graphical image representation.

PubMed

Makhervaks, Vadim; Barequet, Gill; Bruckstein, Alfred

2002-10-01

This paper introduces a novel graphical image representation consisting of a single curve-the one-liner. The first step of the algorithm involves the detection and ranking of image edges. A new edge exploration technique is used to perform both tasks simultaneously. This process is based on image flows. It uses a gradient vector field and a new operator to explore image edges. Estimation of the derivatives of the image is performed by using local Taylor expansions in conjunction with a weighted least-squares method. This process finds all the possible image edges without any pruning, and collects information that allows the edges found to be prioritized. This enables the most important edges to be selected to form a skeleton of the representation sought. The next step connects the selected edges into one continuous curve-the one-liner. It orders the selected edges and determines the curves connecting them. These two problems are solved separately. Since the abstract graph setting of the first problem is NP-complete, we reduce it to a variant of the traveling salesman problem and compute an approximate solution to it. We solve the second problem by using Dijkstra's shortest-path algorithm. The full software implementation for the entire one-liner determination process is available.

Researches on Position Detection for Vacuum Switch Electrode

NASA Astrophysics Data System (ADS)

Dong, Huajun; Guo, Yingjie; Li, Jie; Kong, Yihan

2018-03-01

Form and transformation character of vacuum arc is important influencing factor on the vacuum switch performance, and the dynamic separations of electrode is the chief effecting factor on the transformation of vacuum arcs forms. Consequently, how to detect the position of electrode to calculate the separations in the arcs image is of great significance. However, gray level distribution of vacuum arcs image isn’t even, the gray level of burning arcs is high, but the gray level of electrode is low, meanwhile, the forms of vacuum arcs changes sharply, the problems above restrict electrode position detection precisely. In this paper, algorithm of detecting electrode position base on vacuum arcs image was proposed. The digital image processing technology was used in vacuum switch arcs image analysis, the upper edge and lower edge were detected respectively, then linear fitting was done using the result of edge detection, the fitting result was the position of electrode, thus, accurate position detection of electrode was realized. From the experimental results, we can see that: algorithm described in this paper detected upper and lower edge of arcs successfully and the position of electrode was obtained through calculation.

Adaption of the temporal correlation coefficient calculation for temporal networks (applied to a real-world pig trade network).

PubMed

Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim

2016-01-01

The average topological overlap of two graphs of two consecutive time steps measures the amount of changes in the edge configuration between the two snapshots. This value has to be zero if the edge configuration changes completely and one if the two consecutive graphs are identical. Current methods depend on the number of nodes in the network or on the maximal number of connected nodes in the consecutive time steps. In the first case, this methodology breaks down if there are nodes with no edges. In the second case, it fails if the maximal number of active nodes is larger than the maximal number of connected nodes. In the following, an adaption of the calculation of the temporal correlation coefficient and of the topological overlap of the graph between two consecutive time steps is presented, which shows the expected behaviour mentioned above. The newly proposed adaption uses the maximal number of active nodes, i.e. the number of nodes with at least one edge, for the calculation of the topological overlap. The three methods were compared with the help of vivid example networks to reveal the differences between the proposed notations. Furthermore, these three calculation methods were applied to a real-world network of animal movements in order to detect influences of the network structure on the outcome of the different methods.

Experiences with digital processing of images at INPE

NASA Technical Reports Server (NTRS)

Mascarenhas, N. D. A. (Principal Investigator)

1984-01-01

Four different research experiments with digital image processing at INPE will be described: (1) edge detection by hypothesis testing; (2) image interpolation by finite impulse response filters; (3) spatial feature extraction methods in multispectral classification; and (4) translational image registration by sequential tests of hypotheses.

Face detection in color images using skin color, Laplacian of Gaussian, and Euler number

NASA Astrophysics Data System (ADS)

Saligrama Sundara Raman, Shylaja; Kannanedhi Narasimha Sastry, Balasubramanya Murthy; Subramanyam, Natarajan; Senkutuvan, Ramya; Srikanth, Radhika; John, Nikita; Rao, Prateek

2010-02-01

In this a paper, a feature based approach to face detection has been proposed using an ensemble of algorithms. The method uses chrominance values and edge features to classify the image as skin and nonskin regions. The edge detector used for this purpose is Laplacian of Gaussian (LoG) which is found to be appropriate when images having multiple faces with noise in them. Eight connectivity analysis of these regions will segregate them as probable face or nonface. The procedure is made more robust by identifying local features within these skin regions which include number of holes, percentage of skin and the golden ratio. The method proposed has been tested on color face images of various races obtained from different sources and its performance is found to be encouraging as the color segmentation cleans up almost all the complex facial features. The result obtained has a calculated accuracy of 86.5% on a test set of 230 images.

A novel approach to segmentation and measurement of medical image using level set methods.

PubMed

Chen, Yao-Tien

2017-06-01

The study proposes a novel approach for segmentation and visualization plus value-added surface area and volume measurements for brain medical image analysis. The proposed method contains edge detection and Bayesian based level set segmentation, surface and volume rendering, and surface area and volume measurements for 3D objects of interest (i.e., brain tumor, brain tissue, or whole brain). Two extensions based on edge detection and Bayesian level set are first used to segment 3D objects. Ray casting and a modified marching cubes algorithm are then adopted to facilitate volume and surface visualization of medical-image dataset. To provide physicians with more useful information for diagnosis, the surface area and volume of an examined 3D object are calculated by the techniques of linear algebra and surface integration. Experiment results are finally reported in terms of 3D object extraction, surface and volume rendering, and surface area and volume measurements for medical image analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

Segmentation of 3D ultrasound computer tomography reflection images using edge detection and surface fitting

NASA Astrophysics Data System (ADS)

Hopp, T.; Zapf, M.; Ruiter, N. V.

2014-03-01

An essential processing step for comparison of Ultrasound Computer Tomography images to other modalities, as well as for the use in further image processing, is to segment the breast from the background. In this work we present a (semi-) automated 3D segmentation method which is based on the detection of the breast boundary in coronal slice images and a subsequent surface fitting. The method was evaluated using a software phantom and in-vivo data. The fully automatically processed phantom results showed that a segmentation of approx. 10% of the slices of a dataset is sufficient to recover the overall breast shape. Application to 16 in-vivo datasets was performed successfully using semi-automated processing, i.e. using a graphical user interface for manual corrections of the automated breast boundary detection. The processing time for the segmentation of an in-vivo dataset could be significantly reduced by a factor of four compared to a fully manual segmentation. Comparison to manually segmented images identified a smoother surface for the semi-automated segmentation with an average of 11% of differing voxels and an average surface deviation of 2mm. Limitations of the edge detection may be overcome by future updates of the KIT USCT system, allowing a fully-automated usage of our segmentation approach.

Proceedings of the Third Annual Symposium on Mathematical Pattern Recognition and Image Analysis

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.

1985-01-01

Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters.

Technical Note: Range verification system using edge detection method for a scintillator and a CCD camera system.

PubMed

Saotome, Naoya; Furukawa, Takuji; Hara, Yousuke; Mizushima, Kota; Tansho, Ryohei; Saraya, Yuichi; Shirai, Toshiyuki; Noda, Koji

2016-04-01

Three-dimensional irradiation with a scanned carbon-ion beam has been performed from 2011 at the authors' facility. The authors have developed the rotating-gantry equipped with the scanning irradiation system. The number of combinations of beam properties to measure for the commissioning is more than 7200, i.e., 201 energy steps, 3 intensities, and 12 gantry angles. To compress the commissioning time, quick and simple range verification system is required. In this work, the authors develop a quick range verification system using scintillator and charge-coupled device (CCD) camera and estimate the accuracy of the range verification. A cylindrical plastic scintillator block and a CCD camera were installed on the black box. The optical spatial resolution of the system is 0.2 mm/pixel. The camera control system was connected and communicates with the measurement system that is part of the scanning system. The range was determined by image processing. Reference range for each energy beam was determined by a difference of Gaussian (DOG) method and the 80% of distal dose of the depth-dose distribution that were measured by a large parallel-plate ionization chamber. The authors compared a threshold method and a DOG method. The authors found that the edge detection method (i.e., the DOG method) is best for the range detection. The accuracy of range detection using this system is within 0.2 mm, and the reproducibility of the same energy measurement is within 0.1 mm without setup error. The results of this study demonstrate that the authors' range check system is capable of quick and easy range verification with sufficient accuracy.

Comparison of edge detection techniques for M7 subtype Leukemic cell in terms of noise filters and threshold value

NASA Astrophysics Data System (ADS)

Salam, Afifah Salmi Abdul; Isa, Mohd. Nazrin Md.; Ahmad, Muhammad Imran; Che Ismail, Rizalafande

2017-11-01

This paper will focus on the study and identifying various threshold values for two commonly used edge detection techniques, which are Sobel and Canny Edge detection. The idea is to determine which values are apt in giving accurate results in identifying a particular leukemic cell. In addition, evaluating suitability of edge detectors are also essential as feature extraction of the cell depends greatly on image segmentation (edge detection). Firstly, an image of M7 subtype of Acute Myelocytic Leukemia (AML) is chosen due to its diagnosing which were found lacking. Next, for an enhancement in image quality, noise filters are applied. Hence, by comparing images with no filter, median and average filter, useful information can be acquired. Each threshold value is fixed with value 0, 0.25 and 0.5. From the investigation found, without any filter, Canny with a threshold value of 0.5 yields the best result.

Semantic Information Extraction of Lanes Based on Onboard Camera Videos

NASA Astrophysics Data System (ADS)

Tang, L.; Deng, T.; Ren, C.

2018-04-01

In the field of autonomous driving, semantic information of lanes is very important. This paper proposes a method of automatic detection of lanes and extraction of semantic information from onboard camera videos. The proposed method firstly detects the edges of lanes by the grayscale gradient direction, and improves the Probabilistic Hough transform to fit them; then, it uses the vanishing point principle to calculate the lane geometrical position, and uses lane characteristics to extract lane semantic information by the classification of decision trees. In the experiment, 216 road video images captured by a camera mounted onboard a moving vehicle were used to detect lanes and extract lane semantic information. The results show that the proposed method can accurately identify lane semantics from video images.

Algorithms used in the Airborne Lidar Processing System (ALPS)

USGS Publications Warehouse

Nagle, David B.; Wright, C. Wayne

2016-05-23

The Airborne Lidar Processing System (ALPS) analyzes Experimental Advanced Airborne Research Lidar (EAARL) data—digitized laser-return waveforms, position, and attitude data—to derive point clouds of target surfaces. A full-waveform airborne lidar system, the EAARL seamlessly and simultaneously collects mixed environment data, including submerged, sub-aerial bare earth, and vegetation-covered topographies.ALPS uses three waveform target-detection algorithms to determine target positions within a given waveform: centroid analysis, leading edge detection, and bottom detection using water-column backscatter modeling. The centroid analysis algorithm detects opaque hard surfaces. The leading edge algorithm detects topography beneath vegetation and shallow, submerged topography. The bottom detection algorithm uses water-column backscatter modeling for deeper submerged topography in turbid water.The report describes slant range calculations and explains how ALPS uses laser range and orientation measurements to project measurement points into the Universal Transverse Mercator coordinate system. Parameters used for coordinate transformations in ALPS are described, as are Interactive Data Language-based methods for gridding EAARL point cloud data to derive digital elevation models. Noise reduction in point clouds through use of a random consensus filter is explained, and detailed pseudocode, mathematical equations, and Yorick source code accompany the report.

Results of edge scatter testing for a starshade mission

NASA Astrophysics Data System (ADS)

Casement, Suzanne; Warwick, Steve; Smith, Daniel; Ellis, Scott; Stover, John

2016-07-01

In the field of exoplanet detection and characterization, the use of a starshade, an external occulter in front of a telescope at large separations, has been identified as one of the highly promising methods to achieve the necessary high contrast imagery. Control of scattered sunlight from the edges of the starshade into the telescope has been identified as one of the key technology development areas in order to make the starshade feasible. Modeling of the scattered light has resulted in very different results so a campaign of experimentation with edge samples was undertaken to attempt to understand the discrepancies. Here, we present our results from the measurement of select samples of materials which would be suitable for manufacturing the starshade edge, and related models. We have focused on coating metallic samples for ease of fabrication: Titanium, Aluminum, and a Beryllium Copper alloy. Using standard machine shop methods, we fabricated samples which had sharp edges with radius of curvature (RoC) between 15 and 20 μm. We then had these samples coated by two suppliers to evaluate how well these coating types would conform to the edge and provide scatter suppression. The results of scatter measurements of these coated edge samples are presented. These scatter results have been incorporated into a new geometrical model in FRED which includes the details of the starshade mechanical model. This model predicts both the magnitude and distribution of the scattered sunlight in the image plane of a nominal telescope. We present these results, including a first effort at modeling the Solar System at 10 pc as seen by this mission architecture.

Results of Edge Scatter Testing for a Starshade Mission

NASA Astrophysics Data System (ADS)

Smith, Daniel; Casement, L. Suzanne; Ellis, Scott; Stover, John; Warwick, Steve

2017-01-01

In the field of exoplanet detection and characterization, the use of a starshade, an external occulter in front of a telescope at large separations, has been identified as one of the highly promising methods to achieve the necessary high contrast imagery. Control of scattered sunlight from the edges of the starshade into the telescope has been identified as one of the key technology development areas in order to make the starshade feasible. Modeling of the scattered light has resulted in very different results so a campaign of experimentation with edge samples was undertaken to attempt to understand the discrepancies.Here, we present our results from the measurement of select samples of materials which would be suitable for manufacturing the starshade edge, and related models. We have focused on coating metallic samples for ease of fabrication: Titanium, Aluminum, and a Beryllium Copper alloy. Using standard machine shop methods, we fabricated samples which had sharp edges with radius of curvature (RoC) between 15 and 20 μm. We then had these samples coated by two suppliers to evaluate how well these coating types would conform to the edge and provide scatter suppression. The results of scatter measurements of these coated edge samples are presented. These scatter results have been incorporated into a new geometrical model in FRED which includes the details of the starshade mechanical model. This model predicts both the magnitude and distribution of the scattered sunlight in the image plane of a nominal telescope. We present these results, including a first effort at modeling the Solar System at 10 pc as seen by this mission architecture.

Lidar Measurements of Tropospheric Wind Profiles with the Double Edge Technique

NASA Technical Reports Server (NTRS)

Gentry, Bruce M.; Li, Steven X.; Korb, C. Laurence; Mathur, Savyasachee; Chen, Huailin

1998-01-01

Research has established the importance of global tropospheric wind measurements for large scale improvements in numerical weather prediction. In addition, global wind measurements provide data that are fundamental to the understanding and prediction of global climate change. These tasks are closely linked with the goals of the NASA Earth Science Enterprise and Global Climate Change programs. NASA Goddard has been actively involved in the development of direct detection Doppler lidar methods and technologies to meet the wind observing needs of the atmospheric science community. A variety of direct detection Doppler wind lidar measurements have recently been reported indicating the growing interest in this area. Our program at Goddard has concentrated on the development of the edge technique for lidar wind measurements. Implementations of the edge technique using either the aerosol or molecular backscatter for the Doppler wind measurement have been described. The basic principles have been verified in lab and atmospheric lidar wind experiments. The lidar measurements were obtained with an aerosol edge technique lidar operating at 1064 nm. These measurements demonstrated high spatial resolution (22 m) and high velocity sensitivity (rms variances of 0.1 m/s) in the planetary boundary layer (PBL). The aerosol backscatter is typically high in the PBL and the effects of the molecular backscatter can often be neglected. However, as was discussed in the original edge technique paper, the molecular contribution to the signal is significant above the boundary layer and a correction for the effects of molecular backscatter is required to make wind measurements. In addition, the molecular signal is a dominant source of noise in regions where the molecular to aerosol ratio is large since the energy monitor channel used in the single edge technique measures the sum of the aerosol and molecular signals. To extend the operation of the edge technique into the free troposphere we have developed a variation of the edge technique called the double edge technique. In this paper a ground based aerosol double edge lidar is described and the first measurements of wind profiles in the free troposphere obtained with this lidar will be presented.

Robust foreground detection: a fusion of masked grey world, probabilistic gradient information and extended conditional random field approach.

PubMed

Zulkifley, Mohd Asyraf; Moran, Bill; Rawlinson, David

2012-01-01

Foreground detection has been used extensively in many applications such as people counting, traffic monitoring and face recognition. However, most of the existing detectors can only work under limited conditions. This happens because of the inability of the detector to distinguish foreground and background pixels, especially in complex situations. Our aim is to improve the robustness of foreground detection under sudden and gradual illumination change, colour similarity issue, moving background and shadow noise. Since it is hard to achieve robustness using a single model, we have combined several methods into an integrated system. The masked grey world algorithm is introduced to handle sudden illumination change. Colour co-occurrence modelling is then fused with the probabilistic edge-based background modelling. Colour co-occurrence modelling is good in filtering moving background and robust to gradual illumination change, while an edge-based modelling is used for solving a colour similarity problem. Finally, an extended conditional random field approach is used to filter out shadow and afterimage noise. Simulation results show that our algorithm performs better compared to the existing methods, which makes it suitable for higher-level applications.

Cascaded image analysis for dynamic crack detection in material testing

NASA Astrophysics Data System (ADS)

Hampel, U.; Maas, H.-G.

Concrete probes in civil engineering material testing often show fissures or hairline-cracks. These cracks develop dynamically. Starting at a width of a few microns, they usually cannot be detected visually or in an image of a camera imaging the whole probe. Conventional image analysis techniques will detect fissures only if they show a width in the order of one pixel. To be able to detect and measure fissures with a width of a fraction of a pixel at an early stage of their development, a cascaded image analysis approach has been developed, implemented and tested. The basic idea of the approach is to detect discontinuities in dense surface deformation vector fields. These deformation vector fields between consecutive stereo image pairs, which are generated by cross correlation or least squares matching, show a precision in the order of 1/50 pixel. Hairline-cracks can be detected and measured by applying edge detection techniques such as a Sobel operator to the results of the image matching process. Cracks will show up as linear discontinuities in the deformation vector field and can be vectorized by edge chaining. In practical tests of the method, cracks with a width of 1/20 pixel could be detected, and their width could be determined at a precision of 1/50 pixel.

Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters

NASA Astrophysics Data System (ADS)

Chitsobhuk, Orachat; Kasemsiri, Watjanapong; Glomglome, Sorayut; Lapamonpinyo, Pipatphon

2018-04-01

In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user's defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.

High resolution energy-sensitive digital X-ray

DOEpatents

Nygren, David R.

1995-01-01

An apparatus and method for detecting an x-ray and for determining the depth of penetration of an x-ray into a semiconductor strip detector. In one embodiment, a semiconductor strip detector formed of semiconductor material is disposed in an edge-on orientation towards an x-ray source such that x-rays From the x-ray source are incident upon and substantially perpendicular to the front edge of the semiconductor strip detector. The semiconductor strip detector is formed of a plurality of segments. The segments are coupled together in a collinear arrangement such that the semiconductor strip detector has a length great enough such that substantially all of the x-rays incident on the front edge of the semiconductor strip detector interact with the semiconductor material which forms the semiconductor strip detector. A plurality of electrodes are connected to the semiconductor strip detect or such that each one of the of semiconductor strip detector segments has at least one of the of electrodes coupled thereto. A signal processor is also coupled to each one of the electrodes. The present detector detects an interaction within the semiconductor strip detector, between an x-ray and the semiconductor material, and also indicates the depth of penetration of the x-ray into the semiconductor strip detector at the time of the interaction.

Optically Unraveling the Edge Chirality-Dependent Band Structure and Plasmon Damping in Graphene Edges.

PubMed

Duan, Jiahua; Chen, Runkun; Cheng, Yuan; Yang, Tianzhong; Zhai, Feng; Dai, Qing; Chen, Jianing

2018-05-01

The nontrivial topological origin and pseudospinorial character of electron wavefunctions make edge states possess unusual electronic properties. Twenty years ago, the tight-binding model calculation predicted that zigzag termination of 2D sheets of carbon atoms have peculiar edge states, which show potential application in spintronics and modern information technologies. Although scanning probe microscopy is employed to capture this phenomenon, the experimental demonstration of its optical response remains challenging. Here, the propagating graphene plasmon provides an edge-selective polaritonic probe to directly detect and control the electronic edge state at ambient condition. Compared with armchair, the edge-band structure in the bandgap gives rise to additional optical absorption and strongly absorbed rim at zigzag edge. Furthermore, the optical conductivity is reconstructed and the anisotropic plasmon damping in graphene systems is revealed. The reported approach paves the way for detecting edge-specific phenomena in other van der Waals materials and topological insulators. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Learning a Dictionary of Shape Epitomes with Applications to Image Labeling

PubMed Central

Chen, Liang-Chieh; Papandreou, George; Yuille, Alan L.

2015-01-01

The first main contribution of this paper is a novel method for representing images based on a dictionary of shape epitomes. These shape epitomes represent the local edge structure of the image and include hidden variables to encode shift and rotations. They are learnt in an unsupervised manner from groundtruth edges. This dictionary is compact but is also able to capture the typical shapes of edges in natural images. In this paper, we illustrate the shape epitomes by applying them to the image labeling task. In other work, described in the supplementary material, we apply them to edge detection and image modeling. We apply shape epitomes to image labeling by using Conditional Random Field (CRF) Models. They are alternatives to the superpixel or pixel representations used in most CRFs. In our approach, the shape of an image patch is encoded by a shape epitome from the dictionary. Unlike the superpixel representation, our method avoids making early decisions which cannot be reversed. Our resulting hierarchical CRFs efficiently capture both local and global class co-occurrence properties. We demonstrate its quantitative and qualitative properties of our approach with image labeling experiments on two standard datasets: MSRC-21 and Stanford Background. PMID:26321886

Superconducting transition edge sensors and methods for design and manufacture thereof

NASA Technical Reports Server (NTRS)

Sadleir, John E. (Inventor)

2013-01-01

Methods for forming sensors using transition edge sensors (TES) and sensors therefrom are described. The method includes forming a plurality of sensor arrays includes at least one TES device. The TES device includes a TES device body, a first superconducting lead contacting a first portion of the TES device body, and a second superconducting lead contacting of a second portion of the TES device body, where the first and second superconducting leads separated on the TES device body by a lead spacing. The lead spacing can be selected to be different for at least two of the plurality of sensor arrays. The method also includes determining a transition temperature for each of the plurality of sensor arrays and generating a signal responsive to detecting a change in the electrical characteristics of one of the plurality of sensor arrays meeting a transition temperature criterion.

A novel orthoimage mosaic method using the weighted A* algorithm for UAV imagery

NASA Astrophysics Data System (ADS)

Zheng, Maoteng; Zhou, Shunping; Xiong, Xiaodong; Zhu, Junfeng

2017-12-01

A weighted A* algorithm is proposed to select optimal seam-lines in orthoimage mosaic for UAV (Unmanned Aircraft Vehicle) imagery. The whole workflow includes four steps: the initial seam-line network is firstly generated by standard Voronoi Diagram algorithm; an edge diagram is then detected based on DSM (Digital Surface Model) data; the vertices (conjunction nodes) of initial network are relocated since some of them are on the high objects (buildings, trees and other artificial structures); and, the initial seam-lines are finally refined using the weighted A* algorithm based on the edge diagram and the relocated vertices. The method was tested with two real UAV datasets. Preliminary results show that the proposed method produces acceptable mosaic images in both the urban and mountainous areas, and is better than the result of the state-of-the-art methods on the datasets.

Infrared image enhancement based on the edge detection and mathematical morphology

NASA Astrophysics Data System (ADS)

Zhang, Linlin; Zhao, Yuejin; Dong, Liquan; Liu, Xiaohua; Yu, Xiaomei; Hui, Mei; Chu, Xuhong; Gong, Cheng

2010-11-01

The development of the un-cooled infrared imaging technology from military necessity. At present, It is widely applied in industrial, medicine, scientific and technological research and so on. The infrared radiation temperature distribution of the measured object's surface can be observed visually. The collection of infrared images from our laboratory has following characteristics: Strong spatial correlation, Low contrast , Poor visual effect; Without color or shadows because of gray image , and has low resolution; Low definition compare to the visible light image; Many kinds of noise are brought by the random disturbances of the external environment. Digital image processing are widely applied in many areas, it can now be studied up close and in detail in many research field. It has become one kind of important means of the human visual continuation. Traditional methods for image enhancement cannot capture the geometric information of images and tend to amplify noise. In order to remove noise and improve visual effect. Meanwhile, To overcome the above enhancement issues. The mathematical model of FPA unit was constructed based on matrix transformation theory. According to characteristics of FPA, Image enhancement algorithm which combined with mathematical morphology and edge detection are established. First of all, Image profile is obtained by using the edge detection combine with mathematical morphological operators. And then, through filling the template profile by original image to get the ideal background image, The image noise can be removed on the base of the above method. The experiments show that utilizing the proposed algorithm can enhance image detail and the signal to noise ratio.

Combining Stereo SECCHI COR2 and HI1 Images for Automatic CME Front Edge Tracking

NASA Technical Reports Server (NTRS)

Kirnosov, Vladimir; Chang, Lin-Ching; Pulkkinen, Antti

2016-01-01

COR2 coronagraph images are the most commonly used data for coronal mass ejection (CME) analysis among the various types of data provided by the STEREO (Solar Terrestrial Relations Observatory) SECCHI (Sun-Earth Connection Coronal and Heliospheric Investigation) suite of instruments. The field of view (FOV) in COR2 images covers 215 solar radii (Rs) that allow for tracking the front edge of a CME in its initial stage to forecast the lead-time of a CME and its chances of reaching the Earth. However, estimating the lead-time of a CME using COR2 images gives a larger lead-time, which may be associated with greater uncertainty. To reduce this uncertainty, CME front edge tracking should be continued beyond the FOV of COR2 images. Therefore, heliospheric imager (HI1) data that covers 1590 Rs FOV must be included. In this paper, we propose a novel automatic method that takes both COR2 and HI1 images into account and combine the results to track the front edges of a CME continuously. The method consists of two modules: pre-processing and tracking. The pre-processing module produces a set of segmented images, which contain the signature of a CME, for both COR2 and HI1 separately. In addition, the HI1 images are resized and padded, so that the center of the Sun is the central coordinate of the resized HI1 images. The resulting COR2 andHI1 image set is then fed into the tracking module to estimate the position angle (PA) and track the front edge of a CME. The detected front edge is then used to produce a height-time profile that is used to estimate the speed of a CME. The method was validated using 15 CME events observed in the period from January 1, 2008 to August 31, 2009. The results demonstrate that the proposed method is effective for CME front edge tracking in both COR2 and HI1 images. Using this method, the CME front edge can now be tracked automatically and continuously in a much larger range, i.e., from 2 to 90 Rs, for the first time. These improvement scan greatly help in making the quantitative CME analysis more accurate and have the potential to assist in space weather forecasting.

Metallicity-Corrected Tip of the Red Giant Branch Distances to M66 and M96

NASA Astrophysics Data System (ADS)

Mager, Violet; Madore, Barry F.; Freedman, Wendy L.

2018-06-01

We present distances to M66 and M96 obtained through measurements of the tip of the red giant branch (TRGB) in HST ACS/WFC images, and give details of our method. The TRGB can be difficult to determine in color-magnitude diagrams where it is not a hard, well-defined edge. We discuss our approach to this in our edge-detection algorithm. Furthermore, metals affect the magnitude of the TRGB as a function of color, creating a slope to the edge that has been dealt with in the past by applying a red color cut-off. We instead apply a metallicity correction to the data that removes this effect, increasing the number of useable stars and providing a more accurate distance measurement.

A novel method for resonant inelastic soft X-ray scattering via photoelectron spectroscopy detection

DOE PAGES

Dakovski, Georgi L.; Lin, Ming-Fu; Damiani, Daniel S.; ...

2017-10-05

A method for measuring resonant inelastic X-ray scattering based on the conversion of X-ray photons into photoelectrons is presented in this paper. The setup is compact, relies on commercially available detectors, and offers significant flexibility. Finally, this method is demonstrated at the Linac Coherent Light Source with ~0.5 eV resolution at the cobalt L 3-edge, with signal rates comparable with traditional grating spectrometers.

Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images.

PubMed

Liu, Meiling; Wang, Tiejun; Skidmore, Andrew K; Liu, Xiangnan

2018-05-05

Regional-level information on heavy metal pollution in agro-ecosystems is essential for food security because excessive levels of heavy metals in crops may pose risks to humans. However, collecting this information over large areas is inherently costly. This paper investigates the possibility of applying multi-temporal Sentinel-2 satellite images to detect heavy metal-induced stress (i.e., Cd stress) in rice crops in four study areas in Zhuzhou City, Hunan Province, China. For this purpose, we compared seven Sentinel-2 images acquired in 2016 and 2017 with in situ measured hyper-spectral data, chlorophyll content, rice leaf area index, and heavy metal concentrations in soil collected from 2014 to 2017. Vegetation indices (VIs) related to red edge bands were referred to as the sensitive indicators for screening stressed rice from unstressed rice. The coefficients of spatio-temporal variation (CSTV) derived from the VIs allowed us to discriminate crops exposed to pollution from heavy metals as well as environmental stressors. The results indicate that (i) the red edge chlorophyll index, the red edge position index, and the normalized difference red edge 2 index derived from multi-temporal Sentinel-2 images were good indicators for screening stressed rice from unstressed rice; (ii) Rice under Cd stress remained stable with lower CSTV values of VIs overall growth stages in the experimental region, whereas rice under other stressors (i.e., pests and disease) showed abrupt changes at some growth stages and presented "hot spots" with greater CSTV values; and (iii) the proposed spatio-temporal anomaly detection method was successful at detecting rice under Cd stress; and CSTVs of rice VIs stabilized regardless of whether they were applied to consecutive growth stages or to two different crop years. This study suggests that regional heavy metal stress may be accurately detected using multi-temporal Sentinel-2 images, using VIs sensitive to the spatio-temporal characteristics of crops. Copyright © 2018 Elsevier B.V. All rights reserved.

Variations in the Sea Ice Edge and the Marginal Ice Zone on Different Spatial Scales as Observed from Different Satellite Sensor

NASA Technical Reports Server (NTRS)

Markus, Thorsten; Henrichs, John

2006-01-01

The Marginal sea Ice Zone (MIZ) and the sea ice edge are the most dynamic areas of the sea ice cover. Knowledge of the sea ice edge location is vital for routing shipping in the polar regions. The ice edge is the location of recurrent plankton blooms, and is the habitat for a number of animals, including several which are under severe ecological threat. Polar lows are known to preferentially form along the sea ice edge because of induced atmospheric baroclinicity, and the ice edge is also the location of both vertical and horizontal ocean currents driven by thermal and salinity gradients. Finally, sea ice is both a driver and indicator of climate change and monitoring the position of the ice edge accurately over long time periods enables assessment of the impact of global and regional warming near the poles. Several sensors are currently in orbit that can monitor the sea ice edge. These sensors, though, have different spatial resolutions, different limitations, and different repeat frequencies. Satellite passive microwave sensors can monitor the ice edge on a daily or even twice-daily basis, albeit with low spatial resolution - 25 km for the Special Sensor Microwave Imager (SSM/I) or 12.5 km for the Advanced Microwave Scanning Radiometer (AMSR-E). Although special methods exist that allow the detection of the sea ice edge at a quarter of that nominal resolution (PSSM). Visible and infrared data from the Advanced Very High Resolution Radiometer (AVHRR) and from the Moderate Resolution Imaging Spectroradiometer (MODIS) provide daily coverage at 1 km and 250 m, respectively, but the surface observations me limited to cloud-free periods. The Landsat 7 Enhanced Thematic Mapper (ETM+) has a resolution of 15 to 30 m but is limited to cloud-free periods as well, and does not provide daily coverage. Imagery from Synthetic Aperture Radar (SAR) instruments has resolutions of tens of meters to 100 m, and can be used to distinguish open water and sea ice on the basis of surface and volume scattering characteristics. The Canadian RADARSAT C-band SAR provides data that cover the Arctic Ocean and the MIZ every 3 days. A change-point detection approach was utilized to obtain an ice edge estimate from the RADARSAT data The Quickscat scatterometer provides ice edge information with a resolution of a few kilometers on a near-daily basis. During portions of March and April of 2003 a series of aircraft flights were conducted over the ice edge in the Bering Sea carrying the Polarimetric Scanning Radiometer (PSR), which provides spectral coverage identical with the AMSR-E instrument at a resolution of 500 meters. In this study we investigated these different data sets and analyzed differences in their definition of the sea ice edge and the marginal ice zone and how these differences as well as their individual limitations affect the monitoring of the ice edge dynamics. We also examined how the nature of the sea ice edge, including its location, compactness and shape, changes over the seasons. Our approach was based on calculation of distances between ice edges derived from the satellite and aircraft data sets listed above as well as spectral coherence methods and shape parameters such as tortuosity, curvature, and fractional dimension.

Natural history of optical coherence tomography-detected non-flow-limiting edge dissections following drug-eluting stent implantation.

PubMed

Radu, Maria D; Räber, Lorenz; Heo, Jungho; Gogas, Bill D; Jørgensen, Erik; Kelbæk, Henning; Muramatsu, Takashi; Farooq, Vasim; Helqvist, Steffen; Garcia-Garcia, Hector M; Windecker, Stephan; Saunamäki, Kari; Serruys, Patrick W

2014-01-22

Angiographic evidence of edge dissections has been associated with a risk of early stent thrombosis. Optical coherence tomography (OCT) is a high-resolution technology detecting a greater number of edge dissections--particularly non-flow-limiting--compared to angiography. Their natural history and clinical implications remain unclear. The objectives of the present study were to assess the morphology, healing response, and clinical outcomes of OCT-detected edge dissections using serial OCT imaging at baseline and at one year following drug-eluting stent (DES) implantation. Edge dissections were defined as disruptions of the luminal surface in the 5 mm segments proximal and distal to the stent, and categorised as flaps, cavities, double-lumen dissections or fissures. Qualitative and quantitative OCT analyses were performed every 0.5 mm at baseline and one year, and clinical outcomes were assessed. Sixty-three lesions (57 patients) were studied with OCT at baseline and one-year follow-up. Twenty-two non-flow-limiting edge dissections in 21 lesions (20 patients) were identified by OCT; only two (9%) were angiographically visible. Flaps were found in 96% of cases. The median longitudinal dissection length was 2.9 mm (interquartile range [IQR] 1.6-4.2 mm), whereas the circumferential and axial extensions amounted to 1.2 mm (IQR: 0.9-1.7 mm) and 0.6 mm (IQR: 0.4-0.7 mm), respectively. Dissections extended into the media and adventitia in seven (33%) and four (20%) cases, respectively. Eighteen (82%) OCT-detected edge dissections were also evaluated with intravascular ultrasound which identified nine (50%) of these OCT-detected dissections. No stent thrombosis or target lesion revascularisation occurred up to one year. At follow-up, 20 (90%) edge dissections were completely healed on OCT. The two cases exhibiting persistent dissection had the longest flaps (2.81 mm and 2.42 mm) at baseline. OCT-detected edge dissections which are angiographically silent in the majority of cases are not associated with acute stent thrombosis or restenosis up to one-year follow-up.

Automated feature extraction in color retinal images by a model based approach.

PubMed

Li, Huiqi; Chutatape, Opas

2004-02-01

Color retinal photography is an important tool to detect the evidence of various eye diseases. Novel methods to extract the main features in color retinal images have been developed in this paper. Principal component analysis is employed to locate optic disk; A modified active shape model is proposed in the shape detection of optic disk; A fundus coordinate system is established to provide a better description of the features in the retinal images; An approach to detect exudates by the combined region growing and edge detection is proposed. The success rates of disk localization, disk boundary detection, and fovea localization are 99%, 94%, and 100%, respectively. The sensitivity and specificity of exudate detection are 100% and 71%, correspondingly. The success of the proposed algorithms can be attributed to the utilization of the model-based methods. The detection and analysis could be applied to automatic mass screening and diagnosis of the retinal diseases.

Cutting tool form compensation system and method

DOEpatents

Barkman, W.E.; Babelay, E.F. Jr.; Klages, E.J.

1993-10-19

A compensation system for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along a preprogrammed path during a machining operation utilizes a camera and a vision computer for gathering information at a preselected stage of a machining operation relating to the actual shape and size of the cutting edge of the cutting tool and for altering the preprogrammed path in accordance with detected variations between the actual size and shape of the cutting edge and an assumed size and shape of the cutting edge. The camera obtains an image of the cutting tool against a background so that the cutting tool and background possess contrasting light intensities, and the vision computer utilizes the contrasting light intensities of the image to locate points therein which correspond to points along the actual cutting edge. Following a series of computations involving the determining of a tool center from the points identified along the tool edge, the results of the computations are fed to the controller where the preprogrammed path is altered as aforedescribed. 9 figures.

Cutting tool form compensaton system and method

DOEpatents

Barkman, William E.; Babelay, Jr., Edwin F.; Klages, Edward J.

1993-01-01

A compensation system for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along a preprogrammed path during a machining operation utilizes a camera and a vision computer for gathering information at a preselected stage of a machining operation relating to the actual shape and size of the cutting edge of the cutting tool and for altering the preprogrammed path in accordance with detected variations between the actual size and shape of the cutting edge and an assumed size and shape of the cutting edge. The camera obtains an image of the cutting tool against a background so that the cutting tool and background possess contrasting light intensities, and the vision computer utilizes the contrasting light intensities of the image to locate points therein which correspond to points along the actual cutting edge. Following a series of computations involving the determining of a tool center from the points identified along the tool edge, the results of the computations are fed to the controller where the preprogrammed path is altered as aforedescribed.

Task-Related Edge Density (TED)—A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

PubMed Central

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach “Task-related Edge Density” (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function. PMID:27341204

Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

PubMed

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

Design of compactly supported wavelet to match singularities in medical images

NASA Astrophysics Data System (ADS)

Fung, Carrson C.; Shi, Pengcheng

2002-11-01

Analysis and understanding of medical images has important clinical values for patient diagnosis and treatment, as well as technical implications for computer vision and pattern recognition. One of the most fundamental issues is the detection of object boundaries or singularities, which is often the basis for further processes such as organ/tissue recognition, image registration, motion analysis, measurement of anatomical and physiological parameters, etc. The focus of this work involved taking a correlation based approach toward edge detection, by exploiting some of desirable properties of wavelet analysis. This leads to the possibility of constructing a bank of detectors, consisting of multiple wavelet basis functions of different scales which are optimal for specific types of edges, in order to optimally detect all the edges in an image. Our work involved developing a set of wavelet functions which matches the shape of the ramp and pulse edges. The matching algorithm used focuses on matching the edges in the frequency domain. It was proven that this technique could create matching wavelets applicable at all scales. Results have shown that matching wavelets can be obtained for the pulse edge while the ramp edge requires another matching algorithm.

Iterative image-domain decomposition for dual-energy CT

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

Niu, Tianye; Dong, Xue; Petrongolo, Michael

2014-04-15

Purpose: Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its capability of material decomposition. Direct decomposition via matrix inversion suffers from significant degradation of image signal-to-noise ratios, which reduces clinical values of DECT. Existing denoising algorithms achieve suboptimal performance since they suppress image noise either before or after the decomposition and do not fully explore the noise statistical properties of the decomposition process. In this work, the authors propose an iterative image-domain decomposition method for noise suppression in DECT, using the full variance-covariance matrix of the decomposed images. Methods: The proposed algorithm ismore » formulated in the form of least-square estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, the authors include the inverse of the estimated variance-covariance matrix of the decomposed images as the penalty weight in the least-square term. The regularization term enforces the image smoothness by calculating the square sum of neighboring pixel value differences. To retain the boundary sharpness of the decomposed images, the authors detect the edges in the CT images before decomposition. These edge pixels have small weights in the calculation of the regularization term. Distinct from the existing denoising algorithms applied on the images before or after decomposition, the method has an iterative process for noise suppression, with decomposition performed in each iteration. The authors implement the proposed algorithm using a standard conjugate gradient algorithm. The method performance is evaluated using an evaluation phantom (Catphan©600) and an anthropomorphic head phantom. The results are compared with those generated using direct matrix inversion with no noise suppression, a denoising method applied on the decomposed images, and an existing algorithm with similar formulation as the proposed method but with an edge-preserving regularization term. Results: On the Catphan phantom, the method maintains the same spatial resolution on the decomposed images as that of the CT images before decomposition (8 pairs/cm) while significantly reducing their noise standard deviation. Compared to that obtained by the direct matrix inversion, the noise standard deviation in the images decomposed by the proposed algorithm is reduced by over 98%. Without considering the noise correlation properties in the formulation, the denoising scheme degrades the spatial resolution to 6 pairs/cm for the same level of noise suppression. Compared to the edge-preserving algorithm, the method achieves better low-contrast detectability. A quantitative study is performed on the contrast-rod slice of Catphan phantom. The proposed method achieves lower electron density measurement error as compared to that by the direct matrix inversion, and significantly reduces the error variation by over 97%. On the head phantom, the method reduces the noise standard deviation of decomposed images by over 97% without blurring the sinus structures. Conclusions: The authors propose an iterative image-domain decomposition method for DECT. The method combines noise suppression and material decomposition into an iterative process and achieves both goals simultaneously. By exploring the full variance-covariance properties of the decomposed images and utilizing the edge predetection, the proposed algorithm shows superior performance on noise suppression with high image spatial resolution and low-contrast detectability.« less

A coloured oil level indicator detection method based on simple linear iterative clustering

NASA Astrophysics Data System (ADS)

Liu, Tianli; Li, Dongsong; Jiao, Zhiming; Liang, Tao; Zhou, Hao; Yang, Guoqing

2017-12-01

A detection method of coloured oil level indicator is put forward. The method is applied to inspection robot in substation, which realized the automatic inspection and recognition of oil level indicator. Firstly, the detected image of the oil level indicator is collected, and the detected image is clustered and segmented to obtain the label matrix of the image. Secondly, the detection image is processed by colour space transformation, and the feature matrix of the image is obtained. Finally, the label matrix and feature matrix are used to locate and segment the detected image, and the upper edge of the recognized region is obtained. If the upper limb line exceeds the preset oil level threshold, the alarm will alert the station staff. Through the above-mentioned image processing, the inspection robot can independently recognize the oil level of the oil level indicator, and instead of manual inspection. It embodies the automatic and intelligent level of unattended operation.

A Novel Method for Block Size Forensics Based on Morphological Operations

NASA Astrophysics Data System (ADS)

Luo, Weiqi; Huang, Jiwu; Qiu, Guoping

Passive forensics analysis aims to find out how multimedia data is acquired and processed without relying on pre-embedded or pre-registered information. Since most existing compression schemes for digital images are based on block processing, one of the fundamental steps for subsequent forensics analysis is to detect the presence of block artifacts and estimate the block size for a given image. In this paper, we propose a novel method for blind block size estimation. A 2×2 cross-differential filter is first applied to detect all possible block artifact boundaries, morphological operations are then used to remove the boundary effects caused by the edges of the actual image contents, and finally maximum-likelihood estimation (MLE) is employed to estimate the block size. The experimental results evaluated on over 1300 nature images show the effectiveness of our proposed method. Compared with existing gradient-based detection method, our method achieves over 39% accuracy improvement on average.

Real-time 3D reconstruction of road curvature in far look-ahead distance from analysis of image sequences

NASA Astrophysics Data System (ADS)

Behringer, Reinhold

1995-12-01

A system for visual road recognition in far look-ahead distance, implemented in the autonomous road vehicle VaMP (a passenger car), is described. Visual cues of a road in a video image are the bright lane markings and the edges formed at the road borders. In a distance of more than 100 m, the most relevant road cue is the homogeneous road area, limited by the two border edges. These cues can be detected by the image processing module KRONOS applying edge detection techniques and areal 2D segmentation based on resolution triangles (analogous to a resolution pyramid). An estimation process performs an update of a state vector, which describes spatial road shape and vehicle orientation relative to the road. This state vector is estimated every 40 ms by exploiting knowledge about the vehicle movement (spatio-temporal model of vehicle dynamics) and the road design rules (clothoidal segments). Kalman filter techniques are applied to obtain an optimal estimate of the state vector by evaluating the measurements of the road border positions in the image sequence taken by a set of CCD cameras. The road consists of segments with piecewise constant curvature parameters. The borders between these segments can be detected by applying methods which have been developed for detection of discontinuities during time-discrete measurements. The road recognition system has been tested in autonomous rides with VaMP on public Autobahnen in real traffic at speeds up to 130 km/h.

Active link selection for efficient semi-supervised community detection

NASA Astrophysics Data System (ADS)

Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

2015-03-01

Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches.

Active link selection for efficient semi-supervised community detection

PubMed Central

Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

2015-01-01

Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches. PMID:25761385

Locomotive track detection for underground

NASA Astrophysics Data System (ADS)

Ma, Zhonglei; Lang, Wenhui; Li, Xiaoming; Wei, Xing

2017-08-01

In order to improve the PC-based track detection system, this paper proposes a method to detect linear track for underground locomotive based on DSP + FPGA. Firstly, the analog signal outputted from the camera is sampled by A / D chip. Then the collected digital signal is preprocessed by FPGA. Secondly, the output signal of FPGA is transmitted to DSP via EMIF port. Subsequently, the adaptive threshold edge detection, polar angle and radius constrain based Hough transform are implemented by DSP. Lastly, the detected track information is transmitted to host computer through Ethernet interface. The experimental results show that the system can not only meet the requirements of real-time detection, but also has good robustness.

Enhanced Component Performance Study. Emergency Diesel Generators 1998–2013

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

Schroeder, John Alton

2014-11-01

This report presents an enhanced performance evaluation of emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using Institute of Nuclear Power Operations (INPO) Consolidated Events Database (ICES) data from 1998 through 2013 and maintenance unavailability (UA) performance data using Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2013. The objective is to present an analysis of factors that could influence the system and component trends in addition to annual performance trends of failure rates and probabilities. The factors analyzed for the EDG component are the differences in failuresmore » between all demands and actual unplanned engineered safety feature (ESF) demands, differences among manufacturers, and differences among EDG ratings. Statistical analyses of these differences are performed and results showing whether pooling is acceptable across these factors. In addition, engineering analyses were performed with respect to time period and failure mode. The factors analyzed are: sub-component, failure cause, detection method, recovery, manufacturer, and EDG rating.« less

SU-E-J-168: Automated Pancreas Segmentation Based On Dynamic MRI

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

Gou, S; Rapacchi, S; Hu, P

2014-06-01

Purpose: MRI guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment 2D upper abdominal dynamic MRI is developed for this purpose. Methods: 2D coronal dynamic MR images of 2 healthy volunteers were acquired with a frame rate of 5 f/second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centers of the ROIs were annotated. These center locations were propagatedmore » to the next dynamic MRI frame. 4-neighborhood region transfer growth was performed from these initial seeds for rough segmentation. To improve the results, gradient, edge and shape constraints were applied to the ROIs before final refinement using morphological operations. Results from hGReS and 3 other automated segmentation methods using edge detection, region growth and level set were compared to manual contouring. Results: For the first patient, hGReS resulted in the organ segmentation accuracy as measure by the Dices index (0.77) for the pancreas. The accuracy was slightly superior to the level set method (0.72), and both are significantly more accurate than the edge detection (0.53) and region growth methods (0.42). For the second healthy volunteer, hGReS reliably segmented the pancreatic region, achieving a Dices index of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively, comparing to manual segmentation. Motion trajectories derived from the hGReS, level set and manual segmentation methods showed high correlation to respiratory motion calculated using a lung blood vessel as the reference while the other two methods showed substantial motion tracking errors. hGReS was 10 times faster than level set. Conclusion: We have shown the feasibility of automated segmentation of the pancreas anatomy based on dynamic MRI.« less

Vibration based algorithm for crack detection in cantilever beam containing two different types of cracks

NASA Astrophysics Data System (ADS)

Behzad, Mehdi; Ghadami, Amin; Maghsoodi, Ameneh; Michael Hale, Jack

2013-11-01

In this paper, a simple method for detection of multiple edge cracks in Euler-Bernoulli beams having two different types of cracks is presented based on energy equations. Each crack is modeled as a massless rotational spring using Linear Elastic Fracture Mechanics (LEFM) theory, and a relationship among natural frequencies, crack locations and stiffness of equivalent springs is demonstrated. In the procedure, for detection of m cracks in a beam, 3m equations and natural frequencies of healthy and cracked beam in two different directions are needed as input to the algorithm. The main accomplishment of the presented algorithm is the capability to detect the location, severity and type of each crack in a multi-cracked beam. Concise and simple calculations along with accuracy are other advantages of this method. A number of numerical examples for cantilever beams including one and two cracks are presented to validate the method.

Algorithm research on infrared imaging target extraction based on GAC model

NASA Astrophysics Data System (ADS)

Li, Yingchun; Fan, Youchen; Wang, Yanqing

2016-10-01

Good target detection and tracking technique is significantly meaningful to increase infrared target detection distance and enhance resolution capacity. For the target detection problem about infrared imagining, firstly, the basic principles of level set method and GAC model are is analyzed in great detail. Secondly, "convergent force" is added according to the defect that GAC model is stagnant outside the deep concave region and cannot reach deep concave edge to build the promoted GAC model. Lastly, the self-adaptive detection method in combination of Sobel operation and GAC model is put forward by combining the advantages that subject position of the target could be detected with Sobel operator and the continuous edge of the target could be obtained through GAC model. In order to verify the effectiveness of the model, the two groups of experiments are carried out by selecting the images under different noise effects. Besides, the comparative analysis is conducted with LBF and LIF models. The experimental result shows that target could be better locked through LIF and LBF algorithms for the slight noise effect. The accuracy of segmentation is above 0.8. However, as for the strong noise effect, the target and noise couldn't be distinguished under the strong interference of GAC, LIF and LBF algorithms, thus lots of non-target parts are extracted during iterative process. The accuracy of segmentation is below 0.8. The accurate target position is extracted through the algorithm proposed in this paper. Besides, the accuracy of segmentation is above 0.8.

Habitat edges have weak effects on duck nest survival at local spatial scales

USGS Publications Warehouse

Raquel, Amelia J; Ringelman, Kevin M.; Ackerman, Joshua T.; Eadie, John M.

2015-01-01

Edge effects on nesting success have been documented in breeding birds in a variety of contexts, but there is still uncertainty in how edge type and spatial scale determine the magnitude and detectability of edge effects. Habitat edges are often viewed as predator corridors that surround or penetrate core habitat and increase the risk of predation for nearby nests. We studied the effects of three different types of potential predator corridors (main perimeter roads, field boundaries, and ATV trails within fields) on waterfowl nest survival in California. We measured the distance from duck nests to the nearest edge of each type, and used distance as a covariate in a logistic exposure analysis of nest survival. We found only weak evidence for edge effects due to predation. The best supported model of nest survival included all three distance categories, and while all coefficient estimates were positive (indicating that survival increased with distance from edge), 85% coefficient confidence intervals approached or bounded zero indicating an overall weak effect of habitat edges on nest success. We suggest that given the configuration of edges at our site, there may be few areas far enough from hard edges to be considered ‘core’ habitat, making edge effects on nest survival particularly difficult to detect.

Identifying Hierarchical and Overlapping Protein Complexes Based on Essential Protein-Protein Interactions and “Seed-Expanding” Method

PubMed Central

Ren, Jun; Zhou, Wei; Wang, Jianxin

2014-01-01

Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based on λ-module and “seed-expanding.” First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to a λ-module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameter λ_th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value of λ_th. Experimental results of S. cerevisiae show that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes. PMID:25143945

Finding Statistically Significant Communities in Networks

PubMed Central

Lancichinetti, Andrea; Radicchi, Filippo; Ramasco, José J.; Fortunato, Santo

2011-01-01

Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks. PMID:21559480

Digital gene expression for non-model organisms

PubMed Central

Hong, Lewis Z.; Li, Jun; Schmidt-Küntzel, Anne; Warren, Wesley C.; Barsh, Gregory S.

2011-01-01

Next-generation sequencing technologies offer new approaches for global measurements of gene expression but are mostly limited to organisms for which a high-quality assembled reference genome sequence is available. We present a method for gene expression profiling called EDGE, or EcoP15I-tagged Digital Gene Expression, based on ultra-high-throughput sequencing of 27-bp cDNA fragments that uniquely tag the corresponding gene, thereby allowing direct quantification of transcript abundance. We show that EDGE is capable of assaying for expression in >99% of genes in the genome and achieves saturation after 6–8 million reads. EDGE exhibits very little technical noise, reveals a large (106) dynamic range of gene expression, and is particularly suited for quantification of transcript abundance in non-model organisms where a high-quality annotated genome is not available. In a direct comparison with RNA-seq, both methods provide similar assessments of relative transcript abundance, but EDGE does better at detecting gene expression differences for poorly expressed genes and does not exhibit transcript length bias. Applying EDGE to laboratory mice, we show that a loss-of-function mutation in the melanocortin 1 receptor (Mc1r), recognized as a Mendelian determinant of yellow hair color in many different mammals, also causes reduced expression of genes involved in the interferon response. To illustrate the application of EDGE to a non-model organism, we examine skin biopsy samples from a cheetah (Acinonyx jubatus) and identify genes likely to control differences in the color of spotted versus non-spotted regions. PMID:21844123

Enhancing Community Detection By Affinity-based Edge Weighting Scheme

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

Yoo, Andy; Sanders, Geoffrey; Henson, Van

Community detection refers to an important graph analytics problem of finding a set of densely-connected subgraphs in a graph and has gained a great deal of interest recently. The performance of current community detection algorithms is limited by an inherent constraint of unweighted graphs that offer very little information on their internal community structures. In this paper, we propose a new scheme to address this issue that weights the edges in a given graph based on recently proposed vertex affinity. The vertex affinity quantifies the proximity between two vertices in terms of their clustering strength, and therefore, it is idealmore » for graph analytics applications such as community detection. We also demonstrate that the affinity-based edge weighting scheme can improve the performance of community detection algorithms significantly.« less

Doppler Lidar Measurements of Tropospheric Wind Profiles Using the Aerosol Double Edge Technique

NASA Technical Reports Server (NTRS)

Gentry, Bruce M.; Li, Steven X.; Mathur, Savyasachee; Korb, C. Laurence; Chen, Huailin

2000-01-01

The development of a ground based direct detection Doppler lidar based on the recently described aerosol double edge technique is reported. A pulsed, injection seeded Nd:YAG laser operating at 1064 nm is used to make range resolved measurements of atmospheric winds in the free troposphere. The wind measurements are determined by measuring the Doppler shift of the laser signal backscattered from atmospheric aerosols. The lidar instrument and double edge method are described and initial tropospheric wind profile measurements are presented. Wind profiles are reported for both day and night operation. The measurements extend to altitudes as high as 14 km and are compared to rawinsonde wind profile data from Dulles airport in Virginia. Vertical resolution of the lidar measurements is 330 m and the rms precision of the measurements is a low as 0.6 m/s.

Edge Detection and Geometric Methods in Computer Vision,

DTIC Science & Technology

1985-02-01

enlightening discussion) Derivations or Eqs. 3.29, 3.31, 3.32 (some statistics) Experimental results (pictures)-- not very informative, extensive or useful. lie... neurophysiology and hardware design. If one views 9 the state space as a free vector space on the labels over the field of weights (which we take to be R), then

DiffSLc: A graph centrality method to detect essential proteins of a protein-protein interaction network

USDA-ARS?s Scientific Manuscript database

Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures have been helpful in identifying critical genes and proteins in biomolecular networks. The proposed centrality measure DiffSLc uses the number of interactions of a protein and gen...

Investigation of a driven fermionic system and detecting chiral edge modes in an optical lattice

NASA Astrophysics Data System (ADS)

Görg, Frederik; Messer, Michael; Jotzu, Gregor; Sandholzer, Kilian; Desbuquois, Rémi; Goldman, Nathan; Esslinger, Tilman

2017-04-01

Periodically driven systems of ultracold fermions in optical lattices allow to implement a large variety of effective Hamiltonians through Floquet engineering. An important question is whether this method can be extended to interacting systems. We investigate driven two-body systems in an array of double wells and measure the double occupancy and the spin-spin correlator in the large frequency limit and when driving resonantly to an energy scale of the underlying static Hamiltonian. We analyze whether the emerging states of the driven system can be adiabatically connected to states in the unshaken lattice. In addition, we measure the amplitude of the micromotion which describes the short time dynamics of the system and compare it directly to theory. In another context we propose a method to create topological interfaces and detect chiral edge modes in a two dimensional optical lattice. We illustrate this through an optical lattice realization of the Haldane model for cold atoms, where an additional spatially-varying lattice potential induces distinct topological phases in separated regions of space.

Multi-scales region segmentation for ROI separation in digital mammograms

NASA Astrophysics Data System (ADS)

Zhang, Dapeng; Zhang, Di; Li, Yue; Wang, Wei

2017-02-01

Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer. Segmentation is one of the key steps in the process of developing anatomical models for calculation of safe medical dose of radiation. This paper explores the potential of the statistical region merging segmentation technique for Breast segmentation in digital mammograms. First, the mammograms are pre-processing for regions enhancement, then the enhanced images are segmented using SRM with multi scales, finally these segmentations are combined for region of interest (ROI) separation and edge detection. The proposed algorithm uses multi-scales region segmentation in order to: separate breast region from background region, region edge detection and ROIs separation. The experiments are performed using a data set of mammograms from different patients, demonstrating the validity of the proposed criterion. Results show that, the statistical region merging segmentation algorithm actually can work on the segmentation of medical image and more accurate than another methods. And the outcome shows that the technique has a great potential to become a method of choice for segmentation of mammograms.

Geometric shapes inversion method of space targets by ISAR image segmentation

NASA Astrophysics Data System (ADS)

Huo, Chao-ying; Xing, Xiao-yu; Yin, Hong-cheng; Li, Chen-guang; Zeng, Xiang-yun; Xu, Gao-gui

2017-11-01

The geometric shape of target is an effective characteristic in the process of space targets recognition. This paper proposed a method of shape inversion of space target based on components segmentation from ISAR image. The Radon transformation, Hough transformation, K-means clustering, triangulation will be introduced into ISAR image processing. Firstly, we use Radon transformation and edge detection to extract space target's main body spindle and solar panel spindle from ISAR image. Then the targets' main body, solar panel, rectangular and circular antenna are segmented from ISAR image based on image detection theory. Finally, the sizes of every structural component are computed. The effectiveness of this method is verified using typical targets' simulation data.

A practical method to detect the freezing/thawing onsets of seasonal frozen ground in Alaska

NASA Astrophysics Data System (ADS)

Chen, Xiyu; Liu, Lin

2017-04-01

Microwave remote sensing can provide useful information about freeze/thaw state of soil at the Earth surface. An edge detection method is applied in this study to estimate the onsets of soil freeze/thaw state transition using L band space-borne radiometer data. The Soil Moisture Active Passive (SMAP) mission has a L band radiometer and can provide daily brightness temperature (TB) with horizontal/vertical polarizations. We use the normalized polarization ratios (NPR) calculated based on the Level-1C TB product of SMAP (spatial resolution: 36 km) as the indicator for soil freeze/thaw state, to estimate the freezing and thawing onsets in Alaska in the year of 2015 and 2016. NPR is calculated based on the difference between TB at vertical and horizontal polarizations. Therefore, it is strongly sensitive to liquid water content change in the soil and independent with the soil temperature. Onset estimation is based on the detection of abrupt changes of NPR in transition seasons using edge detection method, and the validation is to compare estimated onsets with the onsets derived from in situ measurement. According to the comparison, the estimated onsets were generally 15 days earlier than the measured onsets in 2015. However, in 2016 there were 4 days in average for the estimation earlier than the measured, which may be due to the less snow cover. Moreover, we extended our estimation to the entire state of Alaska. The estimated freeze/thaw onsets showed a reasonable latitude-dependent distribution although there are still some outliers caused by the noisy variation of NPR. At last, we also try to remove these outliers and improve the performance of the method by smoothing the NPR time series.

Full-waveform data for building roof step edge localization

NASA Astrophysics Data System (ADS)

Słota, Małgorzata

2015-08-01

Airborne laser scanning data perfectly represent flat or gently sloped areas; to date, however, accurate breakline detection is the main drawback of this technique. This issue becomes particularly important in the case of modeling buildings, where accuracy higher than the footprint size is often required. This article covers several issues related to full-waveform data registered on building step edges. First, the full-waveform data simulator was developed and presented in this paper. Second, this article provides a full description of the changes in echo amplitude, echo width and returned power caused by the presence of edges within the laser footprint. Additionally, two important properties of step edge echoes, peak shift and echo asymmetry, were noted and described. It was shown that these properties lead to incorrect echo positioning along the laser center line and can significantly reduce the edge points' accuracy. For these reasons and because all points are aligned with the center of the beam, regardless of the actual target position within the beam footprint, we can state that step edge points require geometric corrections. This article presents a novel algorithm for the refinement of step edge points. The main distinguishing advantage of the developed algorithm is the fact that none of the additional data, such as emitted signal parameters, beam divergence, approximate edge geometry or scanning settings, are required. The proposed algorithm works only on georeferenced profiles of reflected laser energy. Another major advantage is the simplicity of the calculation, allowing for very efficient data processing. Additionally, the developed method of point correction allows for the accurate determination of points lying on edges and edge point densification. For this reason, fully automatic localization of building roof step edges based on LiDAR full-waveform data with higher accuracy than the size of the lidar footprint is feasible.

Dynamical System Approach for Edge Detection Using Coupled FitzHugh-Nagumo Neurons.

PubMed

Li, Shaobai; Dasmahapatra, Srinandan; Maharatna, Koushik

2015-12-01

The prospect of emulating the impressive computational capabilities of biological systems has led to considerable interest in the design of analog circuits that are potentially implementable in very large scale integration CMOS technology and are guided by biologically motivated models. For example, simple image processing tasks, such as the detection of edges in binary and grayscale images, have been performed by networks of FitzHugh-Nagumo-type neurons using the reaction-diffusion models. However, in these studies, the one-to-one mapping of image pixels to component neurons makes the size of the network a critical factor in any such implementation. In this paper, we develop a simplified version of the employed reaction-diffusion model in three steps. In the first step, we perform a detailed study to locate this threshold using continuous Lyapunov exponents from dynamical system theory. Furthermore, we render the diffusion in the system to be anisotropic, with the degree of anisotropy being set by the gradients of grayscale values in each image. The final step involves a simplification of the model that is achieved by eliminating the terms that couple the membrane potentials of adjacent neurons. We apply our technique to detect edges in data sets of artificially generated and real images, and we demonstrate that the performance is as good if not better than that of the previous methods without increasing the size of the network.

LEDs as light source: examining quality of acquired images

NASA Astrophysics Data System (ADS)

Bachnak, Rafic; Funtanilla, Jeng; Hernandez, Jose

2004-05-01

Recent advances in technology have made light emitting diodes (LEDs) viable in a number of applications, including vehicle stoplights, traffic lights, machine-vision-inspection, illumination, and street signs. This paper presents the results of comparing images taken by a videoscope using two different light sources. One of the sources is the internal metal halide lamp and the other is a LED placed at the tip of the insertion tube. Images acquired using these two light sources were quantitatively compared using their histogram, intensity profile along a line segment, and edge detection. Also, images were qualitatively compared using image registration and transformation. The gray-level histogram, edge detection, image profile and image registration do not offer conclusive results. The LED light source, however, produces good images for visual inspection by an operator. The paper will present the results and discuss the usefulness and shortcomings of various comparison methods.

Surface Hold Advisor Using Critical Sections

NASA Technical Reports Server (NTRS)

Law, Caleb Hoi Kei (Inventor); Hsiao, Thomas Kun-Lung (Inventor); Mittler, Nathan C. (Inventor); Couluris, George J. (Inventor)

2013-01-01

The Surface Hold Advisor Using Critical Sections is a system and method for providing hold advisories to surface controllers to prevent gridlock and resolve crossing and merging conflicts among vehicles traversing a vertex-edge graph representing a surface traffic network on an airport surface. The Advisor performs pair-wise comparisons of current position and projected path of each vehicle with other surface vehicles to detect conflicts, determine critical sections, and provide hold advisories to traffic controllers recommending vehicles stop at entry points to protected zones around identified critical sections. A critical section defines a segment of the vertex-edge graph where vehicles are in crossing or merging or opposite direction gridlock contention. The Advisor detects critical sections without reference to scheduled, projected or required times along assigned vehicle paths, and generates hold advisories to prevent conflicts without requiring network path direction-of-movement rules and without requiring rerouting, rescheduling or other network optimization solutions.

On optimal infinite impulse response edge detection filters

NASA Technical Reports Server (NTRS)

Sarkar, Sudeep; Boyer, Kim L.

1991-01-01

The authors outline the design of an optimal, computationally efficient, infinite impulse response edge detection filter. The optimal filter is computed based on Canny's high signal to noise ratio, good localization criteria, and a criterion on the spurious response of the filter to noise. An expression for the width of the filter, which is appropriate for infinite-length filters, is incorporated directly in the expression for spurious responses. The three criteria are maximized using the variational method and nonlinear constrained optimization. The optimal filter parameters are tabulated for various values of the filter performance criteria. A complete methodology for implementing the optimal filter using approximating recursive digital filtering is presented. The approximating recursive digital filter is separable into two linear filters operating in two orthogonal directions. The implementation is very simple and computationally efficient, has a constant time of execution for different sizes of the operator, and is readily amenable to real-time hardware implementation.

Color edges extraction using statistical features and automatic threshold technique: application to the breast cancer cells.

PubMed

Ben Chaabane, Salim; Fnaiech, Farhat

2014-01-23

Color image segmentation has been so far applied in many areas; hence, recently many different techniques have been developed and proposed. In the medical imaging area, the image segmentation may be helpful to provide assistance to doctor in order to follow-up the disease of a certain patient from the breast cancer processed images. The main objective of this work is to rebuild and also to enhance each cell from the three component images provided by an input image. Indeed, from an initial segmentation obtained using the statistical features and histogram threshold techniques, the resulting segmentation may represent accurately the non complete and pasted cells and enhance them. This allows real help to doctors, and consequently, these cells become clear and easy to be counted. A novel method for color edges extraction based on statistical features and automatic threshold is presented. The traditional edge detector, based on the first and the second order neighborhood, describing the relationship between the current pixel and its neighbors, is extended to the statistical domain. Hence, color edges in an image are obtained by combining the statistical features and the automatic threshold techniques. Finally, on the obtained color edges with specific primitive color, a combination rule is used to integrate the edge results over the three color components. Breast cancer cell images were used to evaluate the performance of the proposed method both quantitatively and qualitatively. Hence, a visual and a numerical assessment based on the probability of correct classification (PC), the false classification (Pf), and the classification accuracy (Sens(%)) are presented and compared with existing techniques. The proposed method shows its superiority in the detection of points which really belong to the cells, and also the facility of counting the number of the processed cells. Computer simulations highlight that the proposed method substantially enhances the segmented image with smaller error rates better than other existing algorithms under the same settings (patterns and parameters). Moreover, it provides high classification accuracy, reaching the rate of 97.94%. Additionally, the segmentation method may be extended to other medical imaging types having similar properties.

Object detection via eye tracking and fringe restraint

NASA Astrophysics Data System (ADS)

Pan, Fei; Zhang, Hanming; Zeng, Ying; Tong, Li; Yan, Bin

2017-07-01

Object detection is a computer vision problem which caught a large amount of attention. But the candidate boundingboxes extracted from only image features may end up with false-detection due to the semantic gap between the top-down and the bottom up information. In this paper, we propose a novel method for generating object bounding-boxes proposals using the combination of eye fixation point, saliency detection and edges. The new method obtains a fixation orientated Gaussian map, optimizes the map through single-layer cellular automata, and derives bounding-boxes from the optimized map on three levels. Then we score the boxes by combining all the information above, and choose the box with the highest score to be the final box. We perform an evaluation of our method by comparing with previous state-ofthe art approaches on the challenging POET datasets, the images of which are chosen from PASCAL VOC 2012. Our method outperforms them on small scale objects while comparable to them in general.

Computer-assisted analysis of the vascular endothelial cell motile response to injury.

PubMed

Askey, D B; Herman, I M

1988-12-01

We have developed an automated, user-friendly method to track vascular endothelial cell migration in vitro using an IBM PC/XT with MS DOS. Analog phase-contrast images of the bovine aortic endothelial cells are converted into digital images (8 bit, 250 x 240 pixel resolution) using a Tecmar Video VanGogh A/D board. Digitized images are stored at selected time points following mechanical injury in vitro. FORTRAN and assembly language subroutines have been implemented to automatically detect the wound edge and the edge of each cell nucleus in the phase-contrast, light-microscope field. Detection of the wound edge is accomplished by intensity thresholding following noise reduction in the image and subsequent sampling of the wound. After the range of wound intensities is determined, the entire image is sampled and a histogram of intensities is formed. The histogram peak corresponding to the wound intensities is subtracted, leaving a histogram peak that gives the range of intensities corresponding to the cell nuclei. Rates of cell migration, as well as cellular trajectories and cell surface areas, can be automatically quantitated and analyzed. This inexpensive, automated cell-tracking system should be widely applicable in a variety of cell biologic applications.

Hiding Electronic Patient Record (EPR) in medical images: A high capacity and computationally efficient technique for e-healthcare applications.

PubMed

Loan, Nazir A; Parah, Shabir A; Sheikh, Javaid A; Akhoon, Jahangir A; Bhat, Ghulam M

2017-09-01

A high capacity and semi-reversible data hiding scheme based on Pixel Repetition Method (PRM) and hybrid edge detection for scalable medical images has been proposed in this paper. PRM has been used to scale up the small sized image (seed image) and hybrid edge detection ensures that no important edge information is missed. The scaled up version of seed image has been divided into 2×2 non overlapping blocks. In each block there is one seed pixel whose status decides the number of bits to be embedded in the remaining three pixels of that block. The Electronic Patient Record (EPR)/data have been embedded by using Least Significant and Intermediate Significant Bit Substitution (ISBS). The RC4 encryption has been used to add an additional security layer for embedded EPR/data. The proposed scheme has been tested for various medical and general images and compared with some state of art techniques in the field. The experimental results reveal that the proposed scheme besides being semi-reversible and computationally efficient is capable of handling high payload and as such can be used effectively for electronic healthcare applications. Copyright © 2017. Published by Elsevier Inc.

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

PubMed Central

Wang, Yuliang; Zhang, Zaicheng; Wang, Huimin; Bi, Shusheng

2015-01-01

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells. PMID:26066315

Near Real-Time Automatic Marine Vessel Detection on Optical Satellite Images

NASA Astrophysics Data System (ADS)

Máttyus, G.

2013-05-01

Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR) satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI) can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation). Optical satellite images (OSI) can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width) and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image) on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

Interface Trap Profiles in 4H- and 6H-SiC MOS Capacitors with Nitrogen- and Phosphorus-Doped Gate Oxides

NASA Astrophysics Data System (ADS)

Jiao, C.; Ahyi, A. C.; Dhar, S.; Morisette, D.; Myers-Ward, R.

2017-04-01

We report results on the interface trap density ( D it) of 4H- and 6H-SiC metal-oxide-semiconductor (MOS) capacitors with different interface chemistries. In addition to pure dry oxidation, we studied interfaces formed by annealing thermal oxides in NO or POCl3. The D it profiles, determined by the C- ψ s method, show that, although the as-oxidized 4H-SiC/SiO2 interface has a much higher D it profile than 6H-SiC/SiO2, after postoxidation annealing (POA), both polytypes maintain comparable D it near the conduction band edge for the gate oxides incorporated with nitrogen or phosphorus. Unlike most conventional C- V- or G- ω-based methods, the C- ψ s method is not limited by the maximum probe frequency, therefore taking into account the "fast traps" detected in previous work on 4H-SiC. The results indicate that such fast traps exist near the band edge of 6H-SiC also. For both polytypes, we show that the total interface trap density ( N it) integrated from the C- ψ s method is several times that obtained from the high-low method. The results suggest that the detected fast traps have a detrimental effect on electron transport in metal-oxide-semiconductor field-effect transistor (MOSFET) channels.

Container Surface Evaluation by Function Estimation

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

Wendelberger, James G.

Container images are analyzed for specific surface features, such as, pits, cracks, and corrosion. The detection of these features is confounded with complicating features. These complication features include: shape/curvature, welds, edges, scratches, foreign objects among others. A method is provided to discriminate between the various features. The method consists of estimating the image background, determining a residual image and post processing to determine the features present. The methodology is not finalized but demonstrates the feasibility of a method to determine the kind and size of the features present.

Finite-size effect on the dynamic and sensing performances of graphene resonators: the role of edge stress.

PubMed

Kim, Chang-Wan; Dai, Mai Duc; Eom, Kilho

2016-01-01

We have studied the finite-size effect on the dynamic behavior of graphene resonators and their applications in atomic mass detection using a continuum elastic model such as modified plate theory. In particular, we developed a model based on von Karman plate theory with including the edge stress, which arises from the imbalance between the coordination numbers of bulk atoms and edge atoms of graphene. It is shown that as the size of a graphene resonator decreases, the edge stress depending on the edge structure of a graphene resonator plays a critical role on both its dynamic and sensing performances. We found that the resonance behavior of graphene can be tuned not only through edge stress but also through nonlinear vibration, and that the detection sensitivity of a graphene resonator can be controlled by using the edge stress. Our study sheds light on the important role of the finite-size effect in the effective design of graphene resonators for their mass sensing applications.

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.

A novel algorithm for osteoarthritis detection in Hough domain

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Poria, Nilanjan; Chakraborty, Rajanya; Pratiher, Sawon; Mukherjee, Sukanya; Panigrahi, Prasanta K.

2018-02-01

Background subtraction of knee MRI images has been performed, followed by edge detection through canny edge detector. In order to avoid the discontinuities among edges, Daubechies-4 (Db-4) discrete wavelet transform (DWT) methodology is applied for the smoothening of edges identified through canny edge detector. The approximation coefficients of Db-4, having highest energy is selected to get rid of discontinuities in edges. Hough transform is then applied to find imperfect knee locations, as a function of distance (r) and angle (θ). The final outcome of the linear Hough transform is a two-dimensional array i.e., the accumulator space (r, θ) where one dimension of this matrix is the quantized angle θ and the other dimension is the quantized distance r. A novel algorithm has been suggested such that any deviation from the healthy knee bone structure for diseases like osteoarthritis can clearly be depicted on the accumulator space.

Unsupervised Approaches for Post-Processing in Computationally Efficient Waveform-Similarity-Based Earthquake Detection

NASA Astrophysics Data System (ADS)

Bergen, K.; Yoon, C. E.; OReilly, O. J.; Beroza, G. C.

2015-12-01

Recent improvements in computational efficiency for waveform correlation-based detections achieved by new methods such as Fingerprint and Similarity Thresholding (FAST) promise to allow large-scale blind search for similar waveforms in long-duration continuous seismic data. Waveform similarity search applied to datasets of months to years of continuous seismic data will identify significantly more events than traditional detection methods. With the anticipated increase in number of detections and associated increase in false positives, manual inspection of the detection results will become infeasible. This motivates the need for new approaches to process the output of similarity-based detection. We explore data mining techniques for improved detection post-processing. We approach this by considering similarity-detector output as a sparse similarity graph with candidate events as vertices and similarities as weighted edges. Image processing techniques are leveraged to define candidate events and combine results individually processed at multiple stations. Clustering and graph analysis methods are used to identify groups of similar waveforms and assign a confidence score to candidate detections. Anomaly detection and classification are applied to waveform data for additional false detection removal. A comparison of methods will be presented and their performance will be demonstrated on a suspected induced and non-induced earthquake sequence.

77 FR 33125 - Airworthiness Directives; Bombardier, Inc. Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-05

... along the wing leading edge and the inboard end rib of the wing leading edge due to insufficient clearance. This proposed AD would require inspecting the wire harness along the leading edge for chafing... to detect and correct chafing damage to the wire harness along the wing leading edge which, if not...

Localization and recognition of traffic signs for automated vehicle control systems

NASA Astrophysics Data System (ADS)

Zadeh, Mahmoud M.; Kasvand, T.; Suen, Ching Y.

1998-01-01

We present a computer vision system for detection and recognition of traffic signs. Such systems are required to assist drivers and for guidance and control of autonomous vehicles on roads and city streets. For experiments we use sequences of digitized photographs and off-line analysis. The system contains four stages. First, region segmentation based on color pixel classification called SRSM. SRSM limits the search to regions of interest in the scene. Second, we use edge tracing to find parts of outer edges of signs which are circular or straight, corresponding to the geometrical shapes of traffic signs. The third step is geometrical analysis of the outer edge and preliminary recognition of each candidate region, which may be a potential traffic sign. The final step in recognition uses color combinations within each region and model matching. This system maybe used for recognition of other types of objects, provided that the geometrical shape and color content remain reasonably constant. The method is reliable, easy to implement, and fast, This differs form the road signs recognition method in the PROMETEUS. The overall structure of the approach is sketched.

A Spatial-Spectral Approach for Visualization of Vegetation Stress Resulting from Pipeline Leakage.

PubMed

Van derWerff, Harald; Van der Meijde, Mark; Jansma, Fokke; Van der Meer, Freek; Groothuis, Gert Jan

2008-06-04

Hydrocarbon leakage into the environment has large economic and environmental impact. Traditional methods for investigating seepages and their resulting pollution, such as drilling, are destructive, time consuming and expensive. Remote sensing is an efficient tool that offers a non-destructive investigation method. Optical remote sensing has been extensively tested for exploration of onshore hydrocarbon reservoirs and detection of hydrocarbons at the Earth's surface. In this research, we investigate indirect manifestations of pipeline leakage by way of visualizing vegetation anomalies in airborne hyperspectral imagery. Agricultural land-use causes a heterogeneous landcover; variation in red edge position between fields was much larger than infield red edge position variation that could be related to hydrocarbon pollution. A moving and growing kernel procedure was developed to normalzie red edge values relative to values of neighbouring pixels to enhance pollution related anomalies in the image. Comparison of the spatial distribution of anomalies with geochemical data obtained by drilling showed that 8 out of 10 polluted sites were predicted correctly while 2 out of 30 sites that were predicted clean were actually polluted.

A Spatial-Spectral Approach for Visualization of Vegetation Stress Resulting from Pipeline Leakage

PubMed Central

van der Werff, Harald; van der Meijde, Mark; Jansma, Fokke; van der Meer, Freek; Groothuis, Gert Jan

2008-01-01

Hydrocarbon leakage into the environment has large economic and environmental impact. Traditional methods for investigating seepages and their resulting pollution, such as drilling, are destructive, time consuming and expensive. Remote sensing is an efficient tool that offers a non-destructive investigation method. Optical remote sensing has been extensively tested for exploration of onshore hydrocarbon reservoirs and detection of hydrocarbons at the Earth's surface. In this research, we investigate indirect manifestations of pipeline leakage by way of visualizing vegetation anomalies in airborne hyperspectral imagery. Agricultural land-use causes a heterogeneous landcover; variation in red edge position between fields was much larger than infield red edge position variation that could be related to hydrocarbon pollution. A moving and growing kernel procedure was developed to normalzie red edge values relative to values of neighbouring pixels to enhance pollution related anomalies in the image. Comparison of the spatial distribution of anomalies with geochemical data obtained by drilling showed that 8 out of 10 polluted sites were predicted correctly while 2 out of 30 sites that were predicted clean were actually polluted. PMID:27879905

Natural and artificial spectral edges in exoplanets

NASA Astrophysics Data System (ADS)

Lingam, Manasvi; Loeb, Abraham

2017-09-01

Technological civilizations may rely upon large-scale photovoltaic arrays to harness energy from their host star. Photovoltaic materials, such as silicon, possess distinctive spectral features, including an 'artificial edge' that is characteristically shifted in wavelength shortwards of the 'red edge' of vegetation. Future observations of reflected light from exoplanets would be able to detect both natural and artificial edges photometrically, if a significant fraction of the planet's surface is covered by vegetation or photovoltaic arrays, respectively. The stellar energy thus tapped can be utilized for terraforming activities by transferring heat and light from the day side to the night side on tidally locked exoplanets, thereby producing detectable artefacts.

Detection of fractional solitons in quantum spin Hall systems

NASA Astrophysics Data System (ADS)

Fleckenstein, C.; Traverso Ziani, N.; Trauzettel, B.

2018-03-01

We propose two experimental setups that allow for the implementation and the detection of fractional solitons of the Goldstone-Wilczek type. The first setup is based on two magnetic barriers at the edge of a quantum spin Hall system for generating the fractional soliton. If then a quantum point contact is created with the other edge, the linear conductance shows evidence of the fractional soliton. The second setup consists of a single magnetic barrier covering both edges and implementing a long quantum point contact. In this case, the fractional soliton can unambiguously be detected as a dip in the conductance without the need to control the magnetization of the barrier.

Hypervelocity Impact (HVI). Volume 1; General Introduction

NASA Technical Reports Server (NTRS)

Gorman, Michael R.; Ziola, Steven M.

2007-01-01

During 2003 and 2004, the Johnson Space Center's White Sands Testing Facility in Las Cruces, New Mexico conducted hypervelocity impact tests on the space shuttle wing leading edge. Hypervelocity impact tests were conducted to determine if Micro-Meteoroid/Orbital Debris impacts could be reliably detected and located using simple passive ultrasonic methods. This volume contains an executive summary, overview of the method, brief descriptions of all targets, and highlights of results and conclusions.

Comparative Study of Speckle Filtering Methods in PolSAR Radar Images

NASA Astrophysics Data System (ADS)

Boutarfa, S.; Bouchemakh, L.; Smara, Y.

2015-04-01

Images acquired by polarimetric SAR (PolSAR) radar systems are characterized by the presence of a noise called speckle. This noise has a multiplicative nature, corrupts both the amplitude and phase images, which complicates data interpretation, degrades segmentation performance and reduces the detectability of targets. Hence, the need to preprocess the images by adapted filtering methods before analysis.In this paper, we present a comparative study of implemented methods for reducing speckle in PolSAR images. These developed filters are: refined Lee filter based on the estimation of the minimum mean square error MMSE, improved Sigma filter with detection of strong scatterers based on the calculation of the coherency matrix to detect the different scatterers in order to preserve the polarization signature and maintain structures that are necessary for image interpretation, filtering by stationary wavelet transform SWT using multi-scale edge detection and the technique for improving the wavelet coefficients called SSC (sum of squared coefficients), and Turbo filter which is a combination between two complementary filters the refined Lee filter and the wavelet transform SWT. One filter can boost up the results of the other.The originality of our work is based on the application of these methods to several types of images: amplitude, intensity and complex, from a satellite or an airborne radar, and on the optimization of wavelet filtering by adding a parameter in the calculation of the threshold. This parameter will control the filtering effect and get a good compromise between smoothing homogeneous areas and preserving linear structures.The methods are applied to the fully polarimetric RADARSAT-2 images (HH, HV, VH, VV) acquired on Algiers, Algeria, in C-band and to the three polarimetric E-SAR images (HH, HV, VV) acquired on Oberpfaffenhofen area located in Munich, Germany, in P-band.To evaluate the performance of each filter, we used the following criteria: smoothing homogeneous areas, preserving edges and polarimetric information.Experimental results are included to illustrate the different implemented methods.

Detection of molecular particles in live cells via machine learning.

PubMed

Jiang, Shan; Zhou, Xiaobo; Kirchhausen, Tom; Wong, Stephen T C

2007-08-01

Clathrin-coated pits play an important role in removing proteins and lipids from the plasma membrane and transporting them to the endosomal compartment. It is, however, still unclear whether there exist "hot spots" for the formation of Clathrin-coated pits or the pits and arrays formed randomly on the plasma membrane. To answer this question, first of all, many hundreds of individual pits need to be detected accurately and separated in live-cell microscope movies to capture and monitor how pits and vesicles were formed. Because of the noisy background and the low contrast of the live-cell movies, the existing image analysis methods, such as single threshold, edge detection, and morphological operation, cannot be used. Thus, this paper proposes a machine learning method, which is based on Haar features, to detect the particle's position. Results show that this method can successfully detect most of particles in the image. In order to get the accurate boundaries of these particles, several post-processing methods are applied and signal-to-noise ratio analysis is also performed to rule out the weak spots. Copyright 2007 International Society for Analytical Cytology.

A spectral method to detect community structure based on distance modularity matrix

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

There are many community organizations in social and biological networks. How to identify these community structure in complex networks has become a hot issue. In this paper, an algorithm to detect community structure of networks is proposed by using spectra of distance modularity matrix. The proposed algorithm focuses on the distance of vertices within communities, rather than the most weakly connected vertex pairs or number of edges between communities. The experimental results show that our method achieves better effectiveness to identify community structure for a variety of real-world networks and computer generated networks with a little more time-consumption.

Generic Sensor Modeling Using Pulse Method

NASA Technical Reports Server (NTRS)

Helder, Dennis L.; Choi, Taeyoung

2005-01-01

Recent development of high spatial resolution satellites such as IKONOS, Quickbird and Orbview enable observation of the Earth's surface with sub-meter resolution. Compared to the 30 meter resolution of Landsat 5 TM, the amount of information in the output image was dramatically increased. In this era of high spatial resolution, the estimation of spatial quality of images is gaining attention. Historically, the Modulation Transfer Function (MTF) concept has been used to estimate an imaging system's spatial quality. Sometimes classified by target shapes, various methods were developed in laboratory environment utilizing sinusoidal inputs, periodic bar patterns and narrow slits. On-orbit sensor MTF estimation was performed on 30-meter GSD Landsat4 Thematic Mapper (TM) data from the bridge pulse target as a pulse input . Because of a high resolution sensor s small Ground Sampling Distance (GSD), reasonably sized man-made edge, pulse, and impulse targets can be deployed on a uniform grassy area with accurate control of ground targets using tarps and convex mirrors. All the previous work cited calculated MTF without testing the MTF estimator's performance. In previous report, a numerical generic sensor model had been developed to simulate and improve the performance of on-orbit MTF estimating techniques. Results from the previous sensor modeling report that have been incorporated into standard MTF estimation work include Fermi edge detection and the newly developed 4th order modified Savitzky-Golay (MSG) interpolation technique. Noise sensitivity had been studied by performing simulations on known noise sources and a sensor model. Extensive investigation was done to characterize multi-resolution ground noise. Finally, angle simulation was tested by using synthetic pulse targets with angles from 2 to 15 degrees, several brightness levels, and different noise levels from both ground targets and imaging system. As a continuing research activity using the developed sensor model, this report was dedicated to MTF estimation via pulse input method characterization using the Fermi edge detection and 4th order MSG interpolation method. The relationship between pulse width and MTF value at Nyquist was studied including error detection and correction schemes. Pulse target angle sensitivity was studied by using synthetic targets angled from 2 to 12 degrees. In this report, from the ground and system noise simulation, a minimum SNR value was suggested for a stable MTF value at Nyquist for the pulse method. Target width error detection and adjustment technique based on a smooth transition of MTF profile is presented, which is specifically applicable only to the pulse method with 3 pixel wide targets.

A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor.

PubMed

Kim, Ki Wan; Hong, Hyung Gil; Nam, Gi Pyo; Park, Kang Ryoung

2017-06-30

The necessity for the classification of open and closed eyes is increasing in various fields, including analysis of eye fatigue in 3D TVs, analysis of the psychological states of test subjects, and eye status tracking-based driver drowsiness detection. Previous studies have used various methods to distinguish between open and closed eyes, such as classifiers based on the features obtained from image binarization, edge operators, or texture analysis. However, when it comes to eye images with different lighting conditions and resolutions, it can be difficult to find an optimal threshold for image binarization or optimal filters for edge and texture extraction. In order to address this issue, we propose a method to classify open and closed eye images with different conditions, acquired by a visible light camera, using a deep residual convolutional neural network. After conducting performance analysis on both self-collected and open databases, we have determined that the classification accuracy of the proposed method is superior to that of existing methods.

Algorithm for Automated Detection of Edges of Clouds

NASA Technical Reports Server (NTRS)

Ward, Jennifer G.; Merceret, Francis J.

2006-01-01

An algorithm processes cloud-physics data gathered in situ by an aircraft, along with reflectivity data gathered by ground-based radar, to determine whether the aircraft is inside or outside a cloud at a given time. A cloud edge is deemed to be detected when the in/out state changes, subject to a hysteresis constraint. Such determinations are important in continuing research on relationships among lightning, electric charges in clouds, and decay of electric fields with distance from cloud edges.

Optical Assessment of Soft Contact Lens Edge-Thickness

PubMed Central

Tankam, Patrice; Won, Jungeun; Canavesi, Cristina; Cox, Ian; Rolland, Jannick P.

2016-01-01

Purpose To assess the edge shape of soft contact lenses using Gabor-Domain Optical Coherence Microscopy (GD-OCM) with a 2 μm imaging resolution in three dimensions, and to generate edge-thickness profiles at different distances from the edge tip of soft contact lenses. Methods A high-speed custom-designed GD-OCM system was used to produce 3D images of the edge of an experimental soft contact lens (Bausch + Lomb, Rochester NY) in four different configurations: in air, submerged into water, submerged into saline with contrast agent, and placed onto the cornea of a porcine eyeball. An algorithm to compute the edge-thickness was developed and applied to cross-sectional images. The proposed algorithm includes the accurate detection of the interfaces between the lens and the environment, and the correction of the refraction error. Results The sharply defined edge tip of a soft contact lens was visualized in 3D. Results showed precise thickness measurement of the contact lens edge profile. 50 cross-sectional image frames for each configuration were used to test the robustness of the algorithm in evaluating the edge-thickness at any distance from the edge tip. The precision of the measurements was less than 0.2 μm. Conclusions The results confirmed the ability of GD-OCM to provide high definition images of soft contact lens edges. As a non-destructive, precise, and fast metrology tool for soft contact lens measurement, the integration of GD-OCM in the design and manufacturing of contact lenses will be beneficial for further improvement in edge design and quality control. In the clinical perspective, the in-vivo evaluation of the lens fitted onto the cornea will advance our understanding of how the edge interacts with the ocular surface. The latter will provide insights into the impact of long-term use of contact lenses on the visual performance. PMID:27232902

Edge-based correlation image registration for multispectral imaging

DOEpatents

Nandy, Prabal [Albuquerque, NM

2009-11-17

Registration information for images of a common target obtained from a plurality of different spectral bands can be obtained by combining edge detection and phase correlation. The images are edge-filtered, and pairs of the edge-filtered images are then phase correlated to produce phase correlation images. The registration information can be determined based on these phase correlation images.

Modified slanted-edge method for camera modulation transfer function measurement using nonuniform fast Fourier transform technique

NASA Astrophysics Data System (ADS)

Duan, Yaxuan; Xu, Songbo; Yuan, Suochao; Chen, Yongquan; Li, Hongguang; Da, Zhengshang; Gao, Limin

2018-01-01

ISO 12233 slanted-edge method experiences errors using fast Fourier transform (FFT) in the camera modulation transfer function (MTF) measurement due to tilt angle errors in the knife-edge resulting in nonuniform sampling of the edge spread function (ESF). In order to resolve this problem, a modified slanted-edge method using nonuniform fast Fourier transform (NUFFT) for camera MTF measurement is proposed. Theoretical simulations for images with noise at a different nonuniform sampling rate of ESF are performed using the proposed modified slanted-edge method. It is shown that the proposed method successfully eliminates the error due to the nonuniform sampling of the ESF. An experimental setup for camera MTF measurement is established to verify the accuracy of the proposed method. The experiment results show that under different nonuniform sampling rates of ESF, the proposed modified slanted-edge method has improved accuracy for the camera MTF measurement compared to the ISO 12233 slanted-edge method.

Vision-based method for detecting driver drowsiness and distraction in driver monitoring system

NASA Astrophysics Data System (ADS)

Jo, Jaeik; Lee, Sung Joo; Jung, Ho Gi; Park, Kang Ryoung; Kim, Jaihie

2011-12-01

Most driver-monitoring systems have attempted to detect either driver drowsiness or distraction, although both factors should be considered for accident prevention. Therefore, we propose a new driver-monitoring method considering both factors. We make the following contributions. First, if the driver is looking ahead, drowsiness detection is performed; otherwise, distraction detection is performed. Thus, the computational cost and eye-detection error can be reduced. Second, we propose a new eye-detection algorithm that combines adaptive boosting, adaptive template matching, and blob detection with eye validation, thereby reducing the eye-detection error and processing time significantly, which is hardly achievable using a single method. Third, to enhance eye-detection accuracy, eye validation is applied after initial eye detection, using a support vector machine based on appearance features obtained by principal component analysis (PCA) and linear discriminant analysis (LDA). Fourth, we propose a novel eye state-detection algorithm that combines appearance features obtained using PCA and LDA, with statistical features such as the sparseness and kurtosis of the histogram from the horizontal edge image of the eye. Experimental results showed that the detection accuracies of the eye region and eye states were 99 and 97%, respectively. Both driver drowsiness and distraction were detected with a success rate of 98%.

A synthetic genetic edge detection program.

PubMed

Tabor, Jeffrey J; Salis, Howard M; Simpson, Zachary Booth; Chevalier, Aaron A; Levskaya, Anselm; Marcotte, Edward M; Voigt, Christopher A; Ellington, Andrew D

2009-06-26

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

A Synthetic Genetic Edge Detection Program

PubMed Central

Tabor, Jeffrey J.; Salis, Howard; Simpson, Zachary B.; Chevalier, Aaron A.; Levskaya, Anselm; Marcotte, Edward M.; Voigt, Christopher A.; Ellington, Andrew D.

2009-01-01

Summary Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks. PMID:19563759

Quantitative Detection of Cracks in Steel Using Eddy Current Pulsed Thermography.

PubMed

Shi, Zhanqun; Xu, Xiaoyu; Ma, Jiaojiao; Zhen, Dong; Zhang, Hao

2018-04-02

Small cracks are common defects in steel and often lead to catastrophic accidents in industrial applications. Various nondestructive testing methods have been investigated for crack detection; however, most current methods focus on qualitative crack identification and image processing. In this study, eddy current pulsed thermography (ECPT) was applied for quantitative crack detection based on derivative analysis of temperature variation. The effects of the incentive parameters on the temperature variation were analyzed in the simulation study. The crack profile and position are identified in the thermal image based on the Canny edge detection algorithm. Then, one or more trajectories are determined through the crack profile in order to determine the crack boundary through its temperature distribution. The slope curve along the trajectory is obtained. Finally, quantitative analysis of the crack sizes was performed by analyzing the features of the slope curves. The experimental verification showed that the crack sizes could be quantitatively detected with errors of less than 1%. Therefore, the proposed ECPT method was demonstrated to be a feasible and effective nondestructive approach for quantitative crack detection.

[A research in speech endpoint detection based on boxes-coupling generalization dimension].

PubMed

Wang, Zimei; Yang, Cuirong; Wu, Wei; Fan, Yingle

2008-06-01

In this paper, a new calculating method of generalized dimension, based on boxes-coupling principle, is proposed to overcome the edge effects and to improve the capability of the speech endpoint detection which is based on the original calculating method of generalized dimension. This new method has been applied to speech endpoint detection. Firstly, the length of overlapping border was determined, and through calculating the generalized dimension by covering the speech signal with overlapped boxes, three-dimension feature vectors including the box dimension, the information dimension and the correlation dimension were obtained. Secondly, in the light of the relation between feature distance and similarity degree, feature extraction was conducted by use of common distance. Lastly, bi-threshold method was used to classify the speech signals. The results of experiment indicated that, by comparison with the original generalized dimension (OGD) and the spectral entropy (SE) algorithm, the proposed method is more robust and effective for detecting the speech signals which contain different kinds of noise in different signal noise ratio (SNR), especially in low SNR.

Single-scan patient-specific scatter correction in computed tomography using peripheral detection of scatter and compressed sensing scatter retrieval

PubMed Central

Meng, Bowen; Lee, Ho; Xing, Lei; Fahimian, Benjamin P.

2013-01-01

Purpose: X-ray scatter results in a significant degradation of image quality in computed tomography (CT), representing a major limitation in cone-beam CT (CBCT) and large field-of-view diagnostic scanners. In this work, a novel scatter estimation and correction technique is proposed that utilizes peripheral detection of scatter during the patient scan to simultaneously acquire image and patient-specific scatter information in a single scan, and in conjunction with a proposed compressed sensing scatter recovery technique to reconstruct and correct for the patient-specific scatter in the projection space. Methods: The method consists of the detection of patient scatter at the edges of the field of view (FOV) followed by measurement based compressed sensing recovery of the scatter through-out the projection space. In the prototype implementation, the kV x-ray source of the Varian TrueBeam OBI system was blocked at the edges of the projection FOV, and the image detector in the corresponding blocked region was used for scatter detection. The design enables image data acquisition of the projection data on the unblocked central region of and scatter data at the blocked boundary regions. For the initial scatter estimation on the central FOV, a prior consisting of a hybrid scatter model that combines the scatter interpolation method and scatter convolution model is estimated using the acquired scatter distribution on boundary region. With the hybrid scatter estimation model, compressed sensing optimization is performed to generate the scatter map by penalizing the L1 norm of the discrete cosine transform of scatter signal. The estimated scatter is subtracted from the projection data by soft-tuning, and the scatter-corrected CBCT volume is obtained by the conventional Feldkamp-Davis-Kress algorithm. Experimental studies using image quality and anthropomorphic phantoms on a Varian TrueBeam system were carried out to evaluate the performance of the proposed scheme. Results: The scatter shading artifacts were markedly suppressed in the reconstructed images using the proposed method. On the Catphan©504 phantom, the proposed method reduced the error of CT number to 13 Hounsfield units, 10% of that without scatter correction, and increased the image contrast by a factor of 2 in high-contrast regions. On the anthropomorphic phantom, the spatial nonuniformity decreased from 10.8% to 6.8% after correction. Conclusions: A novel scatter correction method, enabling unobstructed acquisition of the high frequency image data and concurrent detection of the patient-specific low frequency scatter data at the edges of the FOV, is proposed and validated in this work. Relative to blocker based techniques, rather than obstructing the central portion of the FOV which degrades and limits the image reconstruction, compressed sensing is used to solve for the scatter from detection of scatter at the periphery of the FOV, enabling for the highest quality reconstruction in the central region and robust patient-specific scatter correction. PMID:23298098

Markov random field model-based edge-directed image interpolation.

PubMed

Li, Min; Nguyen, Truong Q

2008-07-01

This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the interpolated image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired interpolated image corresponds to the minimal energy state of a 2-D random field given the low-resolution image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the interpolated edges while maintaining a high PSNR level.

Spatial vision processes: From the optical image to the symbolic structures of contour information

NASA Technical Reports Server (NTRS)

Jobson, Daniel J.

1988-01-01

The significance of machine and natural vision is discussed together with the need for a general approach to image acquisition and processing aimed at recognition. An exploratory scheme is proposed which encompasses the definition of spatial primitives, intrinsic image properties and sampling, 2-D edge detection at the smallest scale, the construction of spatial primitives from edges, and the isolation of contour information from textural information. Concepts drawn from or suggested by natural vision at both perceptual and physiological levels are relied upon heavily to guide the development of the overall scheme. The scheme is intended to provide a larger context in which to place the emerging technology of detector array focal-plane processors. The approach differs from many recent efforts in edge detection and image coding by emphasizing smallest scale edge detection as a foundation for multi-scale symbolic processing while diminishing somewhat the importance of image convolutions with multi-scale edge operators. Cursory treatments of information theory illustrate that the direct application of this theory to structural information in images could not be realized.

Forest edges: Effects on vegetation, environmental gradients and local avian communities in the Sierra Juarez, Oaxaca, Mexico

NASA Astrophysics Data System (ADS)

Burcsu, Theresa Katherine

Edge effects are among the most serious threats to forest integrity because as global forest cover decreases overall, forest edge influence increases proportionally, driving habitat change and loss. Edge effects occur at the division between adjacent habitat types. Our understanding of edge effects comes mainly from tropical wet, temperate and boreal forests. Because forest structure in moisture-limited forests differs from wetter forest types, edge dynamics are likely to differ as well. Moreover, dry forests in the tropics have been nearly eliminated or exist only as forest fragments, making edge influence an important conservation and management concern for remaining dry forests. This study addresses this gap in the edge influence knowledge by examining created, regenerating edges associated with forest management in a seasonally dry pine-oak forest of Oaxaca, creating a new data point in edge effects research. In this study I used Landsat TM imagery and a modified semivariance analysis to estimate the distance of edge influence for vegetation. I also used field methods to characterize forest structure and estimate edge influence on canopy and subcanopy vegetation. To finalize the project I extended the study to bird assemblages to identify responses and habitat preferences to local-scale changes associated with regenerating edges created by group-selection timber harvest. Remote sensing analysis estimated that the distance of edge influence was 30-90 m from the edge. Vegetation analysis suggested that edge effects were weak relative to wetter forest types and that remote sensing data did not provide an estimate that was directly applicable to field-measured vegetative edge effects. The bird assemblages likewise responded weakly to habitat change associated with edge effect. Open canopy structure, simple vertical stratigraphy, and topographic variation create forest conditions in which small openings do not create a high contrast to undisturbed forest. Thus, in this seasonally dry, open forest, vegetation and bird communities respond less to small openings than they do in wetter, more closed-canopy forests. Management practices and historical land-use interact and interfere with the detectability of edge influence in our study area. These results support hypotheses proposed for open forest types and suggest that patterns in edge influence in wet forest types may not be applicable to dry sites.

Content modification attacks on consensus seeking multi-agent system with double-integrator dynamics.

PubMed

Dong, Yimeng; Gupta, Nirupam; Chopra, Nikhil

2016-11-01

In this paper, vulnerability of a distributed consensus seeking multi-agent system (MAS) with double-integrator dynamics against edge-bound content modification cyber attacks is studied. In particular, we define a specific edge-bound content modification cyber attack called malignant content modification attack (MCoMA), which results in unbounded growth of an appropriately defined group disagreement vector. Properties of MCoMA are utilized to design detection and mitigation algorithms so as to impart resilience in the considered MAS against MCoMA. Additionally, the proposed detection mechanism is extended to detect the general edge-bound content modification attacks (not just MCoMA). Finally, the efficacies of the proposed results are illustrated through numerical simulations.

Content modification attacks on consensus seeking multi-agent system with double-integrator dynamics

NASA Astrophysics Data System (ADS)

Dong, Yimeng; Gupta, Nirupam; Chopra, Nikhil

2016-11-01

In this paper, vulnerability of a distributed consensus seeking multi-agent system (MAS) with double-integrator dynamics against edge-bound content modification cyber attacks is studied. In particular, we define a specific edge-bound content modification cyber attack called malignant content modification attack (MCoMA), which results in unbounded growth of an appropriately defined group disagreement vector. Properties of MCoMA are utilized to design detection and mitigation algorithms so as to impart resilience in the considered MAS against MCoMA. Additionally, the proposed detection mechanism is extended to detect the general edge-bound content modification attacks (not just MCoMA). Finally, the efficacies of the proposed results are illustrated through numerical simulations.

An approach for traffic prohibition sign detection

NASA Astrophysics Data System (ADS)

Li, Qingquan; Xu, Dihong; Li, Bijun; Zeng, Zhe

2006-10-01

This paper presents an off-line traffic prohibition sign detection approach, whose core is based on combination with the color feature of traffic prohibition signs, shape feature and degree of circularity. Matlab-Image-processing toolbox is used for this purpose. In order to reduce the computational cost, a pre-processing of the image is applied before the core. Then, we employ the obvious redness attribute of prohibition signs to coarsely eliminate the non-redness image in the input data. Again, a edge-detection operator, Canny edge detector, is applied to extract the potential edge. Finally, Degree of circularity is used to verdict the traffic prohibition sign. Experimental results show that our systems offer satisfactory performance.

Linear array optical edge sensor

NASA Technical Reports Server (NTRS)

Bejczy, Antal K. (Inventor); Primus, Howard C. (Inventor)

1987-01-01

A series of independent parallel pairs of light emitting and detecting diodes for a linear pixel array, which is laterally positioned over an edge-like discontinuity in a workpiece to be scanned, is disclosed. These independent pairs of light emitters and detectors sense along intersecting pairs of separate optical axes. A discontinuity, such as an edge in the sensed workpiece, reflects a detectable difference in the amount of light from that discontinuity in comparison to the amount of light that is reflected on either side of the discontinuity. A sequentially sychronized clamping and sampling circuit detects that difference as an electrical signal which is recovered by circuitry that exhibits an improved signal-to-noise capability for the system.

[Comparison of red edge parameters of winter wheat canopy under late frost stress].

PubMed

Wu, Yong-feng; Hu, Xin; Lü, Guo-hua; Ren, De-chao; Jiang, Wei-guo; Song, Ji-qing

2014-08-01

In the present study, late frost experiments were implemented under a range of subfreezing temperatures (-1 - -9 degrees C) by using a field movable climate chamber (FMCC) and a cold climate chamber, respectively. Based on the spectra of winter wheat canopy measured at noon on the first day after the frost experiments, red edge parameters REP, Dr, SDr, Dr(min), Dr/Dr(min) and Dr/SDr were extracted using maximum first derivative spectrum method (FD), linear four-point interpolation method (FPI), polynomial fitting method (POLY), inverted Gaussian fitting method (IG) and linear extrapolation technique (LE), respectively. The capacity of the red edge parameters to detect late frost stress was explicated from the aspects of the early, sensitivity and stability through correlation analysis, linear regression modeling and fluctuation analysis. The result indicates that except for REP calculated from FPI and IG method in Experiment 1, REP from the other methods was correlated with frost temperatures (P < 0.05). Thereinto, significant levels (P) of POLY and LE methods all reached 0.01. Except for POLY method in Experiment 2, Dr/SDr from the other methods were all significantly correlated with frost temperatures (P < 0.01). REP showed a trend to shift to short-wave band with decreasing temperatures. The lower the temperature, the more obvious the trend is. Of all the REP, REP calculated by LE method had the highest correlation with frost temperatures which indicated that LE method is the best for REP extraction. In Experiment 1 and 2, only Dr(min) and Dr/Dr(min), calculated by FD method simultaneously achieved the requirements for the early (their correlations with frost temperatures showed a significant level P < 0.01), sensitivity (abso- lute value of the slope of fluctuation coefficient is greater than 2.0) and stability (their correlations with frost temperatures al- ways keep a consistent direction). Dr/SDr calculated from FD and IG methods always had a low sensitivity in Experiment 2. In Experiment 1, the sensitivity of Dr/SDr from FD was moderate and IG was high. REP calculated from LE method had a lowest sensitivity in the two experiments. Totally, Dr(min) and Dr/Dr(min) calculated by FD method have the strongest detection capacity for frost temperature, which will be helpful to conducting the research on early diagnosis of late frost injury to winter wheat.

Segmenting breast cancerous regions in thermal images using fuzzy active contours

PubMed Central

Ghayoumi Zadeh, Hossein; Haddadnia, Javad; Rahmani Seryasat, Omid; Mostafavi Isfahani, Sayed Mohammad

2016-01-01

Breast cancer is the main cause of death among young women in developing countries. The human body temperature carries critical medical information related to the overall body status. Abnormal rise in total and regional body temperature is a natural symptom in diagnosing many diseases. Thermal imaging (Thermography) utilizes infrared beams which are fast, non-invasive, and non-contact and the output created images by this technique are flexible and useful to monitor the temperature of the human body. In some clinical studies and biopsy tests, it is necessary for the clinician to know the extent of the cancerous area. In such cases, the thermal image is very useful. In the same line, to detect the cancerous tissue core, thermal imaging is beneficial. This paper presents a fully automated approach to detect the thermal edge and core of the cancerous area in thermography images. In order to evaluate the proposed method, 60 patients with an average age of 44/9 were chosen. These cases were suspected of breast tissue disease. These patients referred to Tehran Imam Khomeini Imaging Center. Clinical examinations such as ultrasound, biopsy, questionnaire, and eventually thermography were done precisely on these individuals. Finally, the proposed model is applied for segmenting the proved abnormal area in thermal images. The proposed model is based on a fuzzy active contour designed by fuzzy logic. The presented method can segment cancerous tissue areas from its borders in thermal images of the breast area. In order to evaluate the proposed algorithm, Hausdorff and mean distance between manual and automatic method were used. Estimation of distance was conducted to accurately separate the thermal core and edge. Hausdorff distance between the proposed and the manual method for thermal core and edge was 0.4719 ± 0.4389, 0.3171 ± 0.1056 mm respectively, and the average distance between the proposed and the manual method for core and thermal edge was 0.0845 ± 0.0619, 0.0710 ± 0.0381 mm respectively. Furthermore, the sensitivity in recognizing the thermal pattern in breast tissue masses is 85 % and its accuracy is 91.98 %.A thermal imaging system has been proposed that is able to recognize abnormal breast tissue masses. This system utilizes fuzzy active contours to extract the abnormal regions automatically. PMID:28096784

Active edge maps for medical image registration

NASA Astrophysics Data System (ADS)

Kerwin, William; Yuan, Chun

2001-07-01

Applying edge detection prior to performing image registration yields several advantages over raw intensity- based registration. Advantages include the ability to register multicontrast or multimodality images, immunity to intensity variations, and the potential for computationally efficient algorithms. In this work, a common framework for edge-based image registration is formulated as an adaptation of snakes used in boundary detection. Called active edge maps, the new formulation finds a one-to-one transformation T(x) that maps points in a source image to corresponding locations in a target image using an energy minimization approach. The energy consists of an image component that is small when edge features are well matched in the two images, and an internal term that restricts T(x) to allowable configurations. The active edge map formulation is illustrated here with a specific example developed for affine registration of carotid artery magnetic resonance images. In this example, edges are identified using a magnitude of gradient operator, image energy is determined using a Gaussian weighted distance function, and the internal energy includes separate, adjustable components that control volume preservation and rigidity.

Non-destructive testing of composite materials used in military applications by eddy current thermography method

NASA Astrophysics Data System (ADS)

Swiderski, Waldemar

2016-10-01

Eddy current thermography is a new NDT-technique for the detection of cracks in electro conductive materials. It combines the well-established inspection techniques of eddy current testing and thermography. The technique uses induced eddy currents to heat the sample being tested and defect detection is based on the changes of induced eddy currents flows revealed by thermal visualization captured by an infrared camera. The advantage of this method is to use the high performance of eddy current testing that eliminates the known problem of the edge effect. Especially for components of complex geometry this is an important factor which may overcome the increased expense for inspection set-up. The paper presents the possibility of applying eddy current thermography method for detecting defects in ballistic covers made of carbon fiber reinforced composites used in the construction of military vehicles.

Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications

PubMed Central

Wang, Tao; Zheng, Nanning; Xin, Jingmin; Ma, Zheng

2011-01-01

This paper presents a systematic scheme for fusing millimeter wave (MMW) radar and a monocular vision sensor for on-road obstacle detection. As a whole, a three-level fusion strategy based on visual attention mechanism and driver’s visual consciousness is provided for MMW radar and monocular vision fusion so as to obtain better comprehensive performance. Then an experimental method for radar-vision point alignment for easy operation with no reflection intensity of radar and special tool requirements is put forward. Furthermore, a region searching approach for potential target detection is derived in order to decrease the image processing time. An adaptive thresholding algorithm based on a new understanding of shadows in the image is adopted for obstacle detection, and edge detection is used to assist in determining the boundary of obstacles. The proposed fusion approach is verified through real experimental examples of on-road vehicle/pedestrian detection. In the end, the experimental results show that the proposed method is simple and feasible. PMID:22164117

Integrating millimeter wave radar with a monocular vision sensor for on-road obstacle detection applications.

PubMed

Wang, Tao; Zheng, Nanning; Xin, Jingmin; Ma, Zheng

2011-01-01

This paper presents a systematic scheme for fusing millimeter wave (MMW) radar and a monocular vision sensor for on-road obstacle detection. As a whole, a three-level fusion strategy based on visual attention mechanism and driver's visual consciousness is provided for MMW radar and monocular vision fusion so as to obtain better comprehensive performance. Then an experimental method for radar-vision point alignment for easy operation with no reflection intensity of radar and special tool requirements is put forward. Furthermore, a region searching approach for potential target detection is derived in order to decrease the image processing time. An adaptive thresholding algorithm based on a new understanding of shadows in the image is adopted for obstacle detection, and edge detection is used to assist in determining the boundary of obstacles. The proposed fusion approach is verified through real experimental examples of on-road vehicle/pedestrian detection. In the end, the experimental results show that the proposed method is simple and feasible.

An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture.

PubMed

Zhang, Xiaopu; Lin, Jun; Chen, Zubin; Sun, Feng; Zhu, Xi; Fang, Gengfa

2018-06-05

Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor signal-to-noise ratio (SNR). The other one is concerned with real-time data transmission. Taking these challenges into consideration, an edge-computing-based platform, namely Edge-to-Center LearnReduce, is presented in this work. The platform consists of a data center with many edge components. At the data center, a neural network model combined with convolutional neural network (CNN) and long short-term memory (LSTM) is designed and this model is trained by using previously obtained data. Once the model is fully trained, it is sent to edge components for events detection and data reduction. At each edge component, a probabilistic inference is added to the neural network model to improve its accuracy. Finally, the reduced data is delivered to the data center. Based on experiment results, a high detection accuracy (over 96%) with less transmitted data (about 90%) was achieved by using the proposed approach on a microseismic monitoring system. These results show that the platform can simultaneously improve the accuracy and efficiency of microseismic monitoring.

Strain energy release rate as a function of temperature and preloading history utilizing the edge delamination fatique test method

NASA Technical Reports Server (NTRS)

Zimmerman, Richard S.; Adams, Donald F.

1989-01-01

Static laminate and tension-tension fatigue tests of IM7/8551-7 composite materials was performed. The Edge Delamination Test (EDT) was utilized to evaluate the temperature and preloading history effect on the critical strain energy release rate. Static and fatigue testing was performed at room temperature and 180 F (82 C). Three preloading schemes were used to precondition fatigue test specimens prior to performing the normal tension-tension fatigue EDT testing. Computer software was written to perform all fatigue testing while monitoring the dynamic modulus to detect the onset of delamination and record the test information for later retrieval and reduction.

Energy calibration of organic scintillation detectors for. gamma. rays

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

Gu Jiahui; Xiao Genlai; Liu Jingyi

1988-10-01

An experimental method of calibrating organic detectors is described. A NaI(T1) detector has some advantages of high detection efficiency, good energy resolution, and definite position of the back-scattering peak. The precise position of the Compton edge can be determined by coincidence measurement between the pulse of an organic scintillation detector and the pulse of the back-scattering peak from NaI(T1) detector. It can be used to calibrate various sizes and shapes of organic scintillation detectors simply and reliably. The home-made plastic and organic liquid scintillation detectors are calibrated and positions of the Compton edge as a function of ..gamma..-ray energies aremore » obtained.« less

Reproducibility of ECG-gated ultrasound diameter assessment of small abdominal aortic aneurysms.

PubMed

Bredahl, K; Eldrup, N; Meyer, C; Eiberg, J E; Sillesen, H

2013-03-01

No standardised ultrasound procedure to obtain reliable growth estimates for abdominal aortic aneurysms (AAA) is currently available. We investigated the feasibility and reproducibility of a novel approach controlling for a combination of vessel wall delineation and cardiac cycle variation. Prospective comparative study. Consecutive patients (N = 27) with an AAA, attending their 6-month control as part of a medical treatment trial, were scanned twice by two ultrasound operators. Then, all ultrasound recordings were transferred to a core facility and analysed by a third person. The AAA diameter was determined in four different ways: from the leading edge of adventitia on the anterior wall to either the leading edge of the adventitia (method A) or leading edge of the intima (method B) on the posterior wall, with both measurements performed in systole and diastole. Inter-operator reproducibility was ± 3 mm for all methods applied. There was no difference in outcome between methods A and B; likewise, end-diastolic measurement did not improve reproducibility in preference to peak-systolic measurement. The use of a standardised ultrasound protocol including ECG-gating and subsequent off-line reading with minute calliper placement reduces variability. This may be of use in developing protocols to better detect even small AAA growth rates during clinical trials. Copyright © 2012 European Society for Vascular Surgery. Published by Elsevier Ltd. All rights reserved.

Deferred slanted-edge analysis: a unified approach to spatial frequency response measurement on distorted images and color filter array subsets.

PubMed

van den Bergh, F

2018-03-01

The slanted-edge method of spatial frequency response (SFR) measurement is usually applied to grayscale images under the assumption that any distortion of the expected straight edge is negligible. By decoupling the edge orientation and position estimation step from the edge spread function construction step, it is shown in this paper that the slanted-edge method can be extended to allow it to be applied to images suffering from significant geometric distortion, such as produced by equiangular fisheye lenses. This same decoupling also allows the slanted-edge method to be applied directly to Bayer-mosaicked images so that the SFR of the color filter array subsets can be measured directly without the unwanted influence of demosaicking artifacts. Numerical simulation results are presented to demonstrate the efficacy of the proposed deferred slanted-edge method in relation to existing methods.

A threshold selection method based on edge preserving

NASA Astrophysics Data System (ADS)

Lou, Liantang; Dan, Wei; Chen, Jiaqi

2015-12-01

A method of automatic threshold selection for image segmentation is presented. An optimal threshold is selected in order to preserve edge of image perfectly in image segmentation. The shortcoming of Otsu's method based on gray-level histograms is analyzed. The edge energy function of bivariate continuous function is expressed as the line integral while the edge energy function of image is simulated by discretizing the integral. An optimal threshold method by maximizing the edge energy function is given. Several experimental results are also presented to compare with the Otsu's method.

High resolution energy-sensitive digital X-ray

DOEpatents

Nygren, D.R.

1995-07-18

An apparatus and method for detecting an x-ray and for determining the depth of penetration of an x-ray into a semiconductor strip detector. In one embodiment, a semiconductor strip detector formed of semiconductor material is disposed in an edge-on orientation towards an x-ray source such that x-rays from the x-ray source are incident upon and substantially perpendicular to the front edge of the semiconductor strip detector. The semiconductor strip detector is formed of a plurality of segments. The segments are coupled together in a collinear arrangement such that the semiconductor strip detector has a length great enough such that substantially all of the x-rays incident on the front edge of the semiconductor strip detector interact with the semiconductor material which forms the semiconductor strip detector. A plurality of electrodes are connected to the semiconductor strip detector such that each one of the semiconductor strip detector segments has at least one of the of electrodes coupled thereto. A signal processor is also coupled to each one of the electrodes. The present detector detects an interaction within the semiconductor strip detector, between an x-ray and the semiconductor material, and also indicates the depth of penetration of the x-ray into the semiconductor strip detector at the time of the interaction. 5 figs.

Sampled-data consensus in switching networks of integrators based on edge events

NASA Astrophysics Data System (ADS)

Xiao, Feng; Meng, Xiangyu; Chen, Tongwen

2015-02-01

This paper investigates the event-driven sampled-data consensus in switching networks of multiple integrators and studies both the bidirectional interaction and leader-following passive reaction topologies in a unified framework. In these topologies, each information link is modelled by an edge of the information graph and assigned a sequence of edge events, which activate the mutual data sampling and controller updates of the two linked agents. Two kinds of edge-event-detecting rules are proposed for the general asynchronous data-sampling case and the synchronous periodic event-detecting case. They are implemented in a distributed fashion, and their effectiveness in reducing communication costs and solving consensus problems under a jointly connected topology condition is shown by both theoretical analysis and simulation examples.

Three-dimensional modeling of tea-shoots using images and models.

PubMed

Wang, Jian; Zeng, Xianyin; Liu, Jianbing

2011-01-01

In this paper, a method for three-dimensional modeling of tea-shoots with images and calculation models is introduced. The process is as follows: the tea shoots are photographed with a camera, color space conversion is conducted, using an improved algorithm that is based on color and regional growth to divide the tea shoots in the images, and the edges of the tea shoots extracted with the help of edge detection; after that, using the divided tea-shoot images, the three-dimensional coordinates of the tea shoots are worked out and the feature parameters extracted, matching and calculation conducted according to the model database, and finally the three-dimensional modeling of tea-shoots is completed. According to the experimental results, this method can avoid a lot of calculations and has better visual effects and, moreover, performs better in recovering the three-dimensional information of the tea shoots, thereby providing a new method for monitoring the growth of and non-destructive testing of tea shoots.

Edge theory approach to topological entanglement entropy, mutual information, and entanglement negativity in Chern-Simons theories

NASA Astrophysics Data System (ADS)

Wen, Xueda; Matsuura, Shunji; Ryu, Shinsei

2016-06-01

We develop an approach based on edge theories to calculate the entanglement entropy and related quantities in (2+1)-dimensional topologically ordered phases. Our approach is complementary to, e.g., the existing methods using replica trick and Witten's method of surgery, and applies to a generic spatial manifold of genus g , which can be bipartitioned in an arbitrary way. The effects of fusion and braiding of Wilson lines can be also straightforwardly studied within our framework. By considering a generic superposition of states with different Wilson line configurations, through an interference effect, we can detect, by the entanglement entropy, the topological data of Chern-Simons theories, e.g., the R symbols, monodromy, and topological spins of quasiparticles. Furthermore, by using our method, we calculate other entanglement/correlation measures such as the mutual information and the entanglement negativity. In particular, it is found that the entanglement negativity of two adjacent noncontractible regions on a torus provides a simple way to distinguish Abelian and non-Abelian topological orders.

Real-time edge tracking using a tactile sensor

NASA Technical Reports Server (NTRS)

Berger, Alan D.; Volpe, Richard; Khosla, Pradeep K.

1989-01-01

Object recognition through the use of input from multiple sensors is an important aspect of an autonomous manipulation system. In tactile object recognition, it is necessary to determine the location and orientation of object edges and surfaces. A controller is proposed that utilizes a tactile sensor in the feedback loop of a manipulator to track along edges. In the control system, the data from the tactile sensor is first processed to find edges. The parameters of these edges are then used to generate a control signal to a hybrid controller. Theory is presented for tactile edge detection and an edge tracking controller. In addition, experimental verification of the edge tracking controller is presented.

The application of wavelet denoising in material discrimination system

NASA Astrophysics Data System (ADS)

Fu, Kenneth; Ranta, Dale; Guest, Clark; Das, Pankaj

2010-01-01

Recently, the need for cargo inspection imaging systems to provide a material discrimination function has become desirable. This is done by scanning the cargo container with x-rays at two different energy levels. The ratio of attenuations of the two energy scans can provide information on the composition of the material. However, with the statistical error from noise, the accuracy of such systems can be low. Because the moving source emits two energies of x-rays alternately, images from the two scans will not be identical. That means edges of objects in the two images are not perfectly aligned. Moreover, digitization creates blurry-edge artifacts. Different energy x-rays produce different edge spread functions. Those combined effects contribute to a source of false classification namely, the "edge effect." Other types of false classification are caused by noise, mainly Poisson noise associated with photons. The Poisson noise in xray images can be dealt with using either a Wiener filter or a wavelet shrinkage denoising approach. In this paper, we propose a method that uses the wavelet shrinkage denoising approach to enhance the performance of the material identification system. Test results show that this wavelet-based approach has improved performance in object detection and eliminating false positives due to the edge effects.

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%.

LANDSAT-D investigations in snow hydrology

NASA Technical Reports Server (NTRS)

Dozier, J.

1983-01-01

Progress on the registration of TM data to digital topographic data; on comparison of TM, MSS and NOAA meteorological satellite data for snowcover mapping; and on radiative transfer models for atmospheric correction is reported. Some methods for analyzing spatial contiguity of snow within the snow covered area were selected. The methods are based on a two-channel version of the grey level co-occurence matrix, combined with edge detection derived from an algorithm for computing slopes and exposures from digital terrain data.

78 FR 52419 - Airworthiness Directives; The Boeing Company Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-23

... trailing edge flap area that qualify as structural significant items (SSIs). This AD requires revising the... detect and correct fatigue cracking of the wing trailing edge structure, which could result in... within the wing trailing edge flap area that qualify as structural significant items (SSI). We are...

Analysis of coke beverages by total-reflection X-ray fluorescence

NASA Astrophysics Data System (ADS)

Fernández-Ruiz, Ramón; von Bohlen, Alex; Friedrich K, E. Josue; Redrejo, M. J.

2018-07-01

The influence of the organic content, sample preparation process and the morphology of the depositions of two types of Coke beverage, traditional and light Coke, have been investigated by mean of Total-reflection X-ray Fluorescence (TXRF) spectrometry. Strong distortions of the nominal concentration values, up to 128% for P, have been detected in the analysis of traditional Coke by different preparation methods. These differences have been correlated with the edge X-ray energies of the elements analyzed being more pronounced for the lighter elements. The influence of the organic content (mainly sugar) was evaluated comparing traditional and light Coke analytical TXRF results. Three sample preparation methods have been evaluated as follows: direct TXRF analysis of the sample only adding internal standard, TXRF analysis after open vessel acid digestion and TXRF analysis after high pressure and temperature microwave-assisted acid digestion. Strong correlations were detected between quantitative results, methods of preparation and energies of the X-ray absorption edges of quantified elements. In this way, a decay behavior for the concentration differences between preparation methods and the energies of the X-ray absorption edges of each element were observed. The observed behaviors were modeled with exponential decay functions obtaining R2 correlation coefficients from 0.989 to 0.992. The strong absorption effect observed, and even possible matrix effect, can be explained by the inherent high organic content of the evaluated samples and also by the morphology and average thickness of the TXRF depositions observed. As main conclusion of this work, the analysis of light elements in samples with high organic content by TXRF, i.e. medical, biological, food or any other organic matrixes should be taken carefully. In any case, the direct analysis is not recommended and a previous microwave-assisted acid digestion, or similar, is mandatory, for the correct elemental quantification by TXRF.

Information theoretic analysis of linear shift-invariant edge-detection operators

NASA Astrophysics Data System (ADS)

Jiang, Bo; Rahman, Zia-ur

2012-06-01

Generally, the designs of digital image processing algorithms and image gathering devices remain separate. Consequently, the performance of digital image processing algorithms is evaluated without taking into account the influences by the image gathering process. However, experiments show that the image gathering process has a profound impact on the performance of digital image processing and the quality of the resulting images. Huck et al. proposed one definitive theoretic analysis of visual communication channels, where the different parts, such as image gathering, processing, and display, are assessed in an integrated manner using Shannon's information theory. We perform an end-to-end information theory based system analysis to assess linear shift-invariant edge-detection algorithms. We evaluate the performance of the different algorithms as a function of the characteristics of the scene and the parameters, such as sampling, additive noise etc., that define the image gathering system. The edge-detection algorithm is regarded as having high performance only if the information rate from the scene to the edge image approaches its maximum possible. This goal can be achieved only by jointly optimizing all processes. Our information-theoretic assessment provides a new tool that allows us to compare different linear shift-invariant edge detectors in a common environment.

Image processing techniques for noise removal, enhancement and segmentation of cartilage OCT images

NASA Astrophysics Data System (ADS)

Rogowska, Jadwiga; Brezinski, Mark E.

2002-02-01

Osteoarthritis, whose hallmark is the progressive loss of joint cartilage, is a major cause of morbidity worldwide. Recently, optical coherence tomography (OCT) has demonstrated considerable promise for the assessment of articular cartilage. Among the most important parameters to be assessed is cartilage width. However, detection of the bone cartilage interface is critical for the assessment of cartilage width. At present, the quantitative evaluations of cartilage thickness are being done using manual tracing of cartilage-bone borders. Since data is being obtained near video rate with OCT, automated identification of the bone-cartilage interface is critical. In order to automate the process of boundary detection on OCT images, there is a need for developing new image processing techniques. In this paper we describe the image processing techniques for speckle removal, image enhancement and segmentation of cartilage OCT images. In particular, this paper focuses on rabbit cartilage since this is an important animal model for testing both chondroprotective agents and cartilage repair techniques. In this study, a variety of techniques were examined. Ultimately, by combining an adaptive filtering technique with edge detection (vertical gradient, Sobel edge detection), cartilage edges can be detected. The procedure requires several steps and can be automated. Once the cartilage edges are outlined, the cartilage thickness can be measured.

Edge Modeling by Two Blur Parameters in Varying Contrasts.

PubMed

Seo, Suyoung

2018-06-01

This paper presents a method of modeling edge profiles with two blur parameters, and estimating and predicting those edge parameters with varying brightness combinations and camera-to-object distances (COD). First, the validity of the edge model is proven mathematically. Then, it is proven experimentally with edges from a set of images captured for specifically designed target sheets and with edges from natural images. Estimation of the two blur parameters for each observed edge profile is performed with a brute-force method to find parameters that produce global minimum errors. Then, using the estimated blur parameters, actual blur parameters of edges with arbitrary brightness combinations are predicted using a surface interpolation method (i.e., kriging). The predicted surfaces show that the two blur parameters of the proposed edge model depend on both dark-side edge brightness and light-side edge brightness following a certain global trend. This is similar across varying CODs. The proposed edge model is compared with a one-blur parameter edge model using experiments of the root mean squared error for fitting the edge models to each observed edge profile. The comparison results suggest that the proposed edge model has superiority over the one-blur parameter edge model in most cases where edges have varying brightness combinations.

A method to design blended rolled edges for compact range reflectors

NASA Technical Reports Server (NTRS)

Gupta, Inder J.; Burnside, Walter D.

1989-01-01

A method to design blended rolled edges for arbitrary rim shape compact range reflectors is presented. The reflectors may be center-fed or offset-fed. The method leads to rolled edges with minimal surface discontinuities. It is shown that the reflectors designed using the prescribed method can be defined analytically using simple expressions. A procedure to obtain optimum rolled edges parameter is also presented. The procedure leads to blended rolled edges that minimize the diffracted fields emanating from the junction between the paraboloid and the rolled edge surface while satisfying certain constraints regarding the reflector size and the minimum operating frequency of the system.

A method to design blended rolled edges for compact range reflectors

NASA Technical Reports Server (NTRS)

Gupta, Inder J.; Ericksen, Kurt P.; Burnside, Walter D.

1990-01-01

A method to design blended rolled edges for arbitrary rim shape compact range reflectors is presented. The reflectors may be center-fed or offset-fed. The method leads to rolled edges with minimal surface discontinuities. It is shown that the reflectors designed using the prescribed method can be defined analytically using simple expressions. A procedure to obtain optimum rolled edges parameters is also presented. The procedure leads to blended rolled edges that minimize the diffracted fields emanating from the junction between the paraboloid and the rolled edge surface while satisfying certain constraints regarding the reflector size and the minimum operating frequency of the system.

Autonomous rock detection on mars through region contrast

NASA Astrophysics Data System (ADS)

Xiao, Xueming; Cui, Hutao; Yao, Meibao; Tian, Yang

2017-08-01

In this paper, we present a new autonomous rock detection approach through region contrast. Unlike current state-of-art pixel-level rock segmenting methods, new method deals with this issue in region level, which will significantly reduce the computational cost. Image is firstly splitted into homogeneous regions based on intensity information and spatial layout. Considering the high-water memory constraints of onboard flight processor, only low-level features, average intensity and variation of superpixel, are measured. Region contrast is derived as the integration of intensity contrast and smoothness measurement. Rocks are then segmented from the resulting contrast map by an adaptive threshold. Since the merely intensity-based method may cause false detection in background areas with different illuminations from surroundings, a more reliable method is further proposed by introducing spatial factor and background similarity to the region contrast. Spatial factor demonstrates the locality of contrast, while background similarity calculates the probability of each subregion belonging to background. Our method is efficient in dealing with large images and only few parameters are needed. Preliminary experimental results show that our algorithm outperforms edge-based methods in various grayscale rover images.

Structural Information Detection Based Filter for GF-3 SAR Images

NASA Astrophysics Data System (ADS)

Sun, Z.; Song, Y.

2018-04-01

GF-3 satellite with high resolution, large swath, multi-imaging mode, long service life and other characteristics, can achieve allweather and all day monitoring for global land and ocean. It has become the highest resolution satellite system in the world with the C-band multi-polarized synthetic aperture radar (SAR) satellite. However, due to the coherent imaging system, speckle appears in GF-3 SAR images, and it hinders the understanding and interpretation of images seriously. Therefore, the processing of SAR images has big challenges owing to the appearance of speckle. The high-resolution SAR images produced by the GF-3 satellite are rich in information and have obvious feature structures such as points, edges, lines and so on. The traditional filters such as Lee filter and Gamma MAP filter are not appropriate for the GF-3 SAR images since they ignore the structural information of images. In this paper, the structural information detection based filter is constructed, successively including the point target detection in the smallest window, the adaptive windowing method based on regional characteristics, and the most homogeneous sub-window selection. The despeckling experiments on GF-3 SAR images demonstrate that compared with the traditional filters, the proposed structural information detection based filter can well preserve the points, edges and lines as well as smooth the speckle more sufficiently.

Salient object detection: manifold-based similarity adaptation approach

NASA Astrophysics Data System (ADS)

Zhou, Jingbo; Ren, Yongfeng; Yan, Yunyang; Gao, Shangbing

2014-11-01

A saliency detection algorithm based on manifold-based similarity adaptation is proposed. The proposed algorithm is divided into three steps. First, we segment an input image into superpixels, which are represented as the nodes in a graph. Second, a new similarity measurement is used in the proposed algorithm. The weight matrix of the graph, which indicates the similarities between the nodes, uses a similarity-based method. It also captures the manifold structure of the image patches, in which the graph edges are determined in a data adaptive manner in terms of both similarity and manifold structure. Then, we use local reconstruction method as a diffusion method to obtain the saliency maps. The objective function in the proposed method is based on local reconstruction, with which estimated weights capture the manifold structure. Experiments on four bench-mark databases demonstrate the accuracy and robustness of the proposed method.

Joint detection of anatomical points on surface meshes and color images for visual registration of 3D dental models

NASA Astrophysics Data System (ADS)

Destrez, Raphaël.; Albouy-Kissi, Benjamin; Treuillet, Sylvie; Lucas, Yves

2015-04-01

Computer aided planning for orthodontic treatment requires knowing occlusion of separately scanned dental casts. A visual guided registration is conducted starting by extracting corresponding features in both photographs and 3D scans. To achieve this, dental neck and occlusion surface are firstly extracted by image segmentation and 3D curvature analysis. Then, an iterative registration process is conducted during which feature positions are refined, guided by previously found anatomic edges. The occlusal edge image detection is improved by an original algorithm which follows Canny's poorly detected edges using a priori knowledge of tooth shapes. Finally, the influence of feature extraction and position optimization is evaluated in terms of the quality of the induced registration. Best combination of feature detection and optimization leads to a positioning average error of 1.10 mm and 2.03°.

Segmentation of malignant lesions in 3D breast ultrasound using a depth-dependent model.

PubMed

Tan, Tao; Gubern-Mérida, Albert; Borelli, Cristina; Manniesing, Rashindra; van Zelst, Jan; Wang, Lei; Zhang, Wei; Platel, Bram; Mann, Ritse M; Karssemeijer, Nico

2016-07-01

Automated 3D breast ultrasound (ABUS) has been proposed as a complementary screening modality to mammography for early detection of breast cancers. To facilitate the interpretation of ABUS images, automated diagnosis and detection techniques are being developed, in which malignant lesion segmentation plays an important role. However, automated segmentation of cancer in ABUS is challenging since lesion edges might not be well defined. In this study, the authors aim at developing an automated segmentation method for malignant lesions in ABUS that is robust to ill-defined cancer edges and posterior shadowing. A segmentation method using depth-guided dynamic programming based on spiral scanning is proposed. The method automatically adjusts aggressiveness of the segmentation according to the position of the voxels relative to the lesion center. Segmentation is more aggressive in the upper part of the lesion (close to the transducer) than at the bottom (far away from the transducer), where posterior shadowing is usually visible. The authors used Dice similarity coefficient (Dice) for evaluation. The proposed method is compared to existing state of the art approaches such as graph cut, level set, and smart opening and an existing dynamic programming method without depth dependence. In a dataset of 78 cancers, our proposed segmentation method achieved a mean Dice of 0.73 ± 0.14. The method outperforms an existing dynamic programming method (0.70 ± 0.16) on this task (p = 0.03) and it is also significantly (p < 0.001) better than graph cut (0.66 ± 0.18), level set based approach (0.63 ± 0.20) and smart opening (0.65 ± 0.12). The proposed depth-guided dynamic programming method achieves accurate breast malignant lesion segmentation results in automated breast ultrasound.

Border-oriented post-processing refinement on detected vehicle bounding box for ADAS

NASA Astrophysics Data System (ADS)

Chen, Xinyuan; Zhang, Zhaoning; Li, Minne; Li, Dongsheng

2018-04-01

We investigate a new approach for improving localization accuracy of detected vehicles for object detection in advanced driver assistance systems(ADAS). Specifically, we implement a bounding box refinement as a post-processing of the state-of-the-art object detectors (Faster R-CNN, YOLOv2, etc.). The bounding box refinement is achieved by individually adjusting each border of the detected bounding box to its target location using a regression method. We use HOG features which perform well on the edge detection of vehicles to train the regressor and the regressor is independent of the CNN-based object detectors. Experiment results on the KITTI 2012 benchmark show that we can achieve up to 6% improvements over YOLOv2 and Faster R-CNN object detectors on the IoU threshold of 0.8. Also, the proposed refinement framework is computationally light, allowing for processing one bounding box within a few milliseconds on CPU. Further, this refinement method can be added to any object detectors, especially those with high speed but less accuracy.

Application of Morphological Segmentation to Leaking Defect Detection in Sewer Pipelines

PubMed Central

Su, Tung-Ching; Yang, Ming-Der

2014-01-01

As one of major underground pipelines, sewerage is an important infrastructure in any modern city. The most common problem occurring in sewerage is leaking, whose position and failure level is typically idengified through closed circuit television (CCTV) inspection in order to facilitate rehabilitation process. This paper proposes a novel method of computer vision, morphological segmentation based on edge detection (MSED), to assist inspectors in detecting pipeline defects in CCTV inspection images. In addition to MSED, other mathematical morphology-based image segmentation methods, including opening top-hat operation (OTHO) and closing bottom-hat operation (CBHO), were also applied to the defect detection in vitrified clay sewer pipelines. The CCTV inspection images of the sewer system in the 9th district, Taichung City, Taiwan were selected as the experimental materials. The segmentation results demonstrate that MSED and OTHO are useful for the detection of cracks and open joints, respectively, which are the typical leakage defects found in sewer pipelines. PMID:24841247

Segmentation and classification of brain images using firefly and hybrid kernel-based support vector machine

NASA Astrophysics Data System (ADS)

Selva Bhuvaneswari, K.; Geetha, P.

2017-05-01

Magnetic resonance imaging segmentation refers to a process of assigning labels to set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. In recent years, various brain tumour detection techniques are presented in the literature. The entire segmentation process of our proposed work comprises three phases: threshold generation with dynamic modified region growing phase, texture feature generation phase and region merging phase. by dynamically changing two thresholds in the modified region growing approach, the first phase of the given input image can be performed as dynamic modified region growing process, in which the optimisation algorithm, firefly algorithm help to optimise the two thresholds in modified region growing. After obtaining the region growth segmented image using modified region growing, the edges can be detected with edge detection algorithm. In the second phase, the texture feature can be extracted using entropy-based operation from the input image. In region merging phase, the results obtained from the texture feature-generation phase are combined with the results of dynamic modified region growing phase and similar regions are merged using a distance comparison between regions. After identifying the abnormal tissues, the classification can be done by hybrid kernel-based SVM (Support Vector Machine). The performance analysis of the proposed method will be carried by K-cross fold validation method. The proposed method will be implemented in MATLAB with various images.

Hough transform as a tool support building roof detection. (Polish Title: Transformata Hough'a jako narzędzie wspomagające wykrywanie dachów budynków)

NASA Astrophysics Data System (ADS)

Borowiec, N.

2013-12-01

Gathering information about the roof shapes of the buildings is still current issue. One of the many sources from which we can obtain information about the buildings is the airborne laser scanning. However, detect information from cloud o points about roofs of building automatically is still a complex task. You can perform this task by helping the additional information from other sources, or based only on Lidar data. This article describes how to detect the building roof only from a point cloud. To define the shape of the roof is carried out in three tasks. The first step is to find the location of the building, the second is the precise definition of the edge, while the third is an indication of the roof planes. First step based on the grid analyses. And the next two task based on Hough Transformation. Hough transformation is a method of detecting collinear points, so a perfect match to determine the line describing a roof. To properly determine the shape of the roof is not enough only the edges, but it is necessary to indicate roofs. Thus, in studies Hough Transform, also served as a tool for detection of roof planes. The only difference is that the tool used in this case is a three-dimensional.

An Automated Cloud-edge Detection Algorithm Using Cloud Physics and Radar Data

NASA Technical Reports Server (NTRS)

Ward, Jennifer G.; Merceret, Francis J.; Grainger, Cedric A.

2003-01-01

An automated cloud edge detection algorithm was developed and extensively tested. The algorithm uses in-situ cloud physics data measured by a research aircraft coupled with ground-based weather radar measurements to determine whether the aircraft is in or out of cloud. Cloud edges are determined when the in/out state changes, subject to a hysteresis constraint. The hysteresis constraint prevents isolated transient cloud puffs or data dropouts from being identified as cloud boundaries. The algorithm was verified by detailed manual examination of the data set in comparison to the results from application of the automated algorithm.

High Speed Edge Detection

NASA Technical Reports Server (NTRS)

Prokop, Norman F (Inventor)

2016-01-01

Analog circuits for detecting edges in pixel arrays are disclosed. A comparator may be configured to receive an all pass signal and a low pass signal for a pixel intensity in an array of pixels. A latch may be configured to receive a counter signal and a latching signal from the comparator. The comparator may be configured to send the latching signal to the latch when the all pass signal is below the low pass signal minus an offset. The latch may be configured to hold a last negative edge location when the latching signal is received from the comparator.

High Speed Edge Detection

NASA Technical Reports Server (NTRS)

Prokop, Norman F (Inventor)

2015-01-01

Analog circuits for detecting edges in pixel arrays are disclosed. A comparator may be configured to receive an all pass signal and a low pass signal for a pixel intensity in an array of pixels. A latch may be configured to receive a counter signal and a latching signal from the comparator. The comparator may be configured to send the latching signal to the latch when the all pass signal is below the low pass signal minus an offset. The latch may be configured to hold a last negative edge location when the latching signal is received from the comparator.

A detection method for X-ray images based on wavelet transforms: the case of the ROSAT PSPC.

NASA Astrophysics Data System (ADS)

Damiani, F.; Maggio, A.; Micela, G.; Sciortino, S.

1996-02-01

The authors have developed a method based on wavelet transforms (WT) to detect efficiently sources in PSPC X-ray images. The multiscale approach typical of WT can be used to detect sources with a large range of sizes, and to estimate their size and count rate. Significance thresholds for candidate detections (found as local WT maxima) have been derived from a detailed study of the probability distribution of the WT of a locally uniform background. The use of the exposure map allows good detection efficiency to be retained even near PSPC ribs and edges. The algorithm may also be used to get upper limits to the count rate of undetected objects. Simulations of realistic PSPC images containing either pure background or background+sources were used to test the overall algorithm performances, and to assess the frequency of spurious detections (vs. detection threshold) and the algorithm sensitivity. Actual PSPC images of galaxies and star clusters show the algorithm to have good performance even in cases of extended sources and crowded fields.

A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor

PubMed Central

Kim, Ki Wan; Hong, Hyung Gil; Nam, Gi Pyo; Park, Kang Ryoung

2017-01-01

The necessity for the classification of open and closed eyes is increasing in various fields, including analysis of eye fatigue in 3D TVs, analysis of the psychological states of test subjects, and eye status tracking-based driver drowsiness detection. Previous studies have used various methods to distinguish between open and closed eyes, such as classifiers based on the features obtained from image binarization, edge operators, or texture analysis. However, when it comes to eye images with different lighting conditions and resolutions, it can be difficult to find an optimal threshold for image binarization or optimal filters for edge and texture extraction. In order to address this issue, we propose a method to classify open and closed eye images with different conditions, acquired by a visible light camera, using a deep residual convolutional neural network. After conducting performance analysis on both self-collected and open databases, we have determined that the classification accuracy of the proposed method is superior to that of existing methods. PMID:28665361

Error of the slanted edge method for measuring the modulation transfer function of imaging systems.

PubMed

Xie, Xufen; Fan, Hongda; Wang, Hongyuan; Wang, Zebin; Zou, Nianyu

2018-03-01

The slanted edge method is a basic approach for measuring the modulation transfer function (MTF) of imaging systems; however, its measurement accuracy is limited in practice. Theoretical analysis of the slanted edge MTF measurement method performed in this paper reveals that inappropriate edge angles and random noise reduce this accuracy. The error caused by edge angles is analyzed using sampling and reconstruction theory. Furthermore, an error model combining noise and edge angles is proposed. We verify the analyses and model with respect to (i) the edge angle, (ii) a statistical analysis of the measurement error, (iii) the full width at half-maximum of a point spread function, and (iv) the error model. The experimental results verify the theoretical findings. This research can be referential for applications of the slanted edge MTF measurement method.

Visualization of retinal vascular structure and perfusion with a nonconfocal adaptive optics scanning light ophthalmoscope

PubMed Central

Sulai, Yusufu N.; Scoles, Drew; Harvey, Zachary; Dubra, Alfredo

2015-01-01

Imaging of the retinal vascular structure and perfusion was explored by confocal illumination and nonconfocal detection in an adaptive optics scanning light ophthalmoscope (AOSLO), as an extension of the work by Chui et al. [Biomed. Opt. Express 3, 2537 (2012)]. Five different detection schemes were evaluated at multiple retinal locations: circular mask, annular mask, circular mask with filament, knife-edge, and split-detector. Given the superior image contrast in the reflectance and perfusion maps, the split-detection method was further tested using pupil apodization, polarized detection, and four different wavelengths. None of these variations provided noticeable contrast improvement. The noninvasive visualization of capillary flow and structure provided by AOSLO split-detection shows great promise for studying ocular and systemic conditions that affect the retinal vasculature. PMID:24690655

A novel method about detecting missing holes on the motor carling

NASA Astrophysics Data System (ADS)

Xu, Hongsheng; Tan, Hao; Li, Guirong

2018-03-01

After a deep analysis on how to use an image processing system to detect the missing holes on the motor carling, we design the whole system combined with the actual production conditions of the motor carling. Afterwards we explain the whole system's hardware and software in detail. We introduce the general functions for the system's hardware and software. Analyzed these general functions, we discuss the modules of the system's hardware and software and the theory to design these modules in detail. The measurement to confirm the area to image processing, edge detection, randomized Hough transform to circle detecting is explained in detail. Finally, the system result tested in the laboratory and in the factory is given out.

A hybrid approach for text detection in natural scenes

NASA Astrophysics Data System (ADS)

Wang, Runmin; Sang, Nong; Wang, Ruolin; Kuang, Xiaoqin

2013-10-01

In this paper, a hybrid approach is proposed to detect texts in natural scenes. It is performed by the following steps: Firstly, the edge map and the text saliency region are obtained. Secondly, the text candidate regions are detected by connected components (CC) based method and are identified by an off-line trained HOG classifier. And then, the remaining CCs are grouped into text lines with some heuristic strategies to make up for the false negatives. Finally, the text lines are broken into separate words. The performance of the proposed approach is evaluated on the location detection database of ICDAR 2003 robust reading competition. Experimental results demonstrate the validity of our approach and are competitive with other state-of-the-art algorithms.

On the possibility to detect multipolar order in URu 2 Si 2 by the electric quadrupolar transition of resonant elastic x-ray scattering

DOE PAGES

Wang, Y. L.; Fabbris, G.; Meyers, D.; ...

2017-08-30

Resonant elastic x-ray scattering is a powerful technique for measuring multipolar order parameters. In this paper, we theoretically and experimentally study the possibility of using this technique to detect the proposed multipolar order parameters in URu 2 Si 2 at the U- L 3 edge with the electric quadrupolar transition. Based on an atomic model, we calculate the azimuthal dependence of the quadrupolar transition at the U- L 3 edge. Our results illustrate the potential of this technique for distinguishing different multipolar order parameters. We then perform experiments on ultraclean single crystals of URu 2 Si 2 at the U-more » L 3 edge to search for the predicted signal, but do not detect any indications of multipolar moments within the experimental uncertainty. We also theoretically estimate the orders of magnitude of the cross section and the expected count rate of the quadrupolar transition and compare them to the dipolar transitions at the U- M 4 and U- L 3 edges, clarifying the difficulty in detecting higher order multipolar order parameters in URu 2 Si 2 in the current experimental setup.« less

Contour-Based Corner Detection and Classification by Using Mean Projection Transform

PubMed Central

Kahaki, Seyed Mostafa Mousavi; Nordin, Md Jan; Ashtari, Amir Hossein

2014-01-01

Image corner detection is a fundamental task in computer vision. Many applications require reliable detectors to accurately detect corner points, commonly achieved by using image contour information. The curvature definition is sensitive to local variation and edge aliasing, and available smoothing methods are not sufficient to address these problems properly. Hence, we propose Mean Projection Transform (MPT) as a corner classifier and parabolic fit approximation to form a robust detector. The first step is to extract corner candidates using MPT based on the integral properties of the local contours in both the horizontal and vertical directions. Then, an approximation of the parabolic fit is calculated to localize the candidate corner points. The proposed method presents fewer false-positive (FP) and false-negative (FN) points compared with recent standard corner detection techniques, especially in comparison with curvature scale space (CSS) methods. Moreover, a new evaluation metric, called accuracy of repeatability (AR), is introduced. AR combines repeatability and the localization error (Le) for finding the probability of correct detection in the target image. The output results exhibit better repeatability, localization, and AR for the detected points compared with the criteria in original and transformed images. PMID:24590354

Contour-based corner detection and classification by using mean projection transform.

PubMed

Kahaki, Seyed Mostafa Mousavi; Nordin, Md Jan; Ashtari, Amir Hossein

2014-02-28

Image corner detection is a fundamental task in computer vision. Many applications require reliable detectors to accurately detect corner points, commonly achieved by using image contour information. The curvature definition is sensitive to local variation and edge aliasing, and available smoothing methods are not sufficient to address these problems properly. Hence, we propose Mean Projection Transform (MPT) as a corner classifier and parabolic fit approximation to form a robust detector. The first step is to extract corner candidates using MPT based on the integral properties of the local contours in both the horizontal and vertical directions. Then, an approximation of the parabolic fit is calculated to localize the candidate corner points. The proposed method presents fewer false-positive (FP) and false-negative (FN) points compared with recent standard corner detection techniques, especially in comparison with curvature scale space (CSS) methods. Moreover, a new evaluation metric, called accuracy of repeatability (AR), is introduced. AR combines repeatability and the localization error (Le) for finding the probability of correct detection in the target image. The output results exhibit better repeatability, localization, and AR for the detected points compared with the criteria in original and transformed images.

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

PubMed Central

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-01-01

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies. PMID:29695113

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor.

PubMed

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-04-24

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies.

Leading edge gypsy moth population dynamics

Treesearch

M. R. Carter; F. W. Ravlin; M. L. McManus

1991-01-01

Leading edge gypsy moth populations have been the focus of several intervention programs (MDIPM, AIPM). Knowledge of gypsy moth population dynamics in leading edge area is crucial for effective management. Populations in these areas tend to reach outbreak levels (noticeable defoliation) within three to four years after egg masses are first detected. Pheromone traps...

Improved algorithm for computerized detection and quantification of pulmonary emphysema at high-resolution computed tomography (HRCT)

NASA Astrophysics Data System (ADS)

Tylen, Ulf; Friman, Ola; Borga, Magnus; Angelhed, Jan-Erik

2001-05-01

Emphysema is characterized by destruction of lung tissue with development of small or large holes within the lung. These areas will have Hounsfield values (HU) approaching -1000. It is possible to detect and quantificate such areas using simple density mask technique. The edge enhancement reconstruction algorithm, gravity and motion of the heart and vessels during scanning causes artefacts, however. The purpose of our work was to construct an algorithm that detects such image artefacts and corrects them. The first step is to apply inverse filtering to the image removing much of the effect of the edge enhancement reconstruction algorithm. The next step implies computation of the antero-posterior density gradient caused by gravity and correction for that. Motion artefacts are in a third step corrected for by use of normalized averaging, thresholding and region growing. Twenty healthy volunteers were investigated, 10 with slight emphysema and 10 without. Using simple density mask technique it was not possible to separate persons with disease from those without. Our algorithm improved separation of the two groups considerably. Our algorithm needs further refinement, but may form a basis for further development of methods for computerized diagnosis and quantification of emphysema by HRCT.

Transition edge sensors for quench localization in SRF cavity tests

NASA Astrophysics Data System (ADS)

Furci, H.; Kovács, Z.; Koettig, T.; Vandoni, G.

2017-12-01

Transition Edge Sensors (TES) are bolometers based on the gradual superconducting transition of a thin film alloy. In the frame of improvement of non-contact thermal mapping for quench localisation in SRF cavity tests, TES have been developed in-house at CERN. Based on modern photolithography techniques, a fabrication method has been established and used to produce TES from Au-Sn alloys. The fabricated sensors superconducting transitions were characterised. The sensitive temperature range of the sensors spreads over 100 mK to 200 mK and its centre can be shifted by the bias current applied between 1.5 K and 2.1 K. Maximum sensitivity being in the range of 0.5 mV/mK, it is possible to detect fast temperature variations (in the 50 μs range) below 1 mK. All these characteristics are an asset for the detection of second sound. Second sound was produced by heaters and the TES were able to distinctively detect it. The value of the speed of second sound was determined and corresponds remarkably with literature values. Furthermore, there is a clear correlation between intensity of the signal and distance, opening possibilities for a more precise signal interpretation in quench localisation.

Toward detection of marine vehicles on horizon from buoy camera

NASA Astrophysics Data System (ADS)

Fefilatyev, Sergiy; Goldgof, Dmitry B.; Langebrake, Lawrence

2007-10-01

This paper presents a new technique for automatic detection of marine vehicles in open sea from a buoy camera system using computer vision approach. Users of such system include border guards, military, port safety and flow management, sanctuary protection personnel. The system is intended to work autonomously, taking images of the surrounding ocean surface and analyzing them on the subject of presence of marine vehicles. The goal of the system is to detect an approximate window around the ship and prepare the small image for transmission and human evaluation. The proposed computer vision-based algorithm combines horizon detection method with edge detection and post-processing. The dataset of 100 images is used to evaluate the performance of proposed technique. We discuss promising results of ship detection and suggest necessary improvements for achieving better performance.

SU-D-BRA-04: Fractal Dimension Analysis of Edge-Detected Rectal Cancer CTs for Outcome Prediction

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

Zhong, H; Wang, J; Hu, W

2015-06-15

Purpose: To extract the fractal dimension features from edge-detected rectal cancer CTs, and to examine the predictability of fractal dimensions to outcomes of primary rectal cancer patients. Methods: Ninety-seven rectal cancer patients treated with neo-adjuvant chemoradiation were enrolled in this study. CT images were obtained before chemoradiotherapy. The primary lesions of the rectal cancer were delineated by experienced radiation oncologists. These images were extracted and filtered by six different Laplacian of Gaussian (LoG) filters with different filter values (0.5–3.0: from fine to coarse) to achieve primary lesions in different anatomical scales. Edges of the original images were found at zero-crossingsmore » of the filtered images. Three different fractal dimensions (box-counting dimension, Minkowski dimension, mass dimension) were calculated upon the image slice with the largest cross-section of the primary lesion. The significance of these fractal dimensions in survival, recurrence and metastasis were examined by Student’s t-test. Results: For a follow-up time of two years, 18 of 97 patients had experienced recurrence, 24 had metastasis, and 18 were dead. Minkowski dimensions under large filter values (2.0, 2.5, 3.0) were significantly larger (p=0.014, 0.006, 0.015) in patients with recurrence than those without. For metastasis, only box-counting dimensions under a single filter value (2.5) showed differences (p=0.016) between patients with and without. For overall survival, box-counting dimensions (filter values = 0.5, 1.0, 1.5), Minkowski dimensions (filter values = 0.5, 1.5, 2.0, 2,5) and mass dimensions (filter values = 1.5, 2.0) were all significant (p<0.05). Conclusion: It is feasible to extract shape information by edge detection and fractal dimensions analysis in neo-adjuvant rectal cancer patients. This information can be used to prognosis prediction.« less

Algorithm for parametric community detection in networks.

PubMed

Bettinelli, Andrea; Hansen, Pierre; Liberti, Leo

2012-07-01

Modularity maximization is extensively used to detect communities in complex networks. It has been shown, however, that this method suffers from a resolution limit: Small communities may be undetectable in the presence of larger ones even if they are very dense. To alleviate this defect, various modifications of the modularity function have been proposed as well as multiresolution methods. In this paper we systematically study a simple model (proposed by Pons and Latapy [Theor. Comput. Sci. 412, 892 (2011)] and similar to the parametric model of Reichardt and Bornholdt [Phys. Rev. E 74, 016110 (2006)]) with a single parameter α that balances the fraction of within community edges and the expected fraction of edges according to the configuration model. An exact algorithm is proposed to find optimal solutions for all values of α as well as the corresponding successive intervals of α values for which they are optimal. This algorithm relies upon a routine for exact modularity maximization and is limited to moderate size instances. An agglomerative hierarchical heuristic is therefore proposed to address parametric modularity detection in large networks. At each iteration the smallest value of α for which it is worthwhile to merge two communities of the current partition is found. Then merging is performed and the data are updated accordingly. An implementation is proposed with the same time and space complexity as the well-known Clauset-Newman-Moore (CNM) heuristic [Phys. Rev. E 70, 066111 (2004)]. Experimental results on artificial and real world problems show that (i) communities are detected by both exact and heuristic methods for all values of the parameter α; (ii) the dendrogram summarizing the results of the heuristic method provides a useful tool for substantive analysis, as illustrated particularly on a Les Misérables data set; (iii) the difference between the parametric modularity values given by the exact method and those given by the heuristic is moderate; (iv) the heuristic version of the proposed parametric method, viewed as a modularity maximization tool, gives better results than the CNM heuristic for large instances.

Novel edge treatment method for improving the transmission reconstruction quality in Tomographic Gamma Scanning.

PubMed

Han, Miaomiao; Guo, Zhirong; Liu, Haifeng; Li, Qinghua

2018-05-01

Tomographic Gamma Scanning (TGS) is a method used for the nondestructive assay of radioactive wastes. In TGS, the actual irregular edge voxels are regarded as regular cubic voxels in the traditional treatment method. In this study, in order to improve the performance of TGS, a novel edge treatment method is proposed that considers the actual shapes of these voxels. The two different edge voxel treatment methods were compared by computing the pixel-level relative errors and normalized mean square errors (NMSEs) between the reconstructed transmission images and the ideal images. Both methods were coupled with two different interative algorithms comprising Algebraic Reconstruction Technique (ART) with a non-negativity constraint and Maximum Likelihood Expectation Maximization (MLEM). The results demonstrated that the traditional method for edge voxel treatment can introduce significant error and that the real irregular edge voxel treatment method can improve the performance of TGS by obtaining better transmission reconstruction images. With the real irregular edge voxel treatment method, MLEM algorithm and ART algorithm can be comparable when assaying homogenous matrices, but MLEM algorithm is superior to ART algorithm when assaying heterogeneous matrices. Copyright © 2018 Elsevier Ltd. All rights reserved.

Edge Sharpness Assessment by Parametric Modeling: Application to Magnetic Resonance Imaging.

PubMed

Ahmad, R; Ding, Y; Simonetti, O P

2015-05-01

In biomedical imaging, edge sharpness is an important yet often overlooked image quality metric. In this work, a semi-automatic method to quantify edge sharpness in the presence of significant noise is presented with application to magnetic resonance imaging (MRI). The method is based on parametric modeling of image edges. First, an edge map is automatically generated and one or more edges-of-interest (EOI) are manually selected using graphical user interface. Multiple exclusion criteria are then enforced to eliminate edge pixels that are potentially not suitable for sharpness assessment. Second, at each pixel of the EOI, an image intensity profile is read along a small line segment that runs locally normal to the EOI. Third, the profiles corresponding to all EOI pixels are individually fitted with a sigmoid function characterized by four parameters, including one that represents edge sharpness. Last, the distribution of the sharpness parameter is used to quantify edge sharpness. For validation, the method is applied to simulated data as well as MRI data from both phantom imaging and cine imaging experiments. This method allows for fast, quantitative evaluation of edge sharpness even in images with poor signal-to-noise ratio. Although the utility of this method is demonstrated for MRI, it can be adapted for other medical imaging applications.

Reproducibility of aortic intima-media thickness in infants using edge-detection software and manual caliper measurements.

PubMed

McCloskey, Kate; Ponsonby, Anne-Louise; Carlin, John B; Jachno, Kim; Cheung, Michael; Skilton, Michael R; Koleff, Jane; Vuillermin, Peter; Burgner, David

2014-06-03

Aortic intima-media thickness measured by transabdominal ultrasound (aIMT) is an intermediate phenotype of cardiovascular risk. We aimed to (1) investigate the reproducibility of aIMT in a population-derived cohort of infants; (2) establish the distribution of aIMT in early infancy; (3) compare measurement by edge-detection software to that by manual sonographic calipers; and (4) assess the effect of individual and environmental variables on image quality. Participants were term infants recruited to a population-derived birth cohort study. Transabdominal ultrasound was performed at six weeks of age by one of two trained operators. Thirty participants had ultrasounds performed by both operators on the same day. Data were collected on environmental (infant sleeping, presence of a sibling, use of sucrose, timing during study visit) and individual (post-conception age, weight, gender) variables. Two readers assessed image quality and measured aIMT by edge-detection software and a subset by manual sonographic calipers. Measurements were repeated by the same reader and between readers to obtain intra-observer and inter-observer reliability. Aortic IMT was measured successfully using edge-detection in 814 infants, and 290 of these infants also had aIMT measured using manual sonographic calipers. The intra-reader intra-class correlation (ICC) (n = 20) was 0.90 (95% CI 0.76, 0.96), mean difference 1.5 μm (95% LOA -39, 59). The between reader ICC using edge-detection (n = 20) was 0.92 (95% CI 0.82, 0.97) mean difference 2 μm (95% LOA -45.0, 49.0) and with manual caliper measurement (n = 290) the ICC was 0.84 (95% CI 0.80, 0.87) mean difference 5 μm (95% LOA -51.8, 61.8). Edge-detection measurements were greater than those from manual sonographic calipers (mean aIMT 618 μm (50) versus mean aIMT 563 μm (49) respectively; p < 0.001, mean difference 44 μm, 95% LOA -54, 142). With the exception of infant crying (p = 0.001), no associations were observed between individual and environmental variables and image quality. In a population-derived cohort of term infants, aIMT measurement has a high level of intra and inter-reader reproducibility. Measurement of aIMT using edge-detection software gives higher inter-reader ICC than manual sonographic calipers. Image quality is not substantially affected by individual and environmental factors.

Bayesian Community Detection in the Space of Group-Level Functional Differences

PubMed Central

Venkataraman, Archana; Yang, Daniel Y.-J.; Pelphrey, Kevin A.; Duncan, James S.

2017-01-01

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder. PMID:26955022

Bayesian Community Detection in the Space of Group-Level Functional Differences.

PubMed

Venkataraman, Archana; Yang, Daniel Y-J; Pelphrey, Kevin A; Duncan, James S

2016-08-01

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder.

Community Detection on the GPU

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

Naim, Md; Manne, Fredrik; Halappanavar, Mahantesh

We present and evaluate a new GPU algorithm based on the Louvain method for community detection. Our algorithm is the first for this problem that parallelizes the access to individual edges. In this way we can fine tune the load balance when processing networks with nodes of highly varying degrees. This is achieved by scaling the number of threads assigned to each node according to its degree. Extensive experiments show that we obtain speedups up to a factor of 270 compared to the sequential algorithm. The algorithm consistently outperforms other recent shared memory implementations and is only one order ofmore » magnitude slower than the current fastest parallel Louvain method running on a Blue Gene/Q supercomputer using more than 500K threads.« less

Superpixel edges for boundary detection

DOEpatents

Moya, Mary M.; Koch, Mark W.

2016-07-12

Various embodiments presented herein relate to identifying one or more edges in a synthetic aperture radar (SAR) image comprising a plurality of superpixels. Superpixels sharing an edge (or boundary) can be identified and one or more properties of the shared superpixels can be compared to determine whether the superpixels form the same or two different features. Where the superpixels form the same feature the edge is identified as an internal edge. Where the superpixels form two different features, the edge is identified as an external edge. Based upon classification of the superpixels, the external edge can be further determined to form part of a roof, wall, etc. The superpixels can be formed from a speckle-reduced SAR image product formed from a registered stack of SAR images, which is further segmented into a plurality of superpixels. The edge identification process is applied to the SAR image comprising the superpixels and edges.

Molybdenum X-Ray Absorption Edges from 200 – 20,000 eV, The Benefits of Soft X-Ray Spectroscopy for Chemical Speciation

PubMed Central

George, Simon J.; Drury, Owen B.; Fu, Juxia; Friedrich, Stephan; Doonan, Christian J.; George, Graham N.; White, Jonathan M.; Young, Charles G.; Cramer, Stephen P.

2009-01-01

We have surveyed the chemical utility of the near-edge structure of molybdenum x-ray absorption edges from the hard x-ray K-edge at 20,000 eV down to the soft x-ray M4,5-edges at ~230 eV. We compared, for each edge, the spectra of two tetrahedral anions, MoO4 and MoS42-. We used three criteria for assessing near-edge structure of each edge: (i) the ratio of the observed chemical shift between MoO42- and MoS42- and the linewidth, (ii) the chemical information from analysis of the near-edge structure and (iii) the ease of measurement using fluorescence detection. Not surprisingly, the K-edge was by far the easiest to measure, but it contained the least information. The L2,3-edges, although harder to measure, had benefits with regard to selection rules and chemical speciation in that they had both a greater chemical shift as well as detailed lineshapes which could be theoretically analyzed in terms of Mo ligand field, symmetry, and covalency. The soft x-ray M2,3-edges were perhaps the least useful, in that they were difficult to measure using fluorescence detection and had very similar information content to the corresponding L2,3-edges. Interestingly, the soft x-ray, low energy (~230 eV) M4,5-edges had greatest potential chemical sensitivity and using our high resolution superconducting tunnel junction (STJ) fluorescence detector they appear to be straightforward to measure. The spectra were amenable to analysis using both the TT-multiplet approach and FEFF. The results using FEFF indicate that the sharp near-edge peaks arise from 3d → 5p transitions, while the broad edge structure has predominately 3d → 4f character. A proper understanding of the dependence of these soft x-ray spectra on ligand field and site geometry is necessary before a complete assessment of the utility of the Mo M4,5-edges can be made. This work includes crystallographic characterization of sodium tetrathiomolybdate. PMID:19041140

Automatic detection and recognition of signs from natural scenes.

PubMed

Chen, Xilin; Yang, Jie; Zhang, Jing; Waibel, Alex

2004-01-01

In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.

Eddy current testing for blade edge micro cracks of aircraft engine

NASA Astrophysics Data System (ADS)

Zhang, Wei-min; Xu, Min-dong; Gao, Xuan-yi; Jin, Xin; Qin, Feng

2017-10-01

Based on the problems of low detection efficiency in the micro cracks detection of aircraft engine blades, a differential excitation eddy current testing system was designed and developed. The function and the working principle of the system were described, the problems which contained the manufacture method of simulated cracks, signal generating, signal processing and the signal display method were described. The detection test was carried out by taking a certain model aircraft engine blade with simulated cracks as a tested specimen. The test data was processed by digital low-pass filter in the computer and the crack signals of time domain display and Lissajous figure display were acquired. By comparing the test results, it is verified that Lissajous figure display shows better performance compared to time domain display when the crack angle is small. The test results show that the eddy current testing system designed in this paper is feasible to detect the micro cracks on the aeroengine blade and can effectively improve the detection efficiency of micro cracks in the practical detection work.

New method of 2-dimensional metrology using mask contouring

NASA Astrophysics Data System (ADS)

Matsuoka, Ryoichi; Yamagata, Yoshikazu; Sugiyama, Akiyuki; Toyoda, Yasutaka

2008-10-01

We have developed a new method of accurately profiling and measuring of a mask shape by utilizing a Mask CD-SEM. The method is intended to realize high accuracy, stability and reproducibility of the Mask CD-SEM adopting an edge detection algorithm as the key technology used in CD-SEM for high accuracy CD measurement. In comparison with a conventional image processing method for contour profiling, this edge detection method is possible to create the profiles with much higher accuracy which is comparable with CD-SEM for semiconductor device CD measurement. This method realizes two-dimensional metrology for refined pattern that had been difficult to measure conventionally by utilizing high precision contour profile. In this report, we will introduce the algorithm in general, the experimental results and the application in practice. As shrinkage of design rule for semiconductor device has further advanced, an aggressive OPC (Optical Proximity Correction) is indispensable in RET (Resolution Enhancement Technology). From the view point of DFM (Design for Manufacturability), a dramatic increase of data processing cost for advanced MDP (Mask Data Preparation) for instance and surge of mask making cost have become a big concern to the device manufacturers. This is to say, demands for quality is becoming strenuous because of enormous quantity of data growth with increasing of refined pattern on photo mask manufacture. In the result, massive amount of simulated error occurs on mask inspection that causes lengthening of mask production and inspection period, cost increasing, and long delivery time. In a sense, it is a trade-off between the high accuracy RET and the mask production cost, while it gives a significant impact on the semiconductor market centered around the mask business. To cope with the problem, we propose the best method of a DFM solution using two-dimensional metrology for refined pattern.

PaDe - The particle detection program

NASA Astrophysics Data System (ADS)

Ott, T.; Drolshagen, E.; Koschny, D.; Poppe, B.

2016-01-01

This paper introduces the Particle Detection program PaDe. Its aim is to analyze dust particles in the coma of the Jupiter-family comet 67P/Churyumov-Gerasimenko which were recorded by the two OSIRIS (Optical, Spectroscopic, and Infrared Remote Imaging System) cameras onboard the ESA spacecraft Rosetta, see e.g. Keller et al. (2007). In addition to working with the Rosetta data, the code was modified to work with images from meteors. It was tested with data recorded by the ICCs (Intensified CCD Cameras) of the CILBO-System (Canary Island Long-Baseline Observatory) on the Canary Islands; compare Koschny et al. (2013). This paper presents a new method for the position determination of the observed meteors. The PaDe program was written in Python 3.4. Its original intent is to find the trails of dust particles in space from the OSIRIS images. For that it determines the positions where the trail starts and ends. They were found using a fit following the so-called error function (Andrews, 1998) for the two edges of the profiles. The positions where the intensities fall to the half maximum were found to be the beginning and end of the particle. In the case of meteors, this method can be applied to find the leading edge of the meteor. The proposed method has the potential to increase the accuracy of the position determination of meteors dramatically. Other than the standard method of finding the photometric center, our method is not influenced by any trails or wakes behind the meteor. This paper presents first results of this ongoing work.

Automated and simultaneous fovea center localization and macula segmentation using the new dynamic identification and classification of edges model.

PubMed

Onal, Sinan; Chen, Xin; Satamraju, Veeresh; Balasooriya, Maduka; Dabil-Karacal, Humeyra

2016-07-01

Detecting the position of retinal structures, including the fovea center and macula, in retinal images plays a key role in diagnosing eye diseases such as optic nerve hypoplasia, amblyopia, diabetic retinopathy, and macular edema. However, current detection methods are unreliable for infants or certain ethnic populations. Thus, a methodology is proposed here that may be useful for infants and across ethnicities that automatically localizes the fovea center and segments the macula on digital fundus images. First, dark structures and bright artifacts are removed from the input image using preprocessing operations, and the resulting image is transformed to polar space. Second, the fovea center is identified, and the macula region is segmented using the proposed dynamic identification and classification of edges (DICE) model. The performance of the method was evaluated using 1200 fundus images obtained from the relatively large, diverse, and publicly available Messidor database. In 96.1% of these 1200 cases, the distance between the fovea center identified manually by ophthalmologists and automatically using the proposed method remained within 0 to 8 pixels. The dice similarity index comparing the manually obtained results with those of the model for macula segmentation was 96.12% for these 1200 cases. Thus, the proposed method displayed a high degree of accuracy. The methodology using the DICE model is unique and advantageous over previously reported methods because it simultaneously determines the fovea center and segments the macula region without using any structural information, such as optic disc or blood vessel location, and it may prove useful for all populations, including infants.

Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique.

PubMed

Vikhe, P S; Thool, V R

2016-04-01

Detection of mass in mammogram for early diagnosis of breast cancer is a significant assignment in the reduction of the mortality rate. However, in some cases, screening of mass is difficult task for radiologist, due to variation in contrast, fuzzy edges and noisy mammograms. Masses and micro-calcifications are the distinctive signs for diagnosis of breast cancer. This paper presents, a method for mass enhancement using piecewise linear operator in combination with wavelet processing from mammographic images. The method includes, artifact suppression and pectoral muscle removal based on morphological operations. Finally, mass segmentation for detection using adaptive threshold technique is carried out to separate the mass from background. The proposed method has been tested on 130 (45 + 85) images with 90.9 and 91 % True Positive Fraction (TPF) at 2.35 and 2.1 average False Positive Per Image(FP/I) from two different databases, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). The obtained results show that, the proposed technique gives improved diagnosis in the early breast cancer detection.

Automatic polyp detection in colonoscopy videos

NASA Astrophysics Data System (ADS)

Yuan, Zijie; IzadyYazdanabadi, Mohammadhassan; Mokkapati, Divya; Panvalkar, Rujuta; Shin, Jae Y.; Tajbakhsh, Nima; Gurudu, Suryakanth; Liang, Jianming

2017-02-01

Colon cancer is the second cancer killer in the US [1]. Colonoscopy is the primary method for screening and prevention of colon cancer, but during colonoscopy, a significant number (25% [2]) of polyps (precancerous abnormal growths inside of the colon) are missed; therefore, the goal of our research is to reduce the polyp miss-rate of colonoscopy. This paper presents a method to detect polyp automatically in a colonoscopy video. Our system has two stages: Candidate generation and candidate classification. In candidate generation (stage 1), we chose 3,463 frames (including 1,718 with-polyp frames) from real-time colonoscopy video database. We first applied processing procedures, namely intensity adjustment, edge detection and morphology operations, as pre-preparation. We extracted each connected component (edge contour) as one candidate patch from the pre-processed image. With the help of ground truth (GT) images, 2 constraints were implemented on each candidate patch, dividing and saving them into polyp group and non-polyp group. In candidate classification (stage 2), we trained and tested convolutional neural networks (CNNs) with AlexNet architecture [3] to classify each candidate into with-polyp or non-polyp class. Each with-polyp patch was processed by rotation, translation and scaling for invariant to get a much robust CNNs system. We applied leave-2-patients-out cross-validation on this model (4 of 6 cases were chosen as training set and the rest 2 were as testing set). The system accuracy and sensitivity are 91.47% and 91.76%, respectively.

Detecting complexes from edge-weighted PPI networks via genes expression analysis.

PubMed

Zhang, Zehua; Song, Jian; Tang, Jijun; Xu, Xinying; Guo, Fei

2018-04-24

Identifying complexes from PPI networks has become a key problem to elucidate protein functions and identify signal and biological processes in a cell. Proteins binding as complexes are important roles of life activity. Accurate determination of complexes in PPI networks is crucial for understanding principles of cellular organization. We propose a novel method to identify complexes on PPI networks, based on different co-expression information. First, we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks. Then, we propose some significant features, such as graph information and gene expression analysis, to filter and modify complexes predicted by Markov Cluster Algorithm. To evaluate our method, we test on two experimental yeast PPI networks. On DIP network, our method has Precision and F-Measure values of 0.6004 and 0.5528. On MIPS network, our method has F-Measure and S n values of 0.3774 and 0.3453. Comparing to existing methods, our method improves Precision value by at least 0.1752, F-Measure value by at least 0.0448, S n value by at least 0.0771. Experiments show that our method achieves better results than some state-of-the-art methods for identifying complexes on PPI networks, with the prediction quality improved in terms of evaluation criteria.

SU-C-207-06: In Vivo Quantification of Gold Nanoparticles Using K-Edge Imaging Via Spectrum Shaping by Gold Filter

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

Chen, H; Cormack, R; Bhagwat, M

Purpose: Gold nanoparticles (AuNP) are multifunctional platforms ideal for drug delivery, targeted imaging and radiosensitization. We have investigated quantitative imaging of AuNPs using on board imager (OBI) cone beam computed tomography (CBCT). To this end, we also present, for the first time, a novel method for k-edge imaging of AuNP by filter-based spectral shaping. Methods: We used a digital 25 cm diameter water phantom, embedded with 3 cm spheres filled with AuNPs of different concentrations (0 mg/ml – 16 mg/ml). A poly-energetic X-ray spectrum of 140 kVp from a conventional X-ray tube is shaped by balanced K-edge filters to createmore » an excess of photons right above the K-edge of gold at 80.7 keV. The filters consist of gold, tin, copper and aluminum foils. The phantom with appropriately assigned attenuation coefficients is forward projected onto a detector for each energy bin and then integrated. FKD reconstruction is performed on the integrated projections. Scatter, detector efficiency and noise are included. Results: We found that subtracting the results of two filter sets (Filter A:127 µm gold foil with 254 µm tin, 330 µm copper and 1 mm aluminum, and Filter B: 635 µm tin with 264 µm copper and 1 mm aluminum), provides substantial image contrast. The resulting filtered spectra match well below 80.7 keV, while maintaining sufficient X-ray quanta just above that. Voxel intensities of AuNP containing spheres increase linearly with AuNP concentration. K-edge imaging provides 18% more sensitivity than the tin filter alone, and 38% more sensitivity than the gold filter alone. Conclusion: We have shown that it is feasible to quantitatively detect AuNP distributions in a patient-sized phantom using clinical CBCT and K-edge spectral shaping.« less

Stroke-model-based character extraction from gray-level document images.

PubMed

Ye, X; Cheriet, M; Suen, C Y

2001-01-01

Global gray-level thresholding techniques such as Otsu's method, and local gray-level thresholding techniques such as edge-based segmentation or the adaptive thresholding method are powerful in extracting character objects from simple or slowly varying backgrounds. However, they are found to be insufficient when the backgrounds include sharply varying contours or fonts in different sizes. A stroke-model is proposed to depict the local features of character objects as double-edges in a predefined size. This model enables us to detect thin connected components selectively, while ignoring relatively large backgrounds that appear complex. Meanwhile, since the stroke width restriction is fully factored in, the proposed technique can be used to extract characters in predefined font sizes. To process large volumes of documents efficiently, a hybrid method is proposed for character extraction from various backgrounds. Using the measurement of class separability to differentiate images with simple backgrounds from those with complex backgrounds, the hybrid method can process documents with different backgrounds by applying the appropriate methods. Experiments on extracting handwriting from a check image, as well as machine-printed characters from scene images demonstrate the effectiveness of the proposed model.

Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement.

PubMed

Li, Haiyan; Wu, Jun; Miao, Aimin; Yu, Pengfei; Chen, Jianhua; Zhang, Yufeng

2017-04-17

Ultrasound imaging plays an important role in computer diagnosis since it is non-invasive and cost-effective. However, ultrasound images are inevitably contaminated by noise and speckle during acquisition. Noise and speckle directly impact the physician to interpret the images and decrease the accuracy in clinical diagnosis. Denoising method is an important component to enhance the quality of ultrasound images; however, several limitations discourage the results because current denoising methods can remove noise while ignoring the statistical characteristics of speckle and thus undermining the effectiveness of despeckling, or vice versa. In addition, most existing algorithms do not identify noise, speckle or edge before removing noise or speckle, and thus they reduce noise and speckle while blurring edge details. Therefore, it is a challenging issue for the traditional methods to effectively remove noise and speckle in ultrasound images while preserving edge details. To overcome the above-mentioned limitations, a novel method, called Rayleigh-maximum-likelihood switching bilateral filter (RSBF) is proposed to enhance ultrasound images by two steps: noise, speckle and edge detection followed by filtering. Firstly, a sorted quadrant median vector scheme is utilized to calculate the reference median in a filtering window in comparison with the central pixel to classify the target pixel as noise, speckle or noise-free. Subsequently, the noise is removed by a bilateral filter and the speckle is suppressed by a Rayleigh-maximum-likelihood filter while the noise-free pixels are kept unchanged. To quantitatively evaluate the performance of the proposed method, synthetic ultrasound images contaminated by speckle are simulated by using the speckle model that is subjected to Rayleigh distribution. Thereafter, the corrupted synthetic images are generated by the original image multiplied with the Rayleigh distributed speckle of various signal to noise ratio (SNR) levels and added with Gaussian distributed noise. Meanwhile clinical breast ultrasound images are used to visually evaluate the effectiveness of the method. To examine the performance, comparison tests between the proposed RSBF and six state-of-the-art methods for ultrasound speckle removal are performed on simulated ultrasound images with various noise and speckle levels. The results of the proposed RSBF are satisfying since the Gaussian noise and the Rayleigh speckle are greatly suppressed. The proposed method can improve the SNRs of the enhanced images to nearly 15 and 13 dB compared with images corrupted by speckle as well as images contaminated by speckle and noise under various SNR levels, respectively. The RSBF is effective in enhancing edge while smoothing the speckle and noise in clinical ultrasound images. In the comparison experiments, the proposed method demonstrates its superiority in accuracy and robustness for denoising and edge preserving under various levels of noise and speckle in terms of visual quality as well as numeric metrics, such as peak signal to noise ratio, SNR and root mean squared error. The experimental results show that the proposed method is effective for removing the speckle and the background noise in ultrasound images. The main reason is that it performs a "detect and replace" two-step mechanism. The advantages of the proposed RBSF lie in two aspects. Firstly, each central pixel is classified as noise, speckle or noise-free texture according to the absolute difference between the target pixel and the reference median. Subsequently, the Rayleigh-maximum-likelihood filter and the bilateral filter are switched to eliminate speckle and noise, respectively, while the noise-free pixels are unaltered. Therefore, it is implemented with better accuracy and robustness than the traditional methods. Generally, these traits declare that the proposed RSBF would have significant clinical application.

Directional filtering for block recovery using wavelet features

NASA Astrophysics Data System (ADS)

Hyun, Seung H.; Eom, Il K.; Kim, Yoo S.

2005-07-01

When images compressed with block-based compression techniques are transmitted over a noisy channel, unexpected block losses occur. Conventional methods that do not consider edge directions can cause blocked blurring artifacts. In this paper, we present a post-processing-based block recovery scheme using Haar wavelet features. The adaptive selection of neighboring blocks is performed based on the energy of wavelet subbands (EWS) and difference between DC values (DDC). The lost blocks are recovered by linear interpolation in the spatial domain using selected blocks. The method using only EWS performs well for horizontal and vertical edges, but not as well for diagonal edges. Conversely, only using DDC performs well for diagonal edges with the exception of line- or roof-type edge profiles. Therefore, we combine EWS and DDC for better results. The proposed directional recovery method is effective for the strong edge because exploit the varying neighboring blocks adaptively according to the edges and the directional information in the image. The proposed method outperforms the previous methods that used only fixed blocks.

A fast community detection method in bipartite networks by distance dynamics

NASA Astrophysics Data System (ADS)

Sun, Hong-liang; Ch'ng, Eugene; Yong, Xi; Garibaldi, Jonathan M.; See, Simon; Chen, Duan-bing

2018-04-01

Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extension from unipartite to bipartite networks. Since Jaccard coefficient of distance dynamics model is incapable to measure distances of different types of vertices in bipartite networks, our main contribution is to extend distance dynamics model from unipartite to bipartite networks using a novel measure Local Jaccard Distance (LJD). Furthermore, distances between different types of vertices are not affected by common neighbors in the original method. This new idea makes clear assumptions and yields interpretable results in linear time complexity O(| E |) in sparse networks, where | E | is the number of edges. Experiments on synthetic networks demonstrate it is capable to overcome resolution limit compared with existing other methods. Further research on real networks shows that this model can accurately detect interpretable community structures in a short time.

Simulation of pattern and defect detection in periodic amplitude and phase structures using photorefractive four-wave mixing

NASA Astrophysics Data System (ADS)

Nehmetallah, Georges; Banerjee, Partha; Khoury, Jed

2015-03-01

The nonlinearity inherent in four-wave mixing in photorefractive (PR) materials is used for adaptive filtering. Examples include script enhancement on a periodic pattern, scratch and defect cluster enhancement, periodic pattern dislocation enhancement, etc. through intensity filtering image manipulation. Organic PR materials have large space-bandwidth product, which makes them useful in adaptive filtering techniques in quality control systems. For instance, in the case of edge enhancement, phase conjugation via four-wave mixing suppresses the low spatial frequencies of the Fourier spectrum of an aperiodic image and consequently leads to image edge enhancement. In this work, we model, numerically verify, and simulate the performance of a four wave mixing setup used for edge, defect and pattern detection in periodic amplitude and phase structures. The results show that this technique successfully detects the slightest defects clearly even with no enhancement. This technique should facilitate improvements in applications such as image display sharpness utilizing edge enhancement, production line defect inspection of fabrics, textiles, e-beam lithography masks, surface inspection, and materials characterization.

Detection and Prevalence of Penicillin-Susceptible Staphylococcus aureus in the United States in 2013

PubMed Central

Doern, G. V.; Heilmann, K. P.; Miner, S.; Tendolkar, S.; Riahi, F.; Diekema, D. J.

2016-01-01

Using blaZ PCR as the “gold standard,” the sensitivities of CLSI penicillin zone edge and nitrocefin-based tests for β-lactamase production in Staphylococcus aureus were 64.5% and 35.5%, respectively, with specificity of 99.8% for both methods. In 2013, 13.5% of 3,083 S. aureus isolates from 31 U.S. centers were penicillin susceptible. PMID:26763960

Precise determination of anthropometric dimensions by means of image processing methods for estimating human body segment parameter values.

PubMed

Baca, A

1996-04-01

A method has been developed for the precise determination of anthropometric dimensions from the video images of four different body configurations. High precision is achieved by incorporating techniques for finding the location of object boundaries with sub-pixel accuracy, the implementation of calibration algorithms, and by taking into account the varying distances of the body segments from the recording camera. The system allows automatic segment boundary identification from the video image, if the boundaries are marked on the subject by black ribbons. In connection with the mathematical finite-mass-element segment model of Hatze, body segment parameters (volumes, masses, the three principal moments of inertia, the three local coordinates of the segmental mass centers etc.) can be computed by using the anthropometric data determined videometrically as input data. Compared to other, recently published video-based systems for the estimation of the inertial properties of body segments, the present algorithms reduce errors originating from optical distortions, inaccurate edge-detection procedures, and user-specified upper and lower segment boundaries or threshold levels for the edge-detection. The video-based estimation of human body segment parameters is especially useful in situations where ease of application and rapid availability of comparatively precise parameter values are of importance.

Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection--a first study.

PubMed

Hu, D; Sarder, P; Ronhovde, P; Orthaus, S; Achilefu, S; Nussinov, Z

2014-01-01

Inspired by a multiresolution community detection based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Furthermore, using the proposed method, the mean-square error in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The multiresolution community detection method appeared to perform better than a popular spectral clustering-based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in mean-square error with increasing resolution. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

Automated Ground Penetrating Radar hyperbola detection in complex environment

NASA Astrophysics Data System (ADS)

Mertens, Laurence; Lambot, Sébastien

2015-04-01

Ground Penetrating Radar (GPR) systems are commonly used in many applications to detect, amongst others, buried targets (various types of pipes, landmines, tree roots ...), which, in a cross-section, present theoretically a particular hyperbolic-shaped signature resulting from the antenna radiation pattern. Considering the large quantity of information we can acquire during a field campaign, a manual detection of these hyperbolas is barely possible, therefore we have a real need to have at our disposal a quick and automated detection of these hyperbolas. However, this task may reveal itself laborious in real field data because these hyperbolas are often ill-shaped due to the heterogeneity of the medium and to instrumentation clutter. We propose a new detection algorithm for well- and ill-shaped GPR reflection hyperbolas especially developed for complex field data. This algorithm is based on human recognition pattern to emulate human expertise to identify the hyperbolas apexes. The main principle relies in a fitting process of the GPR image edge dots detected with Canny filter to analytical hyperbolas, considering the object as a punctual disturbance with a physical constraint of the parameters. A long phase of observation of a large number of ill-shaped hyperbolas in various complex media led to the definition of smart criteria characterizing the hyperbolic shape and to the choice of accepted value ranges acceptable for an edge dot to correspond to the apex of a specific hyperbola. These values were defined to fit the ambiguity zone for the human brain and present the particularity of being functional in most heterogeneous media. Furthermore, the irregularity is particularly taken into account by defining a buffer zone around the theoretical hyperbola in which the edge dots need to be encountered to belong to this specific hyperbola. First, the method was tested in laboratory conditions over tree roots and over PVC pipes with both time- and frequency-domain radars used on-ground. Second, we investigated the efficiency of this method for field data taken with a time-domain system connected to 400 and 900 MHz antennas in a forest environment. For all the tests explained above, the computational time is around 56 s for 10000 edge dots detected in a b-scan for the 900 MHz antenna and 228 s for the 400 MHz antenna. This value depends on the complexity of the images. For the given examples, the rate of non-detection is negligible and the rate of false alarms varies from 0 to 8.3% , although it is worth noting that these performance rates become difficult to evaluate for reflections that are ambiguous for our own eyes. Finally, we conducted a sensitivity analysis showing that all these criteria are needed and sufficient for a correct detection. In conclusion, the low computational time and its considerations to take into account the hyperbola irregularities make the proposed algorithm very suitable and robust for complex environments. The false alarms are easily removed by studying the continuity of the reflections between consecutive transects for linear targets such as pipes. This research is funded by the Fonds de la Recherche Scientifique (FNRS, Belgium) and benefits from networking activities carried out within the EU COST Action TU1208 "Civil Engineering Applications of Ground Penetrating Radar".

Detection of metal stress in boreal forest species using the 0.67-micron chlorophyll absorption band

NASA Technical Reports Server (NTRS)

Singhroy, Vernon H.; Kruse, Fred A.

1991-01-01

Several recent studies have shown that a shift of the red-edge inflection near 0.70 micron in vegetation reflectance spectra is an indicator of metal stress, partially attributable to changes in chlorophyll concentration. This 'red-edge shift', however, is difficult to detect and has been reported both toward longer (red) and shorter (blue) wavelengths. Our work demonstrates that direct measurement of the depth and width of the chlorophyll absorption band at 0.67 micron using digital feature extraction and absorption band characterization procedures developed for the analysis of mineral spectra is a more consistent indicator of metal stress. Additionally, the magnitude of these parameters is generally greater than that of the red edge shift and thus should be more amenable to detection and mapping using field and aircraft spectrometers.

High-energy X-ray detection by hafnium-doped organic-inorganic hybrid scintillators prepared by sol-gel method

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

Sun, Yan; Koshimizu, Masanori, E-mail: koshi@qpc.che.tohoku.ac.jp; Yahaba, Natsuna

2014-04-28

With the aim of enhancing the efficiency with which plastic scintillators detect high-energy X-rays, hafnium-doped organic-inorganic hybrid scintillators were fabricated via a sol-gel method. Transmission electron microscopy of sampled material reveals the presence of Hf{sub x}Si{sub 1−x}O{sub 2} nanoparticles, dispersed in a polymer matrix that constitutes the active material of the X-ray detector. With Hf{sub x}Si{sub 1−x}O{sub 2} nanoparticles incorporated in the polymer matrix, the absorption edge and the luminescence wavelength is shifted, which we attribute to Mie scattering. The detection efficiency for 67.4-keV X-rays in a 0.6-mm-thick piece of this material is two times better than the same thicknessmore » of a commercial plastic scintillator-NE142.« less

Detection of Zak phases and topological invariants in a chiral quantum walk of twisted photons.

PubMed

Cardano, Filippo; D'Errico, Alessio; Dauphin, Alexandre; Maffei, Maria; Piccirillo, Bruno; de Lisio, Corrado; De Filippis, Giulio; Cataudella, Vittorio; Santamato, Enrico; Marrucci, Lorenzo; Lewenstein, Maciej; Massignan, Pietro

2017-06-01

Topological insulators are fascinating states of matter exhibiting protected edge states and robust quantized features in their bulk. Here we propose and validate experimentally a method to detect topological properties in the bulk of one-dimensional chiral systems. We first introduce the mean chiral displacement, an observable that rapidly approaches a value proportional to the Zak phase during the free evolution of the system. Then we measure the Zak phase in a photonic quantum walk of twisted photons, by observing the mean chiral displacement in its bulk. Next, we measure the Zak phase in an alternative, inequivalent timeframe and combine the two windings to characterize the full phase diagram of this Floquet system. Finally, we prove the robustness of the measure by introducing dynamical disorder in the system. This detection method is extremely general and readily applicable to all present one-dimensional platforms simulating static or Floquet chiral systems.

Detection of Zak phases and topological invariants in a chiral quantum walk of twisted photons

PubMed Central

Cardano, Filippo; D’Errico, Alessio; Dauphin, Alexandre; Maffei, Maria; Piccirillo, Bruno; de Lisio, Corrado; De Filippis, Giulio; Cataudella, Vittorio; Santamato, Enrico; Marrucci, Lorenzo; Lewenstein, Maciej; Massignan, Pietro

2017-01-01

Topological insulators are fascinating states of matter exhibiting protected edge states and robust quantized features in their bulk. Here we propose and validate experimentally a method to detect topological properties in the bulk of one-dimensional chiral systems. We first introduce the mean chiral displacement, an observable that rapidly approaches a value proportional to the Zak phase during the free evolution of the system. Then we measure the Zak phase in a photonic quantum walk of twisted photons, by observing the mean chiral displacement in its bulk. Next, we measure the Zak phase in an alternative, inequivalent timeframe and combine the two windings to characterize the full phase diagram of this Floquet system. Finally, we prove the robustness of the measure by introducing dynamical disorder in the system. This detection method is extremely general and readily applicable to all present one-dimensional platforms simulating static or Floquet chiral systems. PMID:28569741

Detecting hierarchical levels of connectivity in a population of Acacia tortilis at the northern edge of the species' global distribution: Combining classical population genetics and network analyses.

PubMed

Rodger, Yael S; Greenbaum, Gili; Silver, Micha; Bar-David, Shirli; Winters, Gidon

2018-01-01

Genetic diversity and structure of populations at the edge of the species' spatial distribution are important for potential adaptation to environmental changes and consequently, for the long-term survival of the species. Here, we combined classical population genetic methods with newly developed network analyses to gain complementary insights into the genetic structure and diversity of Acacia tortilis, a keystone desert tree, at the northern edge of its global distribution, where the population is under threat from climatic, ecological, and anthropogenic changes. We sampled A. tortilis from 14 sites along the Dead Sea region and the Arava Valley in Israel and in Jordan. In addition, we obtained samples from Egypt and Sudan, the hypothesized origin of the species. Samples from all sites were genotyped using six polymorphic microsatellite loci.Our results indicate a significant genetic structure in A. tortilis along the Arava Valley. This was detected at different hierarchical levels-from the basic unit of the subpopulation, corresponding to groups of trees within ephemeral rivers (wadis), to groups of subpopulations (communities) that are genetically more connected relative to others. The latter structure mostly corresponds to the partition of the major drainage basins in the area. Network analyses, combined with classical methods, allowed for the identification of key A. tortilis subpopulations in this region, characterized by their relatively high level of genetic diversity and centrality in maintaining gene flow in the population. Characterizing such key subpopulations may enable conservation managers to focus their efforts on certain subpopulations that might be particularly important for the population's long-term persistence, thus contributing to species conservation within its peripheral range.

Automatic Detection of Galaxy Type From Datasets of Galaxies Image Based on Image Retrieval Approach.

PubMed

Abd El Aziz, Mohamed; Selim, I M; Xiong, Shengwu

2017-06-30

This paper presents a new approach for the automatic detection of galaxy morphology from datasets based on an image-retrieval approach. Currently, there are several classification methods proposed to detect galaxy types within an image. However, in some situations, the aim is not only to determine the type of galaxy within the queried image, but also to determine the most similar images for query image. Therefore, this paper proposes an image-retrieval method to detect the type of galaxies within an image and return with the most similar image. The proposed method consists of two stages, in the first stage, a set of features is extracted based on shape, color and texture descriptors, then a binary sine cosine algorithm selects the most relevant features. In the second stage, the similarity between the features of the queried galaxy image and the features of other galaxy images is computed. Our experiments were performed using the EFIGI catalogue, which contains about 5000 galaxies images with different types (edge-on spiral, spiral, elliptical and irregular). We demonstrate that our proposed approach has better performance compared with the particle swarm optimization (PSO) and genetic algorithm (GA) methods.

Tripartite community structure in social bookmarking data

NASA Astrophysics Data System (ADS)

Neubauer, Nicolas; Obermayer, Klaus

2011-12-01

Community detection is a branch of network analysis concerned with identifying strongly connected subnetworks. Social bookmarking sites aggregate datasets of often hundreds of millions of triples (document, user, and tag), which, when interpreted as edges of a graph, give rise to special networks called 3-partite, 3-uniform hypergraphs. We identify challenges and opportunities of generalizing community detection and in particular modularity optimization to these structures. Two methods for community detection are introduced that preserve the hypergraph's special structure to different degrees. Their performance is compared on synthetic datasets, showing the benefits of structure preservation. Furthermore, a tool for interactive exploration of the community detection results is introduced and applied to examples from real datasets. We find additional evidence for the importance of structure preservation and, more generally, demonstrate how tripartite community detection can help understand the structure of social bookmarking data.

Path scanning for the detection of anomalous subgraphs and use of DNS requests and host agents for anomaly/change detection and network situational awareness

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

Neil, Joshua Charles; Fisk, Michael Edward; Brugh, Alexander William

A system, apparatus, computer-readable medium, and computer-implemented method are provided for detecting anomalous behavior in a network. Historical parameters of the network are determined in order to determine normal activity levels. A plurality of paths in the network are enumerated as part of a graph representing the network, where each computing system in the network may be a node in the graph and the sequence of connections between two computing systems may be a directed edge in the graph. A statistical model is applied to the plurality of paths in the graph on a sliding window basis to detect anomalousmore » behavior. Data collected by a Unified Host Collection Agent ("UHCA") may also be used to detect anomalous behavior.« less

Wisps in the outer edge of the Keeler Gap

NASA Astrophysics Data System (ADS)

Tiscareno, M. S.; Arnault, E. G.

2014-12-01

The outer part of Saturn's A ring contains five sharp edges: the inner and outer edges of the Encke Gap and of the Keeler Gap (which contain the moons Pan and Daphnis, respectively), and the outer edge of the A ring itself. Four of these five edges are characterized by structure at moderate to high spatial frequencies, with amplitudes ranging from 2 to 30 km (Tiscareno et al. 2005, DPS). Only the outer edge of the Keeler Gap is reasonably smooth in appearance (Tiscareno et al. 2005, DPS), with occultations indicating residuals less than 1 km upon a possibly non-zero eccentricity (R.G. French, personal communication, 2014). Superposed upon the relatively smooth outer edge of the Keeler Gap are a system of "wisps," which appear to be ring material protruding inward into the gap, usually with a sharp trailing edge and a smooth gradation back to the background edge location on the leading side (Porco et al. 2005, Science). The radial amplitude of wisps is usually 0.5 to 1 km, and their azimuthal extent is approximately a degree of longitude (~2400 km). Wisps are likely caused by an interplay between Daphnis (and perhaps other moons) and embedded moonlets within the ring, though the details remain unclear. We will present a catalogue of wisp detections in Cassini images. We carry out repeated gaussian fits of the radial edge location in order to characterize edge structure (see Figure, which compares our fitted edge to the figure presented by Porco et al. 2005) and visually scan those fitted edges in order to detect wisps. With extensive coverage in longitude and in time, we will report on how wisps evolve and move, both within an orbit period and on longer timescales. We will also report on the frequency and interpretation of wisps that deviate from the standard morphology. We will discuss the implications of our results for the origin and nature of wisps, and for the larger picture of how masses interact within Saturn's rings.

Improved method of edge coating flat ribbon wire

NASA Technical Reports Server (NTRS)

1966-01-01

Method to coat the edges of flat ribbon wire is devised by using enamel with modified flow properties due to addition of 2 to 4 percent silicon. Conventional coating procedes several edge coatings to minimize oxidation and additional conventional coats are applied after edge coating to build up thickness.

78 FR 66859 - Airworthiness Directives; the Boeing Company Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-07

... airplanes. This proposed AD was prompted by reports of bearing damage at certain trailing edge (TE) flap... certain trailing edge (TE) flap support rib assemblies. We are issuing this AD to detect and correct...

Image Motion Detection And Estimation: The Modified Spatio-Temporal Gradient Scheme

NASA Astrophysics Data System (ADS)

Hsin, Cheng-Ho; Inigo, Rafael M.

1990-03-01

The detection and estimation of motion are generally involved in computing a velocity field of time-varying images. A completely new modified spatio-temporal gradient scheme to determine motion is proposed. This is derived by using gradient methods and properties of biological vision. A set of general constraints is proposed to derive motion constraint equations. The constraints are that the second directional derivatives of image intensity at an edge point in the smoothed image will be constant at times t and t+L . This scheme basically has two stages: spatio-temporal filtering, and velocity estimation. Initially, image sequences are processed by a set of oriented spatio-temporal filters which are designed using a Gaussian derivative model. The velocity is then estimated for these filtered image sequences based on the gradient approach. From a computational stand point, this scheme offers at least three advantages over current methods. The greatest advantage of the modified spatio-temporal gradient scheme over the traditional ones is that an infinite number of motion constraint equations are derived instead of only one. Therefore, it solves the aperture problem without requiring any additional assumptions and is simply a local process. The second advantage is that because of the spatio-temporal filtering, the direct computation of image gradients (discrete derivatives) is avoided. Therefore the error in gradients measurement is reduced significantly. The third advantage is that during the processing of motion detection and estimation algorithm, image features (edges) are produced concurrently with motion information. The reliable range of detected velocity is determined by parameters of the oriented spatio-temporal filters. Knowing the velocity sensitivity of a single motion detection channel, a multiple-channel mechanism for estimating image velocity, seldom addressed by other motion schemes in machine vision, can be constructed by appropriately choosing and combining different sets of parameters. By applying this mechanism, a great range of velocity can be detected. The scheme has been tested for both synthetic and real images. The results of simulations are very satisfactory.

Metal-ligand Covalency of Iron Complexes from High-Resolution Resonant Inelastic X-ray Scattering

PubMed Central

Lundberg, Marcus; Kroll, Thomas; DeBeer, Serena; Bergmann, Uwe; Wilson, Samuel A.; Glatzel, Pieter; Nordlund, Dennis; Hedman, Britt; Hodgson, Keith O.; Solomon, Edward I.

2013-01-01

Data from Kα resonant inelastic X-ray scattering (RIXS) have been used to extract electronic structure information, i.e., the covalency of metal-ligand bonds, for four iron complexes using an experimentally based theoretical model. Kα RIXS involves resonant 1s → 3d excitation and detection of the 2p → 1s (Kα) emission. This two-photon process reaches similar final states as single-photon L-edge (2p → 3d) X-ray absorption spectroscopy (XAS), but involves only hard X-rays and can therefore be used to get high-resolution L-edge-like spectra for metal proteins, solution catalysts and their intermediates. To analyze the information content of Kα RIXS spectra, data have been collected for four characteristic σ-donor and π-backdonation complexes; ferrous tacn [FeII(tacn)2]Br2, ferrocyanide [FeII(CN)6]K4, ferric tacn [FeIII(tacn)2]Br3 and ferricyanide [FeIII(CN)6]K3. From these spectra metal-ligand covalencies can be extracted using a charge-transfer multiplet model, without previous information from the L-edge XAS experiment. A direct comparison of L-edge XAS and Kα RIXS spectra show that the latter reaches additional final states, e.g., when exciting into the eg (σ*) orbitals, and the splitting between final states of different symmetry provides an extra dimension that makes Kα RIXS a more sensitive probe of σ-bonding. Another key difference between L-edge XAS and Kα RIXS is the π-backbonding features in ferro- and ferricyanide that are significantly more intense in L-edge XAS compared to Kα RIXS. This shows that two methods are complimentary in assigning electronic structure. The Kα RIXS approach can thus be used as a stand-alone method, in combination with L-edge XAS for strongly covalent systems that are difficult to probe by UV/Vis spectroscopy, or as an extension to conventional absorption spectroscopy for a wide range of transition metal enzymes and catalysts. PMID:24131028

High-Resolution Millimeter Wave Detection of Vertical Cracks in the Space Shuttle External Tank (ET) Spray-on-Foam Insulation (SOFI)

NASA Technical Reports Server (NTRS)

Kharkovsky, S.; Zoughi, R.; Hepburn, Frank L.

2006-01-01

Space Shuttle Columbia's catastrophic failure has been attributed to a piece of spray-on-foam insulation (SOFI) that was dislodged from the external tank (ET) and struck the leading edge of the left wing. A piece of SOFI was also dislodged in the Space Shuttle Discovery's flight in 2005 and recently a crack was detected in its ET foam prior to its successful launch. Millimeter wave nondestructive testing methods have been considered as potential effective inspection tools for evaluating the integrity of the SOFI. Recently, in a specific investigation into the potential of these methods for detecting vertical cracks in SOFI was explored using a focused millimeter wave reflectometer at 150 GHz. The results showed the capability of these methods for detecting tight vertical cracks (also as a function of crack opening dimension) in exposed SOFI panels and while covered by a piece of SOFI ramp simulating a more realistic and challenging situation. Some crack-like anomalies were also detected in a blind SOFI panel. This paper presents the background for these techniques as well as representative images of the vertical crack in the SOFI panel, crack-like anomalies in the blind panel and a discussion of the practical attributes of these inspection methods.

Semi-Metric Topology of the Human Connectome: Sensitivity and Specificity to Autism and Major Depressive Disorder.

PubMed

Simas, Tiago; Chattopadhyay, Shayanti; Hagan, Cindy; Kundu, Prantik; Patel, Ameera; Holt, Rosemary; Floris, Dorothea; Graham, Julia; Ooi, Cinly; Tait, Roger; Spencer, Michael; Baron-Cohen, Simon; Sahakian, Barbara; Bullmore, Ed; Goodyer, Ian; Suckling, John

2015-01-01

The human functional connectome is a graphical representation, consisting of nodes connected by edges, of the inter-relationships of blood oxygenation-level dependent (BOLD) time-series measured by MRI from regions encompassing the cerebral cortices and, often, the cerebellum. Semi-metric analysis of the weighted, undirected connectome distinguishes an edge as either direct (metric), such that there is no alternative path that is accumulatively stronger, or indirect (semi-metric), where one or more alternative paths exist that have greater strength than the direct edge. The sensitivity and specificity of this method of analysis is illustrated by two case-control analyses with independent, matched groups of adolescents with autism spectrum conditions (ASC) and major depressive disorder (MDD). Significance differences in the global percentage of semi-metric edges was observed in both groups, with increases in ASC and decreases in MDD relative to controls. Furthermore, MDD was associated with regional differences in left frontal and temporal lobes, the right limbic system and cerebellum. In contrast, ASC had a broadly increased percentage of semi-metric edges with a more generalised distribution of effects and some areas of reduction. In summary, MDD was characterised by localised, large reductions in the percentage of semi-metric edges, whilst ASC is characterised by more generalised, subtle increases. These differences were corroborated in greater detail by inspection of the semi-metric backbone for each group; that is, the sub-graph of semi-metric edges present in >90% of participants, and by nodal degree differences in the semi-metric connectome. These encouraging results, in what we believe is the first application of semi-metric analysis to neuroimaging data, raise confidence in the methodology as potentially capable of detection and characterisation of a range of neurodevelopmental and psychiatric disorders.

Semi-Metric Topology of the Human Connectome: Sensitivity and Specificity to Autism and Major Depressive Disorder

PubMed Central

Simas, Tiago; Chattopadhyay, Shayanti; Hagan, Cindy; Kundu, Prantik; Patel, Ameera; Holt, Rosemary; Floris, Dorothea; Graham, Julia; Ooi, Cinly; Tait, Roger; Spencer, Michael; Baron-Cohen, Simon; Sahakian, Barbara; Bullmore, Ed; Goodyer, Ian; Suckling, John

2015-01-01

Introduction The human functional connectome is a graphical representation, consisting of nodes connected by edges, of the inter-relationships of blood oxygenation-level dependent (BOLD) time-series measured by MRI from regions encompassing the cerebral cortices and, often, the cerebellum. Semi-metric analysis of the weighted, undirected connectome distinguishes an edge as either direct (metric), such that there is no alternative path that is accumulatively stronger, or indirect (semi-metric), where one or more alternative paths exist that have greater strength than the direct edge. The sensitivity and specificity of this method of analysis is illustrated by two case-control analyses with independent, matched groups of adolescents with autism spectrum conditions (ASC) and major depressive disorder (MDD). Results Significance differences in the global percentage of semi-metric edges was observed in both groups, with increases in ASC and decreases in MDD relative to controls. Furthermore, MDD was associated with regional differences in left frontal and temporal lobes, the right limbic system and cerebellum. In contrast, ASC had a broadly increased percentage of semi-metric edges with a more generalised distribution of effects and some areas of reduction. In summary, MDD was characterised by localised, large reductions in the percentage of semi-metric edges, whilst ASC is characterised by more generalised, subtle increases. These differences were corroborated in greater detail by inspection of the semi-metric backbone for each group; that is, the sub-graph of semi-metric edges present in >90% of participants, and by nodal degree differences in the semi-metric connectome. Conclusion These encouraging results, in what we believe is the first application of semi-metric analysis to neuroimaging data, raise confidence in the methodology as potentially capable of detection and characterisation of a range of neurodevelopmental and psychiatric disorders. PMID:26308854

Factors affecting volume calculation with single photon emission tomography (SPECT) method

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

Liu, T.H.; Lee, K.H.; Chen, D.C.P.

1985-05-01

Several factors may influence the calculation of absolute volumes (VL) from SPECT images. The effect of these factors must be established to optimize the technique. The authors investigated the following on the VL calculations: % of background (BG) subtraction, reconstruction filters, sample activity, angular sampling and edge detection methods. Transaxial images of a liver-trunk phantom filled with Tc-99m from 1 to 3 ..mu..Ci/cc were obtained in 64x64 matrix with a Siemens Rota Camera and MDS computer. Different reconstruction filters including Hanning 20,32, 64 and Butterworth 20, 32 were used. Angular samplings were performed in 3 and 6 degree increments. ROI'smore » were drawn manually and with an automatic edge detection program around the image after BG subtraction. VL's were calculated by multiplying the number of pixels within the ROI by the slice thickness and the x- and y- calibrations of each pixel. One or 2 pixel per slice thickness was applied in the calculation. An inverse correlation was found between the calculated VL and the % of BG subtraction (r=0.99 for 1,2,3 ..mu..Ci/cc activity). Based on the authors' linear regression analysis, the correct liver VL was measured with about 53% BG subtraction. The reconstruction filters, slice thickness and angular sampling had only minor effects on the calculated phantom volumes. Detection of the ROI automatically by the computer was not as accurate as the manual method. The authors conclude that the % of BG subtraction appears to be the most important factor affecting the VL calculation. With good quality control and appropriate reconstruction factors, correct VL calculations can be achieved with SPECT.« less

Context-aided analysis of community evolution in networks

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

Pallotta, Giuliana; Konjevod, Goran; Cadena, Jose

Here, we are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure basedmore » on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. In conclusion, we offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.« less

Context-aided analysis of community evolution in networks

DOE PAGES

Pallotta, Giuliana; Konjevod, Goran; Cadena, Jose; ...

2017-09-15

Here, we are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure basedmore » on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. In conclusion, we offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.« less

Lane detection using Randomized Hough Transform

NASA Astrophysics Data System (ADS)

Mongkonyong, Peerawat; Nuthong, Chaiwat; Siddhichai, Supakorn; Yamakita, Masaki

2018-01-01

According to the report of the Royal Thai Police between 2006 and 2015, lane changing without consciousness is one of the most accident causes. To solve this problem, many methods are considered. Lane Departure Warning System (LDWS) is considered to be one of the potential solutions. LDWS is a mechanism designed to warn the driver when the vehicle begins to move out of its current lane. LDWS contains many parts including lane boundary detection, driver warning and lane marker tracking. This article focuses on the lane boundary detection part. The proposed lane boundary detection detects the lines of the image from the input video and selects the lane marker of the road surface from those lines. Standard Hough Transform (SHT) and Randomized Hough Transform (RHT) are considered in this article. They are used to extract lines of an image. SHT extracts the lines from all of the edge pixels. RHT extracts only the lines randomly picked by the point pairs from edge pixels. RHT algorithm reduces the time and memory usage when compared with SHT. The increase of the threshold value in RHT will increase the voted limit of the line that has a high possibility to be the lane marker, but it also consumes the time and memory. By comparison between SHT and RHT with the different threshold values, 500 frames of input video from the front car camera will be processed. The accuracy and the computational time of RHT are similar to those of SHT in the result of the comparison.

Multi-resolution analysis for region of interest extraction in thermographic nondestructive evaluation

NASA Astrophysics Data System (ADS)

Ortiz-Jaramillo, B.; Fandiño Toro, H. A.; Benitez-Restrepo, H. D.; Orjuela-Vargas, S. A.; Castellanos-Domínguez, G.; Philips, W.

2012-03-01

Infrared Non-Destructive Testing (INDT) is known as an effective and rapid method for nondestructive inspection. It can detect a broad range of near-surface structuring flaws in metallic and composite components. Those flaws are modeled as a smooth contour centered at peaks of stored thermal energy, termed Regions of Interest (ROI). Dedicated methodologies must detect the presence of those ROIs. In this paper, we present a methodology for ROI extraction in INDT tasks. The methodology deals with the difficulties due to the non-uniform heating. The non-uniform heating affects low spatial/frequencies and hinders the detection of relevant points in the image. In this paper, a methodology for ROI extraction in INDT using multi-resolution analysis is proposed, which is robust to ROI low contrast and non-uniform heating. The former methodology includes local correlation, Gaussian scale analysis and local edge detection. In this methodology local correlation between image and Gaussian window provides interest points related to ROIs. We use a Gaussian window because thermal behavior is well modeled by Gaussian smooth contours. Also, the Gaussian scale is used to analyze details in the image using multi-resolution analysis avoiding low contrast, non-uniform heating and selection of the Gaussian window size. Finally, local edge detection is used to provide a good estimation of the boundaries in the ROI. Thus, we provide a methodology for ROI extraction based on multi-resolution analysis that is better or equal compared with the other dedicate algorithms proposed in the state of art.

Model for detection and assessment of abiotic stress caused by uranium mining in European Black Pine landscapes

NASA Astrophysics Data System (ADS)

Filchev, Lachezar; Roumenina, Eugenia

2013-10-01

The article presents the results obtained from a study for detection and assessment of abiotic stress through pollution with heavy metals, metalloids, and natural radionuclides in European Black Pine (Pinus nigra L.) forests caused by uranium mining using ground-based biogeochemical, biophysical, and field spectrometry data. The forests are located on a territory subject to underground and open uranium mining. An operational model of the study is proposed. The areas subject to technogeochemical load are outlined based on the aggregate pollution index Zc. Laboratory and field spectrometry data were used to detect the signals of abiotic stress at pixel level. The methods used for determination of stressed and unstressed black pine forests are: four vegetation indices (TCARI, MCARI, MTVI 2, and PRI 1) for stress detection, and the position, depth, asymmetry, and shift of the red-edge. Based on the "blue shift" and the depth and position of the red-edge, registered by the laboratory analysis and field spectral reflectance, it is established that coniferous forests subject to abiotic stress show an increase in total chlorophyll content and carotene. It has been found that the vegetation indices MTVI 2 and PRI 1, as well as the combination of vegetation indices and pigments may be used as a direct indicator of abiotic stress in coniferous forests caused by uranium mining.

A generalised significance test for individual communities in networks.

PubMed

Kojaku, Sadamori; Masuda, Naoki

2018-05-09

Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various aspects such as the size, density of edges, connectivity to other communities and significance. In the present study, we propose a method to statistically test the significance of individual communities in a given network. Compared to the previous methods, the present algorithm is unique in that it accepts different community-detection algorithms and the corresponding quality function for single communities. The present method requires that a quality of each community can be quantified and that community detection is performed as optimisation of such a quality function summed over the communities. Various community detection algorithms including modularity maximisation and graph partitioning meet this criterion. Our method estimates a distribution of the quality function for randomised networks to calculate a likelihood of each community in the given network. We illustrate our algorithm by synthetic and empirical networks.

Damage assessment in composite laminates via broadband Lamb wave.

PubMed

Gao, Fei; Zeng, Liang; Lin, Jing; Shao, Yongsheng

2018-05-01

Time of flight (ToF) based method for damage detection using Lamb waves is widely used. However, due to the energy dissipation of Lamb waves and the non-ignorable size of damage in composite structure, the performance of damage detection is restricted. The objective of this research is to establish an improved method to locate and assess damages in composite structure. To choose appropriate excitation parameters, the propagation characters of Lamb waves in quasi-isotropic composite laminates are firstly studied and the broadband excitation is designed. Subsequently, the pulse compression technique is adopted for energy concentration and high-accuracy distance estimation. On this basis, the gravity center of intersections of path loci is employed for damage localization and the convex envelop of identified damage edge points is taken for damage contour estimation. As a result, both damage location and size can be evaluated, thereby providing the information for quantitative damage detection. The experiment consisting of five different sizes of damage is carried for method verification and the identified results show the efficiency of the proposed method. Copyright © 2018 Elsevier B.V. All rights reserved.

Improving flow patterns and spillage characteristics of a box-type commercial kitchen hood.

PubMed

Huang, Rong Fung; Chen, Jia-Kun; Han, Meng-Ji; Priyambodo, Yusuf

2014-01-01

A conventional box-type commercial kitchen hood and its improved version (termed the "IQV commercial kitchen hood") were studied using the laser-assisted smoke flow visualization technique and tracer-gas (sulfur hexafluoride) detection methods. The laser-assisted smoke flow visualization technique qualitatively revealed the flow field of the hood and the areas apt for leakages of hood containment. The tracer-gas concentration detection method measured the quantitative leakage levels of the hood containment. The oil mists that were generated in the conventional box-type commercial kitchen hood leaked significantly into the environment from the areas near the front edges of ceiling and side walls. Around these areas, the boundary-layer separation occurred, inducing highly unsteady and turbulent recirculating flow, and leading to spillages of hood containment due to inappropriate aerodynamic design at the front edges of the ceiling and side walls. The tracer-gas concentration measurements on the conventional box-type commercial kitchen hood showed that the sulfur hexafluoride concentrations detected at the hood face attained very large values on an order of magnitude about 10(3)-10(4) ppb. By combining the backward-offset narrow suction slot, deflection plates, and quarter-circular arcs at the hood entrance, the IQV commercial kitchen hood presented a flow field containing four backward-inclined cyclone flow structures. The oil mists generated by cooking were coherently confined in these upward-rising cyclone flow structures and finally exhausted through the narrow suction slot. The tracer-gas concentration measurements on the IQV commercial kitchen hood showed that the order of magnitude of the sulfur hexafluoride concentrations detected at the hood face is negligibly small--only about 10(0) ppb across the whole hood face.

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.

A study on efficient detection of network-based IP spoofing DDoS and malware-infected Systems.

PubMed

Seo, Jung Woo; Lee, Sang Jin

2016-01-01

Large-scale network environments require effective detection and response methods against DDoS attacks. Depending on the advancement of IT infrastructure such as the server or network equipment, DDoS attack traffic arising from a few malware-infected systems capable of crippling the organization's internal network has become a significant threat. This study calculates the frequency of network-based packet attributes and analyzes the anomalies of the attributes in order to detect IP-spoofed DDoS attacks. Also, a method is proposed for the effective detection of malware infection systems triggering IP-spoofed DDoS attacks on an edge network. Detection accuracy and performance of the collected real-time traffic on a core network is analyzed thru the use of the proposed algorithm, and a prototype was developed to evaluate the performance of the algorithm. As a result, DDoS attacks on the internal network were detected in real-time and whether or not IP addresses were spoofed was confirmed. Detecting hosts infected by malware in real-time allowed the execution of intrusion responses before stoppage of the internal network caused by large-scale attack traffic.

Segmentation of Brain Lesions in MRI and CT Scan Images: A Hybrid Approach Using k-Means Clustering and Image Morphology

NASA Astrophysics Data System (ADS)

Agrawal, Ritu; Sharma, Manisha; Singh, Bikesh Kumar

2018-04-01

Manual segmentation and analysis of lesions in medical images is time consuming and subjected to human errors. Automated segmentation has thus gained significant attention in recent years. This article presents a hybrid approach for brain lesion segmentation in different imaging modalities by combining median filter, k means clustering, Sobel edge detection and morphological operations. Median filter is an essential pre-processing step and is used to remove impulsive noise from the acquired brain images followed by k-means segmentation, Sobel edge detection and morphological processing. The performance of proposed automated system is tested on standard datasets using performance measures such as segmentation accuracy and execution time. The proposed method achieves a high accuracy of 94% when compared with manual delineation performed by an expert radiologist. Furthermore, the statistical significance test between lesion segmented using automated approach and that by expert delineation using ANOVA and correlation coefficient achieved high significance values of 0.986 and 1 respectively. The experimental results obtained are discussed in lieu of some recently reported studies.

Accurate detection of blood vessels improves the detection of exudates in color fundus images.

PubMed

Youssef, Doaa; Solouma, Nahed H

2012-12-01

Exudates are one of the earliest and most prevalent symptoms of diseases leading to blindness such as diabetic retinopathy and macular degeneration. Certain areas of the retina with such conditions are to be photocoagulated by laser to stop the disease progress and prevent blindness. Outlining these areas is dependent on outlining the lesions and the anatomic structures of the retina. In this paper, we provide a new method for the detection of blood vessels that improves the detection of exudates in fundus photographs. The method starts with an edge detection algorithm which results in a over segmented image. Then the new feature-based algorithm can be used to accurately detect the blood vessels. This algorithm considers the characteristics of a retinal blood vessel such as its width range, intensities and orientations for the purpose of selective segmentation. Because of its bulb shape and its color similarity with exudates, the optic disc can be detected using the common Hough transform technique. The extracted blood vessel tree and optic disc could be subtracted from the over segmented image to get an initial estimate of exudates. The final estimation of exudates can then be obtained by morphological reconstruction based on the appearance of exudates. This method is shown to be promising since it increases the sensitivity and specificity of exudates detection to 80% and 100% respectively. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Wisps in the outer edge of the Keeler Gap

NASA Astrophysics Data System (ADS)

Tiscareno, Matthew S.; Arnault, Ethan G.

2015-11-01

Superposed upon the relatively smooth outer edge of the Keeler Gap are a system of "wisps," which appear to be ring material protruding inward into the gap, usually with a sharp trailing edge and a smooth gradation back to the background edge location on the leading side (Porco et al. 2005, Science). The radial amplitude of wisps is usually 0.5 to 1 km, and their azimuthal extent is approximately a degree of longitude (~2400 km). Wisps are likely caused by an interplay between Daphnis (and perhaps other moons) and embedded moonlets within the ring, though the details remain unclear.Aside from the wisps, the Keeler Gap outer edge is the only one of the five sharp edges in the outer part of Saturn's A ring that is reasonably smooth in appearance (Tiscareno et al. 2005, DPS), with occultations indicating residuals less than 1 km upon a possibly non-zero eccentricity (R.G. French, personal communication, 2014). The other four (the inner and outer edges of the Encke Gap, the inner edge of the Keeler Gap, and the outer edge of the A ring itself) are characterized by wavy structure at moderate to high spatial frequencies, with amplitudes ranging from 2 to 30 km (Tiscareno et al. 2005, DPS).We will present a catalogue of wisp detections in Cassini images. We carry out repeated gaussian fits of the radial edge location in order to characterize edge structure and visually scan those fitted edges in order to detect wisps. With extensive coverage in longitude and in time, we will report on how wisps evolve and move, both within an orbit period and on longer timescales. We will also report on the frequency and interpretation of wisps that deviate from the standard morphology. We will discuss the implications of our results for the origin and nature of wisps, and for the larger picture of how masses interact within Saturn's rings.

Comparison of birds detected from roadside and off-road point counts in the Shenandoah National Park

USGS Publications Warehouse

Keller, C.M.E.; Fuller, M.R.; Ralph, C. John; Sauer, John R.; Droege, Sam

1995-01-01

Roadside point counts are generally used for large surveys to increase the number of samples. We examined differences in species detected from roadside versus off-road (200-m and 400-ha) point counts in the Shenandoah National Park. We also compared the list of species detected in the first 3 minutes to those detected in 10 minutes for potential species biases. Results from 81 paired roadside and off-road counts indicated that roadside counts had higher numbers of several edge species but did not have lower numbers of nonedge forest species. More individuals and species were detected from roadside points because of this increase in edge species. Sixty-five percent of the species detected in 10 minutes were recorded in the first 3 minutes.

X-Ray Phase Imaging for Breast Cancer Detection

DTIC Science & Technology

2011-09-01

the inline phase contrast imaging has good potential of greatly enhanc - ing the detection sensitivity and reducing radiation doses involved in the...the edge- enhancement generated by phase- contrast is generally useful for imaging the wrap, however, such edge- enhancements may lead interpretation...Kotre and I. P. Birch, “Phase contrast enhancement of x-ray mam- mography: A design study,” Phys. Med. Biol. 44, 2853–2866 (1999). 6F. Arfelli et al

Finding overlapping communities in multilayer networks

PubMed Central

Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

2018-01-01

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks. PMID:29694387

Finding overlapping communities in multilayer networks.

PubMed

Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

2018-01-01

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks.

A combined spectral and object-based approach to transparent cloud removal in an operational setting for Landsat ETM+

NASA Astrophysics Data System (ADS)

Watmough, Gary R.; Atkinson, Peter M.; Hutton, Craig W.

2011-04-01

The automated cloud cover assessment (ACCA) algorithm has provided automated estimates of cloud cover for the Landsat ETM+ mission since 2001. However, due to the lack of a band around 1.375 μm, cloud edges and transparent clouds such as cirrus cannot be detected. Use of Landsat ETM+ imagery for terrestrial land analysis is further hampered by the relatively long revisit period due to a nadir only viewing sensor. In this study, the ACCA threshold parameters were altered to minimise omission errors in the cloud masks. Object-based analysis was used to reduce the commission errors from the extended cloud filters. The method resulted in the removal of optically thin cirrus cloud and cloud edges which are often missed by other methods in sub-tropical areas. Although not fully automated, the principles of the method developed here provide an opportunity for using otherwise sub-optimal or completely unusable Landsat ETM+ imagery for operational applications. Where specific images are required for particular research goals the method can be used to remove cloud and transparent cloud helping to reduce bias in subsequent land cover classifications.

Distributed ice accretion sensor for smart aircraft structures

NASA Technical Reports Server (NTRS)

Gerardi, J. J.; Hickman, G. A.

1989-01-01

A distributed ice accretion sensor is presented, based on the concept of smart structures. Ice accretion is determined using spectral techniques to process signals from piezoelectric sensors integral to the airfoil skin. Frequency shifts in the leading edge structural skin modes are correlated to ice thickness. It is suggested that this method may be used to detect ice over large areas with minimal hardware. Results are presented from preliminary tests to measure simulated ice growth.

Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery

NASA Astrophysics Data System (ADS)

Zarco-Tejada, P. J.; Hornero, A.; Hernández-Clemente, R.; Beck, P. S. A.

2018-03-01

The operational monitoring of forest decline requires the development of remote sensing methods that are sensitive to the spatiotemporal variations of pigment degradation and canopy defoliation. In this context, the red-edge spectral region (RESR) was proposed in the past due to its combined sensitivity to chlorophyll content and leaf area variation. In this study, the temporal dimension of the RESR was evaluated as a function of forest decline using a radiative transfer method with the PROSPECT and 3D FLIGHT models. These models were used to generate synthetic pine stands simulating decline and recovery processes over time and explore the temporal rate of change of the red-edge chlorophyll index (CI) as compared to the trajectories obtained for the structure-related Normalized Difference Vegetation Index (NDVI). The temporal trend method proposed here consisted of using synthetic spectra to calculate the theoretical boundaries of the subspace for healthy and declining pine trees in the temporal domain, defined by CItime=n/CItime=n+1 vs. NDVItime=n/NDVItime=n+1. Within these boundaries, trees undergoing decline and recovery processes showed different trajectories through this subspace. The method was then validated using three high-resolution airborne hyperspectral images acquired at 40 cm resolution and 260 spectral bands of 6.5 nm full-width half-maximum (FWHM) over a forest with widespread tree decline, along with field-based monitoring of chlorosis and defoliation (i.e., 'decline' status) in 663 trees between the years 2015 and 2016. The temporal rate of change of chlorophyll vs. structural indices, based on reflectance spectra extracted from the hyperspectral images, was different for trees undergoing decline, and aligned towards the decline baseline established using the radiative transfer models. By contrast, healthy trees over time aligned towards the theoretically obtained healthy baseline. The applicability of this temporal trend method to the red-edge bands of the MultiSpectral Imager (MSI) instrument on board Sentinel-2a for operational forest status monitoring was also explored by comparing the temporal rate of change of the Sentinel-2-derived CI over areas with declining and healthy trees. Results demonstrated that the Sentinel-2a red-edge region was sensitive to the temporal dimension of forest condition, as the relationships obtained for pixels in healthy condition deviated from those of pixels undergoing decline.

Space-confined fabrication of silver nanodendrites and their enhanced SERS activity

NASA Astrophysics Data System (ADS)

Wang, Shuqi; Xu, Li-Ping; Wen, Yongqiang; Du, Hongwu; Wang, Shutao; Zhang, Xueji

2013-05-01

Here we report a controllable method based on electrodeposition to fabricate Ag nanodendrites (NDs) on a microwell patterned electrode. The microwell patterns on the ITO electrode are fabricated via the microcontact printing technique. By varying the microwell size and electrodeposition time, the morphology of metal deposits on the microwell patterned ITO electrode can be tuned from boulders to dendrites. At the edge of the microwells, the current density was strengthened, which incurs rapid nucleation. The nucleus develops into dendrites because of Mullins-Sekerka instability. However, only boulders were observed at the center of microwells. By reducing the size of the microwells, only NDs were fabricated due to the edge effect. On the basis of understanding the underlying mechanism for dendritic growth in a confined space, our method is used for fabricating other noble metal (Au, Pt) nanodendrites. The controllable synthesis of Au and Pt NDs indicates the universality of this method. Compared with Ag film obtained from electron beam evaporation, the as-prepared Ag NDs exhibit highly enhanced surface-enhanced Raman scattering (SERS) sensitivity when they are used to detect rhodamine 6G (R6G). This approach provides a very controllable, reliable and general way for space-confined fabricating the noble metal nanodendrite arrays which show great promise in catalysis, sensing, biomedicine, electronic and magnetic devices.Here we report a controllable method based on electrodeposition to fabricate Ag nanodendrites (NDs) on a microwell patterned electrode. The microwell patterns on the ITO electrode are fabricated via the microcontact printing technique. By varying the microwell size and electrodeposition time, the morphology of metal deposits on the microwell patterned ITO electrode can be tuned from boulders to dendrites. At the edge of the microwells, the current density was strengthened, which incurs rapid nucleation. The nucleus develops into dendrites because of Mullins-Sekerka instability. However, only boulders were observed at the center of microwells. By reducing the size of the microwells, only NDs were fabricated due to the edge effect. On the basis of understanding the underlying mechanism for dendritic growth in a confined space, our method is used for fabricating other noble metal (Au, Pt) nanodendrites. The controllable synthesis of Au and Pt NDs indicates the universality of this method. Compared with Ag film obtained from electron beam evaporation, the as-prepared Ag NDs exhibit highly enhanced surface-enhanced Raman scattering (SERS) sensitivity when they are used to detect rhodamine 6G (R6G). This approach provides a very controllable, reliable and general way for space-confined fabricating the noble metal nanodendrite arrays which show great promise in catalysis, sensing, biomedicine, electronic and magnetic devices. Electronic supplementary information (ESI) available: SEM images. See DOI: 10.1039/c3nr00313b

Multiscale infrared and visible image fusion using gradient domain guided image filtering

NASA Astrophysics Data System (ADS)

Zhu, Jin; Jin, Weiqi; Li, Li; Han, Zhenghao; Wang, Xia

2018-03-01

For better surveillance with infrared and visible imaging, a novel hybrid multiscale decomposition fusion method using gradient domain guided image filtering (HMSD-GDGF) is proposed in this study. In this method, hybrid multiscale decomposition with guided image filtering and gradient domain guided image filtering of source images are first applied before the weight maps of each scale are obtained using a saliency detection technology and filtering means with three different fusion rules at different scales. The three types of fusion rules are for small-scale detail level, large-scale detail level, and base level. Finally, the target becomes more salient and can be more easily detected in the fusion result, with the detail information of the scene being fully displayed. After analyzing the experimental comparisons with state-of-the-art fusion methods, the HMSD-GDGF method has obvious advantages in fidelity of salient information (including structural similarity, brightness, and contrast), preservation of edge features, and human visual perception. Therefore, visual effects can be improved by using the proposed HMSD-GDGF method.

A novel method for identifying a graph-based representation of 3-D microvascular networks from fluorescence microscopy image stacks.

PubMed

Almasi, Sepideh; Xu, Xiaoyin; Ben-Zvi, Ayal; Lacoste, Baptiste; Gu, Chenghua; Miller, Eric L

2015-02-01

A novel approach to determine the global topological structure of a microvasculature network from noisy and low-resolution fluorescence microscopy data that does not require the detailed segmentation of the vessel structure is proposed here. The method is most appropriate for problems where the tortuosity of the network is relatively low and proceeds by directly computing a piecewise linear approximation to the vasculature skeleton through the construction of a graph in three dimensions whose edges represent the skeletal approximation and vertices are located at Critical Points (CPs) on the microvasculature. The CPs are defined as vessel junctions or locations of relatively large curvature along the centerline of a vessel. Our method consists of two phases. First, we provide a CP detection technique that, for junctions in particular, does not require any a priori geometric information such as direction or degree. Second, connectivity between detected nodes is determined via the solution of a Binary Integer Program (BIP) whose variables determine whether a potential edge between nodes is or is not included in the final graph. The utility function in this problem reflects both intensity-based and structural information along the path connecting the two nodes. Qualitative and quantitative results confirm the usefulness and accuracy of this method. This approach provides a mean of correctly capturing the connectivity patterns in vessels that are missed by more traditional segmentation and binarization schemes because of imperfections in the images which manifest as dim or broken vessels. Copyright © 2014 Elsevier B.V. All rights reserved.

Assessment of AVIRIS data from vegetated sites in the Owens Valley, California

NASA Technical Reports Server (NTRS)

Rock, B. N.; Elvidge, Christopher D.; Defeo, N. J.

1988-01-01

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were acquired from the Bishop, CA area, located at the northern end of the Owens Valley, on July 30, 1987. Radiometrically-corrected AVIRIS data were flat-field corrected, and spectral curves produced and analyzed for pixels taken from both native and cultivated vegetation sites, using the JPS SPAM software program and PC-based spreadsheet programs. Analyses focussed on the chlorophyll well and red edge portions of the spectral curves. Results include the following: AVIRIS spectral data are acquired at sufficient spectral resolution to allow detection of blue shifts of both the chlorophyll well and red edge in moisture-stressed vegetation when compared with non-stressed vegetation; a normalization of selected parameters (chlorophyll well and near infrared shoulder) may be used to emphasize the shift in red edge position; and the presence of the red edge in AVIRIS spectral curves may be useful in detecting small amounts (20 to 30 pct cover) of semi-arid and arid vegetation ground cover. A discussion of possible causes of AVIRIS red edge shifts in respsonse to stress is presented.

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

X-Ray Absorption Spectroscopy As a Probe of Microbial Sulfur Biochemistry: the Nature of Bacterial Sulfur Globules Revisited

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

George, G.N.; Gnida, M.; Bazylinski, D.A.

2009-05-18

The chemical nature of the sulfur in bacterial sulfur globules has been the subject of controversy for a number of years. Sulfur K-edge X-ray absorption spectroscopy (XAS) is a powerful technique for probing the chemical forms of sulfur in situ, but two groups have used it with very different conclusions. The root of the controversy lies with the different detection strategies used by the two groups, which result in very different spectra. This paper seeks to resolve the controversy. We experimentally demonstrate that the use of transmittance detection for sulfur K-edge XAS measurements is highly prone to spectroscopic distortions andmore » that much of the published work on sulfur bacteria is very likely based on distorted data. We also demonstrate that all three detection methods used for X-ray absorption experiments yield essentially identical spectra when the measurements are carried out under conditions where no experimental distortions are expected. Finally, we turn to the original question--the chemical nature of bacterial sulfur. We examine isolated sulfur globules of Allochromatium vinosum and intact cells of a strain of magnetotactic coccus and show that XAS indicates the presence of a chemical form of sulfur resembling S{sub 8}.« less

Linear Phase Sharp Transition BPF to Detect Noninvasive Maternal and Fetal Heart Rate.

PubMed

Marchon, Niyan; Naik, Gourish; Pai, K R

2018-01-01

Fetal heart rate (FHR) detection can be monitored using either direct fetal scalp electrode recording (invasive) or by indirect noninvasive technique. Weeks before delivery, the invasive method poses a risk factor to the fetus, while the latter provides accurate fetal ECG (FECG) information which can help diagnose fetal's well-being. Our technique employs variable order linear phase sharp transition (LPST) FIR band-pass filter which shows improved stopband attenuation at higher filter orders. The fetal frequency fiduciary edges form the band edges of the filter characterized by varying amounts of overlap of maternal ECG (MECG) spectrum. The one with the minimum maternal spectrum overlap was found to be optimum with no power line interference and maximum fetal heart beats being detected. The improved filtering is reflected in the enhancement of the performance of the fetal QRS detector (FQRS). The improvement has also occurred in fetal heart rate obtained using our algorithm which is in close agreement with the true reference (i.e., invasive fetal scalp ECG). The performance parameters of the FQRS detector such as sensitivity (Se), positive predictive value (PPV), and accuracy (F 1 ) were found to improve even for lower filter order. The same technique was extended to evaluate maternal QRS detector (MQRS) and found to yield satisfactory maternal heart rate (MHR) results.

Edge enhancement algorithm for low-dose X-ray fluoroscopic imaging.

PubMed

Lee, Min Seok; Park, Chul Hee; Kang, Moon Gi

2017-12-01

Low-dose X-ray fluoroscopy has continually evolved to reduce radiation risk to patients during clinical diagnosis and surgery. However, the reduction in dose exposure causes quality degradation of the acquired images. In general, an X-ray device has a time-average pre-processor to remove the generated quantum noise. However, this pre-processor causes blurring and artifacts within the moving edge regions, and noise remains in the image. During high-pass filtering (HPF) to enhance edge detail, this noise in the image is amplified. In this study, a 2D edge enhancement algorithm comprising region adaptive HPF with the transient improvement (TI) method, as well as artifacts and noise reduction (ANR), was developed for degraded X-ray fluoroscopic images. The proposed method was applied in a static scene pre-processed by a low-dose X-ray fluoroscopy device. First, the sharpness of the X-ray image was improved using region adaptive HPF with the TI method, which facilitates sharpening of edge details without overshoot problems. Then, an ANR filter that uses an edge directional kernel was developed to remove the artifacts and noise that can occur during sharpening, while preserving edge details. The quantitative and qualitative results obtained by applying the developed method to low-dose X-ray fluoroscopic images and visually and numerically comparing the final images with images improved using conventional edge enhancement techniques indicate that the proposed method outperforms existing edge enhancement methods in terms of objective criteria and subjective visual perception of the actual X-ray fluoroscopic image. The developed edge enhancement algorithm performed well when applied to actual low-dose X-ray fluoroscopic images, not only by improving the sharpness, but also by removing artifacts and noise, including overshoot. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparisons between conventional optical imaging and parametric indirect microscopic imaging on human skin detection

NASA Astrophysics Data System (ADS)

Liu, Guoyan; Gao, Kun; Liu, Xuefeng; Ni, Guoqiang

2016-10-01

We report a new method, polarization parameters indirect microscopic imaging with a high transmission infrared light source, to detect the morphology and component of human skin. A conventional reflection microscopic system is used as the basic optical system, into which a polarization-modulation mechanics is inserted and a high transmission infrared light source is utilized. The near-field structural characteristics of human skin can be delivered by infrared waves and material coupling. According to coupling and conduction physics, changes of the optical wave parameters can be calculated and curves of the intensity of the image can be obtained. By analyzing the near-field polarization parameters in nanoscale, we can finally get the inversion images of human skin. Compared with the conventional direct optical microscope, this method can break diffraction limit and achieve a super resolution of sub-100nm. Besides, the method is more sensitive to the edges, wrinkles, boundaries and impurity particles.

Automatic draft reading based on image processing

NASA Astrophysics Data System (ADS)

Tsujii, Takahiro; Yoshida, Hiromi; Iiguni, Youji

2016-10-01

In marine transportation, a draft survey is a means to determine the quantity of bulk cargo. Automatic draft reading based on computer image processing has been proposed. However, the conventional draft mark segmentation may fail when the video sequence has many other regions than draft marks and a hull, and the estimated waterline is inherently higher than the true one. To solve these problems, we propose an automatic draft reading method that uses morphological operations to detect draft marks and estimate the waterline for every frame with Canny edge detection and a robust estimation. Moreover, we emulate surveyors' draft reading process for getting the understanding of a shipper and a receiver. In an experiment in a towing tank, the draft reading error of the proposed method was <1 cm, showing the advantage of the proposed method. It is also shown that accurate draft reading has been achieved in a real-world scene.

The Double Edge Technique for Doppler lidar wind measurement

NASA Technical Reports Server (NTRS)

Korb, C. Laurence; Gentry, Bruce M.; Li, S. Xingfu; Flesia, Cristina; Chen, Huailin; Mathur, S.

1998-01-01

The edge technique utilizes the edge of a high spectral resolution filter for high accuracy wind measurement using direct detection lidar. The signal is split between an edge filter channel and a broadband energy monitor channel. The energy monitor channel is used for signal normalization. The edge measurement is made as a differential frequency measurement between the outgoing laser signal and the atmospheric backscattered return for each pulse. As a result, the measurement is insensitive to laser and edge filter frequency jitter and drift at a level less than a few parts in 10(exp 10). We will discuss the methodology of the technique in detail, present a broad range of simulation results, and provide preprints of a journal article currently in press.

Performance Characteristics of the Electronic Snow Water Equivalent (SWE) Sensor in Arctic, Marine, and Humid Continental Climates

NASA Astrophysics Data System (ADS)

Johnson, J.; Gelvin, A. B.; Duvoy, P.; Schaefer, G. L.; Poole, G.; Horton, G. D.

2011-12-01

The USA ERDC CRREL and the USDA NRCS developed a 3-m square electronic SWE sensor (e-SWE sensor) consisting of nine perforated panels (a center panel to measure SWE and eight outer panels to buffer edge stress concentrations). Seven e-SWE sensors were installed in five different climate zones including north central and north coastal Alaska, Oregon, Newfoundland, and New York State. With the exception of New York State, the e-SWE sensors accurately measured SWE. The e-SWE sensor at Hogg Pass, OR, accurately measured SWE during five years of observations even when edge stress concentrations occurred. In windy conditions of northern Alaska, the sensor measured losses and gains in SWE with more reliability and higher accuracy than other standard methods. The sensor also detected snowdrift migration (comparing video and sensor measurements). In the thin, icy snow of New York the electronic SWE sensors over-measured SWE during midwinter. Over-measurement errors were caused by edge stress concentrations associated with strong icy layers, a shallow snow cover and possibly using a backfill material with different thermal properties and a large freeboard compared to the surrounding soil . Measurement accuracy improved in spring due to increased snow creep, associated with warming snow temperatures, which reduced edge stress concentrations.

Performance of the EUCAST Disk Diffusion Method, the CLSI Agar Screen Method, and the Vitek 2 Automated Antimicrobial Susceptibility Testing System for Detection of Clinical Isolates of Enterococci with Low- and Medium-Level VanB-Type Vancomycin Resistance: a Multicenter Study

PubMed Central

Giske, Christian G.; Haldorsen, Bjørg; Matuschek, Erika; Schønning, Kristian; Leegaard, Truls M.; Kahlmeter, Gunnar

2014-01-01

Different antimicrobial susceptibility testing methods to detect low-level vancomycin resistance in enterococci were evaluated in a Scandinavian multicenter study (n = 28). A phenotypically and genotypically well-characterized diverse collection of Enterococcus faecalis (n = 12) and Enterococcus faecium (n = 18) strains with and without nonsusceptibility to vancomycin was examined blindly in Danish (n = 5), Norwegian (n = 13), and Swedish (n = 10) laboratories using the EUCAST disk diffusion method (n = 28) and the CLSI agar screen (n = 18) or the Vitek 2 system (bioMérieux) (n = 5). The EUCAST disk diffusion method (very major error [VME] rate, 7.0%; sensitivity, 0.93; major error [ME] rate, 2.4%; specificity, 0.98) and CLSI agar screen (VME rate, 6.6%; sensitivity, 0.93; ME rate, 5.6%; specificity, 0.94) performed significantly better (P = 0.02) than the Vitek 2 system (VME rate, 13%; sensitivity, 0.87; ME rate, 0%; specificity, 1). The performance of the EUCAST disk diffusion method was challenged by differences in vancomycin inhibition zone sizes as well as the experience of the personnel in interpreting fuzzy zone edges as an indication of vancomycin resistance. Laboratories using Oxoid agar (P < 0.0001) or Merck Mueller-Hinton (MH) agar (P = 0.027) for the disk diffusion assay performed significantly better than did laboratories using BBL MH II medium. Laboratories using Difco brain heart infusion (BHI) agar for the CLSI agar screen performed significantly better (P = 0.017) than did those using Oxoid BHI agar. In conclusion, both the EUCAST disk diffusion and CLSI agar screening methods performed acceptably (sensitivity, 0.93; specificity, 0.94 to 0.98) in the detection of VanB-type vancomycin-resistant enterococci with low-level resistance. Importantly, use of the CLSI agar screen requires careful monitoring of the vancomycin concentration in the plates. Moreover, disk diffusion methodology requires that personnel be trained in interpreting zone edges. PMID:24599985

Electrostatically clean solar array

NASA Technical Reports Server (NTRS)

Stern, Theodore Garry (Inventor); Krumweide, Duane Eric (Inventor)

2004-01-01

Provided are methods of manufacturing an electrostatically clean solar array panel and the products resulting from the practice of these methods. The preferred method uses an array of solar cells, each with a coverglass where the method includes machining apertures into a flat, electrically conductive sheet so that each aperture is aligned with and undersized with respect to its matched coverglass sheet and thereby fashion a front side shield with apertures (FSA). The undersized portion about each aperture of the bottom side of the FSA shield is bonded to the topside portions nearest the edges of each aperture's matched coverglass. Edge clips are attached to the front side aperture shield edges with the edge clips electrically and mechanically connecting the tops of the coverglasses to the solar panel substrate. The FSA shield, edge clips and substrate edges are bonded so as to produce a conductively grounded electrostatically clean solar array panel.

Hypervelocity Impact (HVI). Volume 8; Tile Small Targets A-1, Ag-1, B-1, and Bg-1

NASA Technical Reports Server (NTRS)

Gorman, Michael R.; Ziola, Steven M.

2007-01-01

During 2003 and 2004, the Johnson Space Center's White Sands Testing Facility in Las Cruces, New Mexico conducted hypervelocity impact tests on the space shuttle wing leading edge. Hypervelocity impact tests were conducted to determine if Micro-Meteoroid/Orbital Debris impacts could be reliably detected and located using simple passive ultrasonic methods. The objective of Targets A-1, Ag-1, B-1, and Bg-1 was to study hypervelocity impacts on the reinforced Shuttle Heat Shield Tiles of the Wing. Impact damage was detected using lightweight, low power instrumentation capable of being used in flight.