Modeling Of Object- And Scene-Prototypes With Hierarchically Structured Classes

NASA Astrophysics Data System (ADS)

Ren, Z.; Jensch, P.; Ameling, W.

1989-03-01

The success of knowledge-based image analysis methodology and implementation tools depends largely on an appropriately and efficiently built model wherein the domain-specific context information about and the inherent structure of the observed image scene have been encoded. For identifying an object in an application environment a computer vision system needs to know firstly the description of the object to be found in an image or in an image sequence, secondly the corresponding relationships between object descriptions within the image sequence. This paper presents models of image objects scenes by means of hierarchically structured classes. Using the topovisual formalism of graph and higraph, we are currently studying principally the relational aspect and data abstraction of the modeling in order to visualize the structural nature resident in image objects and scenes, and to formalize. their descriptions. The goal is to expose the structure of image scene and the correspondence of image objects in the low level image interpretation. process. The object-based system design approach has been applied to build the model base. We utilize the object-oriented programming language C + + for designing, testing and implementing the abstracted entity classes and the operation structures which have been modeled topovisually. The reference images used for modeling prototypes of objects and scenes are from industrial environments as'well as medical applications.

A focus of attention mechanism for gaze control within a framework for intelligent image analysis tools

NASA Astrophysics Data System (ADS)

Rodrigo, Ranga P.; Ranaweera, Kamal; Samarabandu, Jagath K.

2004-05-01

Focus of attention is often attributed to biological vision system where the entire field of view is first monitored and then the attention is focused to the object of interest. We propose using a similar approach for object recognition in a color image sequence. The intention is to locate an object based on a prior motive, concentrate on the detected object so that the imaging device can be guided toward it. We use the abilities of the intelligent image analysis framework developed in our laboratory to generate an algorithm dynamically to detect the particular type of object based on the user's object description. The proposed method uses color clustering along with segmentation. The segmented image with labeled regions is used to calculate the shape descriptor parameters. These and the color information are matched with the input description. Gaze is then controlled by issuing camera movement commands as appropriate. We present some preliminary results that demonstrate the success of this approach.

Land Cover Analysis by Using Pixel-Based and Object-Based Image Classification Method in Bogor

NASA Astrophysics Data System (ADS)

Amalisana, Birohmatin; Rokhmatullah; Hernina, Revi

2017-12-01

The advantage of image classification is to provide earth’s surface information like landcover and time-series changes. Nowadays, pixel-based image classification technique is commonly performed with variety of algorithm such as minimum distance, parallelepiped, maximum likelihood, mahalanobis distance. On the other hand, landcover classification can also be acquired by using object-based image classification technique. In addition, object-based classification uses image segmentation from parameter such as scale, form, colour, smoothness and compactness. This research is aimed to compare the result of landcover classification and its change detection between parallelepiped pixel-based and object-based classification method. Location of this research is Bogor with 20 years range of observation from 1996 until 2016. This region is famous as urban areas which continuously change due to its rapid development, so that time-series landcover information of this region will be interesting.

Comparison of classification algorithms for various methods of preprocessing radar images of the MSTAR base

NASA Astrophysics Data System (ADS)

Borodinov, A. A.; Myasnikov, V. V.

2018-04-01

The present work is devoted to comparing the accuracy of the known qualification algorithms in the task of recognizing local objects on radar images for various image preprocessing methods. Preprocessing involves speckle noise filtering and normalization of the object orientation in the image by the method of image moments and by a method based on the Hough transform. In comparison, the following classification algorithms are used: Decision tree; Support vector machine, AdaBoost, Random forest. The principal component analysis is used to reduce the dimension. The research is carried out on the objects from the base of radar images MSTAR. The paper presents the results of the conducted studies.

Multiple Object Retrieval in Image Databases Using Hierarchical Segmentation Tree

ERIC Educational Resources Information Center

Chen, Wei-Bang

2012-01-01

The purpose of this research is to develop a new visual information analysis, representation, and retrieval framework for automatic discovery of salient objects of user's interest in large-scale image databases. In particular, this dissertation describes a content-based image retrieval framework which supports multiple-object retrieval. The…

A knowledge-based machine vision system for space station automation

NASA Technical Reports Server (NTRS)

Chipman, Laure J.; Ranganath, H. S.

1989-01-01

A simple knowledge-based approach to the recognition of objects in man-made scenes is being developed. Specifically, the system under development is a proposed enhancement to a robot arm for use in the space station laboratory module. The system will take a request from a user to find a specific object, and locate that object by using its camera input and information from a knowledge base describing the scene layout and attributes of the object types included in the scene. In order to use realistic test images in developing the system, researchers are using photographs of actual NASA simulator panels, which provide similar types of scenes to those expected in the space station environment. Figure 1 shows one of these photographs. In traditional approaches to image analysis, the image is transformed step by step into a symbolic representation of the scene. Often the first steps of the transformation are done without any reference to knowledge of the scene or objects. Segmentation of an image into regions generally produces a counterintuitive result in which regions do not correspond to objects in the image. After segmentation, a merging procedure attempts to group regions into meaningful units that will more nearly correspond to objects. Here, researchers avoid segmenting the image as a whole, and instead use a knowledge-directed approach to locate objects in the scene. The knowledge-based approach to scene analysis is described and the categories of knowledge used in the system are discussed.

Man-made objects cuing in satellite imagery

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

Skurikhin, Alexei N

2009-01-01

We present a multi-scale framework for man-made structures cuing in satellite image regions. The approach is based on a hierarchical image segmentation followed by structural analysis. A hierarchical segmentation produces an image pyramid that contains a stack of irregular image partitions, represented as polygonized pixel patches, of successively reduced levels of detail (LOOs). We are jumping off from the over-segmented image represented by polygons attributed with spectral and texture information. The image is represented as a proximity graph with vertices corresponding to the polygons and edges reflecting polygon relations. This is followed by the iterative graph contraction based on Boruvka'smore » Minimum Spanning Tree (MST) construction algorithm. The graph contractions merge the patches based on their pairwise spectral and texture differences. Concurrently with the construction of the irregular image pyramid, structural analysis is done on the agglomerated patches. Man-made object cuing is based on the analysis of shape properties of the constructed patches and their spatial relations. The presented framework can be used as pre-scanning tool for wide area monitoring to quickly guide the further analysis to regions of interest.« less

Comparison of Object-Based Image Analysis Approaches to Mapping New Buildings in Accra, Ghana Using Multi-Temporal QuickBird Satellite Imagery

PubMed Central

Tsai, Yu Hsin; Stow, Douglas; Weeks, John

2013-01-01

The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings based on QuickBird very high spatial resolution satellite imagery were analyzed: (1) post-classification comparison; and (2) bi-temporal layerstack classification. Feature Analyst software based on a spatial contextual classifier and ENVI Feature Extraction that uses a true object-based image analysis approach of image segmentation and segment classification were evaluated. Final map products representing new building objects were compared and assessed for accuracy using two object-based accuracy measures, completeness and correctness. The bi-temporal layerstack method generated more accurate results compared to the post-classification comparison method due to less confusion with background objects. The spectral/spatial contextual approach (Feature Analyst) outperformed the true object-based feature delineation approach (ENVI Feature Extraction) due to its ability to more reliably delineate individual buildings of various sizes. Semi-automated, object-based detection followed by manual editing appears to be a reliable and efficient approach for detecting and enumerating new building objects. A bivariate regression analysis was performed using neighborhood-level estimates of new building density regressed on a census-derived measure of socio-economic status, yielding an inverse relationship with R2 = 0.31 (n = 27; p = 0.00). The primary utility of the new building delineation results is to support spatial analyses of land cover and land use and demographic change. PMID:24415810

Image sequence analysis workstation for multipoint motion analysis

NASA Astrophysics Data System (ADS)

Mostafavi, Hassan

1990-08-01

This paper describes an application-specific engineering workstation designed and developed to analyze motion of objects from video sequences. The system combines the software and hardware environment of a modem graphic-oriented workstation with the digital image acquisition, processing and display techniques. In addition to automation and Increase In throughput of data reduction tasks, the objective of the system Is to provide less invasive methods of measurement by offering the ability to track objects that are more complex than reflective markers. Grey level Image processing and spatial/temporal adaptation of the processing parameters is used for location and tracking of more complex features of objects under uncontrolled lighting and background conditions. The applications of such an automated and noninvasive measurement tool include analysis of the trajectory and attitude of rigid bodies such as human limbs, robots, aircraft in flight, etc. The system's key features are: 1) Acquisition and storage of Image sequences by digitizing and storing real-time video; 2) computer-controlled movie loop playback, freeze frame display, and digital Image enhancement; 3) multiple leading edge tracking in addition to object centroids at up to 60 fields per second from both live input video or a stored Image sequence; 4) model-based estimation and tracking of the six degrees of freedom of a rigid body: 5) field-of-view and spatial calibration: 6) Image sequence and measurement data base management; and 7) offline analysis software for trajectory plotting and statistical analysis.

The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cooke, diane J.; Ketkar, Nikhil; Aksoy, Selim

2008-01-01

Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

Evaluation of Alzheimer's disease by analysis of MR images using Objective Dialectical Classifiers as an alternative to ADC maps.

PubMed

Dos Santos, Wellington P; de Assis, Francisco M; de Souza, Ricardo E; Dos Santos Filho, Plinio B

2008-01-01

Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted (DW) magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on Objective Dialectical Classifiers, a new method based on Dialectics as defined in the Philosophy of Praxis. A 2-degree polynomial network with supervised training is used to generate the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.

A wavelet-based Bayesian framework for 3D object segmentation in microscopy

NASA Astrophysics Data System (ADS)

Pan, Kangyu; Corrigan, David; Hillebrand, Jens; Ramaswami, Mani; Kokaram, Anil

2012-03-01

In confocal microscopy, target objects are labeled with fluorescent markers in the living specimen, and usually appear with irregular brightness in the observed images. Also, due to the existence of out-of-focus objects in the image, the segmentation of 3-D objects in the stack of image slices captured at different depth levels of the specimen is still heavily relied on manual analysis. In this paper, a novel Bayesian model is proposed for segmenting 3-D synaptic objects from given image stack. In order to solve the irregular brightness and out-offocus problems, the segmentation model employs a likelihood using the luminance-invariant 'wavelet features' of image objects in the dual-tree complex wavelet domain as well as a likelihood based on the vertical intensity profile of the image stack in 3-D. Furthermore, a smoothness 'frame' prior based on the a priori knowledge of the connections of the synapses is introduced to the model for enhancing the connectivity of the synapses. As a result, our model can successfully segment the in-focus target synaptic object from a 3D image stack with irregular brightness.

[Object Separation from Medical X-Ray Images Based on ICA].

PubMed

Li, Yan; Yu, Chun-yu; Miao, Ya-jian; Fei, Bin; Zhuang, Feng-yun

2015-03-01

X-ray medical image can examine diseased tissue of patients and has important reference value for medical diagnosis. With the problems that traditional X-ray images have noise, poor level sense and blocked aliasing organs, this paper proposes a method for the introduction of multi-spectrum X-ray imaging and independent component analysis (ICA) algorithm to separate the target object. Firstly image de-noising preprocessing ensures the accuracy of target extraction based on independent component analysis and sparse code shrinkage. Then according to the main proportion of organ in the images, aliasing thickness matrix of each pixel was isolated. Finally independent component analysis obtains convergence matrix to reconstruct the target object with blind separation theory. In the ICA algorithm, it found that when the number is more than 40, the target objects separate successfully with the aid of subjective evaluation standard. And when the amplitudes of the scale are in the [25, 45] interval, the target images have high contrast and less distortion. The three-dimensional figure of Peak signal to noise ratio (PSNR) shows that the different convergence times and amplitudes have a greater influence on image quality. The contrast and edge information of experimental images achieve better effects with the convergence times 85 and amplitudes 35 in the ICA algorithm.

a Region-Based Multi-Scale Approach for Object-Based Image Analysis

NASA Astrophysics Data System (ADS)

Kavzoglu, T.; Yildiz Erdemir, M.; Tonbul, H.

2016-06-01

Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

Geographic Object-Based Image Analysis - Towards a new paradigm.

PubMed

Blaschke, Thomas; Hay, Geoffrey J; Kelly, Maggi; Lang, Stefan; Hofmann, Peter; Addink, Elisabeth; Queiroz Feitosa, Raul; van der Meer, Freek; van der Werff, Harald; van Coillie, Frieke; Tiede, Dirk

2014-01-01

The amount of scientific literature on (Geographic) Object-based Image Analysis - GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ' per-pixel paradigm ' and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.

Geographic Object-Based Image Analysis – Towards a new paradigm

PubMed Central

Blaschke, Thomas; Hay, Geoffrey J.; Kelly, Maggi; Lang, Stefan; Hofmann, Peter; Addink, Elisabeth; Queiroz Feitosa, Raul; van der Meer, Freek; van der Werff, Harald; van Coillie, Frieke; Tiede, Dirk

2014-01-01

The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm. PMID:24623958

Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images

NASA Astrophysics Data System (ADS)

Maboudi, Mehdi; Amini, Jalal; Malihi, Shirin; Hahn, Michael

2018-04-01

Updated road network as a crucial part of the transportation database plays an important role in various applications. Thus, increasing the automation of the road extraction approaches from remote sensing images has been the subject of extensive research. In this paper, we propose an object based road extraction approach from very high resolution satellite images. Based on the object based image analysis, our approach incorporates various spatial, spectral, and textural objects' descriptors, the capabilities of the fuzzy logic system for handling the uncertainties in road modelling, and the effectiveness and suitability of ant colony algorithm for optimization of network related problems. Four VHR optical satellite images which are acquired by Worldview-2 and IKONOS satellites are used in order to evaluate the proposed approach. Evaluation of the extracted road networks shows that the average completeness, correctness, and quality of the results can reach 89%, 93% and 83% respectively, indicating that the proposed approach is applicable for urban road extraction. We also analyzed the sensitivity of our algorithm to different ant colony optimization parameter values. Comparison of the achieved results with the results of four state-of-the-art algorithms and quantifying the robustness of the fuzzy rule set demonstrate that the proposed approach is both efficient and transferable to other comparable images.

Digital Image Analysis System for Monitoring Crack Growth at Elevated Temperature

DTIC Science & Technology

1988-05-01

The objective of the research work reported here was to develop a new concept, based on Digital Image Analysis , for monitoring the crack-tip position...a 512 x 512 pixel frame. c) Digital Image Analysis software developed to locate and digitize the position of the crack-tip, on the observed image

Demonstrating Change with Astronaut Photography Using Object Based Image Analysis

NASA Technical Reports Server (NTRS)

Hollier, Andi; Jagge, Amy

2017-01-01

Every day, hundreds of images of Earth flood the Crew Earth Observations database as astronauts use hand held digital cameras to capture spectacular frames from the International Space Station. The variety of resolutions and perspectives provide a template for assessing land cover change over decades. We will focus on urban growth in the second fastest growing city in the nation, Houston, TX, using Object-Based Image Analysis. This research will contribute to the land change science community, integrated resource planning, and monitoring of the rapid rate of urban sprawl.

Software for Analyzing Sequences of Flow-Related Images

NASA Technical Reports Server (NTRS)

Klimek, Robert; Wright, Ted

2004-01-01

Spotlight is a computer program for analysis of sequences of images generated in combustion and fluid physics experiments. Spotlight can perform analysis of a single image in an interactive mode or a sequence of images in an automated fashion. The primary type of analysis is tracking of positions of objects over sequences of frames. Features and objects that are typically tracked include flame fronts, particles, droplets, and fluid interfaces. Spotlight automates the analysis of object parameters, such as centroid position, velocity, acceleration, size, shape, intensity, and color. Images can be processed to enhance them before statistical and measurement operations are performed. An unlimited number of objects can be analyzed simultaneously. Spotlight saves results of analyses in a text file that can be exported to other programs for graphing or further analysis. Spotlight is a graphical-user-interface-based program that at present can be executed on Microsoft Windows and Linux operating systems. A version that runs on Macintosh computers is being considered.

Congruence analysis of point clouds from unstable stereo image sequences

NASA Astrophysics Data System (ADS)

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

2014-06-01

This paper deals with the correction of exterior orientation parameters of stereo image sequences over deformed free-form surfaces without control points. Such imaging situation can occur, for example, during photogrammetric car crash test recordings where onboard high-speed stereo cameras are used to measure 3D surfaces. As a result of such measurements 3D point clouds of deformed surfaces are generated for a complete stereo sequence. The first objective of this research focusses on the development and investigation of methods for the detection of corresponding spatial and temporal tie points within the stereo image sequences (by stereo image matching and 3D point tracking) that are robust enough for a reliable handling of occlusions and other disturbances that may occur. The second objective of this research is the analysis of object deformations in order to detect stable areas (congruence analysis). For this purpose a RANSAC-based method for congruence analysis has been developed. This process is based on the sequential transformation of randomly selected point groups from one epoch to another by using a 3D similarity transformation. The paper gives a detailed description of the congruence analysis. The approach has been tested successfully on synthetic and real image data.

Objects Grouping for Segmentation of Roads Network in High Resolution Images of Urban Areas

NASA Astrophysics Data System (ADS)

Maboudi, M.; Amini, J.; Hahn, M.

2016-06-01

Updated road databases are required for many purposes such as urban planning, disaster management, car navigation, route planning, traffic management and emergency handling. In the last decade, the improvement in spatial resolution of VHR civilian satellite sensors - as the main source of large scale mapping applications - was so considerable that GSD has become finer than size of common urban objects of interest such as building, trees and road parts. This technological advancement pushed the development of "Object-based Image Analysis (OBIA)" as an alternative to pixel-based image analysis methods. Segmentation as one of the main stages of OBIA provides the image objects on which most of the following processes will be applied. Therefore, the success of an OBIA approach is strongly affected by the segmentation quality. In this paper, we propose a purpose-dependent refinement strategy in order to group road segments in urban areas using maximal similarity based region merging. For investigations with the proposed method, we use high resolution images of some urban sites. The promising results suggest that the proposed approach is applicable in grouping of road segments in urban areas.

Vision-based object detection and recognition system for intelligent vehicles

NASA Astrophysics Data System (ADS)

Ran, Bin; Liu, Henry X.; Martono, Wilfung

1999-01-01

Recently, a proactive crash mitigation system is proposed to enhance the crash avoidance and survivability of the Intelligent Vehicles. Accurate object detection and recognition system is a prerequisite for a proactive crash mitigation system, as system component deployment algorithms rely on accurate hazard detection, recognition, and tracking information. In this paper, we present a vision-based approach to detect and recognize vehicles and traffic signs, obtain their information, and track multiple objects by using a sequence of color images taken from a moving vehicle. The entire system consist of two sub-systems, the vehicle detection and recognition sub-system and traffic sign detection and recognition sub-system. Both of the sub- systems consist of four models: object detection model, object recognition model, object information model, and object tracking model. In order to detect potential objects on the road, several features of the objects are investigated, which include symmetrical shape and aspect ratio of a vehicle and color and shape information of the signs. A two-layer neural network is trained to recognize different types of vehicles and a parameterized traffic sign model is established in the process of recognizing a sign. Tracking is accomplished by combining the analysis of single image frame with the analysis of consecutive image frames. The analysis of the single image frame is performed every ten full-size images. The information model will obtain the information related to the object, such as time to collision for the object vehicle and relative distance from the traffic sings. Experimental results demonstrated a robust and accurate system in real time object detection and recognition over thousands of image frames.

Using SAR Interferograms and Coherence Images for Object-Based Delineation of Unstable Slopes

NASA Astrophysics Data System (ADS)

Friedl, Barbara; Holbling, Daniel

2015-05-01

This study uses synthetic aperture radar (SAR) interferometric products for the semi-automated identification and delineation of unstable slopes and active landslides. Single-pair interferograms and coherence images are therefore segmented and classified in an object-based image analysis (OBIA) framework. The rule-based classification approach has been applied to landslide-prone areas located in Taiwan and Southern Germany. The semi-automatically obtained results were validated against landslide polygons derived from manual interpretation.

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

A review of supervised object-based land-cover image classification

NASA Astrophysics Data System (ADS)

Ma, Lei; Li, Manchun; Ma, Xiaoxue; Cheng, Liang; Du, Peijun; Liu, Yongxue

2017-08-01

Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial vehicle) or agricultural sites where it also correlates with the number of targeted classes. More than 95.6% of studies involve an area less than 300 ha, and the spatial resolution of images is predominantly between 0 and 2 m. Furthermore, we identify some methods that may advance supervised object-based image classification. For example, deep learning and type-2 fuzzy techniques may further improve classification accuracy. Lastly, scientists are strongly encouraged to report results of uncertainty studies to further explore the effects of varied factors on supervised object-based image classification.

Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

PubMed Central

Toure, Sory I.; Stow, Douglas A.; Weeks, John R.; Kumar, Sunil

2013-01-01

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution. PMID:24403648

Multi-scale image segmentation method with visual saliency constraints and its application

NASA Astrophysics Data System (ADS)

Chen, Yan; Yu, Jie; Sun, Kaimin

2018-03-01

Object-based image analysis method has many advantages over pixel-based methods, so it is one of the current research hotspots. It is very important to get the image objects by multi-scale image segmentation in order to carry out object-based image analysis. The current popular image segmentation methods mainly share the bottom-up segmentation principle, which is simple to realize and the object boundaries obtained are accurate. However, the macro statistical characteristics of the image areas are difficult to be taken into account, and fragmented segmentation (or over-segmentation) results are difficult to avoid. In addition, when it comes to information extraction, target recognition and other applications, image targets are not equally important, i.e., some specific targets or target groups with particular features worth more attention than the others. To avoid the problem of over-segmentation and highlight the targets of interest, this paper proposes a multi-scale image segmentation method with visually saliency graph constraints. Visual saliency theory and the typical feature extraction method are adopted to obtain the visual saliency information, especially the macroscopic information to be analyzed. The visual saliency information is used as a distribution map of homogeneity weight, where each pixel is given a weight. This weight acts as one of the merging constraints in the multi- scale image segmentation. As a result, pixels that macroscopically belong to the same object but are locally different can be more likely assigned to one same object. In addition, due to the constraint of visual saliency model, the constraint ability over local-macroscopic characteristics can be well controlled during the segmentation process based on different objects. These controls will improve the completeness of visually saliency areas in the segmentation results while diluting the controlling effect for non- saliency background areas. Experiments show that this method works better for texture image segmentation than traditional multi-scale image segmentation methods, and can enable us to give priority control to the saliency objects of interest. This method has been used in image quality evaluation, scattered residential area extraction, sparse forest extraction and other applications to verify its validation. All applications showed good results.

Geospatial mapping of Antarctic coastal oasis using geographic object-based image analysis and high resolution satellite imagery

NASA Astrophysics Data System (ADS)

Jawak, Shridhar D.; Luis, Alvarinho J.

2016-04-01

An accurate spatial mapping and characterization of land cover features in cryospheric regions is an essential procedure for many geoscientific studies. A novel semi-automated method was devised by coupling spectral index ratios (SIRs) and geographic object-based image analysis (OBIA) to extract cryospheric geospatial information from very high resolution WorldView 2 (WV-2) satellite imagery. The present study addresses development of multiple rule sets for OBIA-based classification of WV-2 imagery to accurately extract land cover features in the Larsemann Hills, east Antarctica. Multilevel segmentation process was applied to WV-2 image to generate different sizes of geographic image objects corresponding to various land cover features with respect to scale parameter. Several SIRs were applied to geographic objects at different segmentation levels to classify land mass, man-made features, snow/ice, and water bodies. We focus on water body class to identify water areas at the image level, considering their uneven appearance on landmass and ice. The results illustrated that synergetic usage of SIRs and OBIA can provide accurate means to identify land cover classes with an overall classification accuracy of ≍97%. In conclusion, our results suggest that OBIA is a powerful tool for carrying out automatic and semiautomatic analysis for most cryospheric remote-sensing applications, and the synergetic coupling with pixel-based SIRs is found to be a superior method for mining geospatial information.

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

NASA Technical Reports Server (NTRS)

Tescher, Andrew G. (Editor)

1989-01-01

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

An Integrative Object-Based Image Analysis Workflow for Uav Images

NASA Astrophysics Data System (ADS)

Yu, Huai; Yan, Tianheng; Yang, Wen; Zheng, Hong

2016-06-01

In this work, we propose an integrative framework to process UAV images. The overall process can be viewed as a pipeline consisting of the geometric and radiometric corrections, subsequent panoramic mosaicking and hierarchical image segmentation for later Object Based Image Analysis (OBIA). More precisely, we first introduce an efficient image stitching algorithm after the geometric calibration and radiometric correction, which employs a fast feature extraction and matching by combining the local difference binary descriptor and the local sensitive hashing. We then use a Binary Partition Tree (BPT) representation for the large mosaicked panoramic image, which starts by the definition of an initial partition obtained by an over-segmentation algorithm, i.e., the simple linear iterative clustering (SLIC). Finally, we build an object-based hierarchical structure by fully considering the spectral and spatial information of the super-pixels and their topological relationships. Moreover, an optimal segmentation is obtained by filtering the complex hierarchies into simpler ones according to some criterions, such as the uniform homogeneity and semantic consistency. Experimental results on processing the post-seismic UAV images of the 2013 Ya'an earthquake demonstrate the effectiveness and efficiency of our proposed method.

An image analysis toolbox for high-throughput C. elegans assays

PubMed Central

Wählby, Carolina; Kamentsky, Lee; Liu, Zihan H.; Riklin-Raviv, Tammy; Conery, Annie L.; O’Rourke, Eyleen J.; Sokolnicki, Katherine L.; Visvikis, Orane; Ljosa, Vebjorn; Irazoqui, Javier E.; Golland, Polina; Ruvkun, Gary; Ausubel, Frederick M.; Carpenter, Anne E.

2012-01-01

We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available via the open-source CellProfiler project and enables objective scoring of whole-animal high-throughput image-based assays of C. elegans for the study of diverse biological pathways relevant to human disease. PMID:22522656

Seismic zonation of Port-Au-Prince using pixel- and object-based imaging analysis methods on ASTER GDEM

USGS Publications Warehouse

Yong, Alan; Hough, Susan E.; Cox, Brady R.; Rathje, Ellen M.; Bachhuber, Jeff; Dulberg, Ranon; Hulslander, David; Christiansen, Lisa; and Abrams, Michael J.

2011-01-01

We report about a preliminary study to evaluate the use of semi-automated imaging analysis of remotely-sensed DEM and field geophysical measurements to develop a seismic-zonation map of Port-au-Prince, Haiti. For in situ data, VS30 values are derived from the MASW technique deployed in and around the city. For satellite imagery, we use an ASTER GDEM of Hispaniola. We apply both pixel- and object-based imaging methods on the ASTER GDEM to explore local topography (absolute elevation values) and classify terrain types such as mountains, alluvial fans and basins/near-shore regions. We assign NEHRP seismic site class ranges based on available VS30 values. A comparison of results from imagery-based methods to results from traditional geologic-based approaches reveals good overall correspondence. We conclude that image analysis of RS data provides reliable first-order site characterization results in the absence of local data and can be useful to refine detailed site maps with sparse local data.

A multiple-point spatially weighted k-NN method for object-based classification

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.

2016-10-01

Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application

PubMed Central

Maxwell, Susan K.

2010-01-01

Satellite imagery and aerial photography represent a vast resource to significantly enhance environmental mapping and modeling applications for use in understanding spatio-temporal relationships between environment and health. Deriving boundaries of land cover objects, such as trees, buildings, and crop fields, from image data has traditionally been performed manually using a very time consuming process of hand digitizing. Boundary detection algorithms are increasingly being applied using object-based image analysis (OBIA) technology to automate the process. The purpose of this paper is to present an overview and demonstrate the application of OBIA for delineating land cover features at multiple scales using a high resolution aerial photograph (1 m) and a medium resolution Landsat image (30 m) time series in the context of a pesticide spray drift exposure application. PMID:21135917

Model-based object classification using unification grammars and abstract representations

NASA Astrophysics Data System (ADS)

Liburdy, Kathleen A.; Schalkoff, Robert J.

1993-04-01

The design and implementation of a high level computer vision system which performs object classification is described. General object labelling and functional analysis require models of classes which display a wide range of geometric variations. A large representational gap exists between abstract criteria such as `graspable' and current geometric image descriptions. The vision system developed and described in this work addresses this problem and implements solutions based on a fusion of semantics, unification, and formal language theory. Object models are represented using unification grammars, which provide a framework for the integration of structure and semantics. A methodology for the derivation of symbolic image descriptions capable of interacting with the grammar-based models is described and implemented. A unification-based parser developed for this system achieves object classification by determining if the symbolic image description can be unified with the abstract criteria of an object model. Future research directions are indicated.

Rock classification based on resistivity patterns in electrical borehole wall images

NASA Astrophysics Data System (ADS)

Linek, Margarete; Jungmann, Matthias; Berlage, Thomas; Pechnig, Renate; Clauser, Christoph

2007-06-01

Electrical borehole wall images represent grey-level-coded micro-resistivity measurements at the borehole wall. Different scientific methods have been implemented to transform image data into quantitative log curves. We introduce a pattern recognition technique applying texture analysis, which uses second-order statistics based on studying the occurrence of pixel pairs. We calculate so-called Haralick texture features such as contrast, energy, entropy and homogeneity. The supervised classification method is used for assigning characteristic texture features to different rock classes and assessing the discriminative power of these image features. We use classifiers obtained from training intervals to characterize the entire image data set recovered in ODP hole 1203A. This yields a synthetic lithology profile based on computed texture data. We show that Haralick features accurately classify 89.9% of the training intervals. We obtained misclassification for vesicular basaltic rocks. Hence, further image analysis tools are used to improve the classification reliability. We decompose the 2D image signal by the application of wavelet transformation in order to enhance image objects horizontally, diagonally and vertically. The resulting filtered images are used for further texture analysis. This combined classification based on Haralick features and wavelet transformation improved our classification up to a level of 98%. The application of wavelet transformation increases the consistency between standard logging profiles and texture-derived lithology. Texture analysis of borehole wall images offers the potential to facilitate objective analysis of multiple boreholes with the same lithology.

Image processing and classification procedures for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands

USDA-ARS?s Scientific Manuscript database

Using five centimeter resolution images acquired with an unmanned aircraft system (UAS), we developed and evaluated an image processing workflow that included the integration of resolution-appropriate field sampling, feature selection, object-based image analysis, and processing approaches for UAS i...

Comparative analysis of imaging configurations and objectives for Fourier microscopy.

PubMed

Kurvits, Jonathan A; Jiang, Mingming; Zia, Rashid

2015-11-01

Fourier microscopy is becoming an increasingly important tool for the analysis of optical nanostructures and quantum emitters. However, achieving quantitative Fourier space measurements requires a thorough understanding of the impact of aberrations introduced by optical microscopes that have been optimized for conventional real-space imaging. Here we present a detailed framework for analyzing the performance of microscope objectives for several common Fourier imaging configurations. To this end, we model objectives from Nikon, Olympus, and Zeiss using parameters that were inferred from patent literature and confirmed, where possible, by physical disassembly. We then examine the aberrations most relevant to Fourier microscopy, including the alignment tolerances of apodization factors for different objective classes, the effect of magnification on the modulation transfer function, and vignetting-induced reductions of the effective numerical aperture for wide-field measurements. Based on this analysis, we identify an optimal objective class and imaging configuration for Fourier microscopy. In addition, the Zemax files for the objectives and setups used in this analysis have been made publicly available as a resource for future studies.

Moving object detection using dynamic motion modelling from UAV aerial images.

PubMed

Saif, A F M Saifuddin; Prabuwono, Anton Satria; Mahayuddin, Zainal Rasyid

2014-01-01

Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology.

Remote Sensing Image Analysis Without Expert Knowledge - A Web-Based Classification Tool On Top of Taverna Workflow Management System

NASA Astrophysics Data System (ADS)

Selsam, Peter; Schwartze, Christian

2016-10-01

Providing software solutions via internet has been known for quite some time and is now an increasing trend marketed as "software as a service". A lot of business units accept the new methods and streamlined IT strategies by offering web-based infrastructures for external software usage - but geospatial applications featuring very specialized services or functionalities on demand are still rare. Originally applied in desktop environments, the ILMSimage tool for remote sensing image analysis and classification was modified in its communicating structures and enabled for running on a high-power server and benefiting from Tavema software. On top, a GIS-like and web-based user interface guides the user through the different steps in ILMSimage. ILMSimage combines object oriented image segmentation with pattern recognition features. Basic image elements form a construction set to model for large image objects with diverse and complex appearance. There is no need for the user to set up detailed object definitions. Training is done by delineating one or more typical examples (templates) of the desired object using a simple vector polygon. The template can be large and does not need to be homogeneous. The template is completely independent from the segmentation. The object definition is done completely by the software.

An Imaging And Graphics Workstation For Image Sequence Analysis

NASA Astrophysics Data System (ADS)

Mostafavi, Hassan

1990-01-01

This paper describes an application-specific engineering workstation designed and developed to analyze imagery sequences from a variety of sources. The system combines the software and hardware environment of the modern graphic-oriented workstations with the digital image acquisition, processing and display techniques. The objective is to achieve automation and high throughput for many data reduction tasks involving metric studies of image sequences. The applications of such an automated data reduction tool include analysis of the trajectory and attitude of aircraft, missile, stores and other flying objects in various flight regimes including launch and separation as well as regular flight maneuvers. The workstation can also be used in an on-line or off-line mode to study three-dimensional motion of aircraft models in simulated flight conditions such as wind tunnels. The system's key features are: 1) Acquisition and storage of image sequences by digitizing real-time video or frames from a film strip; 2) computer-controlled movie loop playback, slow motion and freeze frame display combined with digital image sharpening, noise reduction, contrast enhancement and interactive image magnification; 3) multiple leading edge tracking in addition to object centroids at up to 60 fields per second from both live input video or a stored image sequence; 4) automatic and manual field-of-view and spatial calibration; 5) image sequence data base generation and management, including the measurement data products; 6) off-line analysis software for trajectory plotting and statistical analysis; 7) model-based estimation and tracking of object attitude angles; and 8) interface to a variety of video players and film transport sub-systems.

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

PubMed Central

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

NASA Technical Reports Server (NTRS)

Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

2012-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.

Histopathological image analysis of chemical-induced hepatocellular hypertrophy in mice.

PubMed

Asaoka, Yoshiji; Togashi, Yuko; Mutsuga, Mayu; Imura, Naoko; Miyoshi, Tomoya; Miyamoto, Yohei

2016-04-01

Chemical-induced hepatocellular hypertrophy is frequently observed in rodents, and is mostly caused by the induction of phase I and phase II drug metabolic enzymes and peroxisomal lipid metabolic enzymes. Liver weight is a sensitive and commonly used marker for detecting hepatocellular hypertrophy, but is also increased by a number of other factors. Histopathological observations subjectively detect changes such as hepatocellular hypertrophy based on the size of a hepatocyte. Therefore, quantitative microscopic observations are required to evaluate histopathological alterations objectively. In the present study, we developed a novel quantitative method for an image analysis of hepatocellular hypertrophy using liver sections stained with hematoxylin and eosin, and demonstrated its usefulness for evaluating hepatocellular hypertrophy induced by phenobarbital (a phase I and phase II enzyme inducer) and clofibrate (a peroxisomal enzyme inducer) in mice. The algorithm of this imaging analysis was designed to recognize an individual hepatocyte through a combination of pixel-based and object-based analyses. Hepatocellular nuclei and the surrounding non-hepatocellular cells were recognized by the pixel-based analysis, while the areas of the recognized hepatocellular nuclei were then expanded until they ran against their expanding neighboring hepatocytes and surrounding non-hepatocellular cells by the object-based analysis. The expanded area of each hepatocellular nucleus was regarded as the size of an individual hepatocyte. The results of this imaging analysis showed that changes in the sizes of hepatocytes corresponded with histopathological observations in phenobarbital and clofibrate-treated mice, and revealed a correlation between hepatocyte size and liver weight. In conclusion, our novel image analysis method is very useful for quantitative evaluations of chemical-induced hepatocellular hypertrophy. Copyright © 2015 Elsevier GmbH. All rights reserved.

Object-Based Change Detection Using High-Resolution Remotely Sensed Data and GIS

NASA Astrophysics Data System (ADS)

Sofina, N.; Ehlers, M.

2012-08-01

High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and - for electro-optical sensors - in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.

[RS estimation of inventory parameters and carbon storage of moso bamboo forest based on synergistic use of object-based image analysis and decision tree].

PubMed

Du, Hua Qiang; Sun, Xiao Yan; Han, Ning; Mao, Fang Jie

2017-10-01

By synergistically using the object-based image analysis (OBIA) and the classification and regression tree (CART) methods, the distribution information, the indexes (including diameter at breast, tree height, and crown closure), and the aboveground carbon storage (AGC) of moso bamboo forest in Shanchuan Town, Anji County, Zhejiang Province were investigated. The results showed that the moso bamboo forest could be accurately delineated by integrating the multi-scale ima ge segmentation in OBIA technique and CART, which connected the image objects at various scales, with a pretty good producer's accuracy of 89.1%. The investigation of indexes estimated by regression tree model that was constructed based on the features extracted from the image objects reached normal or better accuracy, in which the crown closure model archived the best estimating accuracy of 67.9%. The estimating accuracy of diameter at breast and tree height was relatively low, which was consistent with conclusion that estimating diameter at breast and tree height using optical remote sensing could not achieve satisfactory results. Estimation of AGC reached relatively high accuracy, and accuracy of the region of high value achieved above 80%.

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.

Three-dimensional passive sensing photon counting for object classification

NASA Astrophysics Data System (ADS)

Yeom, Seokwon; Javidi, Bahram; Watson, Edward

2007-04-01

In this keynote address, we address three-dimensional (3D) distortion-tolerant object recognition using photon-counting integral imaging (II). A photon-counting linear discriminant analysis (LDA) is discussed for classification of photon-limited images. We develop a compact distortion-tolerant recognition system based on the multiple-perspective imaging of II. Experimental and simulation results have shown that a low level of photons is sufficient to classify out-of-plane rotated objects.

Digital Imaging

NASA Technical Reports Server (NTRS)

1986-01-01

Digital Imaging is the computer processed numerical representation of physical images. Enhancement of images results in easier interpretation. Quantitative digital image analysis by Perceptive Scientific Instruments, locates objects within an image and measures them to extract quantitative information. Applications are CAT scanners, radiography, microscopy in medicine as well as various industrial and manufacturing uses. The PSICOM 327 performs all digital image analysis functions. It is based on Jet Propulsion Laboratory technology, is accurate and cost efficient.

Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application.

PubMed

Maxwell, Susan K

2010-12-01

Satellite imagery and aerial photography represent a vast resource to significantly enhance environmental mapping and modeling applications for use in understanding spatio-temporal relationships between environment and health. Deriving boundaries of land cover objects, such as trees, buildings, and crop fields, from image data has traditionally been performed manually using a very time consuming process of hand digitizing. Boundary detection algorithms are increasingly being applied using object-based image analysis (OBIA) technology to automate the process. The purpose of this paper is to present an overview and demonstrate the application of OBIA for delineating land cover features at multiple scales using a high resolution aerial photograph (1 m) and a medium resolution Landsat image (30 m) time series in the context of a pesticide spray drift exposure application. Copyright © 2010. Published by Elsevier Ltd.

Ruby-Helix: an implementation of helical image processing based on object-oriented scripting language.

PubMed

Metlagel, Zoltan; Kikkawa, Yayoi S; Kikkawa, Masahide

2007-01-01

Helical image analysis in combination with electron microscopy has been used to study three-dimensional structures of various biological filaments or tubes, such as microtubules, actin filaments, and bacterial flagella. A number of packages have been developed to carry out helical image analysis. Some biological specimens, however, have a symmetry break (seam) in their three-dimensional structure, even though their subunits are mostly arranged in a helical manner. We refer to these objects as "asymmetric helices". All the existing packages are designed for helically symmetric specimens, and do not allow analysis of asymmetric helical objects, such as microtubules with seams. Here, we describe Ruby-Helix, a new set of programs for the analysis of "helical" objects with or without a seam. Ruby-Helix is built on top of the Ruby programming language and is the first implementation of asymmetric helical reconstruction for practical image analysis. It also allows easier and semi-automated analysis, performing iterative unbending and accurate determination of the repeat length. As a result, Ruby-Helix enables us to analyze motor-microtubule complexes with higher throughput to higher resolution.

Evaluation of image quality in terahertz pulsed imaging using test objects.

PubMed

Fitzgerald, A J; Berry, E; Miles, R E; Zinovev, N N; Smith, M A; Chamberlain, J M

2002-11-07

As with other imaging modalities, the performance of terahertz (THz) imaging systems is limited by factors of spatial resolution, contrast and noise. The purpose of this paper is to introduce test objects and image analysis methods to evaluate and compare THz image quality in a quantitative and objective way, so that alternative terahertz imaging system configurations and acquisition techniques can be compared, and the range of image parameters can be assessed. Two test objects were designed and manufactured, one to determine the modulation transfer functions (MTF) and the other to derive image signal to noise ratio (SNR) at a range of contrasts. As expected the higher THz frequencies had larger MTFs, and better spatial resolution as determined by the spatial frequency at which the MTF dropped below the 20% threshold. Image SNR was compared for time domain and frequency domain image parameters and time delay based images consistently demonstrated higher SNR than intensity based parameters such as relative transmittance because the latter are more strongly affected by the sources of noise in the THz system such as laser fluctuations and detector shot noise.

A Time of Flight Fast Neutron Imaging System Design Study

NASA Astrophysics Data System (ADS)

Canion, Bonnie; Glenn, Andrew; Sheets, Steven; Wurtz, Ron; Nakae, Les; Hausladen, Paul; McConchie, Seth; Blackston, Matthew; Fabris, Lorenzo; Newby, Jason

2017-09-01

LLNL and ORNL are designing an active/passive fast neutron imaging system that is flexible to non-ideal detector positioning. It is often not possible to move an inspection object in fieldable imager applications such as safeguards, arms control treaty verification, and emergency response. Particularly, we are interested in scenarios which inspectors do not have access to all sides of an inspection object, due to interfering objects or walls. This paper will present the results of a simulation-based design parameter study, that will determine the optimum system design parameters for a fieldable system to perform time-of-flight based imaging analysis. The imaging analysis is based on the use of an associated particle imaging deuterium-tritium (API DT) neutron generator to get the time-of-flight of radiation induced within an inspection object. This design study will investigate the optimum design parameters for such a system (e.g. detector size, ideal placement, etc.), as well as the upper and lower feasible design parameters that the system can expect to provide results within a reasonable amount of time (e.g. minimum/maximum detector efficiency, detector standoff, etc.). Ideally the final prototype from this project will be capable of using full-access techniques, such as transmission imaging, when the measurement circumstances allow, but with the additional capability of producing results at reduced accessibility.

Fourier analysis: from cloaking to imaging

NASA Astrophysics Data System (ADS)

Wu, Kedi; Cheng, Qiluan; Wang, Guo Ping

2016-04-01

Regarding invisibility cloaks as an optical imaging system, we present a Fourier approach to analytically unify both Pendry cloaks and complementary media-based invisibility cloaks into one kind of cloak. By synthesizing different transfer functions, we can construct different devices to realize a series of interesting functions such as hiding objects (events), creating illusions, and performing perfect imaging. In this article, we give a brief review on recent works of applying Fourier approach to analysis invisibility cloaks and optical imaging through scattering layers. We show that, to construct devices to conceal an object, no constructive materials with extreme properties are required, making most, if not all, of the above functions realizable by using naturally occurring materials. As instances, we experimentally verify a method of directionally hiding distant objects and create illusions by using all-dielectric materials, and further demonstrate a non-invasive method of imaging objects completely hidden by scattering layers.

Explicit area-based accuracy assessment for mangrove tree crown delineation using Geographic Object-Based Image Analysis (GEOBIA)

NASA Astrophysics Data System (ADS)

Kamal, Muhammad; Johansen, Kasper

2017-10-01

Effective mangrove management requires spatially explicit information of mangrove tree crown map as a basis for ecosystem diversity study and health assessment. Accuracy assessment is an integral part of any mapping activities to measure the effectiveness of the classification approach. In geographic object-based image analysis (GEOBIA) the assessment of the geometric accuracy (shape, symmetry and location) of the created image objects from image segmentation is required. In this study we used an explicit area-based accuracy assessment to measure the degree of similarity between the results of the classification and reference data from different aspects, including overall quality (OQ), user's accuracy (UA), producer's accuracy (PA) and overall accuracy (OA). We developed a rule set to delineate the mangrove tree crown using WorldView-2 pan-sharpened image. The reference map was obtained by visual delineation of the mangrove tree crowns boundaries form a very high-spatial resolution aerial photograph (7.5cm pixel size). Ten random points with a 10 m radius circular buffer were created to calculate the area-based accuracy assessment. The resulting circular polygons were used to clip both the classified image objects and reference map for area comparisons. In this case, the area-based accuracy assessment resulted 64% and 68% for the OQ and OA, respectively. The overall quality of the calculation results shows the class-related area accuracy; which is the area of correctly classified as tree crowns was 64% out of the total area of tree crowns. On the other hand, the overall accuracy of 68% was calculated as the percentage of all correctly classified classes (tree crowns and canopy gaps) in comparison to the total class area (an entire image). Overall, the area-based accuracy assessment was simple to implement and easy to interpret. It also shows explicitly the omission and commission error variations of object boundary delineation with colour coded polygons.

Seismic-zonation of Port-au-Prince using pixel- and object-based imaging analysis methods on ASTER GDEM

USGS Publications Warehouse

Yong, A.; Hough, S.E.; Cox, B.R.; Rathje, E.M.; Bachhuber, J.; Dulberg, R.; Hulslander, D.; Christiansen, L.; Abrams, M.J.

2011-01-01

We report about a preliminary study to evaluate the use of semi-automated imaging analysis of remotely-sensed DEM and field geophysical measurements to develop a seismic-zonation map of Port-au-Prince, Haiti. For in situ data, Vs30 values are derived from the MASW technique deployed in and around the city. For satellite imagery, we use an ASTER GDEM of Hispaniola. We apply both pixel- and object-based imaging methods on the ASTER GDEM to explore local topography (absolute elevation values) and classify terrain types such as mountains, alluvial fans and basins/near-shore regions. We assign NEHRP seismic site class ranges based on available Vs30 values. A comparison of results from imagery-based methods to results from traditional geologic-based approaches reveals good overall correspondence. We conclude that image analysis of RS data provides reliable first-order site characterization results in the absence of local data and can be useful to refine detailed site maps with sparse local data. ?? 2011 American Society for Photogrammetry and Remote Sensing.

Reducing noise component on medical images

NASA Astrophysics Data System (ADS)

Semenishchev, Evgeny; Voronin, Viacheslav; Dub, Vladimir; Balabaeva, Oksana

2018-04-01

Medical visualization and analysis of medical data is an actual direction. Medical images are used in microbiology, genetics, roentgenology, oncology, surgery, ophthalmology, etc. Initial data processing is a major step towards obtaining a good diagnostic result. The paper considers the approach allows an image filtering with preservation of objects borders. The algorithm proposed in this paper is based on sequential data processing. At the first stage, local areas are determined, for this purpose the method of threshold processing, as well as the classical ICI algorithm, is applied. The second stage uses a method based on based on two criteria, namely, L2 norm and the first order square difference. To preserve the boundaries of objects, we will process the transition boundary and local neighborhood the filtering algorithm with a fixed-coefficient. For example, reconstructed images of CT, x-ray, and microbiological studies are shown. The test images show the effectiveness of the proposed algorithm. This shows the applicability of analysis many medical imaging applications.

Automatic identification of fault surfaces through Object Based Image Analysis of a Digital Elevation Model in the submarine area of the North Aegean Basin

NASA Astrophysics Data System (ADS)

Argyropoulou, Evangelia

2015-04-01

The current study was focused on the seafloor morphology of the North Aegean Basin in Greece, through Object Based Image Analysis (OBIA) using a Digital Elevation Model. The goal was the automatic extraction of morphologic and morphotectonic features, resulting into fault surface extraction. An Object Based Image Analysis approach was developed based on the bathymetric data and the extracted features, based on morphological criteria, were compared with the corresponding landforms derived through tectonic analysis. A digital elevation model of 150 meters spatial resolution was used. At first, slope, profile curvature, and percentile were extracted from this bathymetry grid. The OBIA approach was developed within the eCognition environment. Four segmentation levels were created having as a target "level 4". At level 4, the final classes of geomorphological features were classified: discontinuities, fault-like features and fault surfaces. On previous levels, additional landforms were also classified, such as continental platform and continental slope. The results of the developed approach were evaluated by two methods. At first, classification stability measures were computed within eCognition. Then, qualitative and quantitative comparison of the results took place with a reference tectonic map which has been created manually based on the analysis of seismic profiles. The results of this comparison were satisfactory, a fact which determines the correctness of the developed OBIA approach.

3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging

NASA Astrophysics Data System (ADS)

Aloni, Doron; Jung, Jae-Hyun; Yitzhaky, Yitzhak

2017-10-01

Three dimensional (3D) object segmentation and tracking can be useful in various computer vision applications, such as: object surveillance for security uses, robot navigation, etc. We present a method for 3D multiple-object tracking using computational integral imaging, based on accurate 3D object segmentation. The method does not employ object detection by motion analysis in a video as conventionally performed (such as background subtraction or block matching). This means that the movement properties do not significantly affect the detection quality. The object detection is performed by analyzing static 3D image data obtained through computational integral imaging With regard to previous works that used integral imaging data in such a scenario, the proposed method performs the 3D tracking of objects without prior information about the objects in the scene, and it is found efficient under severe noise conditions.

An approach for quantitative image quality analysis for CT

NASA Astrophysics Data System (ADS)

Rahimi, Amir; Cochran, Joe; Mooney, Doug; Regensburger, Joe

2016-03-01

An objective and standardized approach to assess image quality of Compute Tomography (CT) systems is required in a wide variety of imaging processes to identify CT systems appropriate for a given application. We present an overview of the framework we have developed to help standardize and to objectively assess CT image quality for different models of CT scanners used for security applications. Within this framework, we have developed methods to quantitatively measure metrics that should correlate with feature identification, detection accuracy and precision, and image registration capabilities of CT machines and to identify strengths and weaknesses in different CT imaging technologies in transportation security. To that end we have designed, developed and constructed phantoms that allow for systematic and repeatable measurements of roughly 88 image quality metrics, representing modulation transfer function, noise equivalent quanta, noise power spectra, slice sensitivity profiles, streak artifacts, CT number uniformity, CT number consistency, object length accuracy, CT number path length consistency, and object registration. Furthermore, we have developed a sophisticated MATLAB based image analysis tool kit to analyze CT generated images of phantoms and report these metrics in a format that is standardized across the considered models of CT scanners, allowing for comparative image quality analysis within a CT model or between different CT models. In addition, we have developed a modified sparse principal component analysis (SPCA) method to generate a modified set of PCA components as compared to the standard principal component analysis (PCA) with sparse loadings in conjunction with Hotelling T2 statistical analysis method to compare, qualify, and detect faults in the tested systems.

Mapping urban impervious surface using object-based image analysis with WorldView-3 satellite imagery

NASA Astrophysics Data System (ADS)

Iabchoon, Sanwit; Wongsai, Sangdao; Chankon, Kanoksuk

2017-10-01

Land use and land cover (LULC) data are important to monitor and assess environmental change. LULC classification using satellite images is a method widely used on a global and local scale. Especially, urban areas that have various LULC types are important components of the urban landscape and ecosystem. This study aims to classify urban LULC using WorldView-3 (WV-3) very high-spatial resolution satellite imagery and the object-based image analysis method. A decision rules set was applied to classify the WV-3 images in Kathu subdistrict, Phuket province, Thailand. The main steps were as follows: (1) the image was ortho-rectified with ground control points and using the digital elevation model, (2) multiscale image segmentation was applied to divide the image pixel level into image object level, (3) development of the decision ruleset for LULC classification using spectral bands, spectral indices, spatial and contextual information, and (4) accuracy assessment was computed using testing data, which sampled by statistical random sampling. The results show that seven LULC classes (water, vegetation, open space, road, residential, building, and bare soil) were successfully classified with overall classification accuracy of 94.14% and a kappa coefficient of 92.91%.

Image denoising and deblurring using multispectral data

NASA Astrophysics Data System (ADS)

Semenishchev, E. A.; Voronin, V. V.; Marchuk, V. I.

2017-05-01

Currently decision-making systems get widespread. These systems are based on the analysis video sequences and also additional data. They are volume, change size, the behavior of one or a group of objects, temperature gradient, the presence of local areas with strong differences, and others. Security and control system are main areas of application. A noise on the images strongly influences the subsequent processing and decision making. This paper considers the problem of primary signal processing for solving the tasks of image denoising and deblurring of multispectral data. The additional information from multispectral channels can improve the efficiency of object classification. In this paper we use method of combining information about the objects obtained by the cameras in different frequency bands. We apply method based on simultaneous minimization L2 and the first order square difference sequence of estimates to denoising and restoring the blur on the edges. In case of loss of the information will be applied an approach based on the interpolation of data taken from the analysis of objects located in other areas and information obtained from multispectral camera. The effectiveness of the proposed approach is shown in a set of test images.

View subspaces for indexing and retrieval of 3D models

NASA Astrophysics Data System (ADS)

Dutagaci, Helin; Godil, Afzal; Sankur, Bülent; Yemez, Yücel

2010-02-01

View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithms.

Comparative analysis of numerical simulation techniques for incoherent imaging of extended objects through atmospheric turbulence

NASA Astrophysics Data System (ADS)

Lachinova, Svetlana L.; Vorontsov, Mikhail A.; Filimonov, Grigory A.; LeMaster, Daniel A.; Trippel, Matthew E.

2017-07-01

Computational efficiency and accuracy of wave-optics-based Monte-Carlo and brightness function numerical simulation techniques for incoherent imaging of extended objects through atmospheric turbulence are evaluated. Simulation results are compared with theoretical estimates based on known analytical solutions for the modulation transfer function of an imaging system and the long-exposure image of a Gaussian-shaped incoherent light source. It is shown that the accuracy of both techniques is comparable over the wide range of path lengths and atmospheric turbulence conditions, whereas the brightness function technique is advantageous in terms of the computational speed.

Rotation covariant image processing for biomedical applications.

PubMed

Skibbe, Henrik; Reisert, Marco

2013-01-01

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences.

A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images

NASA Astrophysics Data System (ADS)

Keyport, Ren N.; Oommen, Thomas; Martha, Tapas R.; Sajinkumar, K. S.; Gierke, John S.

2018-02-01

A comparative analysis of landslides detected by pixel-based and object-oriented analysis (OOA) methods was performed using very high-resolution (VHR) remotely sensed aerial images for the San Juan La Laguna, Guatemala, which witnessed widespread devastation during the 2005 Hurricane Stan. A 3-band orthophoto of 0.5 m spatial resolution together with a 115 field-based landslide inventory were used for the analysis. A binary reference was assigned with a zero value for landslide and unity for non-landslide pixels. The pixel-based analysis was performed using unsupervised classification, which resulted in 11 different trial classes. Detection of landslides using OOA includes 2-step K-means clustering to eliminate regions based on brightness; elimination of false positives using object properties such as rectangular fit, compactness, length/width ratio, mean difference of objects, and slope angle. Both overall accuracy and F-score for OOA methods outperformed pixel-based unsupervised classification methods in both landslide and non-landslide classes. The overall accuracy for OOA and pixel-based unsupervised classification was 96.5% and 94.3%, respectively, whereas the best F-score for landslide identification for OOA and pixel-based unsupervised methods: were 84.3% and 77.9%, respectively.Results indicate that the OOA is able to identify the majority of landslides with a few false positive when compared to pixel-based unsupervised classification.

Classification of high resolution remote sensing image based on geo-ontology and conditional random fields

NASA Astrophysics Data System (ADS)

Hong, Liang

2013-10-01

The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

Resolving power of diffraction imaging with an objective: a numerical study.

PubMed

Wang, Wenjin; Liu, Jing; Lu, Jun Qing; Ding, Junhua; Hu, Xin-Hua

2017-05-01

Diffraction imaging in the far-field can detect 3D morphological features of an object for its coherent nature. We describe methods for accurate calculation and analysis of diffraction images of scatterers of single and double spheres by an imaging unit based on microscope objective at non-conjugate positions. A quantitative study of the calculated diffraction imaging in spectral domain has been performed to assess the resolving power of diffraction imaging. It has been shown numerically that with coherent illumination of 532 nm in wavelength the imaging unit can resolve single spheres of 2 μm or larger in diameters and double spheres separated by less than 300 nm between their centers.

Segmentation of touching mycobacterium tuberculosis from Ziehl-Neelsen stained sputum smear images

NASA Astrophysics Data System (ADS)

Xu, Chao; Zhou, Dongxiang; Liu, Yunhui

2015-12-01

Touching Mycobacterium tuberculosis objects in the Ziehl-Neelsen stained sputum smear images present different shapes and invisible boundaries in the adhesion areas, which increases the difficulty in objects recognition and counting. In this paper, we present a segmentation method of combining the hierarchy tree analysis with gradient vector flow snake to address this problem. The skeletons of the objects are used for structure analysis based on the hierarchy tree. The gradient vector flow snake is used to estimate the object edge. Experimental results show that the single objects composing the touching objects are successfully segmented by the proposed method. This work will improve the accuracy and practicability of the computer-aided diagnosis of tuberculosis.

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity

PubMed Central

Wittenberg, Leah A.; Jonsson, Nina J.; Chan, RV Paul; Chiang, Michael F.

2014-01-01

Presence of plus disease in retinopathy of prematurity (ROP) is an important criterion for identifying treatment-requiring ROP. Plus disease is defined by a standard published photograph selected over 20 years ago by expert consensus. However, diagnosis of plus disease has been shown to be subjective and qualitative. Computer-based image analysis, using quantitative methods, has potential to improve the objectivity of plus disease diagnosis. The objective was to review the published literature involving computer-based image analysis for ROP diagnosis. The PubMed and Cochrane library databases were searched for the keywords “retinopathy of prematurity” AND “image analysis” AND/OR “plus disease.” Reference lists of retrieved articles were searched to identify additional relevant studies. All relevant English-language studies were reviewed. There are four main computer-based systems, ROPtool (AU ROC curve, plus tortuosity 0.95, plus dilation 0.87), RISA (AU ROC curve, arteriolar TI 0.71, venular diameter 0.82), Vessel Map (AU ROC curve, arteriolar dilation 0.75, venular dilation 0.96), and CAIAR (AU ROC curve, arteriole tortuosity 0.92, venular dilation 0.91), attempting to objectively analyze vessel tortuosity and dilation in plus disease in ROP. Some of them show promise for identification of plus disease using quantitative methods. This has potential to improve the diagnosis of plus disease, and may contribute to the management of ROP using both traditional binocular indirect ophthalmoscopy and image-based telemedicine approaches. PMID:21366159

Concealed object segmentation and three-dimensional localization with passive millimeter-wave imaging

NASA Astrophysics Data System (ADS)

Yeom, Seokwon

2013-05-01

Millimeter waves imaging draws increasing attention in security applications for weapon detection under clothing. In this paper, concealed object segmentation and three-dimensional localization schemes are reviewed. A concealed object is segmented by the k-means algorithm. A feature-based stereo-matching method estimates the longitudinal distance of the concealed object. The distance is estimated by the discrepancy between the corresponding centers of the segmented objects. Experimental results are provided with the analysis of the depth resolution.

Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Blaschke, Thomas; Tiede, Dirk; Moghaddam, Mohammad Hossein Rezaei

2017-09-01

This article presents a method of object-based image analysis (OBIA) for landslide delineation and landslide-related change detection from multi-temporal satellite images. It uses both spatial and spectral information on landslides, through spectral analysis, shape analysis, textural measurements using a gray-level co-occurrence matrix (GLCM), and fuzzy logic membership functionality. Following an initial segmentation step, particular combinations of various information layers were investigated to generate objects. This was achieved by applying multi-resolution segmentation to IRS-1D, SPOT-5, and ALOS satellite imagery in sequential steps of feature selection and object classification, and using slope and flow direction derivatives from a digital elevation model together with topographically-oriented gray level co-occurrence matrices. Fuzzy membership values were calculated for 11 different membership functions using 20 landslide objects from a landslide training data. Six fuzzy operators were used for the final classification and the accuracies of the resulting landslide maps were compared. A Fuzzy Synthetic Evaluation (FSE) approach was adapted for validation of the results and for an accuracy assessment using the landslide inventory database. The FSE approach revealed that the AND operator performed best with an accuracy of 93.87% for 2005 and 94.74% for 2011, closely followed by the MEAN Arithmetic operator, while the OR and AND (*) operators yielded relatively low accuracies. An object-based change detection was then applied to monitor landslide-related changes that occurred in northern Iran between 2005 and 2011. Knowledge rules to detect possible landslide-related changes were developed by evaluating all possible landslide-related objects for both time steps.

Real-time color image processing for forensic fiber investigations

NASA Astrophysics Data System (ADS)

Paulsson, Nils

1995-09-01

This paper describes a system for automatic fiber debris detection based on color identification. The properties of the system are fast analysis and high selectivity, a necessity when analyzing forensic fiber samples. An ordinary investigation separates the material into well above 100,000 video images to analyze. The system is based on standard techniques such as CCD-camera, motorized sample table, and IBM-compatible PC/AT with add-on-boards for video frame digitalization and stepping motor control as the main parts. It is possible to operate the instrument at full video rate (25 image/s) with aid of the HSI-color system (hue- saturation-intensity) and software optimization. High selectivity is achieved by separating the analysis into several steps. The first step is fast direct color identification of objects in the analyzed video images and the second step analyzes detected objects with a more complex and time consuming stage of the investigation to identify single fiber fragments for subsequent analysis with more selective techniques.

Analysis on the Effect of Sensor Views in Image Reconstruction Produced by Optical Tomography System Using Charge-Coupled Device.

PubMed

Jamaludin, Juliza; Rahim, Ruzairi Abdul; Fazul Rahiman, Mohd Hafiz; Mohd Rohani, Jemmy

2018-04-01

Optical tomography (OPT) is a method to capture a cross-sectional image based on the data obtained by sensors, distributed around the periphery of the analyzed system. This system is based on the measurement of the final light attenuation or absorption of radiation after crossing the measured objects. The number of sensor views will affect the results of image reconstruction, where the high number of sensor views per projection will give a high image quality. This research presents an application of charge-coupled device linear sensor and laser diode in an OPT system. Experiments in detecting solid and transparent objects in crystal clear water were conducted. Two numbers of sensors views, 160 and 320 views are evaluated in this research in reconstructing the images. The image reconstruction algorithms used were filtered images of linear back projection algorithms. Analysis on comparing the simulation and experiments image results shows that, with 320 image views giving less area error than 160 views. This suggests that high image view resulted in the high resolution of image reconstruction.

Relevant Scatterers Characterization in SAR Images

NASA Astrophysics Data System (ADS)

Chaabouni, Houda; Datcu, Mihai

2006-11-01

Recognizing scenes in a single look meter resolution Synthetic Aperture Radar (SAR) images, requires the capability to identify relevant signal signatures in condition of variable image acquisition geometry, arbitrary objects poses and configurations. Among the methods to detect relevant scatterers in SAR images, we can mention the internal coherence. The SAR spectrum splitted in azimuth generates a series of images which preserve high coherence only for particular object scattering. The detection of relevant scatterers can be done by correlation study or Independent Component Analysis (ICA) methods. The present article deals with the state of the art for SAR internal correlation analysis and proposes further extensions using elements of inference based on information theory applied to complex valued signals. The set of azimuth looks images is analyzed using mutual information measures and an equivalent channel capacity is derived. The localization of the "target" requires analysis in a small image window, thus resulting in imprecise estimation of the second order statistics of the signal. For a better precision, a Hausdorff measure is introduced. The method is applied to detect and characterize relevant objects in urban areas.

Syntactic methods of shape feature description and its application in analysis of medical images

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-02-01

The paper presents specialist algorithms of morphologic analysis of shapes of selected organs of abdominal cavity proposed in order to diagnose disease symptoms occurring in the main pancreatic ducts and upper segments of ureters. Analysis of the correct morphology of these structures has been conducted with the use of syntactic methods of pattern recognition. Its main objective is computer-aided support to early diagnosis of neoplastic lesions and pancreatitis based on images taken in the course of examination with the endoscopic retrograde cholangiopancreatography (ERCP) method and a diagnosis of morphological lesions in ureter based on kidney radiogram analysis. In the analysis of ERCP images, the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis. In the case of kidney radiogram analysis the aim is to diagnose local irregularity of ureter lumen. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of shape features description and context-free attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing into diagrams of widths of the examined structures.

Serial grouping of 2D-image regions with object-based attention in humans.

PubMed

Jeurissen, Danique; Self, Matthew W; Roelfsema, Pieter R

2016-06-13

After an initial stage of local analysis within the retina and early visual pathways, the human visual system creates a structured representation of the visual scene by co-selecting image elements that are part of behaviorally relevant objects. The mechanisms underlying this perceptual organization process are only partially understood. We here investigate the time-course of perceptual grouping of two-dimensional image-regions by measuring the reaction times of human participants and report that it is associated with the gradual spread of object-based attention. Attention spreads fastest over large and homogeneous areas and is slowed down at locations that require small-scale processing. We find that the time-course of the object-based selection process is well explained by a 'growth-cone' model, which selects surface elements in an incremental, scale-dependent manner. We discuss how the visual cortical hierarchy can implement this scale-dependent spread of object-based attention, leveraging the different receptive field sizes in distinct cortical areas.

Estimation of the stand ages of tropical secondary forests after shifting cultivation based on the combination of WorldView-2 and time-series Landsat images

NASA Astrophysics Data System (ADS)

Fujiki, Shogoro; Okada, Kei-ichi; Nishio, Shogo; Kitayama, Kanehiro

2016-09-01

We developed a new method to estimate stand ages of secondary vegetation in the Bornean montane zone, where local people conduct traditional shifting cultivation and protected areas are surrounded by patches of recovering secondary vegetation of various ages. Identifying stand ages at the landscape level is critical to improve conservation policies. We combined a high-resolution satellite image (WorldView-2) with time-series Landsat images. We extracted stand ages (the time elapsed since the most recent slash and burn) from a change-detection analysis with Landsat time-series images and superimposed the derived stand ages on the segments classified by object-based image analysis using WorldView-2. We regarded stand ages as a response variable, and object-based metrics as independent variables, to develop regression models that explain stand ages. Subsequently, we classified the vegetation of the target area into six age units and one rubber plantation unit (1-3 yr, 3-5 yr, 5-7 yr, 7-30 yr, 30-50 yr, >50 yr and 'rubber plantation') using regression models and linear discriminant analyses. Validation demonstrated an accuracy of 84.3%. Our approach is particularly effective in classifying highly dynamic pioneer vegetation younger than 7 years into 2-yr intervals, suggesting that rapid changes in vegetation canopies can be detected with high accuracy. The combination of a spectral time-series analysis and object-based metrics based on high-resolution imagery enabled the classification of dynamic vegetation under intensive shifting cultivation and yielded an informative land cover map based on stand ages.

Rapid Disaster Damage Estimation

NASA Astrophysics Data System (ADS)

Vu, T. T.

2012-07-01

The experiences from recent disaster events showed that detailed information derived from high-resolution satellite images could accommodate the requirements from damage analysts and disaster management practitioners. Richer information contained in such high-resolution images, however, increases the complexity of image analysis. As a result, few image analysis solutions can be practically used under time pressure in the context of post-disaster and emergency responses. To fill the gap in employment of remote sensing in disaster response, this research develops a rapid high-resolution satellite mapping solution built upon a dual-scale contextual framework to support damage estimation after a catastrophe. The target objects are building (or building blocks) and their condition. On the coarse processing level, statistical region merging deployed to group pixels into a number of coarse clusters. Based on majority rule of vegetation index, water and shadow index, it is possible to eliminate the irrelevant clusters. The remaining clusters likely consist of building structures and others. On the fine processing level details, within each considering clusters, smaller objects are formed using morphological analysis. Numerous indicators including spectral, textural and shape indices are computed to be used in a rule-based object classification. Computation time of raster-based analysis highly depends on the image size or number of processed pixels in order words. Breaking into 2 level processing helps to reduce the processed number of pixels and the redundancy of processing irrelevant information. In addition, it allows a data- and tasks- based parallel implementation. The performance is demonstrated with QuickBird images captured a disaster-affected area of Phanga, Thailand by the 2004 Indian Ocean tsunami are used for demonstration of the performance. The developed solution will be implemented in different platforms as well as a web processing service for operational uses.

Hierarchical layered and semantic-based image segmentation using ergodicity map

NASA Astrophysics Data System (ADS)

Yadegar, Jacob; Liu, Xiaoqing

2010-04-01

Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects/regions with contextual topological relationships.

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

Approach for scene reconstruction from the analysis of a triplet of still images

NASA Astrophysics Data System (ADS)

Lechat, Patrick; Le Mestre, Gwenaelle; Pele, Danielle

1997-03-01

Three-dimensional modeling of a scene from the automatic analysis of 2D image sequences is a big challenge for future interactive audiovisual services based on 3D content manipulation such as virtual vests, 3D teleconferencing and interactive television. We propose a scheme that computes 3D objects models from stereo analysis of image triplets shot by calibrated cameras. After matching the different views with a correlation based algorithm, a depth map referring to a given view is built by using a fusion criterion taking into account depth coherency, visibility constraints and correlation scores. Because luminance segmentation helps to compute accurate object borders and to detect and improve the unreliable depth values, a two steps segmentation algorithm using both depth map and graylevel image is applied to extract the objects masks. First an edge detection segments the luminance image in regions and a multimodal thresholding method selects depth classes from the depth map. Then the regions are merged and labelled with the different depth classes numbers by using a coherence test on depth values according to the rate of reliable and dominant depth values and the size of the regions. The structures of the segmented objects are obtained with a constrained Delaunay triangulation followed by a refining stage. Finally, texture mapping is performed using open inventor or VRML1.0 tools.

Analysis and Recognition of Curve Type as The Basis of Object Recognition in Image

NASA Astrophysics Data System (ADS)

Nugraha, Nurma; Madenda, Sarifuddin; Indarti, Dina; Dewi Agushinta, R.; Ernastuti

2016-06-01

An object in an image when analyzed further will show the characteristics that distinguish one object with another object in an image. Characteristics that are used in object recognition in an image can be a color, shape, pattern, texture and spatial information that can be used to represent objects in the digital image. The method has recently been developed for image feature extraction on objects that share characteristics curve analysis (simple curve) and use the search feature of chain code object. This study will develop an algorithm analysis and the recognition of the type of curve as the basis for object recognition in images, with proposing addition of complex curve characteristics with maximum four branches that will be used for the process of object recognition in images. Definition of complex curve is the curve that has a point of intersection. By using some of the image of the edge detection, the algorithm was able to do the analysis and recognition of complex curve shape well.

Object Based Image Analysis Combining High Spatial Resolution Imagery and Laser Point Clouds for Urban Land Cover

NASA Astrophysics Data System (ADS)

Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

2016-06-01

With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.

Automatic archaeological feature extraction from satellite VHR images

NASA Astrophysics Data System (ADS)

Jahjah, Munzer; Ulivieri, Carlo

2010-05-01

Archaeological applications need a methodological approach on a variable scale able to satisfy the intra-site (excavation) and the inter-site (survey, environmental research). The increased availability of high resolution and micro-scale data has substantially favoured archaeological applications and the consequent use of GIS platforms for reconstruction of archaeological landscapes based on remotely sensed data. Feature extraction of multispectral remotely sensing image is an important task before any further processing. High resolution remote sensing data, especially panchromatic, is an important input for the analysis of various types of image characteristics; it plays an important role in the visual systems for recognition and interpretation of given data. The methods proposed rely on an object-oriented approach based on a theory for the analysis of spatial structures called mathematical morphology. The term "morphology" stems from the fact that it aims at analysing object shapes and forms. It is mathematical in the sense that the analysis is based on the set theory, integral geometry, and lattice algebra. Mathematical morphology has proven to be a powerful image analysis technique; two-dimensional grey tone images are seen as three-dimensional sets by associating each image pixel with an elevation proportional to its intensity level. An object of known shape and size, called the structuring element, is then used to investigate the morphology of the input set. This is achieved by positioning the origin of the structuring element to every possible position of the space and testing, for each position, whether the structuring element either is included or has a nonempty intersection with the studied set. The shape and size of the structuring element must be selected according to the morphology of the searched image structures. Other two feature extraction techniques were used, eCognition and ENVI module SW, in order to compare the results. These techniques were applied to different archaeological sites in Turkmenistan (Nisa) and in Iraq (Babylon); a further change detection analysis was applied to the Babylon site using two HR images as a pre-post second gulf war. We had different results or outputs, taking into consideration the fact that the operative scale of sensed data determines the final result of the elaboration and the output of the information quality, because each of them was sensitive to specific shapes in each input image, we had mapped linear and nonlinear objects, updating archaeological cartography, automatic change detection analysis for the Babylon site. The discussion of these techniques has the objective to provide the archaeological team with new instruments for the orientation and the planning of a remote sensing application.

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

NASA Astrophysics Data System (ADS)

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

2018-07-01

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

Adaptive Morphological Feature-Based Object Classifier for a Color Imaging System

NASA Technical Reports Server (NTRS)

McDowell, Mark; Gray, Elizabeth

2009-01-01

Utilizing a Compact Color Microscope Imaging System (CCMIS), a unique algorithm has been developed that combines human intelligence along with machine vision techniques to produce an autonomous microscope tool for biomedical, industrial, and space applications. This technique is based on an adaptive, morphological, feature-based mapping function comprising 24 mutually inclusive feature metrics that are used to determine the metrics for complex cell/objects derived from color image analysis. Some of the features include: Area (total numbers of non-background pixels inside and including the perimeter), Bounding Box (smallest rectangle that bounds and object), centerX (x-coordinate of intensity-weighted, center-of-mass of an entire object or multi-object blob), centerY (y-coordinate of intensity-weighted, center-of-mass, of an entire object or multi-object blob), Circumference (a measure of circumference that takes into account whether neighboring pixels are diagonal, which is a longer distance than horizontally or vertically joined pixels), . Elongation (measure of particle elongation given as a number between 0 and 1. If equal to 1, the particle bounding box is square. As the elongation decreases from 1, the particle becomes more elongated), . Ext_vector (extremal vector), . Major Axis (the length of a major axis of a smallest ellipse encompassing an object), . Minor Axis (the length of a minor axis of a smallest ellipse encompassing an object), . Partial (indicates if the particle extends beyond the field of view), . Perimeter Points (points that make up a particle perimeter), . Roundness [(4(pi) x area)/perimeter(squared)) the result is a measure of object roundness, or compactness, given as a value between 0 and 1. The greater the ratio, the rounder the object.], . Thin in center (determines if an object becomes thin in the center, (figure-eight-shaped), . Theta (orientation of the major axis), . Smoothness and color metrics for each component (red, green, blue) the minimum, maximum, average, and standard deviation within the particle are tracked. These metrics can be used for autonomous analysis of color images from a microscope, video camera, or digital, still image. It can also automatically identify tumor morphology of stained images and has been used to detect stained cell phenomena (see figure).

Wavelet-based compression of M-FISH images.

PubMed

Hua, Jianping; Xiong, Zixiang; Wu, Qiang; Castleman, Kenneth R

2005-05-01

Multiplex fluorescence in situ hybridization (M-FISH) is a recently developed technology that enables multi-color chromosome karyotyping for molecular cytogenetic analysis. Each M-FISH image set consists of a number of aligned images of the same chromosome specimen captured at different optical wavelength. This paper presents embedded M-FISH image coding (EMIC), where the foreground objects/chromosomes and the background objects/images are coded separately. We first apply critically sampled integer wavelet transforms to both the foreground and the background. We then use object-based bit-plane coding to compress each object and generate separate embedded bitstreams that allow continuous lossy-to-lossless compression of the foreground and the background. For efficient arithmetic coding of bit planes, we propose a method of designing an optimal context model that specifically exploits the statistical characteristics of M-FISH images in the wavelet domain. Our experiments show that EMIC achieves nearly twice as much compression as Lempel-Ziv-Welch coding. EMIC also performs much better than JPEG-LS and JPEG-2000 for lossless coding. The lossy performance of EMIC is significantly better than that of coding each M-FISH image with JPEG-2000.

Solution of the problem of superposing image and digital map for detection of new objects

NASA Astrophysics Data System (ADS)

Rizaev, I. S.; Miftakhutdinov, D. I.; Takhavova, E. G.

2018-01-01

The problem of superposing the map of the terrain with the image of the terrain is considered. The image of the terrain may be represented in different frequency bands. Further analysis of the results of collation the digital map with the image of the appropriate terrain is described. Also the approach to detection of differences between information represented on the digital map and information of the image of the appropriate area is offered. The algorithm for calculating the values of brightness of the converted image area on the original picture is offered. The calculation is based on using information about the navigation parameters and information according to arranged bench marks. For solving the posed problem the experiments were performed. The results of the experiments are shown in this paper. The presented algorithms are applicable to the ground complex of remote sensing data to assess differences between resulting images and accurate geopositional data. They are also suitable for detecting new objects in the image, based on the analysis of the matching the digital map and the image of corresponding locality.

Spotlight-8 Image Analysis Software

NASA Technical Reports Server (NTRS)

Klimek, Robert; Wright, Ted

2006-01-01

Spotlight is a cross-platform GUI-based software package designed to perform image analysis on sequences of images generated by combustion and fluid physics experiments run in a microgravity environment. Spotlight can perform analysis on a single image in an interactive mode or perform analysis on a sequence of images in an automated fashion. Image processing operations can be employed to enhance the image before various statistics and measurement operations are performed. An arbitrarily large number of objects can be analyzed simultaneously with independent areas of interest. Spotlight saves results in a text file that can be imported into other programs for graphing or further analysis. Spotlight can be run on Microsoft Windows, Linux, and Apple OS X platforms.

Algorithm for Automatic Segmentation of Nuclear Boundaries in Cancer Cells in Three-Channel Luminescent Images

NASA Astrophysics Data System (ADS)

Lisitsa, Y. V.; Yatskou, M. M.; Apanasovich, V. V.; Apanasovich, T. V.

2015-09-01

We have developed an algorithm for segmentation of cancer cell nuclei in three-channel luminescent images of microbiological specimens. The algorithm is based on using a correlation between fluorescence signals in the detection channels for object segmentation, which permits complete automation of the data analysis procedure. We have carried out a comparative analysis of the proposed method and conventional algorithms implemented in the CellProfiler and ImageJ software packages. Our algorithm has an object localization uncertainty which is 2-3 times smaller than for the conventional algorithms, with comparable segmentation accuracy.

The use of neural networks and texture analysis for rapid objective selection of regions of interest in cytoskeletal images.

PubMed

Derkacs, Amanda D Felder; Ward, Samuel R; Lieber, Richard L

2012-02-01

Understanding cytoskeletal dynamics in living tissue is prerequisite to understanding mechanisms of injury, mechanotransduction, and mechanical signaling. Real-time visualization is now possible using transfection with plasmids that encode fluorescent cytoskeletal proteins. Using this approach with the muscle-specific intermediate filament protein desmin, we found that a green fluorescent protein-desmin chimeric protein was unevenly distributed throughout the muscle fiber, resulting in some image areas that were saturated as well as others that lacked any signal. Our goal was to analyze the muscle fiber cytoskeletal network quantitatively in an unbiased fashion. To objectively select areas of the muscle fiber that are suitable for analysis, we devised a method that provides objective classification of regions of images of striated cytoskeletal structures into "usable" and "unusable" categories. This method consists of a combination of spatial analysis of the image using Fourier methods along with a boosted neural network that "decides" on the quality of the image based on previous training. We trained the neural network using the expert opinion of three scientists familiar with these types of images. We found that this method was over 300 times faster than manual classification and that it permitted objective and accurate classification of image regions.

Rotation Covariant Image Processing for Biomedical Applications

PubMed Central

Reisert, Marco

2013-01-01

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences. PMID:23710255

A Review of Sparsity-Based Methods for Analysing Radar Returns from Helicopter Rotor Blades

DTIC Science & Technology

2016-09-01

UNCLASSIFIED A Review of Sparsity-Based Methods for Analysing Radar Returns from Helicopter Rotor Blades Ngoc Hung Nguyen 1, Hai-Tan Tran 2, Kutluyıl...TR–3292 ABSTRACT Radar imaging of rotating blade -like objects, such as helicopter rotors, using narrowband radar has lately been of significant...Methods for Analysing Radar Returns from Helicopter Rotor Blades Executive Summary Signal analysis and radar imaging of fast-rotating objects such as

Regional fringe analysis for improving depth measurement in phase-shifting fringe projection profilometry

NASA Astrophysics Data System (ADS)

Chien, Kuang-Che Chang; Tu, Han-Yen; Hsieh, Ching-Huang; Cheng, Chau-Jern; Chang, Chun-Yen

2018-01-01

This study proposes a regional fringe analysis (RFA) method to detect the regions of a target object in captured shifted images to improve depth measurement in phase-shifting fringe projection profilometry (PS-FPP). In the RFA method, region-based segmentation is exploited to segment the de-fringed image of a target object, and a multi-level fuzzy-based classification with five presented features is used to analyze and discriminate the regions of an object from the segmented regions, which were associated with explicit fringe information. Then, in the experiment, the performance of the proposed method is tested and evaluated on 26 test cases made of five types of materials. The qualitative and quantitative results demonstrate that the proposed RFA method can effectively detect the desired regions of an object to improve depth measurement in the PS-FPP system.

Analysis of objects in binary images. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Leonard, Desiree M.

1991-01-01

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

NASA Astrophysics Data System (ADS)

Tatar, Nurollah; Saadatseresht, Mohammad; Arefi, Hossein; Hadavand, Ahmad

2018-06-01

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C4 index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.

Correction of Atmospheric Haze in RESOURCESAT-1 LISS-4 MX Data for Urban Analysis: AN Improved Dark Object Subtraction Approach

NASA Astrophysics Data System (ADS)

Mustak, S.

2013-09-01

The correction of atmospheric effects is very essential because visible bands of shorter wavelength are highly affected by atmospheric scattering especially of Rayleigh scattering. The objectives of the paper is to find out the haze values present in the all spectral bands and to correct the haze values for urban analysis. In this paper, Improved Dark Object Subtraction method of P. Chavez (1988) is applied for the correction of atmospheric haze in the Resoucesat-1 LISS-4 multispectral satellite image. Dark object Subtraction is a very simple image-based method of atmospheric haze which assumes that there are at least a few pixels within an image which should be black (% reflectance) and such black reflectance termed as dark object which are clear water body and shadows whose DN values zero (0) or Close to zero in the image. Simple Dark Object Subtraction method is a first order atmospheric correction but Improved Dark Object Subtraction method which tends to correct the Haze in terms of atmospheric scattering and path radiance based on the power law of relative scattering effect of atmosphere. The haze values extracted using Simple Dark Object Subtraction method for Green band (Band2), Red band (Band3) and NIR band (band4) are 40, 34 and 18 but the haze values extracted using Improved Dark Object Subtraction method are 40, 18.02 and 11.80 for aforesaid bands. Here it is concluded that the haze values extracted by Improved Dark Object Subtraction method provides more realistic results than Simple Dark Object Subtraction method.

The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data

NASA Technical Reports Server (NTRS)

Kruse, F. A.; Lefkoff, A. B.; Boardman, J. W.; Heidebrecht, K. B.; Shapiro, A. T.; Barloon, P. J.; Goetz, A. F. H.

1993-01-01

The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides 'point and click' operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).

Detecting objects in radiographs for homeland security

NASA Astrophysics Data System (ADS)

Prasad, Lakshman; Snyder, Hans

2005-05-01

We present a general scheme for segmenting a radiographic image into polygons that correspond to visual features. This decomposition provides a vectorized representation that is a high-level description of the image. The polygons correspond to objects or object parts present in the image. This characterization of radiographs allows the direct application of several shape recognition algorithms to identify objects. In this paper we describe the use of constrained Delaunay triangulations as a uniform foundational tool to achieve multiple visual tasks, namely image segmentation, shape decomposition, and parts-based shape matching. Shape decomposition yields parts that serve as tokens representing local shape characteristics. Parts-based shape matching enables the recognition of objects in the presence of occlusions, which commonly occur in radiographs. The polygonal representation of image features affords the efficient design and application of sophisticated geometric filtering methods to detect large-scale structural properties of objects in images. Finally, the representation of radiographs via polygons results in significant reduction of image file sizes and permits the scalable graphical representation of images, along with annotations of detected objects, in the SVG (scalable vector graphics) format that is proposed by the world wide web consortium (W3C). This is a textual representation that can be compressed and encrypted for efficient and secure transmission of information over wireless channels and on the Internet. In particular, our methods described here provide an algorithmic framework for developing image analysis tools for screening cargo at ports of entry for homeland security.

a Cognitive Approach to Teaching a Graduate-Level Geobia Course

NASA Astrophysics Data System (ADS)

Bianchetti, Raechel A.

2016-06-01

Remote sensing image analysis training occurs both in the classroom and the research lab. Education in the classroom for traditional pixel-based image analysis has been standardized across college curriculums. However, with the increasing interest in Geographic Object-Based Image Analysis (GEOBIA), there is a need to develop classroom instruction for this method of image analysis. While traditional remote sensing courses emphasize the expansion of skills and knowledge related to the use of computer-based analysis, GEOBIA courses should examine the cognitive factors underlying visual interpretation. This current paper provides an initial analysis of the development, implementation, and outcomes of a GEOBIA course that considers not only the computational methods of GEOBIA, but also the cognitive factors of expertise, that such software attempts to replicate. Finally, a reflection on the first instantiation of this course is presented, in addition to plans for development of an open-source repository for course materials.

Stochastic HKMDHE: A multi-objective contrast enhancement algorithm

NASA Astrophysics Data System (ADS)

Pratiher, Sawon; Mukhopadhyay, Sabyasachi; Maity, Srideep; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2018-02-01

This contribution proposes a novel extension of the existing `Hyper Kurtosis based Modified Duo-Histogram Equalization' (HKMDHE) algorithm, for multi-objective contrast enhancement of biomedical images. A novel modified objective function has been formulated by joint optimization of the individual histogram equalization objectives. The optimal adequacy of the proposed methodology with respect to image quality metrics such as brightness preserving abilities, peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM) and universal image quality metric has been experimentally validated. The performance analysis of the proposed Stochastic HKMDHE with existing histogram equalization methodologies like Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) has been given for comparative evaluation.

Critical object recognition in millimeter-wave images with robustness to rotation and scale.

PubMed

Mohammadzade, Hoda; Ghojogh, Benyamin; Faezi, Sina; Shabany, Mahdi

2017-06-01

Locating critical objects is crucial in various security applications and industries. For example, in security applications, such as in airports, these objects might be hidden or covered under shields or secret sheaths. Millimeter-wave images can be utilized to discover and recognize the critical objects out of the hidden cases without any health risk due to their non-ionizing features. However, millimeter-wave images usually have waves in and around the detected objects, making object recognition difficult. Thus, regular image processing and classification methods cannot be used for these images and additional pre-processings and classification methods should be introduced. This paper proposes a novel pre-processing method for canceling rotation and scale using principal component analysis. In addition, a two-layer classification method is introduced and utilized for recognition. Moreover, a large dataset of millimeter-wave images is collected and created for experiments. Experimental results show that a typical classification method such as support vector machines can recognize 45.5% of a type of critical objects at 34.2% false alarm rate (FAR), which is a drastically poor recognition. The same method within the proposed recognition framework achieves 92.9% recognition rate at 0.43% FAR, which indicates a highly significant improvement. The significant contribution of this work is to introduce a new method for analyzing millimeter-wave images based on machine vision and learning approaches, which is not yet widely noted in the field of millimeter-wave image analysis.

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.

PubMed

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R

2018-01-01

Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.

A light and faster regional convolutional neural network for object detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Ding, Peng; Zhang, Ye; Deng, Wei-Jian; Jia, Ping; Kuijper, Arjan

2018-07-01

Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications. With the development of deep convolutional neural networks (deep CNNs), the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGG16-Net for improving the precision. We propose an approach to reduce the test-time (detection time) and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles. The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently.

Streak detection and analysis pipeline for space-debris optical images

NASA Astrophysics Data System (ADS)

Virtanen, Jenni; Poikonen, Jonne; Säntti, Tero; Komulainen, Tuomo; Torppa, Johanna; Granvik, Mikael; Muinonen, Karri; Pentikäinen, Hanna; Martikainen, Julia; Näränen, Jyri; Lehti, Jussi; Flohrer, Tim

2016-04-01

We describe a novel data-processing and analysis pipeline for optical observations of moving objects, either of natural (asteroids, meteors) or artificial origin (satellites, space debris). The monitoring of the space object populations requires reliable acquisition of observational data, to support the development and validation of population models and to build and maintain catalogues of orbital elements. The orbital catalogues are, in turn, needed for the assessment of close approaches (for asteroids, with the Earth; for satellites, with each other) and for the support of contingency situations or launches. For both types of populations, there is also increasing interest to detect fainter objects corresponding to the small end of the size distribution. The ESA-funded StreakDet (streak detection and astrometric reduction) activity has aimed at formulating and discussing suitable approaches for the detection and astrometric reduction of object trails, or streaks, in optical observations. Our two main focuses are objects in lower altitudes and space-based observations (i.e., high angular velocities), resulting in long (potentially curved) and faint streaks in the optical images. In particular, we concentrate on single-image (as compared to consecutive frames of the same field) and low-SNR detection of objects. Particular attention has been paid to the process of extraction of all necessary information from one image (segmentation), and subsequently, to efficient reduction of the extracted data (classification). We have developed an automated streak detection and processing pipeline and demonstrated its performance with an extensive database of semisynthetic images simulating streak observations both from ground-based and space-based observing platforms. The average processing time per image is about 13 s for a typical 2k-by-2k image. For long streaks (length >100 pixels), primary targets of the pipeline, the detection sensitivity (true positives) is about 90% for both scenarios for the bright streaks (SNR > 1), while in the low-SNR regime, the sensitivity is still 50% at SNR = 0.5 .

Passive Infrared Thermographic Imaging for Mobile Robot Object Identification

NASA Astrophysics Data System (ADS)

Hinders, M. K.; Fehlman, W. L.

2010-02-01

The usefulness of thermal infrared imaging as a mobile robot sensing modality is explored, and a set of thermal-physical features used to characterize passive thermal objects in outdoor environments is described. Objects that extend laterally beyond the thermal camera's field of view, such as brick walls, hedges, picket fences, and wood walls as well as compact objects that are laterally within the thermal camera's field of view, such as metal poles and tree trunks, are considered. Classification of passive thermal objects is a subtle process since they are not a source for their own emission of thermal energy. A detailed analysis is included of the acquisition and preprocessing of thermal images, as well as the generation and selection of thermal-physical features from these objects within thermal images. Classification performance using these features is discussed, as a precursor to the design of a physics-based model to automatically classify these objects.

Fringe image processing based on structured light series

NASA Astrophysics Data System (ADS)

Gai, Shaoyan; Da, Feipeng; Li, Hongyan

2009-11-01

The code analysis of the fringe image is playing a vital role in the data acquisition of structured light systems, which affects precision, computational speed and reliability of the measurement processing. According to the self-normalizing characteristic, a fringe image processing method based on structured light is proposed. In this method, a series of projective patterns is used when detecting the fringe order of the image pixels. The structured light system geometry is presented, which consist of a white light projector and a digital camera, the former projects sinusoidal fringe patterns upon the object, and the latter acquires the fringe patterns that are deformed by the object's shape. Then the binary images with distinct white and black strips can be obtained and the ability to resist image noise is improved greatly. The proposed method can be implemented easily and applied for profile measurement based on special binary code in a wide field.

Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland mapping and assessment

USDA-ARS?s Scientific Manuscript database

Ultra high resolution digital aerial photography has great potential to complement or replace ground measurements of vegetation cover for rangeland monitoring and assessment. We investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern U.S. arid rangelands...

Feature selection methods for object-based classification of sub-decimeter resolution digital aerial imagery

USDA-ARS?s Scientific Manuscript database

Due to the availability of numerous spectral, spatial, and contextual features, the determination of optimal features and class separabilities can be a time consuming process in object-based image analysis (OBIA). While several feature selection methods have been developed to assist OBIA, a robust c...

Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data

NASA Astrophysics Data System (ADS)

Hamedianfar, Alireza; Shafri, Helmi Zulhaidi Mohd

2016-04-01

This paper integrates decision tree-based data mining (DM) and object-based image analysis (OBIA) to provide a transferable model for the detailed characterization of urban land-cover classes using WorldView-2 (WV-2) satellite images. Many articles have been published on OBIA in recent years based on DM for different applications. However, less attention has been paid to the generation of a transferable model for characterizing detailed urban land cover features. Three subsets of WV-2 images were used in this paper to generate transferable OBIA rule-sets. Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. The developed DT algorithm was applied to object-based classifications in the first study area. After this process, we validated the capability and transferability of the classification rules into second and third subsets. Detailed ground truth samples were collected to assess the classification results. The first, second, and third study areas achieved 88%, 85%, and 85% overall accuracies, respectively. Results from the investigation indicate that DM was an efficient method to provide the optimal and transferable classification rules for OBIA, which accelerates the rule-sets creation stage in the OBIA classification domain.

Object-oriented design of medical imaging software.

PubMed

Ligier, Y; Ratib, O; Logean, M; Girard, C; Perrier, R; Scherrer, J R

1994-01-01

A special software package for interactive display and manipulation of medical images was developed at the University Hospital of Geneva, as part of a hospital wide Picture Archiving and Communication System (PACS). This software package, called Osiris, was especially designed to be easily usable and adaptable to the needs of noncomputer-oriented physicians. The Osiris software has been developed to allow the visualization of medical images obtained from any imaging modality. It provides generic manipulation tools, processing tools, and analysis tools more specific to clinical applications. This software, based on an object-oriented paradigm, is portable and extensible. Osiris is available on two different operating systems: the Unix X-11/OSF-Motif based workstations, and the Macintosh family.

Serial grouping of 2D-image regions with object-based attention in humans

PubMed Central

Jeurissen, Danique; Self, Matthew W; Roelfsema, Pieter R

2016-01-01

After an initial stage of local analysis within the retina and early visual pathways, the human visual system creates a structured representation of the visual scene by co-selecting image elements that are part of behaviorally relevant objects. The mechanisms underlying this perceptual organization process are only partially understood. We here investigate the time-course of perceptual grouping of two-dimensional image-regions by measuring the reaction times of human participants and report that it is associated with the gradual spread of object-based attention. Attention spreads fastest over large and homogeneous areas and is slowed down at locations that require small-scale processing. We find that the time-course of the object-based selection process is well explained by a 'growth-cone' model, which selects surface elements in an incremental, scale-dependent manner. We discuss how the visual cortical hierarchy can implement this scale-dependent spread of object-based attention, leveraging the different receptive field sizes in distinct cortical areas. DOI: http://dx.doi.org/10.7554/eLife.14320.001 PMID:27291188

Object-based Landslide Mapping: Examples, Challenges and Opportunities

NASA Astrophysics Data System (ADS)

Hölbling, Daniel; Eisank, Clemens; Friedl, Barbara; Chang, Kang-Tsung; Tsai, Tsai-Tsung; Birkefeldt Møller Pedersen, Gro; Betts, Harley; Cigna, Francesca; Chiang, Shou-Hao; Aubrey Robson, Benjamin; Bianchini, Silvia; Füreder, Petra; Albrecht, Florian; Spiekermann, Raphael; Weinke, Elisabeth; Blaschke, Thomas; Phillips, Chris

2016-04-01

Over the last decade, object-based image analysis (OBIA) has been increasingly used for mapping landslides that occur after triggering events such as heavy rainfall. The increasing availability and quality of Earth Observation (EO) data in terms of temporal, spatial and spectral resolution allows for comprehensive mapping of landslides at multiple scales. Most often very high resolution (VHR) or high resolution (HR) optical satellite images are used in combination with a digital elevation model (DEM) and its products such as slope and curvature. Semi-automated object-based mapping makes use of various characteristics of image objects that are derived through segmentation. OBIA enables numerous spectral, spatial, contextual and textural image object properties to be applied during an analysis. This is especially useful when mapping complex natural features such as landslides and constitutes an advantage over pixel-based image analysis. However, several drawbacks in the process of object-based landslide mapping have not been overcome yet. The developed classification routines are often rather complex and limited regarding their transferability across areas and sensors. There is still more research needed to further improve present approaches and to fully exploit the capabilities of OBIA for landslide mapping. In this study several examples of object-based landslide mapping from various geographical regions with different characteristics are presented. Examples from the Austrian and Italian Alps are shown, whereby one challenge lies in the detection of small-scale landslides on steep slopes while preventing the classification of false positives with similar spectral properties (construction areas, utilized land, etc.). Further examples feature landslides mapped in Iceland, where the differentiation of landslides from other landscape-altering processes in a highly dynamic volcanic landscape poses a very distinct challenge, and in Norway, which is exposed to multiple types of landslides. Unlike in these northern European countries, landslides in Taiwan can be effectively delineated based on spectral differences as the surrounding is most often densely vegetated. In this tropical/subtropical region the fast information provision after Typhoon events is important. This need can be addressed in OBIA by automatically calculating thresholds based on vegetation indices and using them for a first rough identification of areas affected by landslides. Moreover, the differentiation in landslide source and transportation area is of high relevance in Taiwan. Finally, an example from New Zealand, where landslide inventory mapping is important for estimating surface erosion, will demonstrate the performance of OBIA compared to visual expert interpretation and on-screen mapping. The associated challenges and opportunities related to case studies in each of these regions are discussed and reviewed. In doing so, open research issues in object-based landslide mapping based on EO data are identified and highlighted.

A scale-based connected coherence tree algorithm for image segmentation.

PubMed

Ding, Jundi; Ma, Runing; Chen, Songcan

2008-02-01

This paper presents a connected coherence tree algorithm (CCTA) for image segmentation with no prior knowledge. It aims to find regions of semantic coherence based on the proposed epsilon-neighbor coherence segmentation criterion. More specifically, with an adaptive spatial scale and an appropriate intensity-difference scale, CCTA often achieves several sets of coherent neighboring pixels which maximize the probability of being a single image content (including kinds of complex backgrounds). In practice, each set of coherent neighboring pixels corresponds to a coherence class (CC). The fact that each CC just contains a single equivalence class (EC) ensures the separability of an arbitrary image theoretically. In addition, the resultant CCs are represented by tree-based data structures, named connected coherence tree (CCT)s. In this sense, CCTA is a graph-based image analysis algorithm, which expresses three advantages: 1) its fundamental idea, epsilon-neighbor coherence segmentation criterion, is easy to interpret and comprehend; 2) it is efficient due to a linear computational complexity in the number of image pixels; 3) both subjective comparisons and objective evaluation have shown that it is effective for the tasks of semantic object segmentation and figure-ground separation in a wide variety of images. Those images either contain tiny, long and thin objects or are severely degraded by noise, uneven lighting, occlusion, poor illumination, and shadow.

A software package to improve image quality and isolation of objects of interest for quantitative stereology studies of rat hepatocarcinogenesis.

PubMed

Xu, Yihua; Pitot, Henry C

2006-03-01

In the studies of quantitative stereology of rat hepatocarcinogenesis, we have used image analysis technology (automatic particle analysis) to obtain data such as liver tissue area, size and location of altered hepatic focal lesions (AHF), and nuclei counts. These data are then used for three-dimensional estimation of AHF occurrence and nuclear labeling index analysis. These are important parameters for quantitative studies of carcinogenesis, for screening and classifying carcinogens, and for risk estimation. To take such measurements, structures or cells of interest should be separated from the other components based on the difference of color and density. Common background problems seen on the captured sample image such as uneven light illumination or color shading can cause severe problems in the measurement. Two application programs (BK_Correction and Pixel_Separator) have been developed to solve these problems. With BK_Correction, common background problems such as incorrect color temperature setting, color shading, and uneven light illumination background, can be corrected. With Pixel_Separator different types of objects can be separated from each other in relation to their color, such as seen with different colors in immunohistochemically stained slides. The resultant images of such objects separated from other components are then ready for particle analysis. Objects that have the same darkness but different colors can be accurately differentiated in a grayscale image analysis system after application of these programs.

Applying time series Landsat data for vegetation change analysis in the Florida Everglades Water Conservation Area 2A during 1996-2016

NASA Astrophysics Data System (ADS)

Zhang, Caiyun; Smith, Molly; Lv, Jie; Fang, Chaoyang

2017-05-01

Mapping plant communities and documenting their changes is critical to the on-going Florida Everglades restoration project. In this study, a framework was designed to map dominant vegetation communities and inventory their changes in the Florida Everglades Water Conservation Area 2A (WCA-2A) using time series Landsat images spanning 1996-2016. The object-based change analysis technique was combined in the framework. A hybrid pixel/object-based change detection approach was developed to effectively collect training samples for historical images with sparse reference data. An object-based quantification approach was also developed to assess the expansion/reduction of a specific class such as cattail (an invasive species in the Everglades) from the object-based classifications of two dates of imagery. The study confirmed the results in the literature that cattail was largely expanded during 1996-2007. It also revealed that cattail expansion was constrained after 2007. Application of time series Landsat data is valuable to document vegetation changes for the WCA-2A impoundment. The digital techniques developed will benefit global wetland mapping and change analysis in general, and the Florida Everglades WCA-2A in particular.

Fusion and quality analysis for remote sensing images using contourlet transform

NASA Astrophysics Data System (ADS)

Choi, Yoonsuk; Sharifahmadian, Ershad; Latifi, Shahram

2013-05-01

Recent developments in remote sensing technologies have provided various images with high spatial and spectral resolutions. However, multispectral images have low spatial resolution and panchromatic images have low spectral resolution. Therefore, image fusion techniques are necessary to improve the spatial resolution of spectral images by injecting spatial details of high-resolution panchromatic images. The objective of image fusion is to provide useful information by improving the spatial resolution and the spectral information of the original images. The fusion results can be utilized in various applications, such as military, medical imaging, and remote sensing. This paper addresses two issues in image fusion: i) image fusion method and ii) quality analysis of fusion results. First, a new contourlet-based image fusion method is presented, which is an improvement over the wavelet-based fusion. This fusion method is then applied to a case study to demonstrate its fusion performance. Fusion framework and scheme used in the study are discussed in detail. Second, quality analysis for the fusion results is discussed. We employed various quality metrics in order to analyze the fusion results both spatially and spectrally. Our results indicate that the proposed contourlet-based fusion method performs better than the conventional wavelet-based fusion methods.

Analysis of simulated image sequences from sensors for restricted-visibility operations

NASA Technical Reports Server (NTRS)

Kasturi, Rangachar

1991-01-01

A real time model of the visible output from a 94 GHz sensor, based on a radiometric simulation of the sensor, was developed. A sequence of images as seen from an aircraft as it approaches for landing was simulated using this model. Thirty frames from this sequence of 200 x 200 pixel images were analyzed to identify and track objects in the image using the Cantata image processing package within the visual programming environment provided by the Khoros software system. The image analysis operations are described.

A new generation of intelligent trainable tools for analyzing large scientific image databases

NASA Technical Reports Server (NTRS)

Fayyad, Usama M.; Smyth, Padhraic; Atkinson, David J.

1994-01-01

The focus of this paper is on the detection of natural, as opposed to human-made, objects. The distinction is important because, in the context of image analysis, natural objects tend to possess much greater variability in appearance than human-made objects. Hence, we shall focus primarily on the use of algorithms that 'learn by example' as the basis for image exploration. The 'learn by example' approach is potentially more generally applicable compared to model-based vision methods since domain scientists find it relatively easier to provide examples of what they are searching for versus describing a model.

Silhouette-based approach of 3D image reconstruction for automated image acquisition using robotic arm

NASA Astrophysics Data System (ADS)

Azhar, N.; Saad, W. H. M.; Manap, N. A.; Saad, N. M.; Syafeeza, A. R.

2017-06-01

This study presents the approach of 3D image reconstruction using an autonomous robotic arm for the image acquisition process. A low cost of the automated imaging platform is created using a pair of G15 servo motor connected in series to an Arduino UNO as a main microcontroller. Two sets of sequential images were obtained using different projection angle of the camera. The silhouette-based approach is used in this study for 3D reconstruction from the sequential images captured from several different angles of the object. Other than that, an analysis based on the effect of different number of sequential images on the accuracy of 3D model reconstruction was also carried out with a fixed projection angle of the camera. The effecting elements in the 3D reconstruction are discussed and the overall result of the analysis is concluded according to the prototype of imaging platform.

Tracker: Image-Processing and Object-Tracking System Developed

NASA Technical Reports Server (NTRS)

Klimek, Robert B.; Wright, Theodore W.

1999-01-01

Tracker is an object-tracking and image-processing program designed and developed at the NASA Lewis Research Center to help with the analysis of images generated by microgravity combustion and fluid physics experiments. Experiments are often recorded on film or videotape for analysis later. Tracker automates the process of examining each frame of the recorded experiment, performing image-processing operations to bring out the desired detail, and recording the positions of the objects of interest. It can load sequences of images from disk files or acquire images (via a frame grabber) from film transports, videotape, laser disks, or a live camera. Tracker controls the image source to automatically advance to the next frame. It can employ a large array of image-processing operations to enhance the detail of the acquired images and can analyze an arbitrarily large number of objects simultaneously. Several different tracking algorithms are available, including conventional threshold and correlation-based techniques, and more esoteric procedures such as "snake" tracking and automated recognition of character data in the image. The Tracker software was written to be operated by researchers, thus every attempt was made to make the software as user friendly and self-explanatory as possible. Tracker is used by most of the microgravity combustion and fluid physics experiments performed by Lewis, and by visiting researchers. This includes experiments performed on the space shuttles, Mir, sounding rockets, zero-g research airplanes, drop towers, and ground-based laboratories. This software automates the analysis of the flame or liquid s physical parameters such as position, velocity, acceleration, size, shape, intensity characteristics, color, and centroid, as well as a number of other measurements. It can perform these operations on multiple objects simultaneously. Another key feature of Tracker is that it performs optical character recognition (OCR). This feature is useful in extracting numerical instrumentation data that are embedded in images. All the results are saved in files for further data reduction and graphing. There are currently three Tracking Systems (workstations) operating near the laboratories and offices of Lewis Microgravity Science Division researchers. These systems are used independently by students, scientists, and university-based principal investigators. The researchers bring their tapes or films to the workstation and perform the tracking analysis. The resultant data files generated by the tracking process can then be analyzed on the spot, although most of the time researchers prefer to transfer them via the network to their offices for further analysis or plotting. In addition, many researchers have installed Tracker on computers in their office for desktop analysis of digital image sequences, which can be digitized by the Tracking System or some other means. Tracker has not only provided a capability to efficiently and automatically analyze large volumes of data, saving many hours of tedious work, but has also provided new capabilities to extract valuable information and phenomena that was heretofore undetected and unexploited.

A survey of MRI-based medical image analysis for brain tumor studies

NASA Astrophysics Data System (ADS)

Bauer, Stefan; Wiest, Roland; Nolte, Lutz-P.; Reyes, Mauricio

2013-07-01

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

NASA Astrophysics Data System (ADS)

Ma, Lei; Cheng, Liang; Li, Manchun; Liu, Yongxue; Ma, Xiaoxue

2015-04-01

Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class.

How does c-view image quality compare with conventional 2D FFDM?

PubMed

Nelson, Jeffrey S; Wells, Jered R; Baker, Jay A; Samei, Ehsan

2016-05-01

The FDA approved the use of digital breast tomosynthesis (DBT) in 2011 as an adjunct to 2D full field digital mammography (FFDM) with the constraint that all DBT acquisitions must be paired with a 2D image to assure adequate interpretative information is provided. Recently manufacturers have developed methods to provide a synthesized 2D image generated from the DBT data with the hope of sparing patients the radiation exposure from the FFDM acquisition. While this much needed alternative effectively reduces the total radiation burden, differences in image quality must also be considered. The goal of this study was to compare the intrinsic image quality of synthesized 2D c-view and 2D FFDM images in terms of resolution, contrast, and noise. Two phantoms were utilized in this study: the American College of Radiology mammography accreditation phantom (ACR phantom) and a novel 3D printed anthropomorphic breast phantom. Both phantoms were imaged using a Hologic Selenia Dimensions 3D system. Analysis of the ACR phantom includes both visual inspection and objective automated analysis using in-house software. Analysis of the 3D anthropomorphic phantom includes visual assessment of resolution and Fourier analysis of the noise. Using ACR-defined scoring criteria for the ACR phantom, the FFDM images scored statistically higher than c-view according to both the average observer and automated scores. In addition, between 50% and 70% of c-view images failed to meet the nominal minimum ACR accreditation requirements-primarily due to fiber breaks. Software analysis demonstrated that c-view provided enhanced visualization of medium and large microcalcification objects; however, the benefits diminished for smaller high contrast objects and all low contrast objects. Visual analysis of the anthropomorphic phantom showed a measureable loss of resolution in the c-view image (11 lp/mm FFDM, 5 lp/mm c-view) and loss in detection of small microcalcification objects. Spectral analysis of the anthropomorphic phantom showed higher total noise magnitude in the FFDM image compared with c-view. Whereas the FFDM image contained approximately white noise texture, the c-view image exhibited marked noise reduction at midfrequency and high frequency with far less noise suppression at low frequencies resulting in a mottled noise appearance. Their analysis demonstrates many instances where the c-view image quality differs from FFDM. Compared to FFDM, c-view offers a better depiction of objects of certain size and contrast, but provides poorer overall resolution and noise properties. Based on these findings, the utilization of c-view images in the clinical setting requires careful consideration, especially if considering the discontinuation of FFDM imaging. Not explicitly explored in this study is how the combination of DBT + c-view performs relative to DBT + FFDM or FFDM alone.

Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images

PubMed Central

Peña, José Manuel; Torres-Sánchez, Jorge; de Castro, Ana Isabel; Kelly, Maggi; López-Granados, Francisca

2013-01-01

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance. PMID:24146963

Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.

PubMed

Peña, José Manuel; Torres-Sánchez, Jorge; de Castro, Ana Isabel; Kelly, Maggi; López-Granados, Francisca

2013-01-01

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r(2)=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.

Multiscale Medical Image Fusion in Wavelet Domain

PubMed Central

Khare, Ashish

2013-01-01

Wavelet transforms have emerged as a powerful tool in image fusion. However, the study and analysis of medical image fusion is still a challenging area of research. Therefore, in this paper, we propose a multiscale fusion of multimodal medical images in wavelet domain. Fusion of medical images has been performed at multiple scales varying from minimum to maximum level using maximum selection rule which provides more flexibility and choice to select the relevant fused images. The experimental analysis of the proposed method has been performed with several sets of medical images. Fusion results have been evaluated subjectively and objectively with existing state-of-the-art fusion methods which include several pyramid- and wavelet-transform-based fusion methods and principal component analysis (PCA) fusion method. The comparative analysis of the fusion results has been performed with edge strength (Q), mutual information (MI), entropy (E), standard deviation (SD), blind structural similarity index metric (BSSIM), spatial frequency (SF), and average gradient (AG) metrics. The combined subjective and objective evaluations of the proposed fusion method at multiple scales showed the effectiveness and goodness of the proposed approach. PMID:24453868

Parameterization of Shape and Compactness in Object-based Image Classification Using Quickbird-2 Imagery

NASA Astrophysics Data System (ADS)

Tonbul, H.; Kavzoglu, T.

2016-12-01

In recent years, object based image analysis (OBIA) has spread out and become a widely accepted technique for the analysis of remotely sensed data. OBIA deals with grouping pixels into homogenous objects based on spectral, spatial and textural features of contiguous pixels in an image. The first stage of OBIA, named as image segmentation, is the most prominent part of object recognition. In this study, multiresolution segmentation, which is a region-based approach, was employed to construct image objects. In the application of multi-resolution, three parameters, namely shape, compactness and scale must be set by the analyst. Segmentation quality remarkably influences the fidelity of the thematic maps and accordingly the classification accuracy. Therefore, it is of great importance to search and set optimal values for the segmentation parameters. In the literature, main focus has been on the definition of scale parameter, assuming that the effect of shape and compactness parameters is limited in terms of achieved classification accuracy. The aim of this study is to deeply analyze the influence of shape/compactness parameters by varying their values while using the optimal scale parameter determined by the use of Estimation of Scale Parameter (ESP-2) approach. A pansharpened Qickbird-2 image covering Trabzon, Turkey was employed to investigate the objectives of the study. For this purpose, six different combinations of shape/compactness were utilized to make deductions on the behavior of shape and compactness parameters and optimal setting for all parameters as a whole. Objects were assigned to classes using nearest neighbor classifier in all segmentation observations and equal number of pixels was randomly selected to calculate accuracy metrics. The highest overall accuracy (92.3%) was achieved by setting the shape/compactness criteria to 0.3/0.3. The results of this study indicate that shape/compactness parameters can have significant effect on classification accuracy with 4% change in overall accuracy. Also, statistical significance of differences in accuracy was tested using the McNemar's test and found that the difference between poor and optimal setting of shape/compactness parameters was statistically significant, suggesting a search for optimal parameterization instead of default setting.

Application of GEM-based detectors in full-field XRF imaging

NASA Astrophysics Data System (ADS)

Dąbrowski, W.; Fiutowski, T.; Frączek, P.; Koperny, S.; Lankosz, M.; Mendys, A.; Mindur, B.; Świentek, K.; Wiącek, P.; Wróbel, P. M.

2016-12-01

X-ray fluorescence spectroscopy (XRF) is a commonly used technique for non-destructive elemental analysis of cultural heritage objects. It can be applied to investigations of provenance of historical objects as well as to studies of art techniques. While the XRF analysis can be easily performed locally using standard available equipment there is a growing interest in imaging of spatial distribution of specific elements. Spatial imaging of elemental distrbutions is usually realised by scanning an object with a narrow focused X-ray excitation beam and measuring characteristic fluorescence radiation using a high energy resolution detector, usually a silicon drift detector. Such a technique, called macro-XRF imaging, is suitable for investigation of flat surfaces but it is time consuming because the spatial resolution is basically determined by the spot size of the beam. Another approach is the full-field XRF, which is based on simultaneous irradiation and imaging of large area of an object. The image of the investigated area is projected by a pinhole camera on a position-sensitive and energy dispersive detector. The infinite depth of field of the pinhole camera allows one, in principle, investigation of non-flat surfaces. One of possible detectors to be employed in full-field XRF imaging is a GEM based detector with 2-dimensional readout. In the paper we report on development of an imaging system equipped with a standard 3-stage GEM detector of 10 × 10 cm2 equipped with readout electronics based on dedicated full-custom ASICs and DAQ system. With a demonstrator system we have obtained 2-D spatial resolution of the order of 100 μm and energy resolution at a level of 20% FWHM for 5.9 keV . Limitations of such a detector due to copper fluorescence radiation excited in the copper-clad drift electrode and GEM foils is discussed and performance of the detector using chromium-clad electrodes is reported.

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels

PubMed Central

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V.; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R.

2018-01-01

Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. Conclusions: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods. PMID:29619277

Automated Glacier Mapping using Object Based Image Analysis. Case Studies from Nepal, the European Alps and Norway

NASA Astrophysics Data System (ADS)

Vatle, S. S.

2015-12-01

Frequent and up-to-date glacier outlines are needed for many applications of glaciology, not only glacier area change analysis, but also for masks in volume or velocity analysis, for the estimation of water resources and as model input data. Remote sensing offers a good option for creating glacier outlines over large areas, but manual correction is frequently necessary, especially in areas containing supraglacial debris. We show three different workflows for mapping clean ice and debris-covered ice within Object Based Image Analysis (OBIA). By working at the object level as opposed to the pixel level, OBIA facilitates using contextual, spatial and hierarchical information when assigning classes, and additionally permits the handling of multiple data sources. Our first example shows mapping debris-covered ice in the Manaslu Himalaya, Nepal. SAR Coherence data is used in combination with optical and topographic data to classify debris-covered ice, obtaining an accuracy of 91%. Our second example shows using a high-resolution LiDAR derived DEM over the Hohe Tauern National Park in Austria. Breaks in surface morphology are used in creating image objects; debris-covered ice is then classified using a combination of spectral, thermal and topographic properties. Lastly, we show a completely automated workflow for mapping glacier ice in Norway. The NDSI and NIR/SWIR band ratio are used to map clean ice over the entire country but the thresholds are calculated automatically based on a histogram of each image subset. This means that in theory any Landsat scene can be inputted and the clean ice can be automatically extracted. Debris-covered ice can be included semi-automatically using contextual and morphological information.

Research of second harmonic generation images based on texture analysis

NASA Astrophysics Data System (ADS)

Liu, Yao; Li, Yan; Gong, Haiming; Zhu, Xiaoqin; Huang, Zufang; Chen, Guannan

2014-09-01

Texture analysis plays a crucial role in identifying objects or regions of interest in an image. It has been applied to a variety of medical image processing, ranging from the detection of disease and the segmentation of specific anatomical structures, to differentiation between healthy and pathological tissues. Second harmonic generation (SHG) microscopy as a potential noninvasive tool for imaging biological tissues has been widely used in medicine, with reduced phototoxicity and photobleaching. In this paper, we clarified the principles of texture analysis including statistical, transform, structural and model-based methods and gave examples of its applications, reviewing studies of the technique. Moreover, we tried to apply texture analysis to the SHG images for the differentiation of human skin scar tissues. Texture analysis method based on local binary pattern (LBP) and wavelet transform was used to extract texture features of SHG images from collagen in normal and abnormal scars, and then the scar SHG images were classified into normal or abnormal ones. Compared with other texture analysis methods with respect to the receiver operating characteristic analysis, LBP combined with wavelet transform was demonstrated to achieve higher accuracy. It can provide a new way for clinical diagnosis of scar types. At last, future development of texture analysis in SHG images were discussed.

Use of iris recognition camera technology for the quantification of corneal opacification in mucopolysaccharidoses.

PubMed

Aslam, Tariq Mehmood; Shakir, Savana; Wong, James; Au, Leon; Ashworth, Jane

2012-12-01

Mucopolysaccharidoses (MPS) can cause corneal opacification that is currently difficult to objectively quantify. With newer treatments for MPS comes an increased need for a more objective, valid and reliable index of disease severity for clinical and research use. Clinical evaluation by slit lamp is very subjective and techniques based on colour photography are difficult to standardise. In this article the authors present evidence for the utility of dedicated image analysis algorithms applied to images obtained by a highly sophisticated iris recognition camera that is small, manoeuvrable and adapted to achieve rapid, reliable and standardised objective imaging in a wide variety of patients while minimising artefactual interference in image quality.

Three-dimensional localization and optical imaging of objects in turbid media with independent component analysis.

PubMed

Xu, M; Alrubaiee, M; Gayen, S K; Alfano, R R

2005-04-01

A new approach for optical imaging and localization of objects in turbid media that makes use of the independent component analysis (ICA) from information theory is demonstrated. Experimental arrangement realizes a multisource illumination of a turbid medium with embedded objects and a multidetector acquisition of transmitted light on the medium boundary. The resulting spatial diversity and multiple angular observations provide robust data for three-dimensional localization and characterization of absorbing and scattering inhomogeneities embedded in a turbid medium. ICA of the perturbations in the spatial intensity distribution on the medium boundary sorts out the embedded objects, and their locations are obtained from Green's function analysis based on any appropriate light propagation model. Imaging experiments were carried out on two highly scattering samples of thickness approximately 50 times the transport mean-free path of the respective medium. One turbid medium had two embedded absorptive objects, and the other had four scattering objects. An independent component separation of the signal, in conjunction with diffusive photon migration theory, was used to locate the embedded inhomogeneities. In both cases, improved lateral and axial localizations of the objects over the result obtained by use of common photon migration reconstruction algorithms were achieved. The approach is applicable to different medium geometries, can be used with any suitable photon propagation model, and is amenable to near-real-time imaging applications.

A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery

USDA-ARS?s Scientific Manuscript database

The availability of numerous spectral, spatial, and contextual features with object-based image analysis (OBIA) renders the selection of optimal features a time consuming and subjective process. While several feature election methods have been used in conjunction with OBIA, a robust comparison of th...

Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip.

PubMed

Heo, Young Jin; Lee, Donghyeon; Kang, Junsu; Lee, Keondo; Chung, Wan Kyun

2017-09-14

Imaging flow cytometry (IFC) is an emerging technology that acquires single-cell images at high-throughput for analysis of a cell population. Rich information that comes from high sensitivity and spatial resolution of a single-cell microscopic image is beneficial for single-cell analysis in various biological applications. In this paper, we present a fast image-processing pipeline (R-MOD: Real-time Moving Object Detector) based on deep learning for high-throughput microscopy-based label-free IFC in a microfluidic chip. The R-MOD pipeline acquires all single-cell images of cells in flow, and identifies the acquired images as a real-time process with minimum hardware that consists of a microscope and a high-speed camera. Experiments show that R-MOD has the fast and reliable accuracy (500 fps and 93.3% mAP), and is expected to be used as a powerful tool for biomedical and clinical applications.

Automatic classification of minimally invasive instruments based on endoscopic image sequences

NASA Astrophysics Data System (ADS)

Speidel, Stefanie; Benzko, Julia; Krappe, Sebastian; Sudra, Gunther; Azad, Pedram; Müller-Stich, Beat Peter; Gutt, Carsten; Dillmann, Rüdiger

2009-02-01

Minimally invasive surgery is nowadays a frequently applied technique and can be regarded as a major breakthrough in surgery. The surgeon has to adopt special operation-techniques and deal with difficulties like the complex hand-eye coordination and restricted mobility. To alleviate these constraints we propose to enhance the surgeon's capabilities by providing a context-aware assistance using augmented reality techniques. To analyze the current situation for context-aware assistance, we need intraoperatively gained sensor data and a model of the intervention. A situation consists of information about the performed activity, the used instruments, the surgical objects, the anatomical structures and defines the state of an intervention for a given moment in time. The endoscopic images provide a rich source of information which can be used for an image-based analysis. Different visual cues are observed in order to perform an image-based analysis with the objective to gain as much information as possible about the current situation. An important visual cue is the automatic recognition of the instruments which appear in the scene. In this paper we present the classification of minimally invasive instruments using the endoscopic images. The instruments are not modified by markers. The system segments the instruments in the current image and recognizes the instrument type based on three-dimensional instrument models.

FISH Finder: a high-throughput tool for analyzing FISH images

PubMed Central

Shirley, James W.; Ty, Sereyvathana; Takebayashi, Shin-ichiro; Liu, Xiuwen; Gilbert, David M.

2011-01-01

Motivation: Fluorescence in situ hybridization (FISH) is used to study the organization and the positioning of specific DNA sequences within the cell nucleus. Analyzing the data from FISH images is a tedious process that invokes an element of subjectivity. Automated FISH image analysis offers savings in time as well as gaining the benefit of objective data analysis. While several FISH image analysis software tools have been developed, they often use a threshold-based segmentation algorithm for nucleus segmentation. As fluorescence signal intensities can vary significantly from experiment to experiment, from cell to cell, and within a cell, threshold-based segmentation is inflexible and often insufficient for automatic image analysis, leading to additional manual segmentation and potential subjective bias. To overcome these problems, we developed a graphical software tool called FISH Finder to automatically analyze FISH images that vary significantly. By posing the nucleus segmentation as a classification problem, compound Bayesian classifier is employed so that contextual information is utilized, resulting in reliable classification and boundary extraction. This makes it possible to analyze FISH images efficiently and objectively without adjustment of input parameters. Additionally, FISH Finder was designed to analyze the distances between differentially stained FISH probes. Availability: FISH Finder is a standalone MATLAB application and platform independent software. The program is freely available from: http://code.google.com/p/fishfinder/downloads/list Contact: gilbert@bio.fsu.edu PMID:21310746

A neural network ActiveX based integrated image processing environment.

PubMed

Ciuca, I; Jitaru, E; Alaicescu, M; Moisil, I

2000-01-01

The paper outlines an integrated image processing environment that uses neural networks ActiveX technology for object recognition and classification. The image processing environment which is Windows based, encapsulates a Multiple-Document Interface (MDI) and is menu driven. Object (shape) parameter extraction is focused on features that are invariant in terms of translation, rotation and scale transformations. The neural network models that can be incorporated as ActiveX components into the environment allow both clustering and classification of objects from the analysed image. Mapping neural networks perform an input sensitivity analysis on the extracted feature measurements and thus facilitate the removal of irrelevant features and improvements in the degree of generalisation. The program has been used to evaluate the dimensions of the hydrocephalus in a study for calculating the Evans index and the angle of the frontal horns of the ventricular system modifications.

Analysis of off-axis holographic system based on improved Jamin interferometer

NASA Astrophysics Data System (ADS)

Li, Baosheng; Dong, Hang; Chen, Lijuan; Zhong, Qi

2018-02-01

In this paper, an improved Interferometer was introduced which based on traditional Jamin Interferometer to solve the twin image where appear in on-axis holographic. Adjust the angle of reference light and object light that projected onto the CCD by change the reflector of the system to separate the zero order of diffraction, the virtual image and the real image, so that could eliminate the influence of the twin image. The result of analysis shows that the system could be realized in theory. After actually building the system, the hologram of the calibration plate is reconstructed and the result is shown to be feasible.

Profile fitting in crowded astronomical images

NASA Astrophysics Data System (ADS)

Manish, Raja

Around 18,000 known objects currently populate the near Earth space. These constitute active space assets as well as space debris objects. The tracking and cataloging of such objects relies on observations, most of which are ground based. Also, because of the great distance to the objects, only non-resolved object images can be obtained from the observations. Optical systems consist of telescope optics and a detector. Nowadays, usually CCD detectors are used. The information that is sought to be extracted from the frames are the individual object's astrometric position. In order to do so, the center of the object's image on the CCD frame has to be found. However, the observation frames that are read out of the detector are subject to noise. There are three different sources of noise: celestial background sources, the object signal itself and the sensor noise. The noise statistics are usually modeled as Gaussian or Poisson distributed or their combined distribution. In order to achieve a near real time processing, computationally fast and reliable methods for the so-called centroiding are desired; analytical methods are preferred over numerical ones of comparable accuracy. In this work, an analytic method for the centroiding is investigated and compared to numerical methods. Though the work focuses mainly on astronomical images, same principle could be applied on non-celestial images containing similar data. The method is based on minimizing weighted least squared (LS) error between observed data and the theoretical model of point sources in a novel yet simple way. Synthetic image frames have been simulated. The newly developed method is tested in both crowded and non-crowded fields where former needs additional image handling procedures to separate closely packed objects. Subsequent analysis on real celestial images corroborate the effectiveness of the approach.

Smartphone-based colorimetric analysis for detection of saliva alcohol concentration.

PubMed

Jung, Youngkee; Kim, Jinhee; Awofeso, Olumide; Kim, Huisung; Regnier, Fred; Bae, Euiwon

2015-11-01

A simple device and associated analytical methods are reported. We provide objective and accurate determination of saliva alcohol concentrations using smartphone-based colorimetric imaging. The device utilizes any smartphone with a miniature attachment that positions the sample and provides constant illumination for sample imaging. Analyses of histograms based on channel imaging of red-green-blue (RGB) and hue-saturation-value (HSV) color space provide unambiguous determination of blood alcohol concentration from color changes on sample pads. A smartphone-based sample analysis by colorimetry was developed and tested with blind samples that matched with the training sets. This technology can be adapted to any smartphone and used to conduct color change assays.

Tissue microarrays and quantitative tissue-based image analysis as a tool for oncology biomarker and diagnostic development.

PubMed

Dolled-Filhart, Marisa P; Gustavson, Mark D

2012-11-01

Translational oncology has been improved by using tissue microarrays (TMAs), which facilitate biomarker analysis of large cohorts on a single slide. This has allowed for rapid analysis and validation of potential biomarkers for prognostic and predictive value, as well as for evaluation of biomarker prevalence. Coupled with quantitative analysis of immunohistochemical (IHC) staining, objective and standardized biomarker data from tumor samples can further advance companion diagnostic approaches for the identification of drug-responsive or resistant patient subpopulations. This review covers the advantages, disadvantages and applications of TMAs for biomarker research. Research literature and reviews of TMAs and quantitative image analysis methodology have been surveyed for this review (with an AQUA® analysis focus). Applications such as multi-marker diagnostic development and pathway-based biomarker subpopulation analyses are described. Tissue microarrays are a useful tool for biomarker analyses including prevalence surveys, disease progression assessment and addressing potential prognostic or predictive value. By combining quantitative image analysis with TMAs, analyses will be more objective and reproducible, allowing for more robust IHC-based diagnostic test development. Quantitative multi-biomarker IHC diagnostic tests that can predict drug response will allow for greater success of clinical trials for targeted therapies and provide more personalized clinical decision making.

Automatic thoracic body region localization

NASA Astrophysics Data System (ADS)

Bai, PeiRui; Udupa, Jayaram K.; Tong, YuBing; Xie, ShiPeng; Torigian, Drew A.

2017-03-01

Radiological imaging and image interpretation for clinical decision making are mostly specific to each body region such as head & neck, thorax, abdomen, pelvis, and extremities. For automating image analysis and consistency of results, standardizing definitions of body regions and the various anatomic objects, tissue regions, and zones in them becomes essential. Assuming that a standardized definition of body regions is available, a fundamental early step needed in automated image and object analytics is to automatically trim the given image stack into image volumes exactly satisfying the body region definition. This paper presents a solution to this problem based on the concept of virtual landmarks and evaluates it on whole-body positron emission tomography/computed tomography (PET/CT) scans. The method first selects a (set of) reference object(s), segments it (them) roughly, and identifies virtual landmarks for the object(s). The geometric relationship between these landmarks and the boundary locations of body regions in the craniocaudal direction is then learned through a neural network regressor, and the locations are predicted. Based on low-dose unenhanced CT images of 180 near whole-body PET/CT scans (which includes 34 whole-body PET/CT scans), the mean localization error for the boundaries of superior of thorax (TS) and inferior of thorax (TI), expressed as number of slices (slice spacing ≍ 4mm)), and using either the skeleton or the pleural spaces as reference objects, is found to be 3,2 (using skeleton) and 3, 5 (using pleural spaces) respectively, or in mm 13, 10 mm (using skeleton) and 10.5, 20 mm (using pleural spaces), respectively. Improvements of this performance via optimal selection of objects and virtual landmarks and other object analytics applications are currently being pursued. and the skeleton and pleural spaces used as a reference objects

Trabecular bone analysis in CT and X-ray images of the proximal femur for the assessment of local bone quality.

PubMed

Fritscher, Karl; Grunerbl, Agnes; Hanni, Markus; Suhm, Norbert; Hengg, Clemens; Schubert, Rainer

2009-10-01

Currently, conventional X-ray and CT images as well as invasive methods performed during the surgical intervention are used to judge the local quality of a fractured proximal femur. However, these approaches are either dependent on the surgeon's experience or cannot assist diagnostic and planning tasks preoperatively. Therefore, in this work a method for the individual analysis of local bone quality in the proximal femur based on model-based analysis of CT- and X-ray images of femur specimen will be proposed. A combined representation of shape and spatial intensity distribution of an object and different statistical approaches for dimensionality reduction are used to create a statistical appearance model in order to assess the local bone quality in CT and X-ray images. The developed algorithms are tested and evaluated on 28 femur specimen. It will be shown that the tools and algorithms presented herein are highly adequate to automatically and objectively predict bone mineral density values as well as a biomechanical parameter of the bone that can be measured intraoperatively.

Land Cover/Land Use Classification and Change Detection Analysis with Astronaut Photography and Geographic Object-Based Image Analysis

NASA Technical Reports Server (NTRS)

Hollier, Andi B.; Jagge, Amy M.; Stefanov, William L.; Vanderbloemen, Lisa A.

2017-01-01

For over fifty years, NASA astronauts have taken exceptional photographs of the Earth from the unique vantage point of low Earth orbit (as well as from lunar orbit and surface of the Moon). The Crew Earth Observations (CEO) Facility is the NASA ISS payload supporting astronaut photography of the Earth surface and atmosphere. From aurora to mountain ranges, deltas, and cities, there are over two million images of the Earth's surface dating back to the Mercury missions in the early 1960s. The Gateway to Astronaut Photography of Earth website (eol.jsc.nasa.gov) provides a publically accessible platform to query and download these images at a variety of spatial resolutions and perform scientific research at no cost to the end user. As a demonstration to the science, application, and education user communities we examine astronaut photography of the Washington D.C. metropolitan area for three time steps between 1998 and 2016 using Geographic Object-Based Image Analysis (GEOBIA) to classify and quantify land cover/land use and provide a template for future change detection studies with astronaut photography.

Image Analysis Based on Soft Computing and Applied on Space Shuttle During the Liftoff Process

NASA Technical Reports Server (NTRS)

Dominquez, Jesus A.; Klinko, Steve J.

2007-01-01

Imaging techniques based on Soft Computing (SC) and developed at Kennedy Space Center (KSC) have been implemented on a variety of prototype applications related to the safety operation of the Space Shuttle during the liftoff process. These SC-based prototype applications include detection and tracking of moving Foreign Objects Debris (FOD) during the Space Shuttle liftoff, visual anomaly detection on slidewires used in the emergency egress system for the Space Shuttle at the laJlIlch pad, and visual detection of distant birds approaching the Space Shuttle launch pad. This SC-based image analysis capability developed at KSC was also used to analyze images acquired during the accident of the Space Shuttle Columbia and estimate the trajectory and velocity of the foam that caused the accident.

Object localization in handheld thermal images for fireground understanding

NASA Astrophysics Data System (ADS)

Vandecasteele, Florian; Merci, Bart; Jalalvand, Azarakhsh; Verstockt, Steven

2017-05-01

Despite the broad application of the handheld thermal imaging cameras in firefighting, its usage is mostly limited to subjective interpretation by the person carrying the device. As remedies to overcome this limitation, object localization and classification mechanisms could assist the fireground understanding and help with the automated localization, characterization and spatio-temporal (spreading) analysis of the fire. An automated understanding of thermal images can enrich the conventional knowledge-based firefighting techniques by providing the information from the data and sensing-driven approaches. In this work, transfer learning is applied on multi-labeling convolutional neural network architectures for object localization and recognition in monocular visual, infrared and multispectral dynamic images. Furthermore, the possibility of analyzing fire scene images is studied and their current limitations are discussed. Finally, the understanding of the room configuration (i.e., objects location) for indoor localization in reduced visibility environments and the linking with Building Information Models (BIM) are investigated.

Comparison of performance of object-based image analysis techniques available in open source software (Spring and Orfeo Toolbox/Monteverdi) considering very high spatial resolution data

NASA Astrophysics Data System (ADS)

Teodoro, Ana C.; Araujo, Ricardo

2016-01-01

The use of unmanned aerial vehicles (UAVs) for remote sensing applications is becoming more frequent. However, this type of information can result in several software problems related to the huge amount of data available. Object-based image analysis (OBIA) has proven to be superior to pixel-based analysis for very high-resolution images. The main objective of this work was to explore the potentialities of the OBIA methods available in two different open source software applications, Spring and OTB/Monteverdi, in order to generate an urban land cover map. An orthomosaic derived from UAVs was considered, 10 different regions of interest were selected, and two different approaches were followed. The first one (Spring) uses the region growing segmentation algorithm followed by the Bhattacharya classifier. The second approach (OTB/Monteverdi) uses the mean shift segmentation algorithm followed by the support vector machine (SVM) classifier. Two strategies were followed: four classes were considered using Spring and thereafter seven classes were considered for OTB/Monteverdi. The SVM classifier produces slightly better results and presents a shorter processing time. However, the poor spectral resolution of the data (only RGB bands) is an important factor that limits the performance of the classifiers applied.

Passive Fully Polarimetric W-Band Millimeter-Wave Imaging

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

Bernacki, Bruce E.; Kelly, James F.; Sheen, David M.

2012-04-01

We present the theory, design, and experimental results obtained from a scanning passive W-band fully polarimetric imager. Passive millimeter-wave imaging offers persistent day/nighttime imaging and the ability to penetrate dust, clouds and other obscurants, including clothing and dry soil. The single-pixel scanning imager includes both far-field and near-field fore-optics for investigation of polarization phenomena. Using both fore-optics, a variety of scenes including natural and man-made objects was imaged and these results are presented showing the utility of polarimetric imaging for anomaly detection. Analysis includes conventional Stokes-parameter based approaches as well as multivariate image analysis methods.

An improved level set method for brain MR images segmentation and bias correction.

PubMed

Chen, Yunjie; Zhang, Jianwei; Macione, Jim

2009-10-01

Intensity inhomogeneities cause considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus, bias field estimation is a necessary step before quantitative analysis of MR data can be undertaken. This paper presents a variational level set approach to bias correction and segmentation for images with intensity inhomogeneities. Our method is based on an observation that intensities in a relatively small local region are separable, despite of the inseparability of the intensities in the whole image caused by the overall intensity inhomogeneity. We first define a localized K-means-type clustering objective function for image intensities in a neighborhood around each point. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. The objective function is then integrated over the entire domain to define the data term into the level set framework. Our method is able to capture bias of quite general profiles. Moreover, it is robust to initialization, and thereby allows fully automated applications. The proposed method has been used for images of various modalities with promising results.

Rapid Target Detection in High Resolution Remote Sensing Images Using Yolo Model

NASA Astrophysics Data System (ADS)

Wu, Z.; Chen, X.; Gao, Y.; Li, Y.

2018-04-01

Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

Quadratic grating apodized photon sieves for simultaneous multiplane microscopy

NASA Astrophysics Data System (ADS)

Cheng, Yiguang; Zhu, Jiangping; He, Yu; Tang, Yan; Hu, Song; Zhao, Lixin

2017-10-01

We present a new type of imaging device, named quadratic grating apodized photon sieve (QGPS), used as the objective for simultaneous multiplane imaging in X-rays. The proposed QGPS is structured based on the combination of two concepts: photon sieves and quadratic gratings. Its design principles are also expounded in detail. Analysis of imaging properties of QGPS in terms of point-spread function shows that QGPS can image multiple layers within an object field onto a single image plane. Simulated and experimental results in visible light both demonstrate the feasibility of QGPS for simultaneous multiplane imaging, which is extremely promising to detect dynamic specimens by X-ray microscopy in the physical and life sciences.

Object-based image analysis and data mining for building ontology of informal urban settlements

NASA Astrophysics Data System (ADS)

Khelifa, Dejrriri; Mimoun, Malki

2012-11-01

During recent decades, unplanned settlements have been appeared around the big cities in most developing countries and as consequence, numerous problems have emerged. Thus the identification of different kinds of settlements is a major concern and challenge for authorities of many countries. Very High Resolution (VHR) Remotely Sensed imagery has proved to be a very promising way to detect different kinds of settlements, especially through the using of new objectbased image analysis (OBIA). The most important key is in understanding what characteristics make unplanned settlements differ from planned ones, where most experts characterize unplanned urban areas by small building sizes at high densities, no orderly road arrangement and Lack of green spaces. Knowledge about different kinds of settlements can be captured as a domain ontology that has the potential to organize knowledge in a formal, understandable and sharable way. In this work we focus on extracting knowledge from VHR images and expert's knowledge. We used an object based strategy by segmenting a VHR image taken over urban area into regions of homogenous pixels at adequate scale level and then computing spectral, spatial and textural attributes for each region to create objects. A genetic-based data mining was applied to generate high predictive and comprehensible classification rules based on selected samples from the OBIA result. Optimized intervals of relevant attributes are found, linked with land use types for forming classification rules. The unplanned areas were separated from the planned ones, through analyzing of the line segments detected from the input image. Finally a simple ontology was built based on the previous processing steps. The approach has been tested to VHR images of one of the biggest Algerian cities, that has grown considerably in recent decades.

Improved biliary detection and diagnosis through intelligent machine analysis.

PubMed

Logeswaran, Rajasvaran

2012-09-01

This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Foreign object detection and removal to improve automated analysis of chest radiographs

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

Hogeweg, Laurens; Sanchez, Clara I.; Melendez, Jaime

2013-07-15

Purpose: Chest radiographs commonly contain projections of foreign objects, such as buttons, brassier clips, jewellery, or pacemakers and wires. The presence of these structures can substantially affect the output of computer analysis of these images. An automated method is presented to detect, segment, and remove foreign objects from chest radiographs.Methods: Detection is performed using supervised pixel classification with a kNN classifier, resulting in a probability estimate per pixel to belong to a projected foreign object. Segmentation is performed by grouping and post-processing pixels with a probability above a certain threshold. Next, the objects are replaced by texture inpainting.Results: The methodmore » is evaluated in experiments on 257 chest radiographs. The detection at pixel level is evaluated with receiver operating characteristic analysis on pixels within the unobscured lung fields and an A{sub z} value of 0.949 is achieved. Free response operator characteristic analysis is performed at the object level, and 95.6% of objects are detected with on average 0.25 false positive detections per image. To investigate the effect of removing the detected objects through inpainting, a texture analysis system for tuberculosis detection is applied to images with and without pathology and with and without foreign object removal. Unprocessed, the texture analysis abnormality score of normal images with foreign objects is comparable to those with pathology. After removing foreign objects, the texture score of normal images with and without foreign objects is similar, while abnormal images, whether they contain foreign objects or not, achieve on average higher scores.Conclusions: The authors conclude that removal of foreign objects from chest radiographs is feasible and beneficial for automated image analysis.« less

Benefits of Red-Edge Spectral Band and Texture Features for the Object-based Classification using RapidEye sSatellite Image data

NASA Astrophysics Data System (ADS)

Kim, H. O.; Yeom, J. M.

2014-12-01

Space-based remote sensing in agriculture is particularly relevant to issues such as global climate change, food security, and precision agriculture. Recent satellite missions have opened up new perspectives by offering high spatial resolution, various spectral properties, and fast revisit rates to the same regions. Here, we examine the utility of broadband red-edge spectral information in multispectral satellite image data for classifying paddy rice crops in South Korea. Additionally, we examine how object-based spectral features affect the classification of paddy rice growth stages. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the crop condition in heterogeneous paddy rice crop environments, particularly when single-season image data were used. This positive effect appeared to be offset by the multi-temporal image data. Additional texture information brought only a minor improvement or a slight decline, although it is well known to be advantageous for object-based classification in general. We conclude that broadband red-edge information derived from conventional multispectral satellite data has the potential to improve space-based crop monitoring. Because the positive or negative effects of texture features for object-based crop classification could barely be interpreted, the relationships between the textual properties and paddy rice crop parameters at the field scale should be further examined in depth.

Regional shape-based feature space for segmenting biomedical images using neural networks

NASA Astrophysics Data System (ADS)

Sundaramoorthy, Gopal; Hoford, John D.; Hoffman, Eric A.

1993-07-01

In biomedical images, structure of interest, particularly the soft tissue structures, such as the heart, airways, bronchial and arterial trees often have grey-scale and textural characteristics similar to other structures in the image, making it difficult to segment them using only gray- scale and texture information. However, these objects can be visually recognized by their unique shapes and sizes. In this paper we discuss, what we believe to be, a novel, simple scheme for extracting features based on regional shapes. To test the effectiveness of these features for image segmentation (classification), we use an artificial neural network and a statistical cluster analysis technique. The proposed shape-based feature extraction algorithm computes regional shape vectors (RSVs) for all pixels that meet a certain threshold criteria. The distance from each such pixel to a boundary is computed in 8 directions (or in 26 directions for a 3-D image). Together, these 8 (or 26) values represent the pixel's (or voxel's) RSV. All RSVs from an image are used to train a multi-layered perceptron neural network which uses these features to 'learn' a suitable classification strategy. To clearly distinguish the desired object from other objects within an image, several examples from inside and outside the desired object are used for training. Several examples are presented to illustrate the strengths and weaknesses of our algorithm. Both synthetic and actual biomedical images are considered. Future extensions to this algorithm are also discussed.

Spectral features based tea garden extraction from digital orthophoto maps

NASA Astrophysics Data System (ADS)

Jamil, Akhtar; Bayram, Bulent; Kucuk, Turgay; Zafer Seker, Dursun

2018-05-01

The advancements in the photogrammetry and remote sensing technologies has made it possible to extract useful tangible information from data which plays a pivotal role in various application such as management and monitoring of forests and agricultural lands etc. This study aimed to evaluate the effectiveness of spectral signatures for extraction of tea gardens from 1 : 5000 scaled digital orthophoto maps obtained from Rize city in Turkey. First, the normalized difference vegetation index (NDVI) was derived from the input images to suppress the non-vegetation areas. NDVI values less than zero were discarded and the output images was normalized in the range 0-255. Individual pixels were then mapped into meaningful objects using global region growing technique. The resulting image was filtered and smoothed to reduce the impact of noise. Furthermore, geometrical constraints were applied to remove small objects (less than 500 pixels) followed by morphological opening operator to enhance the results. These objects served as building blocks for further image analysis. Finally, for the classification stage, a range of spectral values were empirically calculated for each band and applied on candidate objects to extract tea gardens. For accuracy assessment, we employed an area based similarity metric by overlapping obtained tea garden boundaries with the manually digitized tea garden boundaries created by experts of photogrammetry. The overall accuracy of the proposed method scored 89 % for tea gardens from 10 sample orthophoto maps. We concluded that exploiting the spectral signatures using object based analysis is an effective technique for extraction of dominant tree species from digital orthophoto maps.

How does C-VIEW image quality compare with conventional 2D FFDM?

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

Nelson, Jeffrey S., E-mail: nelson.jeffrey@duke.edu; Wells, Jered R.; Baker, Jay A.

Purpose: The FDA approved the use of digital breast tomosynthesis (DBT) in 2011 as an adjunct to 2D full field digital mammography (FFDM) with the constraint that all DBT acquisitions must be paired with a 2D image to assure adequate interpretative information is provided. Recently manufacturers have developed methods to provide a synthesized 2D image generated from the DBT data with the hope of sparing patients the radiation exposure from the FFDM acquisition. While this much needed alternative effectively reduces the total radiation burden, differences in image quality must also be considered. The goal of this study was to comparemore » the intrinsic image quality of synthesized 2D C-VIEW and 2D FFDM images in terms of resolution, contrast, and noise. Methods: Two phantoms were utilized in this study: the American College of Radiology mammography accreditation phantom (ACR phantom) and a novel 3D printed anthropomorphic breast phantom. Both phantoms were imaged using a Hologic Selenia Dimensions 3D system. Analysis of the ACR phantom includes both visual inspection and objective automated analysis using in-house software. Analysis of the 3D anthropomorphic phantom includes visual assessment of resolution and Fourier analysis of the noise. Results: Using ACR-defined scoring criteria for the ACR phantom, the FFDM images scored statistically higher than C-VIEW according to both the average observer and automated scores. In addition, between 50% and 70% of C-VIEW images failed to meet the nominal minimum ACR accreditation requirements—primarily due to fiber breaks. Software analysis demonstrated that C-VIEW provided enhanced visualization of medium and large microcalcification objects; however, the benefits diminished for smaller high contrast objects and all low contrast objects. Visual analysis of the anthropomorphic phantom showed a measureable loss of resolution in the C-VIEW image (11 lp/mm FFDM, 5 lp/mm C-VIEW) and loss in detection of small microcalcification objects. Spectral analysis of the anthropomorphic phantom showed higher total noise magnitude in the FFDM image compared with C-VIEW. Whereas the FFDM image contained approximately white noise texture, the C-VIEW image exhibited marked noise reduction at midfrequency and high frequency with far less noise suppression at low frequencies resulting in a mottled noise appearance. Conclusions: Their analysis demonstrates many instances where the C-VIEW image quality differs from FFDM. Compared to FFDM, C-VIEW offers a better depiction of objects of certain size and contrast, but provides poorer overall resolution and noise properties. Based on these findings, the utilization of C-VIEW images in the clinical setting requires careful consideration, especially if considering the discontinuation of FFDM imaging. Not explicitly explored in this study is how the combination of DBT + C-VIEW performs relative to DBT + FFDM or FFDM alone.« less

Cnn Based Retinal Image Upscaling Using Zero Component Analysis

NASA Astrophysics Data System (ADS)

Nasonov, A.; Chesnakov, K.; Krylov, A.

2017-05-01

The aim of the paper is to obtain high quality of image upscaling for noisy images that are typical in medical image processing. A new training scenario for convolutional neural network based image upscaling method is proposed. Its main idea is a novel dataset preparation method for deep learning. The dataset contains pairs of noisy low-resolution images and corresponding noiseless highresolution images. To achieve better results at edges and textured areas, Zero Component Analysis is applied to these images. The upscaling results are compared with other state-of-the-art methods like DCCI, SI-3 and SRCNN on noisy medical ophthalmological images. Objective evaluation of the results confirms high quality of the proposed method. Visual analysis shows that fine details and structures like blood vessels are preserved, noise level is reduced and no artifacts or non-existing details are added. These properties are essential in retinal diagnosis establishment, so the proposed algorithm is recommended to be used in real medical applications.

Computer object segmentation by nonlinear image enhancement, multidimensional clustering, and geometrically constrained contour optimization

NASA Astrophysics Data System (ADS)

Bruynooghe, Michel M.

1998-04-01

In this paper, we present a robust method for automatic object detection and delineation in noisy complex images. The proposed procedure is a three stage process that integrates image segmentation by multidimensional pixel clustering and geometrically constrained optimization of deformable contours. The first step is to enhance the original image by nonlinear unsharp masking. The second step is to segment the enhanced image by multidimensional pixel clustering, using our reducible neighborhoods clustering algorithm that has a very interesting theoretical maximal complexity. Then, candidate objects are extracted and initially delineated by an optimized region merging algorithm, that is based on ascendant hierarchical clustering with contiguity constraints and on the maximization of average contour gradients. The third step is to optimize the delineation of previously extracted and initially delineated objects. Deformable object contours have been modeled by cubic splines. An affine invariant has been used to control the undesired formation of cusps and loops. Non linear constrained optimization has been used to maximize the external energy. This avoids the difficult and non reproducible choice of regularization parameters, that are required by classical snake models. The proposed method has been applied successfully to the detection of fine and subtle microcalcifications in X-ray mammographic images, to defect detection by moire image analysis, and to the analysis of microrugosities of thin metallic films. The later implementation of the proposed method on a digital signal processor associated to a vector coprocessor would allow the design of a real-time object detection and delineation system for applications in medical imaging and in industrial computer vision.

Earthquake Damage Assessment Using Objective Image Segmentation: A Case Study of 2010 Haiti Earthquake

NASA Technical Reports Server (NTRS)

Oommen, Thomas; Rebbapragada, Umaa; Cerminaro, Daniel

2012-01-01

In this study, we perform a case study on imagery from the Haiti earthquake that evaluates a novel object-based approach for characterizing earthquake induced surface effects of liquefaction against a traditional pixel based change technique. Our technique, which combines object-oriented change detection with discriminant/categorical functions, shows the power of distinguishing earthquake-induced surface effects from changes in buildings using the object properties concavity, convexity, orthogonality and rectangularity. Our results suggest that object-based analysis holds promise in automatically extracting earthquake-induced damages from high-resolution aerial/satellite imagery.

Design and applications of a multimodality image data warehouse framework.

PubMed

Wong, Stephen T C; Hoo, Kent Soo; Knowlton, Robert C; Laxer, Kenneth D; Cao, Xinhau; Hawkins, Randall A; Dillon, William P; Arenson, Ronald L

2002-01-01

A comprehensive data warehouse framework is needed, which encompasses imaging and non-imaging information in supporting disease management and research. The authors propose such a framework, describe general design principles and system architecture, and illustrate a multimodality neuroimaging data warehouse system implemented for clinical epilepsy research. The data warehouse system is built on top of a picture archiving and communication system (PACS) environment and applies an iterative object-oriented analysis and design (OOAD) approach and recognized data interface and design standards. The implementation is based on a Java CORBA (Common Object Request Broker Architecture) and Web-based architecture that separates the graphical user interface presentation, data warehouse business services, data staging area, and backend source systems into distinct software layers. To illustrate the practicality of the data warehouse system, the authors describe two distinct biomedical applications--namely, clinical diagnostic workup of multimodality neuroimaging cases and research data analysis and decision threshold on seizure foci lateralization. The image data warehouse framework can be modified and generalized for new application domains.

Design and Applications of a Multimodality Image Data Warehouse Framework

PubMed Central

Wong, Stephen T.C.; Hoo, Kent Soo; Knowlton, Robert C.; Laxer, Kenneth D.; Cao, Xinhau; Hawkins, Randall A.; Dillon, William P.; Arenson, Ronald L.

2002-01-01

A comprehensive data warehouse framework is needed, which encompasses imaging and non-imaging information in supporting disease management and research. The authors propose such a framework, describe general design principles and system architecture, and illustrate a multimodality neuroimaging data warehouse system implemented for clinical epilepsy research. The data warehouse system is built on top of a picture archiving and communication system (PACS) environment and applies an iterative object-oriented analysis and design (OOAD) approach and recognized data interface and design standards. The implementation is based on a Java CORBA (Common Object Request Broker Architecture) and Web-based architecture that separates the graphical user interface presentation, data warehouse business services, data staging area, and backend source systems into distinct software layers. To illustrate the practicality of the data warehouse system, the authors describe two distinct biomedical applications—namely, clinical diagnostic workup of multimodality neuroimaging cases and research data analysis and decision threshold on seizure foci lateralization. The image data warehouse framework can be modified and generalized for new application domains. PMID:11971885

Bacterial cell identification in differential interference contrast microscopy images.

PubMed

Obara, Boguslaw; Roberts, Mark A J; Armitage, Judith P; Grau, Vicente

2013-04-23

Microscopy image segmentation lays the foundation for shape analysis, motion tracking, and classification of biological objects. Despite its importance, automated segmentation remains challenging for several widely used non-fluorescence, interference-based microscopy imaging modalities. For example in differential interference contrast microscopy which plays an important role in modern bacterial cell biology. Therefore, new revolutions in the field require the development of tools, technologies and work-flows to extract and exploit information from interference-based imaging data so as to achieve new fundamental biological insights and understanding. We have developed and evaluated a high-throughput image analysis and processing approach to detect and characterize bacterial cells and chemotaxis proteins. Its performance was evaluated using differential interference contrast and fluorescence microscopy images of Rhodobacter sphaeroides. Results demonstrate that the proposed approach provides a fast and robust method for detection and analysis of spatial relationship between bacterial cells and their chemotaxis proteins.

Tradeoff between noise reduction and inartificial visualization in a model-based iterative reconstruction algorithm on coronary computed tomography angiography.

PubMed

Hirata, Kenichiro; Utsunomiya, Daisuke; Kidoh, Masafumi; Funama, Yoshinori; Oda, Seitaro; Yuki, Hideaki; Nagayama, Yasunori; Iyama, Yuji; Nakaura, Takeshi; Sakabe, Daisuke; Tsujita, Kenichi; Yamashita, Yasuyuki

2018-05-01

We aimed to evaluate the image quality performance of coronary CT angiography (CTA) under the different settings of forward-projected model-based iterative reconstruction solutions (FIRST).Thirty patients undergoing coronary CTA were included. Each image was reconstructed using filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR-3D), and 2 model-based iterative reconstructions including FIRST-body and FIRST-cardiac sharp (CS). CT number and noise were measured in the coronary vessels and plaque. Subjective image-quality scores were obtained for noise and structure visibility.In the objective image analysis, FIRST-body produced the significantly highest contrast-to-noise ratio. Regarding subjective image quality, FIRST-CS had the highest score for structure visibility, although the image noise score was inferior to that of FIRST-body.In conclusion, FIRST provides significant improvements in objective and subjective image quality compared with FBP and AIDR-3D. FIRST-body effectively reduces image noise, but the structure visibility with FIRST-CS was superior to FIRST-body.

Automatic segmentation of colon glands using object-graphs.

PubMed

Gunduz-Demir, Cigdem; Kandemir, Melih; Tosun, Akif Burak; Sokmensuer, Cenk

2010-02-01

Gland segmentation is an important step to automate the analysis of biopsies that contain glandular structures. However, this remains a challenging problem as the variation in staining, fixation, and sectioning procedures lead to a considerable amount of artifacts and variances in tissue sections, which may result in huge variances in gland appearances. In this work, we report a new approach for gland segmentation. This approach decomposes the tissue image into a set of primitive objects and segments glands making use of the organizational properties of these objects, which are quantified with the definition of object-graphs. As opposed to the previous literature, the proposed approach employs the object-based information for the gland segmentation problem, instead of using the pixel-based information alone. Working with the images of colon tissues, our experiments demonstrate that the proposed object-graph approach yields high segmentation accuracies for the training and test sets and significantly improves the segmentation performance of its pixel-based counterparts. The experiments also show that the object-based structure of the proposed approach provides more tolerance to artifacts and variances in tissues.

Muon tomography imaging algorithms for nuclear threat detection inside large volume containers with the Muon Portal detector

NASA Astrophysics Data System (ADS)

Riggi, S.; Antonuccio-Delogu, V.; Bandieramonte, M.; Becciani, U.; Costa, A.; La Rocca, P.; Massimino, P.; Petta, C.; Pistagna, C.; Riggi, F.; Sciacca, E.; Vitello, F.

2013-11-01

Muon tomographic visualization techniques try to reconstruct a 3D image as close as possible to the real localization of the objects being probed. Statistical algorithms under test for the reconstruction of muon tomographic images in the Muon Portal Project are discussed here. Autocorrelation analysis and clustering algorithms have been employed within the context of methods based on the Point Of Closest Approach (POCA) reconstruction tool. An iterative method based on the log-likelihood approach was also implemented. Relative merits of all such methods are discussed, with reference to full GEANT4 simulations of different scenarios, incorporating medium and high-Z objects inside a container.

3D Surface Reconstruction for Lower Limb Prosthetic Model using Radon Transform

NASA Astrophysics Data System (ADS)

Sobani, S. S. Mohd; Mahmood, N. H.; Zakaria, N. A.; Razak, M. A. Abdul

2018-03-01

This paper describes the idea to realize three-dimensional surfaces of objects with cylinder-based shapes where the techniques adopted and the strategy developed for a non-rigid three-dimensional surface reconstruction of an object from uncalibrated two-dimensional image sequences using multiple-view digital camera and turntable setup. The surface of an object is reconstructed based on the concept of tomography with the aid of performing several digital image processing algorithms on the two-dimensional images captured by a digital camera in thirty-six different projections and the three-dimensional structure of the surface is analysed. Four different objects are used as experimental models in the reconstructions and each object is placed on a manually rotated turntable. The results shown that the proposed method has successfully reconstruct the three-dimensional surface of the objects and practicable. The shape and size of the reconstructed three-dimensional objects are recognizable and distinguishable. The reconstructions of objects involved in the test are strengthened with the analysis where the maximum percent error obtained from the computation is approximately 1.4 % for the height whilst 4.0%, 4.79% and 4.7% for the diameters at three specific heights of the objects.

SHERPA: an image segmentation and outline feature extraction tool for diatoms and other objects

PubMed Central

2014-01-01

Background Light microscopic analysis of diatom frustules is widely used both in basic and applied research, notably taxonomy, morphometrics, water quality monitoring and paleo-environmental studies. In these applications, usually large numbers of frustules need to be identified and/or measured. Although there is a need for automation in these applications, and image processing and analysis methods supporting these tasks have previously been developed, they did not become widespread in diatom analysis. While methodological reports for a wide variety of methods for image segmentation, diatom identification and feature extraction are available, no single implementation combining a subset of these into a readily applicable workflow accessible to diatomists exists. Results The newly developed tool SHERPA offers a versatile image processing workflow focused on the identification and measurement of object outlines, handling all steps from image segmentation over object identification to feature extraction, and providing interactive functions for reviewing and revising results. Special attention was given to ease of use, applicability to a broad range of data and problems, and supporting high throughput analyses with minimal manual intervention. Conclusions Tested with several diatom datasets from different sources and of various compositions, SHERPA proved its ability to successfully analyze large amounts of diatom micrographs depicting a broad range of species. SHERPA is unique in combining the following features: application of multiple segmentation methods and selection of the one giving the best result for each individual object; identification of shapes of interest based on outline matching against a template library; quality scoring and ranking of resulting outlines supporting quick quality checking; extraction of a wide range of outline shape descriptors widely used in diatom studies and elsewhere; minimizing the need for, but enabling manual quality control and corrections. Although primarily developed for analyzing images of diatom valves originating from automated microscopy, SHERPA can also be useful for other object detection, segmentation and outline-based identification problems. PMID:24964954

SHERPA: an image segmentation and outline feature extraction tool for diatoms and other objects.

PubMed

Kloster, Michael; Kauer, Gerhard; Beszteri, Bánk

2014-06-25

Light microscopic analysis of diatom frustules is widely used both in basic and applied research, notably taxonomy, morphometrics, water quality monitoring and paleo-environmental studies. In these applications, usually large numbers of frustules need to be identified and/or measured. Although there is a need for automation in these applications, and image processing and analysis methods supporting these tasks have previously been developed, they did not become widespread in diatom analysis. While methodological reports for a wide variety of methods for image segmentation, diatom identification and feature extraction are available, no single implementation combining a subset of these into a readily applicable workflow accessible to diatomists exists. The newly developed tool SHERPA offers a versatile image processing workflow focused on the identification and measurement of object outlines, handling all steps from image segmentation over object identification to feature extraction, and providing interactive functions for reviewing and revising results. Special attention was given to ease of use, applicability to a broad range of data and problems, and supporting high throughput analyses with minimal manual intervention. Tested with several diatom datasets from different sources and of various compositions, SHERPA proved its ability to successfully analyze large amounts of diatom micrographs depicting a broad range of species. SHERPA is unique in combining the following features: application of multiple segmentation methods and selection of the one giving the best result for each individual object; identification of shapes of interest based on outline matching against a template library; quality scoring and ranking of resulting outlines supporting quick quality checking; extraction of a wide range of outline shape descriptors widely used in diatom studies and elsewhere; minimizing the need for, but enabling manual quality control and corrections. Although primarily developed for analyzing images of diatom valves originating from automated microscopy, SHERPA can also be useful for other object detection, segmentation and outline-based identification problems.

Retinal status analysis method based on feature extraction and quantitative grading in OCT images.

PubMed

Fu, Dongmei; Tong, Hejun; Zheng, Shuang; Luo, Ling; Gao, Fulin; Minar, Jiri

2016-07-22

Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis. This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images. This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status. This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosis.

Contour sensitive saliency and depth application in image retargeting

NASA Astrophysics Data System (ADS)

Lu, Hongju; Yue, Pengfei; Zhao, Yanhui; Liu, Rui; Fu, Yuanbin; Zheng, Yuanjie; Cui, Jia

2018-04-01

Image retargeting technique requires important information preservation and less edge distortion during increasing/decreasing image size. The major existed content-aware methods perform well. However, there are two problems should be improved: the slight distortion appeared at the object edges and the structure distortion in the nonsalient area. According to psychological theories, people evaluate image quality based on multi-level judgments and comparison between different areas, both image content and image structure. The paper proposes a new standard: the structure preserving in non-salient area. After observation and image analysis, blur (slight blur) is generally existed at the edge of objects. The blur feature is used to estimate the depth cue, named blur depth descriptor. It can be used in the process of saliency computation for balanced image retargeting result. In order to keep the structure information in nonsalient area, the salient edge map is presented in Seam Carving process, instead of field-based saliency computation. The derivative saliency from x- and y-direction can avoid the redundant energy seam around salient objects causing structure distortion. After the comparison experiments between classical approaches and ours, the feasibility of our algorithm is proved.

Method of center localization for objects containing concentric arcs

NASA Astrophysics Data System (ADS)

Kuznetsova, Elena G.; Shvets, Evgeny A.; Nikolaev, Dmitry P.

2015-02-01

This paper proposes a method for automatic center location of objects containing concentric arcs. The method utilizes structure tensor analysis and voting scheme optimized with Fast Hough Transform. Two applications of the proposed method are considered: (i) wheel tracking in video-based system for automatic vehicle classification and (ii) tree growth rings analysis on a tree cross cut image.

Analysis of source data resolution on photogrammetric products quality of architectural object. (Polish Title: Analiza wpęywu rozdzielczości danych śródłowych na jakość produktów fotogrametrycznych obiektu architektury)

NASA Astrophysics Data System (ADS)

Markiewicz, J. S.; Kowalczyk, M.; Podlasiak, P.; Bakuła, K.; Zawieska, D.; Bujakiewicz, A.; Andrzejewska, E.

2013-12-01

Due to considerable development of the non - invasion measurement technologies, taking advantages from the distance measurement, the possibility of data acquisition increased and at the same time the measurement period has been reduced. This, by combination of close range laser scanning data and images, enabled the wider expansion of photogrammetric methods effectiveness in registration and analysis of cultural heritage objects. Mentioned integration allows acquisition of objects three - dimensional models and in addition digital image maps - true - ortho and vector products. The quality of photogrammetric products is defined by accuracy and the range of content, therefore by number and the minuteness of detail. That always depends on initial data geometrical resolution. The research results presented in the following paper concern the quality valuation of two products, image of true - ortho and vector data, created for selected parts of architectural object. Source data is represented by point collection i n cloud, acquired from close range laser scanning and photo images. Both data collections has been acquired with diversified resolutions. The exterior orientation of images and several versions of the true - ortho are based on numeric models of the object, acquired with specified resolutions. The comparison of these products gives the opportunity to rate the influence of initial data resolution on their quality (accuracy, information volume). Additional analysis will be performed on the base of vector product s comparison, acquired from monoplotting and true - ortho images. As a conclusion of experiment it was proved that geometric resolution has significant impact on the possibility of generation and on the accuracy of relative orientation TLS scans. If creation of high - resolution products is considered, scanning resolution of about 2 mm should be applied and in case of architecture details - 1 mm. It was also noted that scanning angle and object structure has significant influence on accuracy and completeness of the data. For creation of true - orthoimages for architecture purposes high - resolution ground - based images in geometry close to normal case are recommended to improve their quality. The use of grayscale true - orthoimages with values from scanner intensity is not advised. Presented research proved also that accuracy of manual and automated vectorisation results depend significantly on the resolution of the generated orthoimages (scans and images resolution) and mainly of blur effect and possible pixel size.

Multi-object segmentation using coupled nonparametric shape and relative pose priors

NASA Astrophysics Data System (ADS)

Uzunbas, Mustafa Gökhan; Soldea, Octavian; Çetin, Müjdat; Ünal, Gözde; Erçil, Aytül; Unay, Devrim; Ekin, Ahmet; Firat, Zeynep

2009-02-01

We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes.

Wavelet-based image analysis system for soil texture analysis

NASA Astrophysics Data System (ADS)

Sun, Yun; Long, Zhiling; Jang, Ping-Rey; Plodinec, M. John

2003-05-01

Soil texture is defined as the relative proportion of clay, silt and sand found in a given soil sample. It is an important physical property of soil that affects such phenomena as plant growth and agricultural fertility. Traditional methods used to determine soil texture are either time consuming (hydrometer), or subjective and experience-demanding (field tactile evaluation). Considering that textural patterns observed at soil surfaces are uniquely associated with soil textures, we propose an innovative approach to soil texture analysis, in which wavelet frames-based features representing texture contents of soil images are extracted and categorized by applying a maximum likelihood criterion. The soil texture analysis system has been tested successfully with an accuracy of 91% in classifying soil samples into one of three general categories of soil textures. In comparison with the common methods, this wavelet-based image analysis approach is convenient, efficient, fast, and objective.

Follow-up of solar lentigo depigmentation with a retinaldehyde-based cream by clinical evaluation and calibrated colour imaging.

PubMed

Questel, E; Durbise, E; Bardy, A-L; Schmitt, A-M; Josse, G

2015-05-01

To assess an objective method evaluating the effects of a retinaldehyde-based cream (RA-cream) on solar lentigines; 29 women randomly applied RA-cream on lentigines of one hand and a control cream on the other, once daily for 3 months. A specific method enabling a reliable visualisation of the lesions was proposed, using high-magnification colour-calibrated camera imaging. Assessment was performed using clinical evaluation by Physician Global Assessment score and image analysis. Luminance determination on the numeric images was performed either on the basis of 5 independent expert's consensus borders or probability map analysis via an algorithm automatically detecting the pigmented area. Both image analysis methods showed a similar lightening of ΔL* = 2 after a 3-month treatment by RA-cream, in agreement with single-blind clinical evaluation. High-magnification colour-calibrated camera imaging combined with probability map analysis is a fast and precise method to follow lentigo depigmentation. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

An object-based storage model for distributed remote sensing images

NASA Astrophysics Data System (ADS)

Yu, Zhanwu; Li, Zhongmin; Zheng, Sheng

2006-10-01

It is very difficult to design an integrated storage solution for distributed remote sensing images to offer high performance network storage services and secure data sharing across platforms using current network storage models such as direct attached storage, network attached storage and storage area network. Object-based storage, as new generation network storage technology emerged recently, separates the data path, the control path and the management path, which solves the bottleneck problem of metadata existed in traditional storage models, and has the characteristics of parallel data access, data sharing across platforms, intelligence of storage devices and security of data access. We use the object-based storage in the storage management of remote sensing images to construct an object-based storage model for distributed remote sensing images. In the storage model, remote sensing images are organized as remote sensing objects stored in the object-based storage devices. According to the storage model, we present the architecture of a distributed remote sensing images application system based on object-based storage, and give some test results about the write performance comparison of traditional network storage model and object-based storage model.

New approach to gallbladder ultrasonic images analysis and lesions recognition.

PubMed

Bodzioch, Sławomir; Ogiela, Marek R

2009-03-01

This paper presents a new approach to gallbladder ultrasonic image processing and analysis towards detection of disease symptoms on processed images. First, in this paper, there is presented a new method of filtering gallbladder contours from USG images. A major stage in this filtration is to segment and section off areas occupied by the said organ. In most cases this procedure is based on filtration that plays a key role in the process of diagnosing pathological changes. Unfortunately ultrasound images present among the most troublesome methods of analysis owing to the echogenic inconsistency of structures under observation. This paper provides for an inventive algorithm for the holistic extraction of gallbladder image contours. The algorithm is based on rank filtration, as well as on the analysis of histogram sections on tested organs. The second part concerns detecting lesion symptoms of the gallbladder. Automating a process of diagnosis always comes down to developing algorithms used to analyze the object of such diagnosis and verify the occurrence of symptoms related to given affection. Usually the final stage is to make a diagnosis based on the detected symptoms. This last stage can be carried out through either dedicated expert systems or more classic pattern analysis approach like using rules to determine illness basing on detected symptoms. This paper discusses the pattern analysis algorithms for gallbladder image interpretation towards classification of the most frequent illness symptoms of this organ.

Building Change Detection in Very High Resolution Satellite Stereo Image Time Series

NASA Astrophysics Data System (ADS)

Tian, J.; Qin, R.; Cerra, D.; Reinartz, P.

2016-06-01

There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satellite image time series (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.

Small blob identification in medical images using regional features from optimum scale.

PubMed

Zhang, Min; Wu, Teresa; Bennett, Kevin M

2015-04-01

Recent advances in medical imaging technology have greatly enhanced imaging-based diagnosis which requires computational effective and accurate algorithms to process the images (e.g., measure the objects) for quantitative assessment. In this research, we are interested in one type of imaging objects: small blobs. Examples of small blob objects are cells in histopathology images, glomeruli in MR images, etc. This problem is particularly challenging because the small blobs often have in homogeneous intensity distribution and an indistinct boundary against the background. Yet, in general, these blobs have similar sizes. Motivated by this finding, we propose a novel detector termed Hessian-based Laplacian of Gaussian (HLoG) using scale space theory as the foundation. Like most imaging detectors, an image is first smoothed via LoG. Hessian analysis is then launched to identify the single optimal scale on which a presegmentation is conducted. The advantage of the Hessian process is that it is capable of delineating the blobs. As a result, regional features can be retrieved. These features enable an unsupervised clustering algorithm for postpruning which should be more robust and sensitive than the traditional threshold-based postpruning commonly used in most imaging detectors. To test the performance of the proposed HLoG, two sets of 2-D grey medical images are studied. HLoG is compared against three state-of-the-art detectors: generalized LoG, Radial-Symmetry and LoG using precision, recall, and F-score metrics.We observe that HLoG statistically outperforms the compared detectors.

Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation

NASA Astrophysics Data System (ADS)

Chen, Y.; Luo, M.; Xu, L.; Zhou, X.; Ren, J.; Zhou, J.

2018-04-01

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

Semi-automated extraction of landslides in Taiwan based on SPOT imagery and DEMs

NASA Astrophysics Data System (ADS)

Eisank, Clemens; Hölbling, Daniel; Friedl, Barbara; Chen, Yi-Chin; Chang, Kang-Tsung

2014-05-01

The vast availability and improved quality of optical satellite data and digital elevation models (DEMs), as well as the need for complete and up-to-date landslide inventories at various spatial scales have fostered the development of semi-automated landslide recognition systems. Among the tested approaches for designing such systems, object-based image analysis (OBIA) stepped out to be a highly promising methodology. OBIA offers a flexible, spatially enabled framework for effective landslide mapping. Most object-based landslide mapping systems, however, have been tailored to specific, mainly small-scale study areas or even to single landslides only. Even though reported mapping accuracies tend to be higher than for pixel-based approaches, accuracy values are still relatively low and depend on the particular study. There is still room to improve the applicability and objectivity of object-based landslide mapping systems. The presented study aims at developing a knowledge-based landslide mapping system implemented in an OBIA environment, i.e. Trimble eCognition. In comparison to previous knowledge-based approaches, the classification of segmentation-derived multi-scale image objects relies on digital landslide signatures. These signatures hold the common operational knowledge on digital landslide mapping, as reported by 25 Taiwanese landslide experts during personal semi-structured interviews. Specifically, the signatures include information on commonly used data layers, spectral and spatial features, and feature thresholds. The signatures guide the selection and implementation of mapping rules that were finally encoded in Cognition Network Language (CNL). Multi-scale image segmentation is optimized by using the improved Estimation of Scale Parameter (ESP) tool. The approach described above is developed and tested for mapping landslides in a sub-region of the Baichi catchment in Northern Taiwan based on SPOT imagery and a high-resolution DEM. An object-based accuracy assessment is conducted by quantitatively comparing extracted landslide objects with landslide polygons that were visually interpreted by local experts. The applicability and transferability of the mapping system are evaluated by comparing initial accuracies with those achieved for the following two tests: first, usage of a SPOT image from the same year, but for a different area within the Baichi catchment; second, usage of SPOT images from multiple years for the same region. The integration of the common knowledge via digital landslide signatures is new in object-based landslide studies. In combination with strategies to optimize image segmentation this may lead to a more objective, transferable and stable knowledge-based system for the mapping of landslides from optical satellite data and DEMs.

Exploration of mineral resource deposits based on analysis of aerial and satellite image data employing artificial intelligence methods

NASA Astrophysics Data System (ADS)

Osipov, Gennady

2013-04-01

We propose a solution to the problem of exploration of various mineral resource deposits, determination of their forms / classification of types (oil, gas, minerals, gold, etc.) with the help of satellite photography of the region of interest. Images received from satellite are processed and analyzed to reveal the presence of specific signs of deposits of various minerals. Course of data processing and making forecast can be divided into some stages: Pre-processing of images. Normalization of color and luminosity characteristics, determination of the necessary contrast level and integration of a great number of separate photos into a single map of the region are performed. Construction of semantic map image. Recognition of bitmapped image and allocation of objects and primitives known to system are realized. Intelligent analysis. At this stage acquired information is analyzed with the help of a knowledge base, which contain so-called "attention landscapes" of experts. Used methods of recognition and identification of images: a) combined method of image recognition, b)semantic analysis of posterized images, c) reconstruction of three-dimensional objects from bitmapped images, d)cognitive technology of processing and interpretation of images. This stage is fundamentally new and it distinguishes suggested technology from all others. Automatic registration of allocation of experts` attention - registration of so-called "attention landscape" of experts - is the base of the technology. Landscapes of attention are, essentially, highly effective filters that cut off unnecessary information and emphasize exactly the factors used by an expert for making a decision. The technology based on denoted principles involves the next stages, which are implemented in corresponding program agents. Training mode -> Creation of base of ophthalmologic images (OI) -> Processing and making generalized OI (GOI) -> Mode of recognition and interpretation of unknown images. Training mode includes noncontact registration of eye motion, reconstruction of "attention landscape" fixed by the expert, recording the comments of the expert who is a specialist in the field of images` interpretation, and transfer this information into knowledge base.Creation of base of ophthalmologic images (OI) includes making semantic contacts from great number of OI based on analysis of OI and expert's comments.Processing of OI and making generalized OI (GOI) is realized by inductive logic algorithms and consists in synthesis of structural invariants of OI. The mode of recognition and interpretation of unknown images consists of several stages, which include: comparison of unknown image with the base of structural invariants of OI; revealing of structural invariants in unknown images; ynthesis of interpretive message of the structural invariants base and OI base (the experts` comments stored in it). We want to emphasize that the training mode does not assume special involvement of experts to teach the system - it is realized in the process of regular experts` work on image interpretation and it becomes possible after installation of a special apparatus for non contact registration of experts` attention. Consequently, the technology, which principles is described there, provides fundamentally new effective solution to the problem of exploration of mineral resource deposits based on computer analysis of aerial and satellite image data.

Extraction and Analysis of Major Autumn Crops in Jingxian County Based on Multi - Temporal gf - 1 Remote Sensing Image and Object-Oriented

NASA Astrophysics Data System (ADS)

Ren, B.; Wen, Q.; Zhou, H.; Guan, F.; Li, L.; Yu, H.; Wang, Z.

2018-04-01

The purpose of this paper is to provide decision support for the adjustment and optimization of crop planting structure in Jingxian County. The object-oriented information extraction method is used to extract corn and cotton from Jingxian County of Hengshui City in Hebei Province, based on multi-period GF-1 16-meter images. The best time of data extraction was screened by analyzing the spectral characteristics of corn and cotton at different growth stages based on multi-period GF-116-meter images, phenological data, and field survey data. The results showed that the total classification accuracy of corn and cotton was up to 95.7 %, the producer accuracy was 96 % and 94 % respectively, and the user precision was 95.05 % and 95.9 % respectively, which satisfied the demand of crop monitoring application. Therefore, combined with multi-period high-resolution images and object-oriented classification can be a good extraction of large-scale distribution of crop information for crop monitoring to provide convenient and effective technical means.

A novel visual saliency analysis model based on dynamic multiple feature combination strategy

NASA Astrophysics Data System (ADS)

Lv, Jing; Ye, Qi; Lv, Wen; Zhang, Libao

2017-06-01

The human visual system can quickly focus on a small number of salient objects. This process was known as visual saliency analysis and these salient objects are called focus of attention (FOA). The visual saliency analysis mechanism can be used to extract the salient regions and analyze saliency of object in an image, which is time-saving and can avoid unnecessary costs of computing resources. In this paper, a novel visual saliency analysis model based on dynamic multiple feature combination strategy is introduced. In the proposed model, we first generate multi-scale feature maps of intensity, color and orientation features using Gaussian pyramids and the center-surround difference. Then, we evaluate the contribution of all feature maps to the saliency map according to the area of salient regions and their average intensity, and attach different weights to different features according to their importance. Finally, we choose the largest salient region generated by the region growing method to perform the evaluation. Experimental results show that the proposed model cannot only achieve higher accuracy in saliency map computation compared with other traditional saliency analysis models, but also extract salient regions with arbitrary shapes, which is of great value for the image analysis and understanding.

Getting a grip on reality: Grasping movements directed to real objects and images rely on dissociable neural representations.

PubMed

Freud, Erez; Macdonald, Scott N; Chen, Juan; Quinlan, Derek J; Goodale, Melvyn A; Culham, Jody C

2018-01-01

In the current era of touchscreen technology, humans commonly execute visually guided actions directed to two-dimensional (2D) images of objects. Although real, three-dimensional (3D), objects and images of the same objects share high degree of visual similarity, they differ fundamentally in the actions that can be performed on them. Indeed, previous behavioral studies have suggested that simulated grasping of images relies on different representations than actual grasping of real 3D objects. Yet the neural underpinnings of this phenomena have not been investigated. Here we used functional magnetic resonance imaging (fMRI) to investigate how brain activation patterns differed for grasping and reaching actions directed toward real 3D objects compared to images. Multivoxel Pattern Analysis (MVPA) revealed that the left anterior intraparietal sulcus (aIPS), a key region for visually guided grasping, discriminates between both the format in which objects were presented (real/image) and the motor task performed on them (grasping/reaching). Interestingly, during action planning, the representations of real 3D objects versus images differed more for grasping movements than reaching movements, likely because grasping real 3D objects involves fine-grained planning and anticipation of the consequences of a real interaction. Importantly, this dissociation was evident in the planning phase, before movement initiation, and was not found in any other regions, including motor and somatosensory cortices. This suggests that the dissociable representations in the left aIPS were not based on haptic, motor or proprioceptive feedback. Together, these findings provide novel evidence that actions, particularly grasping, are affected by the realness of the target objects during planning, perhaps because real targets require a more elaborate forward model based on visual cues to predict the consequences of real manipulation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessment methods for the evaluation of vitiligo.

PubMed

Alghamdi, K M; Kumar, A; Taïeb, A; Ezzedine, K

2012-12-01

There is no standardized method for assessing vitiligo. In this article, we review the literature from 1981 to 2011 on different vitiligo assessment methods. We aim to classify the techniques available for vitiligo assessment as subjective, semi-objective or objective; microscopic or macroscopic; and as based on morphometry or colorimetry. Macroscopic morphological measurements include visual assessment, photography in natural or ultraviolet light, photography with computerized image analysis and tristimulus colorimetry or spectrophotometry. Non-invasive micromorphological methods include confocal laser microscopy (CLM). Subjective methods include clinical evaluation by a dermatologist and a vitiligo disease activity score. Semi-objective methods include the Vitiligo Area Scoring Index (VASI) and point-counting methods. Objective methods include software-based image analysis, tristimulus colorimetry, spectrophotometry and CLM. Morphometry is the measurement of the vitiliginous surface area, whereas colorimetry quantitatively analyses skin colour changes caused by erythema or pigment. Most methods involve morphometry, except for the chromameter method, which assesses colorimetry. Some image analysis software programs can assess both morphometry and colorimetry. The details of these programs (Corel Draw, Image Pro Plus, AutoCad and Photoshop) are discussed in the review. Reflectance confocal microscopy provides real-time images and has great potential for the non-invasive assessment of pigmentary lesions. In conclusion, there is no single best method for assessing vitiligo. This review revealed that VASI, the rule of nine and Wood's lamp are likely to be the best techniques available for assessing the degree of pigmentary lesions and measuring the extent and progression of vitiligo in the clinic and in clinical trials. © 2012 The Authors. Journal of the European Academy of Dermatology and Venereology © 2012 European Academy of Dermatology and Venereology.

GEOBIA For Land Use Mapping Using Worldview2 Image In Bengkak Village Coastal, Banyuwangi Regency, East Java

NASA Astrophysics Data System (ADS)

Alrassi, Fitzastri; Salim, Emil; Nina, Anastasia; Alwi, Luthfi; Danoedoro, Projo; Kamal, Muhammad

2016-11-01

The east coast of Banyuwangi regency has a diverse variety of land use such as ponds, mangroves, agricultural fields and settlements. WorldView-2 is a multispectral image with high spatial resolution that can display detailed information of land use. Geographic Object Based Image Analysis (GEOBIA) classification technique uses object segments as the smallest unit of analysis. The segmentation and classification process is not only based on spectral value of the image but also considering other elements of the image interpretation. This gives GEOBIA an opportunities and challenges in the mapping and monitoring of land use. This research aims to assess the GEOBIA classification method for generating the classification of land use in coastal areas of Banyuwangi. The result of this study is land use classification map produced by GEOBIA classification. We verified the accuracy of the resulted land use map by comparing the map with result from visual interpretation of the image that have been validated through field surveys. Variation of land use in most of the east coast of Banyuwangi regency is dominated by mangrove, agricultural fields, mixed farms, settlements and ponds.

Study on the algorithm of computational ghost imaging based on discrete fourier transform measurement matrix

NASA Astrophysics Data System (ADS)

Zhang, Leihong; Liang, Dong; Li, Bei; Kang, Yi; Pan, Zilan; Zhang, Dawei; Gao, Xiumin; Ma, Xiuhua

2016-07-01

On the basis of analyzing the cosine light field with determined analytic expression and the pseudo-inverse method, the object is illuminated by a presetting light field with a determined discrete Fourier transform measurement matrix, and the object image is reconstructed by the pseudo-inverse method. The analytic expression of the algorithm of computational ghost imaging based on discrete Fourier transform measurement matrix is deduced theoretically, and compared with the algorithm of compressive computational ghost imaging based on random measurement matrix. The reconstruction process and the reconstruction error are analyzed. On this basis, the simulation is done to verify the theoretical analysis. When the sampling measurement number is similar to the number of object pixel, the rank of discrete Fourier transform matrix is the same as the one of the random measurement matrix, the PSNR of the reconstruction image of FGI algorithm and PGI algorithm are similar, the reconstruction error of the traditional CGI algorithm is lower than that of reconstruction image based on FGI algorithm and PGI algorithm. As the decreasing of the number of sampling measurement, the PSNR of reconstruction image based on FGI algorithm decreases slowly, and the PSNR of reconstruction image based on PGI algorithm and CGI algorithm decreases sharply. The reconstruction time of FGI algorithm is lower than that of other algorithms and is not affected by the number of sampling measurement. The FGI algorithm can effectively filter out the random white noise through a low-pass filter and realize the reconstruction denoising which has a higher denoising capability than that of the CGI algorithm. The FGI algorithm can improve the reconstruction accuracy and the reconstruction speed of computational ghost imaging.

UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning

NASA Astrophysics Data System (ADS)

Fernandez Galarreta, J.; Kerle, N.; Gerke, M.

2015-06-01

Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAVs). 3-D point-cloud assessment for the entire building is combined with detailed object-based image analysis (OBIA) of façades and roofs. This research focuses not on automatic damage assessment, but on creating a methodology that supports the often ambiguous classification of intermediate damage levels, aiming at producing comprehensive per-building damage scores. We identify completely damaged structures in the 3-D point cloud, and for all other cases provide the OBIA-based damage indicators to be used as auxiliary information by damage analysts. The results demonstrate the usability of the 3-D point-cloud data to identify major damage features. Also the UAV-derived and OBIA-processed oblique images are shown to be a suitable basis for the identification of detailed damage features on façades and roofs. Finally, we also demonstrate the possibility of aggregating the multi-perspective damage information at building level.

Use of laser range finders and range image analysis in automated assembly tasks

NASA Technical Reports Server (NTRS)

Alvertos, Nicolas; Dcunha, Ivan

1990-01-01

A proposition to study the effect of filtering processes on range images and to evaluate the performance of two different laser range mappers is made. Median filtering was utilized to remove noise from the range images. First and second order derivatives are then utilized to locate the similarities and dissimilarities between the processed and the original images. Range depth information is converted into spatial coordinates, and a set of coefficients which describe 3-D objects is generated using the algorithm developed in the second phase of this research. Range images of spheres and cylinders are used for experimental purposes. An algorithm was developed to compare the performance of two different laser range mappers based upon the range depth information of surfaces generated by each of the mappers. Furthermore, an approach based on 2-D analytic geometry is also proposed which serves as a basis for the recognition of regular 3-D geometric objects.

DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research.

PubMed

Fedorov, Andriy; Clunie, David; Ulrich, Ethan; Bauer, Christian; Wahle, Andreas; Brown, Bartley; Onken, Michael; Riesmeier, Jörg; Pieper, Steve; Kikinis, Ron; Buatti, John; Beichel, Reinhard R

2016-01-01

Background. Imaging biomarkers hold tremendous promise for precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation tasks motivate integration of the clinical and imaging data, and the use of standardized approaches to support annotation and sharing of the analysis results and semantics. We developed the methodology and tools to support these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging and Communications in Medicine (DICOM(®)) international standard and free open-source software. Methods. Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor using manual and semi-automatic approaches, automatic segmentation of the reference regions, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data. Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results and relevant clinical data. A number of correction proposals to the standard were developed. The open-source DICOM toolkit (DCMTK) was improved to simplify the task of DICOM encoding by introducing new API abstractions. Conversion and visualization tools utilizing this toolkit were developed. The encoded objects were validated for consistency and interoperability. The resulting dataset was deposited in the QIN-HEADNECK collection of The Cancer Imaging Archive (TCIA). Supporting tools for data analysis and DICOM conversion were made available as free open-source software. Discussion. We presented a detailed investigation of the development and application of the DICOM model, as well as the supporting open-source tools and toolkits, to accommodate representation of the research data in QI biomarker development. We demonstrated that the DICOM standard can be used to represent the types of data relevant in HNC QI biomarker development, and encode their complex relationships. The resulting annotated objects are amenable to data mining applications, and are interoperable with a variety of systems that support the DICOM standard.

[Object-oriented aquatic vegetation extracting approach based on visible vegetation indices.

PubMed

Jing, Ran; Deng, Lei; Zhao, Wen Ji; Gong, Zhao Ning

2016-05-01

Using the estimation of scale parameters (ESP) image segmentation tool to determine the ideal image segmentation scale, the optimal segmented image was created by the multi-scale segmentation method. Based on the visible vegetation indices derived from mini-UAV imaging data, we chose a set of optimal vegetation indices from a series of visible vegetation indices, and built up a decision tree rule. A membership function was used to automatically classify the study area and an aquatic vegetation map was generated. The results showed the overall accuracy of image classification using the supervised classification was 53.7%, and the overall accuracy of object-oriented image analysis (OBIA) was 91.7%. Compared with pixel-based supervised classification method, the OBIA method improved significantly the image classification result and further increased the accuracy of extracting the aquatic vegetation. The Kappa value of supervised classification was 0.4, and the Kappa value based OBIA was 0.9. The experimental results demonstrated that using visible vegetation indices derived from the mini-UAV data and OBIA method extracting the aquatic vegetation developed in this study was feasible and could be applied in other physically similar areas.

Extracting contours of oval-shaped objects by Hough transform and minimal path algorithms

NASA Astrophysics Data System (ADS)

Tleis, Mohamed; Verbeek, Fons J.

2014-04-01

Circular and oval-like objects are very common in cell and micro biology. These objects need to be analyzed, and to that end, digitized images from the microscope are used so as to come to an automated analysis pipeline. It is essential to detect all the objects in an image as well as to extract the exact contour of each individual object. In this manner it becomes possible to perform measurements on these objects, i.e. shape and texture features. Our measurement objective is achieved by probing contour detection through dynamic programming. In this paper we describe a method that uses Hough transform and two minimal path algorithms to detect contours of (ovoid-like) objects. These algorithms are based on an existing grey-weighted distance transform and a new algorithm to extract the circular shortest path in an image. The methods are tested on an artificial dataset of a 1000 images, with an F1-score of 0.972. In a case study with yeast cells, contours from our methods were compared with another solution using Pratt's figure of merit. Results indicate that our methods were more precise based on a comparison with a ground-truth dataset. As far as yeast cells are concerned, the segmentation and measurement results enable, in future work, to retrieve information from different developmental stages of the cell using complex features.

Image Analysis and Modeling

DTIC Science & Technology

1976-03-01

This report summarizes the results of the research program on Image Analysis and Modeling supported by the Defense Advanced Research Projects Agency...The objective is to achieve a better understanding of image structure and to use this knowledge to develop improved image models for use in image ... analysis and processing tasks such as information extraction, image enhancement and restoration, and coding. The ultimate objective of this research is

Introduction to the local enhancement of underwater imagery

NASA Astrophysics Data System (ADS)

Schmalz, Mark S.

1995-06-01

Image-based detection of submerged objects is frequently confounded by optical distortions in the aqueous medium. For example, scattering can severly degrade contrast and resolution in underwater (UW) images when illumination systems and cameras are not range-gated. Prior to the development of range-gated imaging, much research emphasis was placed upon the analysis of greyscale imagery acquired under incoherent illumination. Primarily as a result of current emphasis on coherent optical technologies, the progress of image processing (IP) research that pertains to UW imagery has lagged IP hardware and software development. In this paper, we summarize methods for the digital clarification of images that portray actively illuminated UW scenes, i.e., images of floodlit objects. We model the primary UW image components as: a) contrast degradation resulting from illuminant backscattering from the water column, b) a return signal that results from backscattering of the illuminant from the object of regard, and c) resolution loss, due to forward scattering of the return signal. Letting items a) and c) consititute error sources, one can locally apply the appropriate filters to reduce the contribution of such errors. Our technique emphasized local enhancement, as opposed to the global methods used in previous imaging practice. Our enhancement filters are based upon image-algebraic templates that are designed to compensate for the effects of single and multiple scattering as well as absorption within the water column. Discussion is based upon image clarity, algorithmic complexity, and computational efficiency.

Noise and analyzer-crystal angular position analysis for analyzer-based phase-contrast imaging

NASA Astrophysics Data System (ADS)

Majidi, Keivan; Li, Jun; Muehleman, Carol; Brankov, Jovan G.

2014-04-01

The analyzer-based phase-contrast x-ray imaging (ABI) method is emerging as a potential alternative to conventional radiography. Like many of the modern imaging techniques, ABI is a computed imaging method (meaning that images are calculated from raw data). ABI can simultaneously generate a number of planar parametric images containing information about absorption, refraction, and scattering properties of an object. These images are estimated from raw data acquired by measuring (sampling) the angular intensity profile of the x-ray beam passed through the object at different angular positions of the analyzer crystal. The noise in the estimated ABI parametric images depends upon imaging conditions like the source intensity (flux), measurements angular positions, object properties, and the estimation method. In this paper, we use the Cramér-Rao lower bound (CRLB) to quantify the noise properties in parametric images and to investigate the effect of source intensity, different analyzer-crystal angular positions and object properties on this bound, assuming a fixed radiation dose delivered to an object. The CRLB is the minimum bound for the variance of an unbiased estimator and defines the best noise performance that one can obtain regardless of which estimation method is used to estimate ABI parametric images. The main result of this paper is that the variance (hence the noise) in parametric images is directly proportional to the source intensity and only a limited number of analyzer-crystal angular measurements (eleven for uniform and three for optimal non-uniform) are required to get the best parametric images. The following angular measurements only spread the total dose to the measurements without improving or worsening CRLB, but the added measurements may improve parametric images by reducing estimation bias. Next, using CRLB we evaluate the multiple-image radiography, diffraction enhanced imaging and scatter diffraction enhanced imaging estimation techniques, though the proposed methodology can be used to evaluate any other ABI parametric image estimation technique.

Noise and Analyzer-Crystal Angular Position Analysis for Analyzer-Based Phase-Contrast Imaging

PubMed Central

Majidi, Keivan; Li, Jun; Muehleman, Carol; Brankov, Jovan G.

2014-01-01

The analyzer-based phase-contrast X-ray imaging (ABI) method is emerging as a potential alternative to conventional radiography. Like many of the modern imaging techniques, ABI is a computed imaging method (meaning that images are calculated from raw data). ABI can simultaneously generate a number of planar parametric images containing information about absorption, refraction, and scattering properties of an object. These images are estimated from raw data acquired by measuring (sampling) the angular intensity profile (AIP) of the X-ray beam passed through the object at different angular positions of the analyzer crystal. The noise in the estimated ABI parametric images depends upon imaging conditions like the source intensity (flux), measurements angular positions, object properties, and the estimation method. In this paper, we use the Cramér-Rao lower bound (CRLB) to quantify the noise properties in parametric images and to investigate the effect of source intensity, different analyzer-crystal angular positions and object properties on this bound, assuming a fixed radiation dose delivered to an object. The CRLB is the minimum bound for the variance of an unbiased estimator and defines the best noise performance that one can obtain regardless of which estimation method is used to estimate ABI parametric images. The main result of this manuscript is that the variance (hence the noise) in parametric images is directly proportional to the source intensity and only a limited number of analyzer-crystal angular measurements (eleven for uniform and three for optimal non-uniform) are required to get the best parametric images. The following angular measurements only spread the total dose to the measurements without improving or worsening CRLB, but the added measurements may improve parametric images by reducing estimation bias. Next, using CRLB we evaluate the Multiple-Image Radiography (MIR), Diffraction Enhanced Imaging (DEI) and Scatter Diffraction Enhanced Imaging (S-DEI) estimation techniques, though the proposed methodology can be used to evaluate any other ABI parametric image estimation technique. PMID:24651402

Bundle Adjustment-Based Stability Analysis Method with a Case Study of a Dual Fluoroscopy Imaging System

NASA Astrophysics Data System (ADS)

Al-Durgham, K.; Lichti, D. D.; Detchev, I.; Kuntze, G.; Ronsky, J. L.

2018-05-01

A fundamental task in photogrammetry is the temporal stability analysis of a camera/imaging-system's calibration parameters. This is essential to validate the repeatability of the parameters' estimation, to detect any behavioural changes in the camera/imaging system and to ensure precise photogrammetric products. Many stability analysis methods exist in the photogrammetric literature; each one has different methodological bases, and advantages and disadvantages. This paper presents a simple and rigorous stability analysis method that can be straightforwardly implemented for a single camera or an imaging system with multiple cameras. The basic collinearity model is used to capture differences between two calibration datasets, and to establish the stability analysis methodology. Geometric simulation is used as a tool to derive image and object space scenarios. Experiments were performed on real calibration datasets from a dual fluoroscopy (DF; X-ray-based) imaging system. The calibration data consisted of hundreds of images and thousands of image observations from six temporal points over a two-day period for a precise evaluation of the DF system stability. The stability of the DF system - for a single camera analysis - was found to be within a range of 0.01 to 0.66 mm in terms of 3D coordinates root-mean-square-error (RMSE), and 0.07 to 0.19 mm for dual cameras analysis. It is to the authors' best knowledge that this work is the first to address the topic of DF stability analysis.

Object recognition based on Google's reverse image search and image similarity

NASA Astrophysics Data System (ADS)

Horváth, András.

2015-12-01

Image classification is one of the most challenging tasks in computer vision and a general multiclass classifier could solve many different tasks in image processing. Classification is usually done by shallow learning for predefined objects, which is a difficult task and very different from human vision, which is based on continuous learning of object classes and one requires years to learn a large taxonomy of objects which are not disjunct nor independent. In this paper I present a system based on Google image similarity algorithm and Google image database, which can classify a large set of different objects in a human like manner, identifying related classes and taxonomies.

An interactive method based on the live wire for segmentation of the breast in mammography images.

PubMed

Zewei, Zhang; Tianyue, Wang; Li, Guo; Tingting, Wang; Lu, Xu

2014-01-01

In order to improve accuracy of computer-aided diagnosis of breast lumps, the authors introduce an improved interactive segmentation method based on Live Wire. This paper presents the Gabor filters and FCM clustering algorithm is introduced to the Live Wire cost function definition. According to the image FCM analysis for image edge enhancement, we eliminate the interference of weak edge and access external features clear segmentation results of breast lumps through improving Live Wire on two cases of breast segmentation data. Compared with the traditional method of image segmentation, experimental results show that the method achieves more accurate segmentation of breast lumps and provides more accurate objective basis on quantitative and qualitative analysis of breast lumps.

Virtual Surveyor based Object Extraction from Airborne LiDAR data

NASA Astrophysics Data System (ADS)

Habib, Md. Ahsan

Topographic feature detection of land cover from LiDAR data is important in various fields - city planning, disaster response and prevention, soil conservation, infrastructure or forestry. In recent years, feature classification, compliant with Object-Based Image Analysis (OBIA) methodology has been gaining traction in remote sensing and geographic information science (GIS). In OBIA, the LiDAR image is first divided into meaningful segments called object candidates. This results, in addition to spectral values, in a plethora of new information such as aggregated spectral pixel values, morphology, texture, context as well as topology. Traditional nonparametric segmentation methods rely on segmentations at different scales to produce a hierarchy of semantically significant objects. Properly tuned scale parameters are, therefore, imperative in these methods for successful subsequent classification. Recently, some progress has been made in the development of methods for tuning the parameters for automatic segmentation. However, researchers found that it is very difficult to automatically refine the tuning with respect to each object class present in the scene. Moreover, due to the relative complexity of real-world objects, the intra-class heterogeneity is very high, which leads to over-segmentation. Therefore, the method fails to deliver correctly many of the new segment features. In this dissertation, a new hierarchical 3D object segmentation algorithm called Automatic Virtual Surveyor based Object Extracted (AVSOE) is presented. AVSOE segments objects based on their distinct geometric concavity/convexity. This is achieved by strategically mapping the sloping surface, which connects the object to its background. Further analysis produces hierarchical decomposition of objects to its sub-objects at a single scale level. Extensive qualitative and qualitative results are presented to demonstrate the efficacy of this hierarchical segmentation approach.

A Biologically Plausible Transform for Visual Recognition that is Invariant to Translation, Scale, and Rotation.

PubMed

Sountsov, Pavel; Santucci, David M; Lisman, John E

2011-01-01

Visual object recognition occurs easily despite differences in position, size, and rotation of the object, but the neural mechanisms responsible for this invariance are not known. We have found a set of transforms that achieve invariance in a neurally plausible way. We find that a transform based on local spatial frequency analysis of oriented segments and on logarithmic mapping, when applied twice in an iterative fashion, produces an output image that is unique to the object and that remains constant as the input image is shifted, scaled, or rotated.

A Biologically Plausible Transform for Visual Recognition that is Invariant to Translation, Scale, and Rotation

PubMed Central

Sountsov, Pavel; Santucci, David M.; Lisman, John E.

2011-01-01

Visual object recognition occurs easily despite differences in position, size, and rotation of the object, but the neural mechanisms responsible for this invariance are not known. We have found a set of transforms that achieve invariance in a neurally plausible way. We find that a transform based on local spatial frequency analysis of oriented segments and on logarithmic mapping, when applied twice in an iterative fashion, produces an output image that is unique to the object and that remains constant as the input image is shifted, scaled, or rotated. PMID:22125522

Dynamic feature analysis for Voyager at the Image Processing Laboratory

NASA Technical Reports Server (NTRS)

Yagi, G. M.; Lorre, J. J.; Jepsen, P. L.

1978-01-01

Voyager 1 and 2 were launched from Cape Kennedy to Jupiter, Saturn, and beyond on September 5, 1977 and August 20, 1977. The role of the Image Processing Laboratory is to provide the Voyager Imaging Team with the necessary support to identify atmospheric features (tiepoints) for Jupiter and Saturn data, and to analyze and display them in a suitable form. This support includes the software needed to acquire and store tiepoints, the hardware needed to interactively display images and tiepoints, and the general image processing environment necessary for decalibration and enhancement of the input images. The objective is an understanding of global circulation in the atmospheres of Jupiter and Saturn. Attention is given to the Voyager imaging subsystem, the Voyager imaging science objectives, hardware, software, display monitors, a dynamic feature study, decalibration, navigation, and data base.

Optical nanoscopy with contact Mie-particles: Resolution analysis

NASA Astrophysics Data System (ADS)

Maslov, Alexey V.; Astratov, Vasily N.

2017-06-01

The theoretical limits of resolution available in microspherical nanoscopy are explored using incoherent point emitters in the air. The images are calculated using a two-dimensional model and solving the Maxwell equations which account for the wave effects on the sub-wavelength scale of the emitter-microsphere interaction. Based on our results, we propose to use small dielectric particles with diameters λ ≲ D ≲ 2 λ made of a high-refractive-index material n ˜2 for imaging sub-wavelength objects. It is shown that such particles form virtual images below and real images above them. At wavelengths of the Mie resonances, these images have slightly better than ˜λ/4 resolution that can be attributed to the image magnification in close proximity to the object and contributions of its near field. The resonant super-resolution imaging of various point-like objects, such as dye molecules, fluorophores, or nanoplasmonic particles, can be realized by using narrow bandpass optical filters spectrally aligned with the Mie resonances.

Three-dimensional surface contouring of macroscopic objects by means of phase-difference images.

PubMed

Velásquez Prieto, Daniel; Garcia-Sucerquia, Jorge

2006-09-01

We report a technique to determine the 3D contour of objects with dimensions of at least 4 orders of magnitude larger than the illumination optical wavelength. Our proposal is based on the numerical reconstruction of the optical wave field of digitally recorded holograms. The required modulo 2pi phase map in any contouring process is obtained by means of the direct subtraction of two phase-contrast images under different illumination angles to create a phase-difference image of a still object. Obtaining the phase-difference images is only possible by using the capability of numerical reconstruction of the complex optical field provided by digital holography. This unique characteristic leads us to a robust, reliable, and fast procedure that requires only two images. A theoretical analysis of the contouring system is shown, with verification by means of numerical and experimental results.

Development of Automated Image Analysis Software for Suspended Marine Particle Classification

DTIC Science & Technology

2002-09-30

Development of Automated Image Analysis Software for Suspended Marine Particle Classification Scott Samson Center for Ocean Technology...and global water column. 1 OBJECTIVES The project’s objective is to develop automated image analysis software to reduce the effort and time

Urban Land Cover/use Change Detection Using High Resolution SPOT 5 and SPOT 6 Images and Urban Atlas Nomenclature

NASA Astrophysics Data System (ADS)

Akay, S. S.; Sertel, E.

2016-06-01

Urban land cover/use changes like urbanization and urban sprawl have been impacting the urban ecosystems significantly therefore determination of urban land cover/use changes is an important task to understand trends and status of urban ecosystems, to support urban planning and to aid decision-making for urban-based projects. High resolution satellite images could be used to accurately, periodically and quickly map urban land cover/use and their changes by time. This paper aims to determine urban land cover/use changes in Gaziantep city centre between 2010 and 2105 using object based images analysis and high resolution SPOT 5 and SPOT 6 images. 2.5 m SPOT 5 image obtained in 5th of June 2010 and 1.5 m SPOT 6 image obtained in 7th of July 2015 were used in this research to precisely determine land changes in five-year period. In addition to satellite images, various ancillary data namely Normalized Difference Vegetation Index (NDVI), Difference Water Index (NDWI) maps, cadastral maps, OpenStreetMaps, road maps and Land Cover maps, were integrated into the classification process to produce high accuracy urban land cover/use maps for these two years. Both images were geometrically corrected to fulfil the 1/10,000 scale geometric accuracy. Decision tree based object oriented classification was applied to identify twenty different urban land cover/use classes defined in European Urban Atlas project. Not only satellite images and satellite image-derived indices but also different thematic maps were integrated into decision tree analysis to create rule sets for accurate mapping of each class. Rule sets of each satellite image for the object based classification involves spectral, spatial and geometric parameter to automatically produce urban map of the city centre region. Total area of each class per related year and their changes in five-year period were determined and change trend in terms of class transformation were presented. Classification accuracy assessment was conducted by creating a confusion matrix to illustrate the thematic accuracy of each class.

Object-based class modelling for multi-scale riparian forest habitat mapping

NASA Astrophysics Data System (ADS)

Strasser, Thomas; Lang, Stefan

2015-05-01

Object-based class modelling allows for mapping complex, hierarchical habitat systems. The riparian zone, including forests, represents such a complex ecosystem. Forests within riparian zones are biologically high productive and characterized by a rich biodiversity; thus considered of high community interest with an imperative to be protected and regularly monitored. Satellite earth observation (EO) provides tools for capturing the current state of forest habitats such as forest composition including intermixture of non-native tree species. Here we present a semi-automated object based image analysis (OBIA) approach for the mapping of riparian forests by applying class modelling of habitats based on the European Nature Information System (EUNIS) habitat classifications and the European Habitats Directive (HabDir) Annex 1. A very high resolution (VHR) WorldView-2 satellite image provided the required spatial and spectral details for a multi-scale image segmentation and rule-base composition to generate a six-level hierarchical representation of riparian forest habitats. Thereby habitats were hierarchically represented within an image object hierarchy as forest stands, stands of homogenous tree species and single trees represented by sunlit tree crowns. 522 EUNIS level 3 (EUNIS-3) habitat patches with a mean patch size (MPS) of 12,349.64 m2 were modelled from 938 forest stand patches (MPS = 6868.20 m2) and 43,742 tree stand patches (MPS = 140.79 m2). The delineation quality of the modelled EUNIS-3 habitats (focal level) was quantitatively assessed to an expert-based visual interpretation showing a mean deviation of 11.71%.

Photogrammetric Analysis of Historical Image Repositories for Virtual Reconstruction in the Field of Digital Humanities

NASA Astrophysics Data System (ADS)

Maiwald, F.; Vietze, T.; Schneider, D.; Henze, F.; Münster, S.; Niebling, F.

2017-02-01

Historical photographs contain high density of information and are of great importance as sources in humanities research. In addition to the semantic indexing of historical images based on metadata, it is also possible to reconstruct geometric information about the depicted objects or the camera position at the time of the recording by employing photogrammetric methods. The approach presented here is intended to investigate (semi-) automated photogrammetric reconstruction methods for heterogeneous collections of historical (city) photographs and photographic documentation for the use in the humanities, urban research and history sciences. From a photogrammetric point of view, these images are mostly digitized photographs. For a photogrammetric evaluation, therefore, the characteristics of scanned analog images with mostly unknown camera geometry, missing or minimal object information and low radiometric and geometric resolution have to be considered. In addition, these photographs have not been created specifically for documentation purposes and so the focus of these images is often not on the object to be evaluated. The image repositories must therefore be subjected to a preprocessing analysis of their photogrammetric usability. Investigations are carried out on the basis of a repository containing historical images of the Kronentor ("crown gate") of the Dresden Zwinger. The initial step was to assess the quality and condition of available images determining their appropriateness for generating three-dimensional point clouds from historical photos using a structure-from-motion evaluation (SfM). Then, the generated point clouds were assessed by comparing them with current measurement data of the same object.

Material classification and automatic content enrichment of images using supervised learning and knowledge bases

NASA Astrophysics Data System (ADS)

Mallepudi, Sri Abhishikth; Calix, Ricardo A.; Knapp, Gerald M.

2011-02-01

In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

Water Vapor Sensitivity Analysis for AVIRIS Radiactive-Transfer-Model-Based Reflectance Inversion

NASA Technical Reports Server (NTRS)

Green, R.

2000-01-01

As with other imaging spectrometers, AVIRIS measures the upwelling specral radiance incident at the sensor. Most research and applications objectives for AVIRIS are based on the molecular absorption and scattering features expressed in the surface reflectance.

Evaluation of browning ratio in an image analysis of apple slices at different stages of instant controlled pressure drop-assisted hot-air drying (AD-DIC).

PubMed

Gao, Kun; Zhou, Linyan; Bi, Jinfeng; Yi, Jianyong; Wu, Xinye; Zhou, Mo; Wang, Xueyuan; Liu, Xuan

2017-06-01

Computer vision-based image analysis systems are widely used in food processing to evaluate quality changes. They are able to objectively measure the surface colour of various products since, providing some obvious advantages with their objectivity and quantitative capabilities. In this study, a computer vision-based image analysis system was used to investigate the colour changes of apple slices dried by instant controlled pressure drop-assisted hot air drying (AD-DIC). The CIE L* value and polyphenol oxidase activity in apple slices decreased during the entire drying process, whereas other colour indexes, including CIE a*, b*, ΔE and C* values, increased. The browning ratio calculated by image analysis increased during the drying process, and a sharp increment was observed for the DIC process. The change in 5-hydroxymethylfurfural (5-HMF) and fluorescent compounds (FIC) showed the same trend with browning ratio due to Maillard reaction. Moreover, the concentrations of 5-HMF and FIC both had a good quadratic correlation (R 2  > 0.998) with the browning ratio. Browning ratio was a reliable indicator of 5-HMF and FIC changes in apple slices during drying. The image analysis system could be used to monitor colour changes, 5-HMF and FIC in dehydrated apple slices during the AD-DIC process. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

A Monocular Vision Measurement System of Three-Degree-of-Freedom Air-Bearing Test-Bed Based on FCCSP

NASA Astrophysics Data System (ADS)

Gao, Zhanyu; Gu, Yingying; Lv, Yaoyu; Xu, Zhenbang; Wu, Qingwen

2018-06-01

A monocular vision-based pose measurement system is provided for real-time measurement of a three-degree-of-freedom (3-DOF) air-bearing test-bed. Firstly, a circular plane cooperative target is designed. An image of a target fixed on the test-bed is then acquired. Blob analysis-based image processing is used to detect the object circles on the target. A fast algorithm (FCCSP) based on pixel statistics is proposed to extract the centers of object circles. Finally, pose measurements can be obtained when combined with the centers and the coordinate transformation relation. Experiments show that the proposed method is fast, accurate, and robust enough to satisfy the requirement of the pose measurement.

Measurement of two-dimensional thickness of micro-patterned thin film based on image restoration in a spectroscopic imaging reflectometer.

PubMed

Kim, Min-Gab; Kim, Jin-Yong

2018-05-01

In this paper, we introduce a method to overcome the limitation of thickness measurement of a micro-patterned thin film. A spectroscopic imaging reflectometer system that consists of an acousto-optic tunable filter, a charge-coupled-device camera, and a high-magnitude objective lens was proposed, and a stack of multispectral images was generated. To secure improved accuracy and lateral resolution in the reconstruction of a two-dimensional thin film thickness, prior to the analysis of spectral reflectance profiles from each pixel of multispectral images, the image restoration based on an iterative deconvolution algorithm was applied to compensate for image degradation caused by blurring.

Adaptive thresholding image series from fluorescence confocal scanning laser microscope using orientation intensity profiles

NASA Astrophysics Data System (ADS)

Feng, Judy J.; Ip, Horace H.; Cheng, Shuk H.

2004-05-01

Many grey-level thresholding methods based on histogram or other statistic information about the interest image such as maximum entropy and so on have been proposed in the past. However, most methods based on statistic analysis of the images concerned little about the characteristics of morphology of interest objects, which sometimes could provide very important indication which can help to find the optimum threshold, especially for those organisms which have special texture morphologies such as vasculature, neuro-network etc. in medical imaging. In this paper, we propose a novel method for thresholding the fluorescent vasculature image series recorded from Confocal Scanning Laser Microscope. After extracting the basic orientation of the slice of vessels inside a sub-region partitioned from the images, we analysis the intensity profiles perpendicular to the vessel orientation to get the reasonable initial threshold for each region. Then the threshold values of those regions near the interest one both in x-y and optical directions have been referenced to get the final result of thresholds of the region, which makes the whole stack of images look more continuous. The resulting images are characterized by suppressing both noise and non-interest tissues conglutinated to vessels, while improving the vessel connectivities and edge definitions. The value of the method for idealized thresholding the fluorescence images of biological objects is demonstrated by a comparison of the results of 3D vascular reconstruction.

Extraction of Extended Small-Scale Objects in Digital Images

NASA Astrophysics Data System (ADS)

Volkov, V. Y.

2015-05-01

Detection and localization problem of extended small-scale objects with different shapes appears in radio observation systems which use SAR, infra-red, lidar and television camera. Intensive non-stationary background is the main difficulty for processing. Other challenge is low quality of images, blobs, blurred boundaries; in addition SAR images suffer from a serious intrinsic speckle noise. Statistics of background is not normal, it has evident skewness and heavy tails in probability density, so it is hard to identify it. The problem of extraction small-scale objects is solved here on the basis of directional filtering, adaptive thresholding and morthological analysis. New kind of masks is used which are open-ended at one side so it is possible to extract ends of line segments with unknown length. An advanced method of dynamical adaptive threshold setting is investigated which is based on isolated fragments extraction after thresholding. Hierarchy of isolated fragments on binary image is proposed for the analysis of segmentation results. It includes small-scale objects with different shape, size and orientation. The method uses extraction of isolated fragments in binary image and counting points in these fragments. Number of points in extracted fragments is normalized to the total number of points for given threshold and is used as effectiveness of extraction for these fragments. New method for adaptive threshold setting and control maximises effectiveness of extraction. It has optimality properties for objects extraction in normal noise field and shows effective results for real SAR images.

D Tracking Based Augmented Reality for Cultural Heritage Data Management

NASA Astrophysics Data System (ADS)

Battini, C.; Landi, G.

2015-02-01

The development of contactless documentation techniques is allowing researchers to collect high volumes of three-dimensional data in a short time but with high levels of accuracy. The digitalisation of cultural heritage opens up the possibility of using image processing and analysis, and computer graphics techniques, to preserve this heritage for future generations; augmenting it with additional information or with new possibilities for its enjoyment and use. The collection of precise datasets about cultural heritage status is crucial for its interpretation, its conservation and during the restoration processes. The application of digital-imaging solutions for various feature extraction, image data-analysis techniques, and three-dimensional reconstruction of ancient artworks, allows the creation of multidimensional models that can incorporate information coming from heterogeneous data sets, research results and historical sources. Real objects can be scanned and reconstructed virtually, with high levels of data accuracy and resolution. Real-time visualisation software and hardware is rapidly evolving and complex three-dimensional models can be interactively visualised and explored on applications developed for mobile devices. This paper will show how a 3D reconstruction of an object, with multiple layers of information, can be stored and visualised through a mobile application that will allow interaction with a physical object for its study and analysis, using 3D Tracking based Augmented Reality techniques.

Practical considerations of image analysis and quantification of signal transduction IHC staining.

PubMed

Grunkin, Michael; Raundahl, Jakob; Foged, Niels T

2011-01-01

The dramatic increase in computer processing power in combination with the availability of high-quality digital cameras during the last 10 years has fertilized the grounds for quantitative microscopy based on digital image analysis. With the present introduction of robust scanners for whole slide imaging in both research and routine, the benefits of automation and objectivity in the analysis of tissue sections will be even more obvious. For in situ studies of signal transduction, the combination of tissue microarrays, immunohistochemistry, digital imaging, and quantitative image analysis will be central operations. However, immunohistochemistry is a multistep procedure including a lot of technical pitfalls leading to intra- and interlaboratory variability of its outcome. The resulting variations in staining intensity and disruption of original morphology are an extra challenge for the image analysis software, which therefore preferably should be dedicated to the detection and quantification of histomorphometrical end points.

An Integrated Centroid Finding and Particle Overlap Decomposition Algorithm for Stereo Imaging Velocimetry

NASA Technical Reports Server (NTRS)

McDowell, Mark

2004-01-01

An integrated algorithm for decomposing overlapping particle images (multi-particle objects) along with determining each object s constituent particle centroid(s) has been developed using image analysis techniques. The centroid finding algorithm uses a modified eight-direction search method for finding the perimeter of any enclosed object. The centroid is calculated using the intensity-weighted center of mass of the object. The overlap decomposition algorithm further analyzes the object data and breaks it down into its constituent particle centroid(s). This is accomplished with an artificial neural network, feature based technique and provides an efficient way of decomposing overlapping particles. Combining the centroid finding and overlap decomposition routines into a single algorithm allows us to accurately predict the error associated with finding the centroid(s) of particles in our experiments. This algorithm has been tested using real, simulated, and synthetic data and the results are presented and discussed.

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis.

PubMed

Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica; Campbell, J Peter; Bozkurt, Alican; Erdogmus, Deniz; Kalpathy-Cramer, Jayashree; Patel, Samir; Jonas, Karyn; Chan, R V Paul; Ostmo, Susan; Chiang, Michael F

2015-11-01

We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis. A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the "i-ROP" system. Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%). This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination. Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists.

Tensor-based dynamic reconstruction method for electrical capacitance tomography

NASA Astrophysics Data System (ADS)

Lei, J.; Mu, H. P.; Liu, Q. B.; Li, Z. H.; Liu, S.; Wang, X. Y.

2017-03-01

Electrical capacitance tomography (ECT) is an attractive visualization measurement method, in which the acquisition of high-quality images is beneficial for the understanding of the underlying physical or chemical mechanisms of the dynamic behaviors of the measurement objects. In real-world measurement environments, imaging objects are often in a dynamic process, and the exploitation of the spatial-temporal correlations related to the dynamic nature will contribute to improving the imaging quality. Different from existing imaging methods that are often used in ECT measurements, in this paper a dynamic image sequence is stacked into a third-order tensor that consists of a low rank tensor and a sparse tensor within the framework of the multiple measurement vectors model and the multi-way data analysis method. The low rank tensor models the similar spatial distribution information among frames, which is slowly changing over time, and the sparse tensor captures the perturbations or differences introduced in each frame, which is rapidly changing over time. With the assistance of the Tikhonov regularization theory and the tensor-based multi-way data analysis method, a new cost function, with the considerations of the multi-frames measurement data, the dynamic evolution information of a time-varying imaging object and the characteristics of the low rank tensor and the sparse tensor, is proposed to convert the imaging task in the ECT measurement into a reconstruction problem of a third-order image tensor. An effective algorithm is developed to search for the optimal solution of the proposed cost function, and the images are reconstructed via a batching pattern. The feasibility and effectiveness of the developed reconstruction method are numerically validated.

A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches

NASA Astrophysics Data System (ADS)

Ye, Su; Pontius, Robert Gilmore; Rakshit, Rahul

2018-07-01

Object-based image analysis (OBIA) has gained widespread popularity for creating maps from remotely sensed data. Researchers routinely claim that OBIA procedures outperform pixel-based procedures; however, it is not immediately obvious how to evaluate the degree to which an OBIA map compares to reference information in a manner that accounts for the fact that the OBIA map consists of objects that vary in size and shape. Our study reviews 209 journal articles concerning OBIA published between 2003 and 2017. We focus on the three stages of accuracy assessment: (1) sampling design, (2) response design and (3) accuracy analysis. First, we report the literature's overall characteristics concerning OBIA accuracy assessment. Simple random sampling was the most used method among probability sampling strategies, slightly more than stratified sampling. Office interpreted remotely sensed data was the dominant reference source. The literature reported accuracies ranging from 42% to 96%, with an average of 85%. A third of the articles failed to give sufficient information concerning accuracy methodology such as sampling scheme and sample size. We found few studies that focused specifically on the accuracy of the segmentation. Second, we identify a recent increase of OBIA articles in using per-polygon approaches compared to per-pixel approaches for accuracy assessment. We clarify the impacts of the per-pixel versus the per-polygon approaches respectively on sampling, response design and accuracy analysis. Our review defines the technical and methodological needs in the current per-polygon approaches, such as polygon-based sampling, analysis of mixed polygons, matching of mapped with reference polygons and assessment of segmentation accuracy. Our review summarizes and discusses the current issues in object-based accuracy assessment to provide guidance for improved accuracy assessments for OBIA.

Object-Based Image Analysis of WORLDVIEW-2 Satellite Data for the Classification of Mangrove Areas in the City of SÃO LUÍS, MARANHÃO State, Brazil

NASA Astrophysics Data System (ADS)

Kux, H. J. H.; Souza, U. D. V.

2012-07-01

Taking into account the importance of mangrove environments for the biodiversity of coastal areas, the objective of this paper is to classify the different types of irregular human occupation on the areas of mangrove vegetation in São Luis, capital of Maranhão State, Brazil, considering the OBIA (Object-based Image Analysis) approach with WorldView-2 satellite data and using InterIMAGE, a free image analysis software. A methodology for the study of the area covered by mangroves at the northern portion of the city was proposed to identify the main targets of this area, such as: marsh areas (known locally as Apicum), mangrove forests, tidal channels, blockhouses (irregular constructions), embankments, paved streets and different condominiums. Initially a databank including information on the main types of occupation and environments was established for the area under study. An image fusion (multispectral bands with panchromatic band) was done, to improve the information content of WorldView-2 data. Following an ortho-rectification was made with the dataset used, in order to compare with cartographical data from the municipality, using Ground Control Points (GCPs) collected during field survey. Using the data mining software GEODMA, a series of attributes which characterize the targets of interest was established. Afterwards the classes were structured, a knowledge model was created and the classification performed. The OBIA approach eased mapping of such sensitive areas, showing the irregular occupations and embankments of mangrove forests, reducing its area and damaging the marine biodiversity.

Objected-oriented remote sensing image classification method based on geographic ontology model

NASA Astrophysics Data System (ADS)

Chu, Z.; Liu, Z. J.; Gu, H. Y.

2016-11-01

Nowadays, with the development of high resolution remote sensing image and the wide application of laser point cloud data, proceeding objected-oriented remote sensing classification based on the characteristic knowledge of multi-source spatial data has been an important trend on the field of remote sensing image classification, which gradually replaced the traditional method through improving algorithm to optimize image classification results. For this purpose, the paper puts forward a remote sensing image classification method that uses the he characteristic knowledge of multi-source spatial data to build the geographic ontology semantic network model, and carries out the objected-oriented classification experiment to implement urban features classification, the experiment uses protégé software which is developed by Stanford University in the United States, and intelligent image analysis software—eCognition software as the experiment platform, uses hyperspectral image and Lidar data that is obtained through flight in DaFeng City of JiangSu as the main data source, first of all, the experiment uses hyperspectral image to obtain feature knowledge of remote sensing image and related special index, the second, the experiment uses Lidar data to generate nDSM(Normalized DSM, Normalized Digital Surface Model),obtaining elevation information, the last, the experiment bases image feature knowledge, special index and elevation information to build the geographic ontology semantic network model that implement urban features classification, the experiment results show that, this method is significantly higher than the traditional classification algorithm on classification accuracy, especially it performs more evidently on the respect of building classification. The method not only considers the advantage of multi-source spatial data, for example, remote sensing image, Lidar data and so on, but also realizes multi-source spatial data knowledge integration and application of the knowledge to the field of remote sensing image classification, which provides an effective way for objected-oriented remote sensing image classification in the future.

Image analysis of multiple moving wood pieces in real time

NASA Astrophysics Data System (ADS)

Wang, Weixing

2006-02-01

This paper presents algorithms for image processing and image analysis of wood piece materials. The algorithms were designed for auto-detection of wood piece materials on a moving conveyor belt or a truck. When wood objects on moving, the hard task is to trace the contours of the objects in n optimal way. To make the algorithms work efficiently in the plant, a flexible online system was designed and developed, which mainly consists of image acquisition, image processing, object delineation and analysis. A number of newly-developed algorithms can delineate wood objects with high accuracy and high speed, and in the wood piece analysis part, each wood piece can be characterized by a number of visual parameters which can also be used for constructing experimental models directly in the system.

Design and Implementation of CNEOST Image Database Based on NoSQL System

NASA Astrophysics Data System (ADS)

Wang, X.

2013-07-01

The China Near Earth Object Survey Telescope (CNEOST) is the largest Schmidt telescope in China, and it has acquired more than 3 TB astronomical image data since it saw the first light in 2006. After the upgradation of the CCD camera in 2013, over 10 TB data will be obtained every year. The management of massive images is not only an indispensable part of data processing pipeline but also the basis of data sharing. Based on the analysis of requirement, an image management system is designed and implemented by employing the non-relational database.

Design and Implementation of CNEOST Image Database Based on NoSQL System

NASA Astrophysics Data System (ADS)

Wang, Xin

2014-04-01

The China Near Earth Object Survey Telescope is the largest Schmidt telescope in China, and it has acquired more than 3 TB astronomical image data since it saw the first light in 2006. After the upgrade of the CCD camera in 2013, over 10 TB data will be obtained every year. The management of the massive images is not only an indispensable part of data processing pipeline but also the basis of data sharing. Based on the analysis of requirement, an image management system is designed and implemented by employing the non-relational database.

Mapping forested wetlands in the Great Zhan River Basin through integrating optical, radar, and topographical data classification techniques.

PubMed

Na, X D; Zang, S Y; Wu, C S; Li, W L

2015-11-01

Knowledge of the spatial extent of forested wetlands is essential to many studies including wetland functioning assessment, greenhouse gas flux estimation, and wildlife suitable habitat identification. For discriminating forested wetlands from their adjacent land cover types, researchers have resorted to image analysis techniques applied to numerous remotely sensed data. While with some success, there is still no consensus on the optimal approaches for mapping forested wetlands. To address this problem, we examined two machine learning approaches, random forest (RF) and K-nearest neighbor (KNN) algorithms, and applied these two approaches to the framework of pixel-based and object-based classifications. The RF and KNN algorithms were constructed using predictors derived from Landsat 8 imagery, Radarsat-2 advanced synthetic aperture radar (SAR), and topographical indices. The results show that the objected-based classifications performed better than per-pixel classifications using the same algorithm (RF) in terms of overall accuracy and the difference of their kappa coefficients are statistically significant (p<0.01). There were noticeably omissions for forested and herbaceous wetlands based on the per-pixel classifications using the RF algorithm. As for the object-based image analysis, there were also statistically significant differences (p<0.01) of Kappa coefficient between results performed based on RF and KNN algorithms. The object-based classification using RF provided a more visually adequate distribution of interested land cover types, while the object classifications based on the KNN algorithm showed noticeably commissions for forested wetlands and omissions for agriculture land. This research proves that the object-based classification with RF using optical, radar, and topographical data improved the mapping accuracy of land covers and provided a feasible approach to discriminate the forested wetlands from the other land cover types in forestry area.

Object-based image analysis for cadastral mapping using satellite images

NASA Astrophysics Data System (ADS)

Kohli, D.; Crommelinck, S.; Bennett, R.; Koeva, M.; Lemmen, C.

2017-10-01

Cadasters together with land registry form a core ingredient of any land administration system. Cadastral maps comprise of the extent, ownership and value of land which are essential for recording and updating land records. Traditional methods for cadastral surveying and mapping often prove to be labor, cost and time intensive: alternative approaches are thus being researched for creating such maps. With the advent of very high resolution (VHR) imagery, satellite remote sensing offers a tremendous opportunity for (semi)-automation of cadastral boundaries detection. In this paper, we explore the potential of object-based image analysis (OBIA) approach for this purpose by applying two segmentation methods, i.e. MRS (multi-resolution segmentation) and ESP (estimation of scale parameter) to identify visible cadastral boundaries. Results show that a balance between high percentage of completeness and correctness is hard to achieve: a low error of commission often comes with a high error of omission. However, we conclude that the resulting segments/land use polygons can potentially be used as a base for further aggregation into tenure polygons using participatory mapping.

ROBOSIGHT: Robotic Vision System For Inspection And Manipulation

NASA Astrophysics Data System (ADS)

Trivedi, Mohan M.; Chen, ChuXin; Marapane, Suresh

1989-02-01

Vision is an important sensory modality that can be used for deriving information critical to the proper, efficient, flexible, and safe operation of an intelligent robot. Vision systems are uti-lized for developing higher level interpretation of the nature of a robotic workspace using images acquired by cameras mounted on a robot. Such information can be useful for tasks such as object recognition, object location, object inspection, obstacle avoidance and navigation. In this paper we describe efforts directed towards developing a vision system useful for performing various robotic inspection and manipulation tasks. The system utilizes gray scale images and can be viewed as a model-based system. It includes general purpose image analysis modules as well as special purpose, task dependent object status recognition modules. Experiments are described to verify the robust performance of the integrated system using a robotic testbed.

Object based image analysis for the classification of the growth stages of Avocado crop, in Michoacán State, Mexico

NASA Astrophysics Data System (ADS)

Gao, Yan; Marpu, Prashanth; Morales Manila, Luis M.

2014-11-01

This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and without the four new spectral bands. Classification accuracy assessment results show that object-based classification with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%) method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.

Tree crown mapping in managed woodlands (parklands) of semi-arid West Africa using WorldView-2 imagery and geographic object based image analysis.

PubMed

Karlson, Martin; Reese, Heather; Ostwald, Madelene

2014-11-28

Detailed information on tree cover structure is critical for research and monitoring programs targeting African woodlands, including agroforestry parklands. High spatial resolution satellite imagery represents a potentially effective alternative to field-based surveys, but requires the development of accurate methods to automate information extraction. This study presents a method for tree crown mapping based on Geographic Object Based Image Analysis (GEOBIA) that use spectral and geometric information to detect and delineate individual tree crowns and crown clusters. The method was implemented on a WorldView-2 image acquired over the parklands of Saponé, Burkina Faso, and rigorously evaluated against field reference data. The overall detection rate was 85.4% for individual tree crowns and crown clusters, with lower accuracies in areas with high tree density and dense understory vegetation. The overall delineation error (expressed as the difference between area of delineated object and crown area measured in the field) was 45.6% for individual tree crowns and 61.5% for crown clusters. Delineation accuracies were higher for medium (35-100 m(2)) and large (≥100 m(2)) trees compared to small (<35 m(2)) trees. The results indicate potential of GEOBIA and WorldView-2 imagery for tree crown mapping in parkland landscapes and similar woodland areas.

Tree Crown Mapping in Managed Woodlands (Parklands) of Semi-Arid West Africa Using WorldView-2 Imagery and Geographic Object Based Image Analysis

PubMed Central

Karlson, Martin; Reese, Heather; Ostwald, Madelene

2014-01-01

Detailed information on tree cover structure is critical for research and monitoring programs targeting African woodlands, including agroforestry parklands. High spatial resolution satellite imagery represents a potentially effective alternative to field-based surveys, but requires the development of accurate methods to automate information extraction. This study presents a method for tree crown mapping based on Geographic Object Based Image Analysis (GEOBIA) that use spectral and geometric information to detect and delineate individual tree crowns and crown clusters. The method was implemented on a WorldView-2 image acquired over the parklands of Saponé, Burkina Faso, and rigorously evaluated against field reference data. The overall detection rate was 85.4% for individual tree crowns and crown clusters, with lower accuracies in areas with high tree density and dense understory vegetation. The overall delineation error (expressed as the difference between area of delineated object and crown area measured in the field) was 45.6% for individual tree crowns and 61.5% for crown clusters. Delineation accuracies were higher for medium (35–100 m2) and large (≥100 m2) trees compared to small (<35 m2) trees. The results indicate potential of GEOBIA and WorldView-2 imagery for tree crown mapping in parkland landscapes and similar woodland areas. PMID:25460815

AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques.

PubMed

Khan, Arif Ul Maula; Torelli, Angelo; Wolf, Ivo; Gretz, Norbert

2018-05-08

In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust image processing/analysis method. We present AutoCellSeg (based on MATLAB) that implements a supervised automatic and robust image segmentation method. AutoCellSeg utilizes multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. It is usable in different operation modes and intuitively enables the user to select object features interactively for supervised image segmentation method. It allows the user to correct results with a graphical interface. This publicly available tool outperforms tools like OpenCFU and CellProfiler in terms of accuracy and provides many additional useful features for end-users.

Fast processing of microscopic images using object-based extended depth of field.

PubMed

Intarapanich, Apichart; Kaewkamnerd, Saowaluck; Pannarut, Montri; Shaw, Philip J; Tongsima, Sissades

2016-12-22

Microscopic analysis requires that foreground objects of interest, e.g. cells, are in focus. In a typical microscopic specimen, the foreground objects may lie on different depths of field necessitating capture of multiple images taken at different focal planes. The extended depth of field (EDoF) technique is a computational method for merging images from different depths of field into a composite image with all foreground objects in focus. Composite images generated by EDoF can be applied in automated image processing and pattern recognition systems. However, current algorithms for EDoF are computationally intensive and impractical, especially for applications such as medical diagnosis where rapid sample turnaround is important. Since foreground objects typically constitute a minor part of an image, the EDoF technique could be made to work much faster if only foreground regions are processed to make the composite image. We propose a novel algorithm called object-based extended depths of field (OEDoF) to address this issue. The OEDoF algorithm consists of four major modules: 1) color conversion, 2) object region identification, 3) good contrast pixel identification and 4) detail merging. First, the algorithm employs color conversion to enhance contrast followed by identification of foreground pixels. A composite image is constructed using only these foreground pixels, which dramatically reduces the computational time. We used 250 images obtained from 45 specimens of confirmed malaria infections to test our proposed algorithm. The resulting composite images with all in-focus objects were produced using the proposed OEDoF algorithm. We measured the performance of OEDoF in terms of image clarity (quality) and processing time. The features of interest selected by the OEDoF algorithm are comparable in quality with equivalent regions in images processed by the state-of-the-art complex wavelet EDoF algorithm; however, OEDoF required four times less processing time. This work presents a modification of the extended depth of field approach for efficiently enhancing microscopic images. This selective object processing scheme used in OEDoF can significantly reduce the overall processing time while maintaining the clarity of important image features. The empirical results from parasite-infected red cell images revealed that our proposed method efficiently and effectively produced in-focus composite images. With the speed improvement of OEDoF, this proposed algorithm is suitable for processing large numbers of microscope images, e.g., as required for medical diagnosis.

Automatic Cell Segmentation in Fluorescence Images of Confluent Cell Monolayers Using Multi-object Geometric Deformable Model.

PubMed

Yang, Zhen; Bogovic, John A; Carass, Aaron; Ye, Mao; Searson, Peter C; Prince, Jerry L

2013-03-13

With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. This method addresses several challenges through a combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as initial seeds and then using a multi-object geometric deformable model (MGDM) for final segmentation. 2) To deal with different defects in the fluorescence images, the cell junctions are enhanced by applying an order-statistic filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient over all distinguishable cells is 0.88.

A model-based approach for detection of runways and other objects in image sequences acquired using an on-board camera

NASA Technical Reports Server (NTRS)

Kasturi, Rangachar; Devadiga, Sadashiva; Tang, Yuan-Liang

1994-01-01

This research was initiated as a part of the Advanced Sensor and Imaging System Technology (ASSIST) program at NASA Langley Research Center. The primary goal of this research is the development of image analysis algorithms for the detection of runways and other objects using an on-board camera. Initial effort was concentrated on images acquired using a passive millimeter wave (PMMW) sensor. The images obtained using PMMW sensors under poor visibility conditions due to atmospheric fog are characterized by very low spatial resolution but good image contrast compared to those images obtained using sensors operating in the visible spectrum. Algorithms developed for analyzing these images using a model of the runway and other objects are described in Part 1 of this report. Experimental verification of these algorithms was limited to a sequence of images simulated from a single frame of PMMW image. Subsequent development and evaluation of algorithms was done using video image sequences. These images have better spatial and temporal resolution compared to PMMW images. Algorithms for reliable recognition of runways and accurate estimation of spatial position of stationary objects on the ground have been developed and evaluated using several image sequences. These algorithms are described in Part 2 of this report. A list of all publications resulting from this work is also included.

Beam Characterization at the Neutron Radiography Facility

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

Sarah Morgan; Jeffrey King

The quality of a neutron imaging beam directly impacts the quality of radiographic images produced using that beam. Fully characterizing a neutron beam, including determination of the beam’s effective length-to-diameter ratio, neutron flux profile, energy spectrum, image quality, and beam divergence, is vital for producing quality radiographic images. This project characterized the east neutron imaging beamline at the Idaho National Laboratory Neutron Radiography Reactor (NRAD). The experiments which measured the beam’s effective length-to-diameter ratio and image quality are based on American Society for Testing and Materials (ASTM) standards. An analysis of the image produced by a calibrated phantom measured themore » beam divergence. The energy spectrum measurements consist of a series of foil irradiations using a selection of activation foils, compared to the results produced by a Monte Carlo n-Particle (MCNP) model of the beamline. Improvement of the existing NRAD MCNP beamline model includes validation of the model’s energy spectrum and the development of enhanced image simulation methods. The image simulation methods predict the radiographic image of an object based on the foil reaction rate data obtained by placing a model of the object in front of the image plane in an MCNP beamline model.« less

Object-based land-use/land-cover change detection using Landsat imagery: a case study of Ardabil, Namin, and Nir counties in northwest Iran.

PubMed

Aslami, Farnoosh; Ghorbani, Ardavan

2018-06-03

In this study, land-use/land-cover (LULC) change in the Ardabil, Namin, and Nir counties, in the Ardabil province in the northwest of Iran, was detected using an object-based method. Landsat images including Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM + ), and Operational Land Imager (OLI) were used. Preprocessing methods, including geometric and radiometric correction, and topographic normalization were performed. Image processing was conducted according to object-based image analysis using the nearest neighbor algorithm. An accuracy assessment was conducted using overall accuracy and Kappa statistics. Results show that maps obtained from images for 1987, 2002, and 2013 had an overall accuracy of 91.76, 91.06, and 93.00%, and a Kappa coefficient of 0.90, 0.83, and 0.91, respectively. Change detection between 1987 and 2013 shows that most of the rangelands (97,156.6 ha) have been converted to dry farming; moreover, residential and other urban land uses have also increased. The largest change in land use has occurred for irrigated farming, rangelands, and dry farming, of which approximately 3539.8, 3086.9, and 2271.9 ha, respectively, have given way to urban land use for each of the studied years.

Automatic target recognition apparatus and method

DOEpatents

Baumgart, Chris W.; Ciarcia, Christopher A.

2000-01-01

An automatic target recognition apparatus (10) is provided, having a video camera/digitizer (12) for producing a digitized image signal (20) representing an image containing therein objects which objects are to be recognized if they meet predefined criteria. The digitized image signal (20) is processed within a video analysis subroutine (22) residing in a computer (14) in a plurality of parallel analysis chains such that the objects are presumed to be lighter in shading than the background in the image in three of the chains and further such that the objects are presumed to be darker than the background in the other three chains. In two of the chains the objects are defined by surface texture analysis using texture filter operations. In another two of the chains the objects are defined by background subtraction operations. In yet another two of the chains the objects are defined by edge enhancement processes. In each of the analysis chains a calculation operation independently determines an error factor relating to the probability that the objects are of the type which should be recognized, and a probability calculation operation combines the results of the analysis chains.

Northeast Artificial Intelligence Consortium Annual Report for 1987. Volume 4. Research in Automated Photointerpretation

DTIC Science & Technology

1989-03-01

KOWLEDGE INFERENCE IMAGE DAAAEENGINE DATABASE Automated Photointerpretation Testbed. 4.1.7 Fig. .1.1-2 An Initial Segmentation of an Image / zx...MRF) theory provide a powerful alternative texture model and have resulted in intensive research activity in MRF model- based texture analysis...interpretation process. 5. Additional, and perhaps more powerful , features have to be incorporated into the image segmentation procedure. 6. Object detection

Complex dark-field contrast and its retrieval in x-ray phase contrast imaging implemented with Talbot interferometry.

PubMed

Yang, Yi; Tang, Xiangyang

2014-10-01

Under the existing theoretical framework of x-ray phase contrast imaging methods implemented with Talbot interferometry, the dark-field contrast refers to the reduction in interference fringe visibility due to small-angle x-ray scattering of the subpixel microstructures of an object to be imaged. This study investigates how an object's subpixel microstructures can also affect the phase of the intensity oscillations. Instead of assuming that the object's subpixel microstructures distribute in space randomly, the authors' theoretical derivation starts by assuming that an object's attenuation projection and phase shift vary at a characteristic size that is not smaller than the period of analyzer grating G₂ and a characteristic length dc. Based on the paraxial Fresnel-Kirchhoff theory, the analytic formulae to characterize the zeroth- and first-order Fourier coefficients of the x-ray irradiance recorded at each detector cell are derived. Then the concept of complex dark-field contrast is introduced to quantify the influence of the object's microstructures on both the interference fringe visibility and the phase of intensity oscillations. A method based on the phase-attenuation duality that holds for soft tissues and high x-ray energies is proposed to retrieve the imaginary part of the complex dark-field contrast for imaging. Through computer simulation study with a specially designed numerical phantom, they evaluate and validate the derived analytic formulae and the proposed retrieval method. Both theoretical analysis and computer simulation study show that the effect of an object's subpixel microstructures on x-ray phase contrast imaging method implemented with Talbot interferometry can be fully characterized by a complex dark-field contrast. The imaginary part of complex dark-field contrast quantifies the influence of the object's subpixel microstructures on the phase of intensity oscillations. Furthermore, at relatively high energies, for soft tissues it can be retrieved for imaging with a method based on the phase-attenuation duality. The analytic formulae derived in this work to characterize the complex dark-field contrast in x-ray phase contrast imaging method implemented with Talbot interferometry are of significance, which may initiate more activities in the research and development of x-ray differential phase contrast imaging for extensive biomedical applications.

McIDAS-eXplorer: A version of McIDAS for planetary applications

NASA Technical Reports Server (NTRS)

Limaye, Sanjay S.; Saunders, R. Stephen; Sromovsky, Lawrence A.; Martin, Michael

1994-01-01

McIDAS-eXplorer is a set of software tools developed for analysis of planetary data published by the Planetary Data System on CD-ROM's. It is built upon McIDAS-X, an environment which has been in use nearly two decades now for earth weather satellite data applications in research and routine operations. The environment allows convenient access, navigation, analysis, display, and animation of planetary data by utilizing the full calibration data accompanying the planetary data. Support currently exists for Voyager images of the giant planets and their satellites; Magellan radar images (F-MIDR and C-MIDR's, global map products (GxDR's), and altimetry data (ARCDR's)); Galileo SSI images of the earth, moon, and Venus; Viking Mars images and MDIM's as well as most earth based telescopic images of solar system objects (FITS). The NAIF/JPL SPICE kernels are used for image navigation when available. For data without the SPICE kernels (such as the bulk of the Voyager Jupiter and Saturn imagery and Pioneer Orbiter images of Venus), tools based on NAIF toolkit allow the user to navigate the images interactively. Multiple navigation types can be attached to a given image (e.g., for ring navigation and planet navigation in the same image). Tools are available to perform common image processing tasks such as digital filtering, cartographic mapping, map overlays, and data extraction. It is also possible to have different planetary radii for an object such as Venus which requires a different radius for the surface and for the cloud level. A graphical user interface based on Tel-Tk scripting language is provided (UNIX only at present) for using the environment and also to provide on-line help. It is possible for end users to add applications of their own to the environment at any time.

Large-Scale Overlays and Trends: Visually Mining, Panning and Zooming the Observable Universe.

PubMed

Luciani, Timothy Basil; Cherinka, Brian; Oliphant, Daniel; Myers, Sean; Wood-Vasey, W Michael; Labrinidis, Alexandros; Marai, G Elisabeta

2014-07-01

We introduce a web-based computing infrastructure to assist the visual integration, mining and interactive navigation of large-scale astronomy observations. Following an analysis of the application domain, we design a client-server architecture to fetch distributed image data and to partition local data into a spatial index structure that allows prefix-matching of spatial objects. In conjunction with hardware-accelerated pixel-based overlays and an online cross-registration pipeline, this approach allows the fetching, displaying, panning and zooming of gigabit panoramas of the sky in real time. To further facilitate the integration and mining of spatial and non-spatial data, we introduce interactive trend images-compact visual representations for identifying outlier objects and for studying trends within large collections of spatial objects of a given class. In a demonstration, images from three sky surveys (SDSS, FIRST and simulated LSST results) are cross-registered and integrated as overlays, allowing cross-spectrum analysis of astronomy observations. Trend images are interactively generated from catalog data and used to visually mine astronomy observations of similar type. The front-end of the infrastructure uses the web technologies WebGL and HTML5 to enable cross-platform, web-based functionality. Our approach attains interactive rendering framerates; its power and flexibility enables it to serve the needs of the astronomy community. Evaluation on three case studies, as well as feedback from domain experts emphasize the benefits of this visual approach to the observational astronomy field; and its potential benefits to large scale geospatial visualization in general.

Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification

NASA Astrophysics Data System (ADS)

Liu, Tao; Abd-Elrahman, Amr

2018-05-01

Deep convolutional neural network (DCNN) requires massive training datasets to trigger its image classification power, while collecting training samples for remote sensing application is usually an expensive process. When DCNN is simply implemented with traditional object-based image analysis (OBIA) for classification of Unmanned Aerial systems (UAS) orthoimage, its power may be undermined if the number training samples is relatively small. This research aims to develop a novel OBIA classification approach that can take advantage of DCNN by enriching the training dataset automatically using multi-view data. Specifically, this study introduces a Multi-View Object-based classification using Deep convolutional neural network (MODe) method to process UAS images for land cover classification. MODe conducts the classification on multi-view UAS images instead of directly on the orthoimage, and gets the final results via a voting procedure. 10-fold cross validation results show the mean overall classification accuracy increasing substantially from 65.32%, when DCNN was applied on the orthoimage to 82.08% achieved when MODe was implemented. This study also compared the performances of the support vector machine (SVM) and random forest (RF) classifiers with DCNN under traditional OBIA and the proposed multi-view OBIA frameworks. The results indicate that the advantage of DCNN over traditional classifiers in terms of accuracy is more obvious when these classifiers were applied with the proposed multi-view OBIA framework than when these classifiers were applied within the traditional OBIA framework.

Kernel-aligned multi-view canonical correlation analysis for image recognition

NASA Astrophysics Data System (ADS)

Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

2016-09-01

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

Computerized quantitative evaluation of mammographic accreditation phantom images

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

Lee, Yongbum; Tsai, Du-Yih; Shinohara, Norimitsu

2010-12-15

Purpose: The objective was to develop and investigate an automated scoring scheme of the American College of Radiology (ACR) mammographic accreditation phantom (RMI 156, Middleton, WI) images. Methods: The developed method consisted of background subtraction, determination of region of interest, classification of fiber and mass objects by Mahalanobis distance, detection of specks by template matching, and rule-based scoring. Fifty-one phantom images were collected from 51 facilities for this study (one facility provided one image). A medical physicist and two radiologic technologists also scored the images. The human and computerized scores were compared. Results: In terms of meeting the ACR's criteria,more » the accuracies of the developed method for computerized evaluation of fiber, mass, and speck were 90%, 80%, and 98%, respectively. Contingency table analysis revealed significant association between observer and computer scores for microcalcifications (p<5%) but not for masses and fibers. Conclusions: The developed method may achieve a stable assessment of visibility for test objects in mammographic accreditation phantom image in whether the phantom image meets the ACR's criteria in the evaluation test, although there is room left for improvement in the approach for fiber and mass objects.« less

Polarization-sensitive optical coherence tomography-based imaging, parameterization, and quantification of human cartilage degeneration

NASA Astrophysics Data System (ADS)

Brill, Nicolai; Wirtz, Mathias; Merhof, Dorit; Tingart, Markus; Jahr, Holger; Truhn, Daniel; Schmitt, Robert; Nebelung, Sven

2016-07-01

Polarization-sensitive optical coherence tomography (PS-OCT) is a light-based, high-resolution, real-time, noninvasive, and nondestructive imaging modality yielding quasimicroscopic cross-sectional images of cartilage. As yet, comprehensive parameterization and quantification of birefringence and tissue properties have not been performed on human cartilage. PS-OCT and algorithm-based image analysis were used to objectively grade human cartilage degeneration in terms of surface irregularity, tissue homogeneity, signal attenuation, as well as birefringence coefficient and band width, height, depth, and number. Degeneration-dependent changes were noted for the former three parameters exclusively, thereby questioning the diagnostic value of PS-OCT in the assessment of human cartilage degeneration.

Lessons learned in historical mapping of conifer and oak in the North Coast

Treesearch

Melissa V. Eitzel; Maggi Kelly; Lenya N. Quinn-Davidson

2015-01-01

Conifer encroachment into oak woodlands is becoming a pressing concern for oak conservation, particularly in California's north coast. We use Object-Based Image Analysis (OBIA) with historical aerial imagery from 1948 and recent high-spatial-resolution images from 2009 to explore the potential for mapping encroachment using remote sensing. We find that pre-...

A building extraction approach for Airborne Laser Scanner data utilizing the Object Based Image Analysis paradigm

NASA Astrophysics Data System (ADS)

Tomljenovic, Ivan; Tiede, Dirk; Blaschke, Thomas

2016-10-01

In the past two decades Object-Based Image Analysis (OBIA) established itself as an efficient approach for the classification and extraction of information from remote sensing imagery and, increasingly, from non-image based sources such as Airborne Laser Scanner (ALS) point clouds. ALS data is represented in the form of a point cloud with recorded multiple returns and intensities. In our work, we combined OBIA with ALS point cloud data in order to identify and extract buildings as 2D polygons representing roof outlines in a top down mapping approach. We performed rasterization of the ALS data into a height raster for the purpose of the generation of a Digital Surface Model (DSM) and a derived Digital Elevation Model (DEM). Further objects were generated in conjunction with point statistics from the linked point cloud. With the use of class modelling methods, we generated the final target class of objects representing buildings. The approach was developed for a test area in Biberach an der Riß (Germany). In order to point out the possibilities of the adaptation-free transferability to another data set, the algorithm has been applied ;as is; to the ISPRS Benchmarking data set of Toronto (Canada). The obtained results show high accuracies for the initial study area (thematic accuracies of around 98%, geometric accuracy of above 80%). The very high performance within the ISPRS Benchmark without any modification of the algorithm and without any adaptation of parameters is particularly noteworthy.

The information extraction of Gannan citrus orchard based on the GF-1 remote sensing image

NASA Astrophysics Data System (ADS)

Wang, S.; Chen, Y. L.

2017-02-01

The production of Gannan oranges is the largest in China, which occupied an important part in the world. The extraction of citrus orchard quickly and effectively has important significance for fruit pathogen defense, fruit production and industrial planning. The traditional spectra extraction method of citrus orchard based on pixel has a lower classification accuracy, difficult to avoid the “pepper phenomenon”. In the influence of noise, the phenomenon that different spectrums of objects have the same spectrum is graveness. Taking Xunwu County citrus fruit planting area of Ganzhou as the research object, aiming at the disadvantage of the lower accuracy of the traditional method based on image element classification method, a decision tree classification method based on object-oriented rule set is proposed. Firstly, multi-scale segmentation is performed on the GF-1 remote sensing image data of the study area. Subsequently the sample objects are selected for statistical analysis of spectral features and geometric features. Finally, combined with the concept of decision tree classification, a variety of empirical values of single band threshold, NDVI, band combination and object geometry characteristics are used hierarchically to execute the information extraction of the research area, and multi-scale segmentation and hierarchical decision tree classification is implemented. The classification results are verified with the confusion matrix, and the overall Kappa index is 87.91%.

3D shape recovery of a newborn skull using thin-plate splines.

PubMed

Lapeer, R J; Prager, R W

2000-01-01

The objective of this paper is to construct a mesh-model of a newborn skull for finite element analysis to study its deformation when subjected to the forces present during labour. The current state of medical imaging technology has reached a level which allows accurate visualisation and shape recovery of biological organs and body-parts. However, a sufficiently large set of medical images cannot always be obtained, often because of practical or ethical reasons, and the requirement to recover the shape of the biological object of interest has to be met by other means. Such is the case for a newborn skull. A method to recover the three-dimensional (3D) shape from (minimum) two orthogonal atlas images of the object of interest and a homologous object is described. This method is based on matching landmarks and curves on the orthogonal images of the object of interest with corresponding landmarks and curves on the homologous or 'master'-object which is fully defined in 3D space. On the basis of this set of corresponding landmarks, a thin-plate spline function can be derived to warp from the 'master'-object space to the 'slave'-object space. This method is applied to recover the 3D shape of a newborn skull. Images from orthogonal view-planes are obtained from an atlas. The homologous object is an adult skull, obtained from CT-images made available by the Visible Human Project. After shape recovery, a mesh-model of the newborn skull is generated.

Mapping Nearshore Seagrass and Colonized Hard Bottom Spatial Distribution and Percent Biological Cover in Florida, USA Using Object Based Image Analysis of WorldView-2 Satellite Imagery

NASA Astrophysics Data System (ADS)

Baumstark, R. D.; Duffey, R.; Pu, R.

2016-12-01

The offshore extent of seagrass habitat along the West Florida (USA) coast represents an important corridor for inshore-offshore migration of economically important fish and shellfish. Surviving at the fringe of light requirements, offshore seagrass beds are sensitive to changes in water clarity. Beyond and intermingled with the offshore seagrass areas are large swaths of colonized hard bottom. These offshore habitats of the West Florida coast have lacked mapping efforts needed for status and trends monitoring. The objective of this study was to propose an object-based classification method for mapping offshore habitats and to compare results to traditional photo-interpreted maps. Benthic maps depicting the spatial distribution and percent biological cover were created from WorldView-2 satellite imagery using Object Based Image Analysis (OBIA) method and a visual photo-interpretation method. A logistic regression analysis identified depth and distance from shore as significant parameters for discriminating spectrally similar seagrass and colonized hard bottom features. Seagrass, colonized hard bottom and unconsolidated sediment (sand) were mapped with 78% overall accuracy using the OBIA method compared to 71% overall accuracy using the photo-interpretation method. This study presents an alternative for mapping deeper, offshore habitats capable of producing higher thematic (percent biological cover) and spatial resolution maps compared to those created with the traditional photo-interpretation method.

Evaluation of automated threshold selection methods for accurately sizing microscopic fluorescent cells by image analysis.

PubMed Central

Sieracki, M E; Reichenbach, S E; Webb, K L

1989-01-01

The accurate measurement of bacterial and protistan cell biomass is necessary for understanding their population and trophic dynamics in nature. Direct measurement of fluorescently stained cells is often the method of choice. The tedium of making such measurements visually on the large numbers of cells required has prompted the use of automatic image analysis for this purpose. Accurate measurements by image analysis require an accurate, reliable method of segmenting the image, that is, distinguishing the brightly fluorescing cells from a dark background. This is commonly done by visually choosing a threshold intensity value which most closely coincides with the outline of the cells as perceived by the operator. Ideally, an automated method based on the cell image characteristics should be used. Since the optical nature of edges in images of light-emitting, microscopic fluorescent objects is different from that of images generated by transmitted or reflected light, it seemed that automatic segmentation of such images may require special considerations. We tested nine automated threshold selection methods using standard fluorescent microspheres ranging in size and fluorescence intensity and fluorochrome-stained samples of cells from cultures of cyanobacteria, flagellates, and ciliates. The methods included several variations based on the maximum intensity gradient of the sphere profile (first derivative), the minimum in the second derivative of the sphere profile, the minimum of the image histogram, and the midpoint intensity. Our results indicated that thresholds determined visually and by first-derivative methods tended to overestimate the threshold, causing an underestimation of microsphere size. The method based on the minimum of the second derivative of the profile yielded the most accurate area estimates for spheres of different sizes and brightnesses and for four of the five cell types tested. A simple model of the optical properties of fluorescing objects and the video acquisition system is described which explains how the second derivative best approximates the position of the edge. Images PMID:2516431

DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research

PubMed Central

Clunie, David; Ulrich, Ethan; Bauer, Christian; Wahle, Andreas; Brown, Bartley; Onken, Michael; Riesmeier, Jörg; Pieper, Steve; Kikinis, Ron; Buatti, John; Beichel, Reinhard R.

2016-01-01

Background. Imaging biomarkers hold tremendous promise for precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation tasks motivate integration of the clinical and imaging data, and the use of standardized approaches to support annotation and sharing of the analysis results and semantics. We developed the methodology and tools to support these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging and Communications in Medicine (DICOM®) international standard and free open-source software. Methods. Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor using manual and semi-automatic approaches, automatic segmentation of the reference regions, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data. Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results and relevant clinical data. A number of correction proposals to the standard were developed. The open-source DICOM toolkit (DCMTK) was improved to simplify the task of DICOM encoding by introducing new API abstractions. Conversion and visualization tools utilizing this toolkit were developed. The encoded objects were validated for consistency and interoperability. The resulting dataset was deposited in the QIN-HEADNECK collection of The Cancer Imaging Archive (TCIA). Supporting tools for data analysis and DICOM conversion were made available as free open-source software. Discussion. We presented a detailed investigation of the development and application of the DICOM model, as well as the supporting open-source tools and toolkits, to accommodate representation of the research data in QI biomarker development. We demonstrated that the DICOM standard can be used to represent the types of data relevant in HNC QI biomarker development, and encode their complex relationships. The resulting annotated objects are amenable to data mining applications, and are interoperable with a variety of systems that support the DICOM standard. PMID:27257542

The adaptive parallel UKF inversion method for the shape of space objects based on the ground-based photometric data

NASA Astrophysics Data System (ADS)

Du, Xiaoping; Wang, Yang; Liu, Hao

2018-04-01

The space object in highly elliptical orbit is always presented as an image point on the ground-based imaging equipment so that it is difficult to resolve and identify the shape and attitude directly. In this paper a novel algorithm is presented for the estimation of spacecraft shape. The apparent magnitude model suitable for the inversion of object information such as shape and attitude is established based on the analysis of photometric characteristics. A parallel adaptive shape inversion algorithm based on UKF was designed after the achievement of dynamic equation of the nonlinear, Gaussian system involved with the influence of various dragging forces. The result of a simulation study demonstrate the viability and robustness of the new filter and its fast convergence rate. It realizes the inversion of combination shape with high accuracy, especially for the bus of cube and cylinder. Even though with sparse photometric data, it still can maintain a higher success rate of inversion.

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

PubMed Central

Holmström, Oscar; Linder, Nina; Ngasala, Billy; Mårtensson, Andreas; Linder, Ewert; Lundin, Mikael; Moilanen, Hannu; Suutala, Antti; Diwan, Vinod; Lundin, Johan

2017-01-01

ABSTRACT Background: Microscopy remains the gold standard in the diagnosis of neglected tropical diseases. As resource limited, rural areas often lack laboratory equipment and trained personnel, new diagnostic techniques are needed. Low-cost, point-of-care imaging devices show potential in the diagnosis of these diseases. Novel, digital image analysis algorithms can be utilized to automate sample analysis. Objective: Evaluation of the imaging performance of a miniature digital microscopy scanner for the diagnosis of soil-transmitted helminths and Schistosoma haematobium, and training of a deep learning-based image analysis algorithm for automated detection of soil-transmitted helminths in the captured images. Methods: A total of 13 iodine-stained stool samples containing Ascaris lumbricoides, Trichuris trichiura and hookworm eggs and 4 urine samples containing Schistosoma haematobium were digitized using a reference whole slide-scanner and the mobile microscopy scanner. Parasites in the images were identified by visual examination and by analysis with a deep learning-based image analysis algorithm in the stool samples. Results were compared between the digital and visual analysis of the images showing helminth eggs. Results: Parasite identification by visual analysis of digital slides captured with the mobile microscope was feasible for all analyzed parasites. Although the spatial resolution of the reference slide-scanner is higher, the resolution of the mobile microscope is sufficient for reliable identification and classification of all parasites studied. Digital image analysis of stool sample images captured with the mobile microscope showed high sensitivity for detection of all helminths studied (range of sensitivity = 83.3–100%) in the test set (n = 217) of manually labeled helminth eggs. Conclusions: In this proof-of-concept study, the imaging performance of a mobile, digital microscope was sufficient for visual detection of soil-transmitted helminths and Schistosoma haematobium. Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images. PMID:28838305

Novel CT-based objective imaging biomarkers of long term radiation-induced lung damage.

PubMed

Veiga, Catarina; Landau, David; Devaraj, Anand; Doel, Tom; White, Jared; Ngai, Yenting; Hawkes, David J; McClelland, Jamie R

2018-06-14

and Purpose: Recent improvements in lung cancer survival have spurred an interest in understanding and minimizing long term radiation-induced lung damage (RILD). However, there is still no objective criteria to quantify RILD leading to variable reporting across centres and trials. We propose a set of objective imaging biomarkers to quantify common radiological findings observed 12-months after lung cancer radiotherapy (RT). Baseline and 12-month CT scans of 27 patients from a phase I/II clinical trial of isotoxic chemoradiation were included in this study. To detect and measure the severity of RILD, twelve quantitative imaging biomarkers were developed. These describe basic CT findings including parenchymal change, volume reduction and pleural change. The imaging biomarkers were implemented as semi-automated image analysis pipelines and assessed against visual assessment of the occurrence of each change. The majority of the biomarkers were measurable in each patient. Their continuous nature allows objective scoring of severity for each patient. For each imaging biomarker the cohort was split into two groups according to the presence or absence of the biomarker by visual assessment, testing the hypothesis that the imaging biomarkers were different in these two groups. All features were statistically significant except for rotation of the main bronchus and diaphragmatic curvature. The majority of the biomarkers were not strongly correlated with each other suggesting that each of the biomarkers is measuring a separate element of RILD pathology. We developed objective CT-based imaging biomarkers that quantify the severity of radiological lung damage after RT. These biomarkers are representative of typical radiological findings of RILD. Copyright © 2018. Published by Elsevier Inc.

Robust object tracking techniques for vision-based 3D motion analysis applications

NASA Astrophysics Data System (ADS)

Knyaz, Vladimir A.; Zheltov, Sergey Y.; Vishnyakov, Boris V.

2016-04-01

Automated and accurate spatial motion capturing of an object is necessary for a wide variety of applications including industry and science, virtual reality and movie, medicine and sports. For the most part of applications a reliability and an accuracy of the data obtained as well as convenience for a user are the main characteristics defining the quality of the motion capture system. Among the existing systems for 3D data acquisition, based on different physical principles (accelerometry, magnetometry, time-of-flight, vision-based), optical motion capture systems have a set of advantages such as high speed of acquisition, potential for high accuracy and automation based on advanced image processing algorithms. For vision-based motion capture accurate and robust object features detecting and tracking through the video sequence are the key elements along with a level of automation of capturing process. So for providing high accuracy of obtained spatial data the developed vision-based motion capture system "Mosca" is based on photogrammetric principles of 3D measurements and supports high speed image acquisition in synchronized mode. It includes from 2 to 4 technical vision cameras for capturing video sequences of object motion. The original camera calibration and external orientation procedures provide the basis for high accuracy of 3D measurements. A set of algorithms as for detecting, identifying and tracking of similar targets, so for marker-less object motion capture is developed and tested. The results of algorithms' evaluation show high robustness and high reliability for various motion analysis tasks in technical and biomechanics applications.

Implementation of a Synchronized Oscillator Circuit for Fast Sensing and Labeling of Image Objects

PubMed Central

Kowalski, Jacek; Strzelecki, Michal; Kim, Hyongsuk

2011-01-01

We present an application-specific integrated circuit (ASIC) CMOS chip that implements a synchronized oscillator cellular neural network with a matrix size of 32 × 32 for object sensing and labeling in binary images. Networks of synchronized oscillators are a recently developed tool for image segmentation and analysis. Its parallel network operation is based on a “temporary correlation” theory that attempts to describe scene recognition as if performed by the human brain. The synchronized oscillations of neuron groups attract a person’s attention if he or she is focused on a coherent stimulus (image object). For more than one perceived stimulus, these synchronized patterns switch in time between different neuron groups, thus forming temporal maps that code several features of the analyzed scene. In this paper, a new oscillator circuit based on a mathematical model is proposed, and the network architecture and chip functional blocks are presented and discussed. The proposed chip is implemented in AMIS 0.35 μm C035M-D 5M/1P technology. An application of the proposed network chip for the segmentation of insulin-producing pancreatic islets in magnetic resonance liver images is presented. PMID:22163803

Feature Selection for Object-Based Classification of High-Resolution Remote Sensing Images Based on the Combination of a Genetic Algorithm and Tabu Search

PubMed Central

Shi, Lei; Wan, Youchuan; Gao, Xianjun

2018-01-01

In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy. PMID:29581721

Objective definition of rosette shape variation using a combined computer vision and data mining approach.

PubMed

Camargo, Anyela; Papadopoulou, Dimitra; Spyropoulou, Zoi; Vlachonasios, Konstantinos; Doonan, John H; Gay, Alan P

2014-01-01

Computer-vision based measurements of phenotypic variation have implications for crop improvement and food security because they are intrinsically objective. It should be possible therefore to use such approaches to select robust genotypes. However, plants are morphologically complex and identification of meaningful traits from automatically acquired image data is not straightforward. Bespoke algorithms can be designed to capture and/or quantitate specific features but this approach is inflexible and is not generally applicable to a wide range of traits. In this paper, we have used industry-standard computer vision techniques to extract a wide range of features from images of genetically diverse Arabidopsis rosettes growing under non-stimulated conditions, and then used statistical analysis to identify those features that provide good discrimination between ecotypes. This analysis indicates that almost all the observed shape variation can be described by 5 principal components. We describe an easily implemented pipeline including image segmentation, feature extraction and statistical analysis. This pipeline provides a cost-effective and inherently scalable method to parameterise and analyse variation in rosette shape. The acquisition of images does not require any specialised equipment and the computer routines for image processing and data analysis have been implemented using open source software. Source code for data analysis is written using the R package. The equations to calculate image descriptors have been also provided.

Methods of Hematoxylin and Erosin Image Information Acquisition and Optimization in Confocal Microscopy

PubMed Central

Yoon, Woong Bae; Kim, Hyunjin; Kim, Kwang Gi; Choi, Yongdoo; Chang, Hee Jin

2016-01-01

Objectives We produced hematoxylin and eosin (H&E) staining-like color images by using confocal laser scanning microscopy (CLSM), which can obtain the same or more information in comparison to conventional tissue staining. Methods We improved images by using several image converting techniques, including morphological methods, color space conversion methods, and segmentation methods. Results An image obtained after image processing showed coloring very similar to that in images produced by H&E staining, and it is advantageous to conduct analysis through fluorescent dye imaging and microscopy rather than analysis based on single microscopic imaging. Conclusions The colors used in CLSM are different from those seen in H&E staining, which is the method most widely used for pathologic diagnosis and is familiar to pathologists. Computer technology can facilitate the conversion of images by CLSM to be very similar to H&E staining images. We believe that the technique used in this study has great potential for application in clinical tissue analysis. PMID:27525165

An ECG storage and retrieval system embedded in client server HIS utilizing object-oriented DB.

PubMed

Wang, C; Ohe, K; Sakurai, T; Nagase, T; Kaihara, S

1996-02-01

In the University of Tokyo Hospital, the improved client server HIS has been applied to clinical practice and physicians can order prescription, laboratory examination, ECG examination and radiographic examination, etc. directly by themselves and read results of these examinations, except medical signal waves, schema and image, on UNIX workstations. Recently, we designed and developed an ECG storage and retrieval system embedded in the client server HIS utilizing object-oriented database to take the first step in dealing with digitized signal, schema and image data and show waves, graphics, and images directly to physicians by the client server HIS. The system was developed based on object-oriented analysis and design, and implemented with object-oriented database management system (OODMS) and C++ programming language. In this paper, we describe the ECG data model, functions of the storage and retrieval system, features of user interface and the result of its implementation in the HIS.

A motion artefact study and locally deforming objects in computerized tomography

NASA Astrophysics Data System (ADS)

Hahn, Bernadette N.

2017-11-01

Movements of the object during the data collection in computerized tomography can introduce motion artefacts in the reconstructed image. They can be reduced by employing information about the dynamic behaviour within the reconstruction step. However, inaccuracies concerning the movement are inevitable in practice. In this article, we give an explicit characterization of what is visible in an image obtained by a reconstruction algorithm with incorrect motion information. Then, we use this result to study in detail the situation of locally deforming objects, i.e. individual parts of the object have a different dynamic behaviour. In this context, we prove that additional artefacts arise due to the global nature of the Radon transform, even if the motion is exactly known. Based on our analysis, we propose a numerical scheme to reduce these artefacts in the reconstructed image. All our results are illustrated by numerical examples.

The optimal algorithm for Multi-source RS image fusion.

PubMed

Fu, Wei; Huang, Shui-Guang; Li, Zeng-Shun; Shen, Hao; Li, Jun-Shuai; Wang, Peng-Yuan

2016-01-01

In order to solve the issue which the fusion rules cannot be self-adaptively adjusted by using available fusion methods according to the subsequent processing requirements of Remote Sensing (RS) image, this paper puts forward GSDA (genetic-iterative self-organizing data analysis algorithm) by integrating the merit of genetic arithmetic together with the advantage of iterative self-organizing data analysis algorithm for multi-source RS image fusion. The proposed algorithm considers the wavelet transform of the translation invariance as the model operator, also regards the contrast pyramid conversion as the observed operator. The algorithm then designs the objective function by taking use of the weighted sum of evaluation indices, and optimizes the objective function by employing GSDA so as to get a higher resolution of RS image. As discussed above, the bullet points of the text are summarized as follows.•The contribution proposes the iterative self-organizing data analysis algorithm for multi-source RS image fusion.•This article presents GSDA algorithm for the self-adaptively adjustment of the fusion rules.•This text comes up with the model operator and the observed operator as the fusion scheme of RS image based on GSDA. The proposed algorithm opens up a novel algorithmic pathway for multi-source RS image fusion by means of GSDA.

Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images

NASA Astrophysics Data System (ADS)

Ardila, Juan P.; Bijker, Wietske; Tolpekin, Valentyn A.; Stein, Alfred

2012-04-01

Municipalities need accurate and updated inventories of urban vegetation in order to manage green resources and estimate their return on investment in urban forestry activities. Earlier studies have shown that semi-automatic tree detection using remote sensing is a challenging task. This study aims to develop a reproducible geographic object-based image analysis (GEOBIA) methodology to locate and delineate tree crowns in urban areas using high resolution imagery. We propose a GEOBIA approach that considers the spectral, spatial and contextual characteristics of tree objects in the urban space. The study presents classification rules that exploit object features at multiple segmentation scales modifying the labeling and shape of image-objects. The GEOBIA methodology was implemented on QuickBird images acquired over the cities of Enschede and Delft (The Netherlands), resulting in an identification rate of 70% and 82% respectively. False negative errors concentrated on small trees and false positive errors in private gardens. The quality of crown boundaries was acceptable, with an overall delineation error <0.24 outside of gardens and backyards.

Hybrid Optimization of Object-Based Classification in High-Resolution Images Using Continous ANT Colony Algorithm with Emphasis on Building Detection

NASA Astrophysics Data System (ADS)

Tamimi, E.; Ebadi, H.; Kiani, A.

2017-09-01

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

Saliency-Guided Change Detection of Remotely Sensed Images Using Random Forest

NASA Astrophysics Data System (ADS)

Feng, W.; Sui, H.; Chen, X.

2018-04-01

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

Detection of reflecting surfaces by a statistical model

NASA Astrophysics Data System (ADS)

He, Qiang; Chu, Chee-Hung H.

2009-02-01

Remote sensing is widely used assess the destruction from natural disasters and to plan relief and recovery operations. How to automatically extract useful features and segment interesting objects from digital images, including remote sensing imagery, becomes a critical task for image understanding. Unfortunately, current research on automated feature extraction is ignorant of contextual information. As a result, the fidelity of populating attributes corresponding to interesting features and objects cannot be satisfied. In this paper, we present an exploration on meaningful object extraction integrating reflecting surfaces. Detection of specular reflecting surfaces can be useful in target identification and then can be applied to environmental monitoring, disaster prediction and analysis, military, and counter-terrorism. Our method is based on a statistical model to capture the statistical properties of specular reflecting surfaces. And then the reflecting surfaces are detected through cluster analysis.

Application of composite small calibration objects in traffic accident scene photogrammetry.

PubMed

Chen, Qiang; Xu, Hongguo; Tan, Lidong

2015-01-01

In order to address the difficulty of arranging large calibration objects and the low measurement accuracy of small calibration objects in traffic accident scene photogrammetry, a photogrammetric method based on a composite of small calibration objects is proposed. Several small calibration objects are placed around the traffic accident scene, and the coordinate system of the composite calibration object is given based on one of them. By maintaining the relative position and coplanar relationship of the small calibration objects, the local coordinate system of each small calibration object is transformed into the coordinate system of the composite calibration object. The two-dimensional direct linear transformation method is improved based on minimizing the reprojection error of the calibration points of all objects. A rectified image is obtained using the nonlinear optimization method. The increased accuracy of traffic accident scene photogrammetry using a composite small calibration object is demonstrated through the analysis of field experiments and case studies.

Multiobject relative fuzzy connectedness and its implications in image segmentation

NASA Astrophysics Data System (ADS)

Udupa, Jayaram K.; Saha, Punam K.

2001-07-01

The notion of fuzzy connectedness captures the idea of hanging-togetherness of image elements in an object by assigning a strength of connectedness to every possible path between every possible pair of image elements. This concept leads to powerful image segmentation algorithms based on dynamic programming whose effectiveness has been demonstrated on 1000s of images in a variety of applications. In a previous framework, we introduced the notion of relative fuzzy connectedness for separating a foreground object from a background object. In this framework, an image element c is considered to belong to that among these two objects with respect to whose reference image element c has the higher strength of connectedness. In fuzzy connectedness, a local fuzzy reflation called affinity is used on the image domain. This relation was required for theoretical reasons to be of fixed form in the previous framework. In the present paper, we generalize relative connectedness to multiple objects, allowing all objects (of importance) to compete among themselves to grab membership of image elements based on their relative strength of connectedness to reference elements. We also allow affinity to be tailored to the individual objects. We present a theoretical and algorithmic framework and demonstrate that the objects defined are independent of the reference elements chosen as long as they are not in the fuzzy boundary between objects. Examples from medical imaging are presented to illustrate visually the effectiveness of multiple object relative fuzzy connectedness. A quantitative evaluation based on 160 mathematical phantom images demonstrates objectively the effectiveness of relative fuzzy connectedness with object- tailored affinity relation.

Microscopic image analysis for reticulocyte based on watershed algorithm

NASA Astrophysics Data System (ADS)

Wang, J. Q.; Liu, G. F.; Liu, J. G.; Wang, G.

2007-12-01

We present a watershed-based algorithm in the analysis of light microscopic image for reticulocyte (RET), which will be used in an automated recognition system for RET in peripheral blood. The original images, obtained by micrography, are segmented by modified watershed algorithm and are recognized in term of gray entropy and area of connective area. In the process of watershed algorithm, judgment conditions are controlled according to character of the image, besides, the segmentation is performed by morphological subtraction. The algorithm was simulated with MATLAB software. It is similar for automated and manual scoring and there is good correlation(r=0.956) between the methods, which is resulted from 50 pieces of RET images. The result indicates that the algorithm for peripheral blood RETs is comparable to conventional manual scoring, and it is superior in objectivity. This algorithm avoids time-consuming calculation such as ultra-erosion and region-growth, which will speed up the computation consequentially.

Subpixel based defocused points removal in photon-limited volumetric dataset

NASA Astrophysics Data System (ADS)

Muniraj, Inbarasan; Guo, Changliang; Malallah, Ra'ed; Maraka, Harsha Vardhan R.; Ryle, James P.; Sheridan, John T.

2017-03-01

The asymptotic property of the maximum likelihood estimator (MLE) has been utilized to reconstruct three-dimensional (3D) sectional images in the photon counting imaging (PCI) regime. At first, multiple 2D intensity images, known as Elemental images (EI), are captured. Then the geometric ray-tracing method is employed to reconstruct the 3D sectional images at various depth cues. We note that a 3D sectional image consists of both focused and defocused regions, depending on the reconstructed depth position. The defocused portion is redundant and should be removed in order to facilitate image analysis e.g., 3D object tracking, recognition, classification and navigation. In this paper, we present a subpixel level three-step based technique (i.e. involving adaptive thresholding, boundary detection and entropy based segmentation) to discard the defocused sparse-samples from the reconstructed photon-limited 3D sectional images. Simulation results are presented demonstrating the feasibility and efficiency of the proposed method.

Object representations in ventral and dorsal visual streams: fMRI repetition effects depend on attention and part–whole configuration

PubMed Central

Thoma, Volker; Henson, Richard N.

2011-01-01

The effects of attention and object configuration on the neural responses to short-lag visual image repetition were investigated with fMRI. Attention to one of two object images in a prime display was cued spatially. The images were either intact or split vertically; a manipulation that negates the influence of view-based representations. A subsequent single intact probe image was named covertly. Behavioural priming observed as faster button presses was found for attended primes in both intact and split configurations, but only for uncued primes in the intact configuration. In a voxel-wise analysis, fMRI repetition suppression (RS) was observed in a left mid-fusiform region for attended primes, both intact and split, whilst a right intraparietal region showed repetition enhancement (RE) for intact primes, regardless of attention. In a factorial analysis across regions of interest (ROIs) defined from independent localiser contrasts, RS for attended objects in the ventral stream was significantly left-lateralised, whilst repetition effects in ventral and dorsal ROIs correlated with the amount of priming in specific conditions. These fMRI results extend hybrid theories of object recognition, implicating left ventral stream regions in analytic processing (requiring attention), consistent with prior hypotheses about hemispheric specialisation, and implicating dorsal stream regions in holistic processing (independent of attention). PMID:21554967

Phase contrast imaging simulation and measurements using polychromatic sources with small source-object distances

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

Golosio, Bruno; Carpinelli, Massimo; Masala, Giovanni Luca

Phase contrast imaging is a technique widely used in synchrotron facilities for nondestructive analysis. Such technique can also be implemented through microfocus x-ray tube systems. Recently, a relatively new type of compact, quasimonochromatic x-ray sources based on Compton backscattering has been proposed for phase contrast imaging applications. In order to plan a phase contrast imaging system setup, to evaluate the system performance and to choose the experimental parameters that optimize the image quality, it is important to have reliable software for phase contrast imaging simulation. Several software tools have been developed and tested against experimental measurements at synchrotron facilities devotedmore » to phase contrast imaging. However, many approximations that are valid in such conditions (e.g., large source-object distance, small transverse size of the object, plane wave approximation, monochromatic beam, and Gaussian-shaped source focal spot) are not generally suitable for x-ray tubes and other compact systems. In this work we describe a general method for the simulation of phase contrast imaging using polychromatic sources based on a spherical wave description of the beam and on a double-Gaussian model of the source focal spot, we discuss the validity of some possible approximations, and we test the simulations against experimental measurements using a microfocus x-ray tube on three types of polymers (nylon, poly-ethylene-terephthalate, and poly-methyl-methacrylate) at varying source-object distance. It will be shown that, as long as all experimental conditions are described accurately in the simulations, the described method yields results that are in good agreement with experimental measurements.« less

Mapping Robinia pseudoacacia forest health in the Yellow River delta by using high-resolution IKONOS imagery and object-based image analysis

NASA Astrophysics Data System (ADS)

Wang, Hong; Lu, Kaiyu; Pu, Ruiliang

2016-10-01

The Robinia pseudoacacia forest in the Yellow River delta of China has been planted since the 1970s, and a large area of dieback of the forest has occurred since the 1990s. To assess the condition of the R. pseudoacacia forest in three forest areas (i.e., Gudao, Machang, and Abandoned Yellow River) in the delta, we combined an estimation of scale parameters tool and geometry/topology assessment criteria to determine the optimal scale parameters, selected optimal predictive variables determined by stepwise discriminant analysis, and compared object-based image analysis (OBIA) and pixel-based approaches using IKONOS data. The experimental results showed that the optimal segmentation scale is 5 for both the Gudao and Machang forest areas, and 12 for the Abandoned Yellow River forest area. The results produced by the OBIA method were much better than those created by the pixel-based method. The overall accuracy of the OBIA method was 93.7% (versus 85.4% by the pixel-based) for Gudao, 89.0% (versus 72.7%) for Abandoned Yellow River, and 91.7% (versus 84.4%) for Machang. Our analysis results demonstrated that the OBIA method was an effective tool for rapidly mapping and assessing the health levels of forest.

An enhanced multi-view vertical line locus matching algorithm of object space ground primitives based on positioning consistency for aerial and space images

NASA Astrophysics Data System (ADS)

Zhang, Ka; Sheng, Yehua; Wang, Meizhen; Fu, Suxia

2018-05-01

The traditional multi-view vertical line locus (TMVLL) matching method is an object-space-based method that is commonly used to directly acquire spatial 3D coordinates of ground objects in photogrammetry. However, the TMVLL method can only obtain one elevation and lacks an accurate means of validating the matching results. In this paper, we propose an enhanced multi-view vertical line locus (EMVLL) matching algorithm based on positioning consistency for aerial or space images. The algorithm involves three components: confirming candidate pixels of the ground primitive in the base image, multi-view image matching based on the object space constraints for all candidate pixels, and validating the consistency of the object space coordinates with the multi-view matching result. The proposed algorithm was tested using actual aerial images and space images. Experimental results show that the EMVLL method successfully solves the problems associated with the TMVLL method, and has greater reliability, accuracy and computing efficiency.

CMEIAS color segmentation: an improved computing technology to process color images for quantitative microbial ecology studies at single-cell resolution.

PubMed

Gross, Colin A; Reddy, Chandan K; Dazzo, Frank B

2010-02-01

Quantitative microscopy and digital image analysis are underutilized in microbial ecology largely because of the laborious task to segment foreground object pixels from background, especially in complex color micrographs of environmental samples. In this paper, we describe an improved computing technology developed to alleviate this limitation. The system's uniqueness is its ability to edit digital images accurately when presented with the difficult yet commonplace challenge of removing background pixels whose three-dimensional color space overlaps the range that defines foreground objects. Image segmentation is accomplished by utilizing algorithms that address color and spatial relationships of user-selected foreground object pixels. Performance of the color segmentation algorithm evaluated on 26 complex micrographs at single pixel resolution had an overall pixel classification accuracy of 99+%. Several applications illustrate how this improved computing technology can successfully resolve numerous challenges of complex color segmentation in order to produce images from which quantitative information can be accurately extracted, thereby gain new perspectives on the in situ ecology of microorganisms. Examples include improvements in the quantitative analysis of (1) microbial abundance and phylotype diversity of single cells classified by their discriminating color within heterogeneous communities, (2) cell viability, (3) spatial relationships and intensity of bacterial gene expression involved in cellular communication between individual cells within rhizoplane biofilms, and (4) biofilm ecophysiology based on ribotype-differentiated radioactive substrate utilization. The stand-alone executable file plus user manual and tutorial images for this color segmentation computing application are freely available at http://cme.msu.edu/cmeias/ . This improved computing technology opens new opportunities of imaging applications where discriminating colors really matter most, thereby strengthening quantitative microscopy-based approaches to advance microbial ecology in situ at individual single-cell resolution.

Scale-based fuzzy connectivity: a novel image segmentation methodology and its validation

NASA Astrophysics Data System (ADS)

Saha, Punam K.; Udupa, Jayaram K.

1999-05-01

This paper extends a previously reported theory and algorithms for fuzzy connected object definition. It introduces `object scale' for determining the neighborhood size for defining affinity, the degree of local hanging togetherness between image elements. Object scale allows us to use a varying neighborhood size in different parts of the image. This paper argues that scale-based fuzzy connectivity is natural in object definition and demonstrates that this leads to a more effective object segmentation than without using scale in fuzzy concentrations. Affinity is described as consisting of a homogeneity-based and an object-feature- based component. Families of non scale-based and scale-based affinity relations are constructed. An effective method for giving a rough estimate of scale at different locations in the image is presented. The original theoretical and algorithmic framework remains more-or-less the same but considerably improved segmentations result. A quantitative statistical comparison between the non scale-based and the scale-based methods was made based on phantom images generated from patient MR brain studies by first segmenting the objects, and then by adding noise and blurring, and background component. Both the statistical and the subjective tests clearly indicate the superiority of scale- based method in capturing details and in robustness to noise.

Navigation integrity monitoring and obstacle detection for enhanced-vision systems

NASA Astrophysics Data System (ADS)

Korn, Bernd; Doehler, Hans-Ullrich; Hecker, Peter

2001-08-01

Typically, Enhanced Vision (EV) systems consist of two main parts, sensor vision and synthetic vision. Synthetic vision usually generates a virtual out-the-window view using databases and accurate navigation data, e. g. provided by differential GPS (DGPS). The reliability of the synthetic vision highly depends on both, the accuracy of the used database and the integrity of the navigation data. But especially in GPS based systems, the integrity of the navigation can't be guaranteed. Furthermore, only objects that are stored in the database can be displayed to the pilot. Consequently, unexpected obstacles are invisible and this might cause severe problems. Therefore, additional information has to be extracted from sensor data to overcome these problems. In particular, the sensor data analysis has to identify obstacles and has to monitor the integrity of databases and navigation. Furthermore, if a lack of integrity arises, navigation data, e.g. the relative position of runway and aircraft, has to be extracted directly from the sensor data. The main contribution of this paper is about the realization of these three sensor data analysis tasks within our EV system, which uses the HiVision 35 GHz MMW radar of EADS, Ulm as the primary EV sensor. For the integrity monitoring, objects extracted from radar images are registered with both database objects and objects (e. g. other aircrafts) transmitted via data link. This results in a classification into known and unknown radar image objects and consequently, in a validation of the integrity of database and navigation. Furthermore, special runway structures are searched for in the radar image where they should appear. The outcome of this runway check contributes to the integrity analysis, too. Concurrent to this investigation a radar image based navigation is performed without using neither precision navigation nor detailed database information to determine the aircraft's position relative to the runway. The performance of our approach is demonstrated with real data acquired during extensive flight tests to several airports in Northern Germany.

Experimental research of digital holographic microscopic measuring

NASA Astrophysics Data System (ADS)

Zhu, Xueliang; Chen, Feifei; Li, Jicheng

2013-06-01

Digital holography is a new imaging technique, which is developed on the base of optical holography, Digital processing, and Computer techniques. It is using CCD instead of the conventional silver to record hologram, and then reproducing the 3D contour of the object by the way of computer simulation. Compared with the traditional optical holographic, the whole process is of simple measuring, lower production cost, faster the imaging speed, and with the advantages of non-contact real-time measurement. At present, it can be used in the fields of the morphology detection of tiny objects, micro deformation analysis, and biological cells shape measurement. It is one of the research hot spot at home and abroad. This paper introduced the basic principles and relevant theories about the optical holography and Digital holography, and researched the basic questions which influence the reproduce images in the process of recording and reconstructing of the digital holographic microcopy. In order to get a clear digital hologram, by analyzing the optical system structure, we discussed the recording distance and of the hologram. On the base of the theoretical studies, we established a measurement and analyzed the experimental conditions, then adjusted them to the system. To achieve a precise measurement of tiny object in three-dimension, we measured MEMS micro device for example, and obtained the reproduction three-dimensional contour, realized the three dimensional profile measurement of tiny object. According to the experiment results consider: analysis the reference factors between the zero-order term and a pair of twin-images by the choice of the object light and the reference light and the distance of the recording and reconstructing and the characteristics of reconstruction light on the measurement, the measurement errors were analyzed. The research result shows that the device owns certain reliability.

Diagnostic analysis of liver B ultrasonic texture features based on LM neural network

NASA Astrophysics Data System (ADS)

Chi, Qingyun; Hua, Hu; Liu, Menglin; Jiang, Xiuying

2017-03-01

In this study, B ultrasound images of 124 benign and malignant patients were randomly selected as the study objects. The B ultrasound images of the liver were treated by enhanced de-noising. By constructing the gray level co-occurrence matrix which reflects the information of each angle, Principal Component Analysis of 22 texture features were extracted and combined with LM neural network for diagnosis and classification. Experimental results show that this method is a rapid and effective diagnostic method for liver imaging, which provides a quantitative basis for clinical diagnosis of liver diseases.

Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects

NASA Astrophysics Data System (ADS)

Joseph, R.; Courbin, F.; Starck, J.-L.

2016-05-01

We introduce a new algorithm for colour separation and deblending of multi-band astronomical images called MuSCADeT which is based on Morpho-spectral Component Analysis of multi-band images. The MuSCADeT algorithm takes advantage of the sparsity of astronomical objects in morphological dictionaries such as wavelets and their differences in spectral energy distribution (SED) across multi-band observations. This allows us to devise a model independent and automated approach to separate objects with different colours. We show with simulations that we are able to separate highly blended objects and that our algorithm is robust against SED variations of objects across the field of view. To confront our algorithm with real data, we use HST images of the strong lensing galaxy cluster MACS J1149+2223 and we show that MuSCADeT performs better than traditional profile-fitting techniques in deblending the foreground lensing galaxies from background lensed galaxies. Although the main driver for our work is the deblending of strong gravitational lenses, our method is fit to be used for any purpose related to deblending of objects in astronomical images. An example of such an application is the separation of the red and blue stellar populations of a spiral galaxy in the galaxy cluster Abell 2744. We provide a python package along with all simulations and routines used in this paper to contribute to reproducible research efforts. Codes can be found at http://lastro.epfl.ch/page-126973.html

Miniature injection-molded optics for fiber-optic, in vivo confocal microscopy

NASA Astrophysics Data System (ADS)

Chidley, Matthew D.; Liang, Chen; Descour, Michael R.; Sung, Kung-Bin; Richards-Kortum, Rebecca R.; Gillenwater, Ann

2002-12-01

In collaboration with the Department of Biomedical Engineering at the University of Texas at Austin and the UT MD Anderson Cancer Center, a laser scanning fiber confocal reflectance microscope (FCRM) system has been designed and tested for in vivo detection of cervical and oral pre-cancers. This system along with specially developed diagnosis algorithms and techniques can achieve an unprecedented specificity and sensitivity for the diagnosis of pre-cancers in epithelial tissue. The FCRM imaging system consists of an NdYAG laser (1064 nm), scanning mirrors/optics, precision pinhole, detector, and an endoscopic probe (the objective). The objective is connected to the rest of the imaging system via a fiber bundle. The fiber bundle allows the rest of the system to be remotely positioned in a convenient location. Only the objective comes into contact with the patient. It is our intent that inexpensive mass-produced disposable endoscopic probes would be produced for large clinical trials. This paper touches on the general design process of developing a miniature, high numerical aperture, injection-molded (IM) objective. These IM optical designs are evaluated and modified based on manufacturing and application constraints. Based on these driving criteria, one specific optical design was chosen and a detailed tolerance analysis was conducted. The tolerance analysis was custom built to create a realistic statistical analysis for integrated IM lens elements that can be stacked one on top of another using micro-spheres resting in tiny circular grooves. These configurations allow each lens element to be rotated and possibly help compensate for predicted manufacturing errors. This research was supported by a grant from the National Institutes of Health (RO1 CA82880). Special thanks go to Applied Image Group/Optics for the numerous fabrication meetings concerning the miniature IM objective.

Determination of Surface Tension of Surfactant Solutions through Capillary Rise Measurements: An Image-Processing Undergraduate Laboratory Experiment

ERIC Educational Resources Information Center

Huck-Iriart, Cristia´n; De-Candia, Ariel; Rodriguez, Javier; Rinaldi, Carlos

2016-01-01

In this work, we described an image processing procedure for the measurement of surface tension of the air-liquid interface using isothermal capillary action. The experiment, designed for an undergraduate course, is based on the analysis of a series of solutions with diverse surfactant concentrations at different ionic strengths. The objective of…

Cerebrovascular plaque segmentation using object class uncertainty snake in MR images

NASA Astrophysics Data System (ADS)

Das, Bipul; Saha, Punam K.; Wolf, Ronald; Song, Hee Kwon; Wright, Alexander C.; Wehrli, Felix W.

2005-04-01

Atherosclerotic cerebrovascular disease leads to formation of lipid-laden plaques that can form emboli when ruptured causing blockage to cerebral vessels. The clinical manifestation of this event sequence is stroke; a leading cause of disability and death. In vivo MR imaging provides detailed image of vascular architecture for the carotid artery making it suitable for analysis of morphological features. Assessing the status of carotid arteries that supplies blood to the brain is of primary interest to such investigations. Reproducible quantification of carotid artery dimensions in MR images is essential for plaque analysis. Manual segmentation being the only method presently makes it time consuming and sensitive to inter and intra observer variability. This paper presents a deformable model for lumen and vessel wall segmentation of carotid artery from MR images. The major challenges of carotid artery segmentation are (a) low signal-to-noise ratio, (b) background intensity inhomogeneity and (c) indistinct inner and/or outer vessel wall. We propose a new, effective object-class uncertainty based deformable model with additional features tailored toward this specific application. Object-class uncertainty optimally utilizes MR intensity characteristics of various anatomic entities that enable the snake to avert leakage through fuzzy boundaries. To strengthen the deformable model for this application, some other properties are attributed to it in the form of (1) fully arc-based deformation using a Gaussian model to maximally exploit vessel wall smoothness, (2) construction of a forbidden region for outer-wall segmentation to reduce interferences by prominent lumen features and (3) arc-based landmark for efficient user interaction. The algorithm has been tested upon T1- and PD- weighted images. Measures of lumen area and vessel wall area are computed from segmented data of 10 patient MR images and their accuracy and reproducibility are examined. These results correspond exceptionally well with manual segmentation completed by radiology experts. Reproducibility of the proposed method is estimated for both intra- and inter-operator studies.

Automatic landslide detection from LiDAR DTM derivatives by geographic-object-based image analysis based on open-source software

NASA Astrophysics Data System (ADS)

Knevels, Raphael; Leopold, Philip; Petschko, Helene

2017-04-01

With high-resolution airborne Light Detection and Ranging (LiDAR) data more commonly available, many studies have been performed to facilitate the detailed information on the earth surface and to analyse its limitation. Specifically in the field of natural hazards, digital terrain models (DTM) have been used to map hazardous processes such as landslides mainly by visual interpretation of LiDAR DTM derivatives. However, new approaches are striving towards automatic detection of landslides to speed up the process of generating landslide inventories. These studies usually use a combination of optical imagery and terrain data, and are designed in commercial software packages such as ESRI ArcGIS, Definiens eCognition, or MathWorks MATLAB. The objective of this study was to investigate the potential of open-source software for automatic landslide detection based only on high-resolution LiDAR DTM derivatives in a study area within the federal state of Burgenland, Austria. The study area is very prone to landslides which have been mapped with different methodologies in recent years. The free development environment R was used to integrate open-source geographic information system (GIS) software, such as SAGA (System for Automated Geoscientific Analyses), GRASS (Geographic Resources Analysis Support System), or TauDEM (Terrain Analysis Using Digital Elevation Models). The implemented geographic-object-based image analysis (GEOBIA) consisted of (1) derivation of land surface parameters, such as slope, surface roughness, curvature, or flow direction, (2) finding optimal scale parameter by the use of an objective function, (3) multi-scale segmentation, (4) classification of landslide parts (main scarp, body, flanks) by k-mean thresholding, (5) assessment of the classification performance using a pre-existing landslide inventory, and (6) post-processing analysis for the further use in landslide inventories. The results of the developed open-source approach demonstrated good success rates to objectively detect landslides in high-resolution topography data by GEOBIA.

The NOAO Data Lab PHAT Photometry Database

NASA Astrophysics Data System (ADS)

Olsen, Knut; Williams, Ben; Fitzpatrick, Michael; PHAT Team

2018-01-01

We present a database containing both the combined photometric object catalog and the single epoch measurements from the Panchromatic Hubble Andromeda Treasury (PHAT). This database is hosted by the NOAO Data Lab (http://datalab.noao.edu), and as such exposes a number of data services to the PHAT photometry, including access through a Table Access Protocol (TAP) service, direct PostgreSQL queries, web-based and programmatic query interfaces, remote storage space for personal database tables and files, and a JupyterHub-based Notebook analysis environment, as well as image access through a Simple Image Access (SIA) service. We show how the Data Lab database and Jupyter Notebook environment allow for straightforward and efficient analyses of PHAT catalog data, including maps of object density, depth, and color, extraction of light curves of variable objects, and proper motion exploration.

RAPTOR-scan: Identifying and Tracking Objects Through Thousands of Sky Images

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

Davidoff, Sherri; Wozniak, Przemyslaw

2004-09-28

The RAPTOR-scan system mines data for optical transients associated with gamma-ray bursts and is used to create a catalog for the RAPTOR telescope system. RAPTOR-scan can detect and track individual astronomical objects across data sets containing millions of observed points.Accurately identifying a real object over many optical images (clustering the individual appearances) is necessary in order to analyze object light curves. To achieve this, RAPTOR telescope observations are sent in real time to a database. Each morning, a program based on the DBSCAN algorithm clusters the observations and labels each one with an object identifier. Once clustering is complete, themore » analysis program may be used to query the database and produce light curves, maps of the sky field, or other informative displays.Although RAPTOR-scan was designed for the RAPTOR optical telescope system, it is a general tool designed to identify objects in a collection of astronomical data and facilitate quick data analysis. RAPTOR-scan will be released as free software under the GNU General Public License.« less

Tracking target objects orbiting earth using satellite-based telescopes

DOEpatents

De Vries, Willem H; Olivier, Scot S; Pertica, Alexander J

2014-10-14

A system for tracking objects that are in earth orbit via a constellation or network of satellites having imaging devices is provided. An object tracking system includes a ground controller and, for each satellite in the constellation, an onboard controller. The ground controller receives ephemeris information for a target object and directs that ephemeris information be transmitted to the satellites. Each onboard controller receives ephemeris information for a target object, collects images of the target object based on the expected location of the target object at an expected time, identifies actual locations of the target object from the collected images, and identifies a next expected location at a next expected time based on the identified actual locations of the target object. The onboard controller processes the collected image to identify the actual location of the target object and transmits the actual location information to the ground controller.

Twelve tips for creating trigger images for problem-based learning cases.

PubMed

Azer, Samy A

2007-03-01

A trigger is the starting point of problem-based learning (PBL) cases. It is usually in the form of 5-6 text lines that provide the key information about the main character (usually the patient), including 3-4 of patient's presenting problems. In addition to the trigger text, most programs using PBL include a visual trigger. This might be in the form of a single image, a series of images, a video clip, a cartoon, or even one of the patient's investigation results (e.g. chest X-ray, pathology report, or urine sample analysis). The main educational objectives of the trigger image are as follows: (1) to introduce the patient to the students; (2) to enhance students' observation skills; (3) to provide them with new information to add to the cues obtained from the trigger text; and (4) to stimulate students to ask questions as they develop their enquiry plan. When planned and delivered effectively, trigger images should be engaging and stimulate group discussion. Understanding the educational objectives of using trigger images and choosing appropriate images are the keys for constructing successful PBL cases. These twelve tips highlight the key steps in the successful creation of trigger images.

Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment.

PubMed

Larkin, Timothy J; Canuto, Holly C; Kettunen, Mikko I; Booth, Thomas C; Hu, De-En; Krishnan, Anant S; Bohndiek, Sarah E; Neves, André A; McLachlan, Charles; Hobson, Michael P; Brindle, Kevin M

2014-01-01

The acquisition of ever increasing volumes of high resolution magnetic resonance imaging (MRI) data has created an urgent need to develop automated and objective image analysis algorithms that can assist in determining tumor margins, diagnosing tumor stage, and detecting treatment response. We have shown previously that Minkowski functionals, which are precise morphological and structural descriptors of image heterogeneity, can be used to enhance the detection, in T1 -weighted images, of a targeted Gd(3+) -chelate-based contrast agent for detecting tumor cell death. We have used Minkowski functionals here to characterize heterogeneity in T2 -weighted images acquired before and after drug treatment, and obtained without contrast agent administration. We show that Minkowski functionals can be used to characterize the changes in image heterogeneity that accompany treatment of tumors with a vascular disrupting agent, combretastatin A4-phosphate, and with a cytotoxic drug, etoposide. Parameterizing changes in the heterogeneity of T2 -weighted images can be used to detect early responses of tumors to drug treatment, even when there is no change in tumor size. The approach provides a quantitative and therefore objective assessment of treatment response that could be used with other types of MR image and also with other imaging modalities. Copyright © 2013 Wiley Periodicals, Inc.

Research on image complexity evaluation method based on color information

NASA Astrophysics Data System (ADS)

Wang, Hao; Duan, Jin; Han, Xue-hui; Xiao, Bo

2017-11-01

In order to evaluate the complexity of a color image more effectively and find the connection between image complexity and image information, this paper presents a method to compute the complexity of image based on color information.Under the complexity ,the theoretical analysis first divides the complexity from the subjective level, divides into three levels: low complexity, medium complexity and high complexity, and then carries on the image feature extraction, finally establishes the function between the complexity value and the color characteristic model. The experimental results show that this kind of evaluation method can objectively reconstruct the complexity of the image from the image feature research. The experimental results obtained by the method of this paper are in good agreement with the results of human visual perception complexity,Color image complexity has a certain reference value.

Vision-Based Geo-Monitoring - A New Approach for an Automated System

NASA Astrophysics Data System (ADS)

Wagner, A.; Reiterer, A.; Wasmeier, P.; Rieke-Zapp, D.; Wunderlich, T.

2012-04-01

The necessity for monitoring geo-risk areas such as rock slides is growing due to the increasing probability of such events caused by environmental change. Life with threat becomes to a calculable risk by geodetic deformation monitoring. An in-depth monitoring concept with modern measurement technologies allows the estimation of the hazard potential and the prediction of life-threatening situations. The movements can be monitored by sensors, placed in the unstable slope area. In most cases, it is necessary to enter the regions at risk in order to place the sensors and maintain them. Using long-range monitoring systems (e.g. terrestrial laser scanners, total stations, ground based synthetic aperture radar) allows avoiding this risk. To close the gap between the existing low-resolution, medium-accuracy sensors and conventional (co-operative target-based) surveying methods, image-assisted total stations (IATS) are a suggestive solution. IATS offer the user (e.g. metrology expert) an image capturing system (CCD/CMOS camera) in addition to 3D point measurements. The images of the telescope's visual field are projected onto the camera's chip. With appropriate calibration, these images are accurately geo-referenced and oriented since the horizontal and vertical angles of rotation are continuously recorded. The oriented images can directly be used for direction measurements with no need for object control points or further photogrammetric orientation processes. IATS are able to provide high density deformation fields with high accuracy (down to mm range), in all three coordinate directions. Tests have shown that with suitable image processing measurements a precision of 0.05 pixel ± 0.04·σ is possible (which corresponds to 0.03 mgon ± 0.04·σ). These results have to be seen under the consideration that such measurements are image-based only. For measuring in 3D object space the precision of pointing has to be taken into account. IATS can be used in two different ways: (1) combining two measurement systems and measuring object points by spatial intersection, or (2) using one measurement system and combining image-based techniques with the integrated distance measurement unit. Beside the system configuration, the detection of features inside the captured images can be done on the basis of different approaches, e.g. template-, edge-, and/or point-based methods. Our system is able to select a suitable algorithm based on different object characteristics, such as object geometry, texture, behaviour, etc. The long-term objective is the research, development and installation of a fully-automated measurement system, including a data analysis and interpretation component. Acknowledgments: The presented research has been supported by the Alexander von Humboldt Foundation, and by the European Sciences Foundation (ESF).

Covariance of lucky images for increasing objects contrast: diffraction-limited images in ground-based telescopes

NASA Astrophysics Data System (ADS)

Cagigal, Manuel P.; Valle, Pedro J.; Colodro-Conde, Carlos; Villó-Pérez, Isidro; Pérez-Garrido, Antonio

2016-01-01

Images of stars adopt shapes far from the ideal Airy pattern due to atmospheric density fluctuations. Hence, diffraction-limited images can only be achieved by telescopes without atmospheric influence, e.g. spatial telescopes, or by using techniques like adaptive optics or lucky imaging. In this paper, we propose a new computational technique based on the evaluation of the COvariancE of Lucky Images (COELI). This technique allows us to discover companions to main stars by taking advantage of the atmospheric fluctuations. We describe the algorithm and we carry out a theoretical analysis of the improvement in contrast. We have used images taken with 2.2-m Calar Alto telescope as a test bed for the technique resulting that, under certain conditions, telescope diffraction limit is clearly reached.

Determining the 3-D structure and motion of objects using a scanning laser range sensor

NASA Technical Reports Server (NTRS)

Nandhakumar, N.; Smith, Philip W.

1993-01-01

In order for the EVAHR robot to autonomously track and grasp objects, its vision system must be able to determine the 3-D structure and motion of an object from a sequence of sensory images. This task is accomplished by the use of a laser radar range sensor which provides dense range maps of the scene. Unfortunately, the currently available laser radar range cameras use a sequential scanning approach which complicates image analysis. Although many algorithms have been developed for recognizing objects from range images, none are suited for use with single beam, scanning, time-of-flight sensors because all previous algorithms assume instantaneous acquisition of the entire image. This assumption is invalid since the EVAHR robot is equipped with a sequential scanning laser range sensor. If an object is moving while being imaged by the device, the apparent structure of the object can be significantly distorted due to the significant non-zero delay time between sampling each image pixel. If an estimate of the motion of the object can be determined, this distortion can be eliminated; but, this leads to the motion-structure paradox - most existing algorithms for 3-D motion estimation use the structure of objects to parameterize their motions. The goal of this research is to design a rigid-body motion recovery technique which overcomes this limitation. The method being developed is an iterative, linear, feature-based approach which uses the non-zero image acquisition time constraint to accurately recover the motion parameters from the distorted structure of the 3-D range maps. Once the motion parameters are determined, the structural distortion in the range images is corrected.

Model-based iterative reconstruction in low-dose CT colonography-feasibility study in 65 patients for symptomatic investigation.

PubMed

Vardhanabhuti, Varut; James, Julia; Nensey, Rehaan; Hyde, Christopher; Roobottom, Carl

2015-05-01

To compare image quality on computed tomographic colonography (CTC) acquired at standard dose (STD) and low dose (LD) using filtered-back projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction (MBIR) techniques. A total of 65 symptomatic patients were prospectively enrolled for the study and underwent STD and LD CTC with filtered-back projection, adaptive statistical iterative reconstruction, and MBIR to allow direct per-patient comparison. Objective image noise, subjective image analyses, and polyp detection were assessed. Objective image noise analysis demonstrates significant noise reduction using MBIR technique (P < .05) despite being acquired at lower doses. Subjective image analyses were superior for LD MBIR in all parameters except visibility of extracolonic lesions (two-dimensional) and visibility of colonic wall (three-dimensional) where there were no significant differences. There was no significant difference in polyp detection rates (P > .05). Doses: LD (dose-length product, 257.7), STD (dose-length product, 483.6). LD MBIR CTC objectively shows improved image noise using parameters in our study. Subjectively, image quality is maintained. Polyp detection shows no significant difference but because of small numbers needs further validation. Average dose reduction of 47% can be achieved. This study confirms feasibility of using MBIR in this context of CTC in symptomatic population. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Poka Yoke system based on image analysis and object recognition

NASA Astrophysics Data System (ADS)

Belu, N.; Ionescu, L. M.; Misztal, A.; Mazăre, A.

2015-11-01

Poka Yoke is a method of quality management which is related to prevent faults from arising during production processes. It deals with “fail-sating” or “mistake-proofing”. The Poka-yoke concept was generated and developed by Shigeo Shingo for the Toyota Production System. Poka Yoke is used in many fields, especially in monitoring production processes. In many cases, identifying faults in a production process involves a higher cost than necessary cost of disposal. Usually, poke yoke solutions are based on multiple sensors that identify some nonconformities. This means the presence of different equipment (mechanical, electronic) on production line. As a consequence, coupled with the fact that the method itself is an invasive, affecting the production process, would increase its price diagnostics. The bulky machines are the means by which a Poka Yoke system can be implemented become more sophisticated. In this paper we propose a solution for the Poka Yoke system based on image analysis and identification of faults. The solution consists of a module for image acquisition, mid-level processing and an object recognition module using associative memory (Hopfield network type). All are integrated into an embedded system with AD (Analog to Digital) converter and Zync 7000 (22 nm technology).

Nationwide Hybrid Change Detection of Buildings

NASA Astrophysics Data System (ADS)

Hron, V.; Halounova, L.

2016-06-01

The Fundamental Base of Geographic Data of the Czech Republic (hereinafter FBGD) is a national 2D geodatabase at a 1:10,000 scale with more than 100 geographic objects. This paper describes the design of the permanent updating mechanism of buildings in FBGD. The proposed procedure belongs to the category of hybrid change detection (HCD) techniques which combine pixel-based and object-based evaluation. The main sources of information for HCD are cadastral information and bi-temporal vertical digital aerial photographs. These photographs have great information potential because they contain multispectral, position and also elevation information. Elevation information represents a digital surface model (DSM) which can be obtained using the image matching technique. Pixel-based evaluation of bi-temporal DSMs enables fast localization of places with potential building changes. These coarse results are subsequently classified through the object-based image analysis (OBIA) using spectral, textural and contextual features and GIS tools. The advantage of the two-stage evaluation is the pre-selection of locations where image segmentation (a computationally demanding part of OBIA) is performed. It is not necessary to apply image segmentation to the entire scene, but only to the surroundings of detected changes, which contributes to significantly faster processing and lower hardware requirements. The created technology is based on open-source software solutions that allow easy portability on multiple computers and parallelization of processing. This leads to significant savings of financial resources which can be expended on the further development of FBGD.

Iris recognition and what is next? Iris diagnosis: a new challenging topic for machine vision from image acquisition to image interpretation

NASA Astrophysics Data System (ADS)

Perner, Petra

2017-03-01

Molecular image-based techniques are widely used in medicine to detect specific diseases. Look diagnosis is an important issue but also the analysis of the eye plays an important role in order to detect specific diseases. These topics are important topics in medicine and the standardization of these topics by an automatic system can be a new challenging field for machine vision. Compared to iris recognition has the iris diagnosis much more higher demands for the image acquisition and interpretation of the iris. One understands by iris diagnosis (Iridology) the investigation and analysis of the colored part of the eye, the iris, to discover factors, which play an important role for the prevention and treatment of illnesses, but also for the preservation of an optimum health. An automatic system would pave the way for a much wider use of the iris diagnosis for the diagnosis of illnesses and for the purpose of individual health protection. With this paper, we describe our work towards an automatic iris diagnosis system. We describe the image acquisition and the problems with it. Different ways are explained for image acquisition and image preprocessing. We describe the image analysis method for the detection of the iris. The meta-model for image interpretation is given. Based on this model we show the many tasks for image analysis that range from different image-object feature analysis, spatial image analysis to color image analysis. Our first results for the recognition of the iris are given. We describe how detecting the pupil and not wanted lamp spots. We explain how to recognize orange blue spots in the iris and match them against the topological map of the iris. Finally, we give an outlook for further work.

Validation of voxel-based morphometry (VBM) based on MRI

NASA Astrophysics Data System (ADS)

Yang, Xueyu; Chen, Kewei; Guo, Xiaojuan; Yao, Li

2007-03-01

Voxel-based morphometry (VBM) is an automated and objective image analysis technique for detecting differences in regional concentration or volume of brain tissue composition based on structural magnetic resonance (MR) images. VBM has been used widely to evaluate brain morphometric differences between different populations, but there isn't an evaluation system for its validation until now. In this study, a quantitative and objective evaluation system was established in order to assess VBM performance. We recruited twenty normal volunteers (10 males and 10 females, age range 20-26 years, mean age 22.6 years). Firstly, several focal lesions (hippocampus, frontal lobe, anterior cingulate, back of hippocampus, back of anterior cingulate) were simulated in selected brain regions using real MRI data. Secondly, optimized VBM was performed to detect structural differences between groups. Thirdly, one-way ANOVA and post-hoc test were used to assess the accuracy and sensitivity of VBM analysis. The results revealed that VBM was a good detective tool in majority of brain regions, even in controversial brain region such as hippocampus in VBM study. Generally speaking, much more severity of focal lesion was, better VBM performance was. However size of focal lesion had little effects on VBM analysis.

Image BOSS: a biomedical object storage system

NASA Astrophysics Data System (ADS)

Stacy, Mahlon C.; Augustine, Kurt E.; Robb, Richard A.

1997-05-01

Researchers using biomedical images have data management needs which are oriented perpendicular to clinical PACS. The image BOSS system is designed to permit researchers to organize and select images based on research topic, image metadata, and a thumbnail of the image. Image information is captured from existing images in a Unix based filesystem, stored in an object oriented database, and presented to the user in a familiar laboratory notebook metaphor. In addition, the ImageBOSS is designed to provide an extensible infrastructure for future content-based queries directly on the images.

3D pose estimation and motion analysis of the articulated human hand-forearm limb in an industrial production environment

NASA Astrophysics Data System (ADS)

Hahn, Markus; Barrois, Björn; Krüger, Lars; Wöhler, Christian; Sagerer, Gerhard; Kummert, Franz

2010-09-01

This study introduces an approach to model-based 3D pose estimation and instantaneous motion analysis of the human hand-forearm limb in the application context of safe human-robot interaction. 3D pose estimation is performed using two approaches: The Multiocular Contracting Curve Density (MOCCD) algorithm is a top-down technique based on pixel statistics around a contour model projected into the images from several cameras. The Iterative Closest Point (ICP) algorithm is a bottom-up approach which uses a motion-attributed 3D point cloud to estimate the object pose. Due to their orthogonal properties, a fusion of these algorithms is shown to be favorable. The fusion is performed by a weighted combination of the extracted pose parameters in an iterative manner. The analysis of object motion is based on the pose estimation result and the motion-attributed 3D points belonging to the hand-forearm limb using an extended constraint-line approach which does not rely on any temporal filtering. A further refinement is obtained using the Shape Flow algorithm, a temporal extension of the MOCCD approach, which estimates the temporal pose derivative based on the current and the two preceding images, corresponding to temporal filtering with a short response time of two or at most three frames. Combining the results of the two motion estimation stages provides information about the instantaneous motion properties of the object. Experimental investigations are performed on real-world image sequences displaying several test persons performing different working actions typically occurring in an industrial production scenario. In all example scenes, the background is cluttered, and the test persons wear various kinds of clothes. For evaluation, independently obtained ground truth data are used. [Figure not available: see fulltext.

The design and performance characteristics of a cellular logic 3-D image classification processor

NASA Astrophysics Data System (ADS)

Ankeney, L. A.

1981-04-01

The introduction of high resolution scanning laser radar systems which are capable of collecting range and reflectivity images, is predicted to significantly influence the development of processors capable of performing autonomous target classification tasks. Actively sensed range images are shown to be superior to passively collected infrared images in both image stability and information content. An illustrated tutorial introduces cellular logic (neighborhood) transformations and two and three dimensional erosion and dilation operations which are used for noise filters and geometric shape measurement. A unique 'cookbook' approach to selecting a sequence of neighborhood transformations suitable for object measurement is developed and related to false alarm rate and algorithm effectiveness measures. The cookbook design approach is used to develop an algorithm to classify objects based upon their 3-D geometrical features. A Monte Carlo performance analysis is used to demonstrate the utility of the design approach by characterizing the ability of the algorithm to classify randomly positioned three dimensional objects in the presence of additive noise, scale variations, and other forms of image distortion.

Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection

PubMed Central

Wei, Pan; Anderson, Derek T.

2018-01-01

A significant challenge in object detection is accurate identification of an object’s position in image space, whereas one algorithm with one set of parameters is usually not enough, and the fusion of multiple algorithms and/or parameters can lead to more robust results. Herein, a new computational intelligence fusion approach based on the dynamic analysis of agreement among object detection outputs is proposed. Furthermore, we propose an online versus just in training image augmentation strategy. Experiments comparing the results both with and without fusion are presented. We demonstrate that the augmented and fused combination results are the best, with respect to higher accuracy rates and reduction of outlier influences. The approach is demonstrated in the context of cone, pedestrian and box detection for Advanced Driver Assistance Systems (ADAS) applications. PMID:29562609

IRIS: a novel spectral imaging system for the analysis of cultural heritage objects

NASA Astrophysics Data System (ADS)

Papadakis, V. M.; Orphanos, Y.; Kogou, S.; Melessanaki, K.; Pouli, P.; Fotakis, C.

2011-06-01

A new portable spectral imaging system is herein presented capable of acquiring images of high resolution (2MPixels) ranging from 380 nm up to 950 nm. The system consists of a digital color CCD camera, 15 interference filters covering all the sensitivity range of the detector and a robust filter changing system. The acquisition software has been developed in "LabView" programming language allowing easy handling and modification by end-users. The system has been tested and evaluated on a series of objects of Cultural Heritage (CH) value including paintings, encrusted stonework, ceramics etc. This paper aims to present the system, as well as, its application and advantages in the analysis of artworks with emphasis on the detailed compositional and structural information of layered surfaces based on reflection & fluorescence spectroscopy. Specific examples will be presented and discussed on the basis of system improvements.

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images

PubMed Central

Kather, Jakob Nikolas; Weis, Cleo-Aron; Marx, Alexander; Schuster, Alexander K.; Schad, Lothar R.; Zöllner, Frank Gerrit

2015-01-01

Background Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions. Methods and Results In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin—3,3’-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images. Validation To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images. Context Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics. PMID:26717571

New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images.

PubMed

Kather, Jakob Nikolas; Weis, Cleo-Aron; Marx, Alexander; Schuster, Alexander K; Schad, Lothar R; Zöllner, Frank Gerrit

2015-01-01

Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions. In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin-3,3'-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images. To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images. Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.

Toward semantic-based retrieval of visual information: a model-based approach

NASA Astrophysics Data System (ADS)

Park, Youngchoon; Golshani, Forouzan; Panchanathan, Sethuraman

2002-07-01

This paper center around the problem of automated visual content classification. To enable classification based image or visual object retrieval, we propose a new image representation scheme called visual context descriptor (VCD) that is a multidimensional vector in which each element represents the frequency of a unique visual property of an image or a region. VCD utilizes the predetermined quality dimensions (i.e., types of features and quantization level) and semantic model templates mined in priori. Not only observed visual cues, but also contextually relevant visual features are proportionally incorporated in VCD. Contextual relevance of a visual cue to a semantic class is determined by using correlation analysis of ground truth samples. Such co-occurrence analysis of visual cues requires transformation of a real-valued visual feature vector (e.g., color histogram, Gabor texture, etc.,) into a discrete event (e.g., terms in text). Good-feature to track, rule of thirds, iterative k-means clustering and TSVQ are involved in transformation of feature vectors into unified symbolic representations called visual terms. Similarity-based visual cue frequency estimation is also proposed and used for ensuring the correctness of model learning and matching since sparseness of sample data causes the unstable results of frequency estimation of visual cues. The proposed method naturally allows integration of heterogeneous visual or temporal or spatial cues in a single classification or matching framework, and can be easily integrated into a semantic knowledge base such as thesaurus, and ontology. Robust semantic visual model template creation and object based image retrieval are demonstrated based on the proposed content description scheme.

Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters

NASA Astrophysics Data System (ADS)

Ierodiaconou, Daniel; Schimel, Alexandre C. G.; Kennedy, David; Monk, Jacquomo; Gaylard, Grace; Young, Mary; Diesing, Markus; Rattray, Alex

2018-06-01

Habitat mapping data are increasingly being recognised for their importance in underpinning marine spatial planning. The ability to collect ultra-high resolution (cm) multibeam echosounder (MBES) data in shallow waters has facilitated understanding of the fine-scale distribution of benthic habitats in these areas that are often prone to human disturbance. Developing quantitative and objective approaches to integrate MBES data with ground observations for predictive modelling is essential for ensuring repeatability and providing confidence measures for habitat mapping products. Whilst supervised classification approaches are becoming more common, users are often faced with a decision whether to implement a pixel based (PB) or an object based (OB) image analysis approach, with often limited understanding of the potential influence of that decision on final map products and relative importance of data inputs to patterns observed. In this study, we apply an ensemble learning approach capable of integrating PB and OB Image Analysis from ultra-high resolution MBES bathymetry and backscatter data for mapping benthic habitats in Refuge Cove, a temperate coastal embayment in south-east Australia. We demonstrate the relative importance of PB and OB seafloor derivatives for the five broad benthic habitats that dominate the site. We found that OB and PB approaches performed well with differences in classification accuracy but not discernible statistically. However, a model incorporating elements of both approaches proved to be significantly more accurate than OB or PB methods alone and demonstrate the benefits of using MBES bathymetry and backscatter combined for class discrimination.

Electron Tomography: A Three-Dimensional Analytic Tool for Hard and Soft Materials Research.

PubMed

Ercius, Peter; Alaidi, Osama; Rames, Matthew J; Ren, Gang

2015-10-14

Three-dimensional (3D) structural analysis is essential to understand the relationship between the structure and function of an object. Many analytical techniques, such as X-ray diffraction, neutron spectroscopy, and electron microscopy imaging, are used to provide structural information. Transmission electron microscopy (TEM), one of the most popular analytic tools, has been widely used for structural analysis in both physical and biological sciences for many decades, in which 3D objects are projected into two-dimensional (2D) images. In many cases, 2D-projection images are insufficient to understand the relationship between the 3D structure and the function of nanoscale objects. Electron tomography (ET) is a technique that retrieves 3D structural information from a tilt series of 2D projections, and is gradually becoming a mature technology with sub-nanometer resolution. Distinct methods to overcome sample-based limitations have been separately developed in both physical and biological science, although they share some basic concepts of ET. This review discusses the common basis for 3D characterization, and specifies difficulties and solutions regarding both hard and soft materials research. It is hoped that novel solutions based on current state-of-the-art techniques for advanced applications in hybrid matter systems can be motivated. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Electron Tomography: A Three-Dimensional Analytic Tool for Hard and Soft Materials Research

PubMed Central

Alaidi, Osama; Rames, Matthew J.

2016-01-01

Three-dimensional (3D) structural analysis is essential to understand the relationship between the structure and function of an object. Many analytical techniques, such as X-ray diffraction, neutron spectroscopy, and electron microscopy imaging, are used to provide structural information. Transmission electron microscopy (TEM), one of the most popular analytic tools, has been widely used for structural analysis in both physical and biological sciences for many decades, in which 3D objects are projected into two-dimensional (2D) images. In many cases, 2D-projection images are insufficient to understand the relationship between the 3D structure and the function of nanoscale objects. Electron tomography (ET) is a technique that retrieves 3D structural information from a tilt series of 2D projections, and is gradually becoming a mature technology with sub-nanometer resolution. Distinct methods to overcome sample-based limitations have been separately developed in both physical and biological science, although they share some basic concepts of ET. This review discusses the common basis for 3D characterization, and specifies difficulties and solutions regarding both hard and soft materials research. It is hoped that novel solutions based on current state-of-the-art techniques for advanced applications in hybrid matter systems can be motivated. PMID:26087941

A Low-Power Wireless Image Sensor Node with Noise-Robust Moving Object Detection and a Region-of-Interest Based Rate Controller

DTIC Science & Technology

2017-03-01

A Low- Power Wireless Image Sensor Node with Noise-Robust Moving Object Detection and a Region-of-Interest Based Rate Controller Jong Hwan Ko...Atlanta, GA 30332 USA Contact Author Email: jonghwan.ko@gatech.edu Abstract: This paper presents a low- power wireless image sensor node for...present a low- power wireless image sensor node with a noise-robust moving object detection and region-of-interest based rate controller [Fig. 1]. The

Texture- and deformability-based surface recognition by tactile image analysis.

PubMed

Khasnobish, Anwesha; Pal, Monalisa; Tibarewala, D N; Konar, Amit; Pal, Kunal

2016-08-01

Deformability and texture are two unique object characteristics which are essential for appropriate surface recognition by tactile exploration. Tactile sensation is required to be incorporated in artificial arms for rehabilitative and other human-computer interface applications to achieve efficient and human-like manoeuvring. To accomplish the same, surface recognition by tactile data analysis is one of the prerequisites. The aim of this work is to develop effective technique for identification of various surfaces based on deformability and texture by analysing tactile images which are obtained during dynamic exploration of the item by artificial arms whose gripper is fitted with tactile sensors. Tactile data have been acquired, while human beings as well as a robot hand fitted with tactile sensors explored the objects. The tactile images are pre-processed, and relevant features are extracted from the tactile images. These features are provided as input to the variants of support vector machine (SVM), linear discriminant analysis and k-nearest neighbour (kNN) for classification. Based on deformability, six household surfaces are recognized from their corresponding tactile images. Moreover, based on texture five surfaces of daily use are classified. The method adopted in the former two cases has also been applied for deformability- and texture-based recognition of four biomembranes, i.e. membranes prepared from biomaterials which can be used for various applications such as drug delivery and implants. Linear SVM performed best for recognizing surface deformability with an accuracy of 83 % in 82.60 ms, whereas kNN classifier recognizes surfaces of daily use having different textures with an accuracy of 89 % in 54.25 ms and SVM with radial basis function kernel recognizes biomembranes with an accuracy of 78 % in 53.35 ms. The classifiers are observed to generalize well on the unseen test datasets with very high performance to achieve efficient material recognition based on its deformability and texture.

Objective measurement of bread crumb texture

NASA Astrophysics Data System (ADS)

Wang, Jian; Coles, Graeme D.

1995-01-01

Evaluation of bread crumb texture plays an important role in judging bread quality. This paper discusses the application of image analysis methods to the objective measurement of the visual texture of bread crumb. The application of Fast Fourier Transform and mathematical morphology methods have been discussed by the authors in their previous work, and a commercial bread texture measurement system has been developed. Based on the nature of bread crumb texture, we compare the advantages and disadvantages of the two methods, and a third method based on features derived directly from statistics of edge density in local windows of the bread image. The analysis of various methods and experimental results provides an insight into the characteristics of the bread texture image and interconnection between texture measurement algorithms. The usefulness of the application of general stochastic process modelling of texture is thus revealed; it leads to more reliable and accurate evaluation of bread crumb texture. During the development of these methods, we also gained useful insights into how subjective judges form opinions about bread visual texture. These are discussed here.

Image Understanding Research and Its Application to Cartography and Computer-Based Analysis of Aerial Imagery

DTIC Science & Technology

1983-05-01

Parallel Computation that Assign Canonical Object-Based Frames of Refer- ence," Proc. 7th it. .nt. Onf. on Artifcial Intellig nce (IJCAI-81), Vol. 2...Perception of Linear Struc- ture in Imaged Data ." TN 276, Artiflci!.a Intelligence Center, SRI International, Feb. 1983. [Fram75] J.P. Frain and E.S...1983 May 1983 D C By: Martin A. Fischler, Program Director S ELECTE Principal Investigator, (415)859-5106 MAY 2 21990 Artificial Intelligence Center

Acquiring 3-D information about thick objects from differential interference contrast images using texture extraction

NASA Astrophysics Data System (ADS)

Sierra, Heidy; Brooks, Dana; Dimarzio, Charles

2010-07-01

The extraction of 3-D morphological information about thick objects is explored in this work. We extract this information from 3-D differential interference contrast (DIC) images by applying a texture detection method. Texture extraction methods have been successfully used in different applications to study biological samples. A 3-D texture image is obtained by applying a local entropy-based texture extraction method. The use of this method to detect regions of blastocyst mouse embryos that are used in assisted reproduction techniques such as in vitro fertilization is presented as an example. Results demonstrate the potential of using texture detection methods to improve morphological analysis of thick samples, which is relevant to many biomedical and biological studies. Fluorescence and optical quadrature microscope phase images are used for validation.

Information recovery through image sequence fusion under wavelet transformation

NASA Astrophysics Data System (ADS)

He, Qiang

2010-04-01

Remote sensing is widely applied to provide information of areas with limited ground access with applications such as to assess the destruction from natural disasters and to plan relief and recovery operations. However, the data collection of aerial digital images is constrained by bad weather, atmospheric conditions, and unstable camera or camcorder. Therefore, how to recover the information from the low-quality remote sensing images and how to enhance the image quality becomes very important for many visual understanding tasks, such like feature detection, object segmentation, and object recognition. The quality of remote sensing imagery can be improved through meaningful combination of the employed images captured from different sensors or from different conditions through information fusion. Here we particularly address information fusion to remote sensing images under multi-resolution analysis in the employed image sequences. The image fusion is to recover complete information by integrating multiple images captured from the same scene. Through image fusion, a new image with high-resolution or more perceptive for human and machine is created from a time series of low-quality images based on image registration between different video frames.

Polarization sensitive spectroscopic optical coherence tomography for multimodal imaging

NASA Astrophysics Data System (ADS)

Strąkowski, Marcin R.; Kraszewski, Maciej; Strąkowska, Paulina; Trojanowski, Michał

2015-03-01

Optical coherence tomography (OCT) is a non-invasive method for 3D and cross-sectional imaging of biological and non-biological objects. The OCT measurements are provided in non-contact and absolutely safe way for the tested sample. Nowadays, the OCT is widely applied in medical diagnosis especially in ophthalmology, as well as dermatology, oncology and many more. Despite of great progress in OCT measurements there are still a vast number of issues like tissue recognition or imaging contrast enhancement that have not been solved yet. Here we are going to present the polarization sensitive spectroscopic OCT system (PS-SOCT). The PS-SOCT combines the polarization sensitive analysis with time-frequency analysis. Unlike standard polarization sensitive OCT the PS-SOCT delivers spectral information about measured quantities e.g. tested object birefringence changes over the light spectra. This solution overcomes the limits of polarization sensitive analysis applied in standard PS-OCT. Based on spectral data obtained from PS-SOCT the exact value of birefringence can be calculated even for the objects that provide higher order of retardation. In this contribution the benefits of using the combination of time-frequency and polarization sensitive analysis are being expressed. Moreover, the PS-SOCT system features, as well as OCT measurement examples are presented.

Blackboard architecture for medical image interpretation

NASA Astrophysics Data System (ADS)

Davis, Darryl N.; Taylor, Christopher J.

1991-06-01

There is a growing interest in using sophisticated knowledge-based systems for biomedical image interpretation. We present a principled attempt to use artificial intelligence methodologies in interpreting lateral skull x-ray images. Such radiographs are routinely used in cephalometric analysis to provide quantitative measurements useful to clinical orthodontists. Manual and interactive methods of analysis are known to be error prone and previous attempts to automate this analysis typically fail to capture the expertise and adaptability required to cope with the variability in biological structure and image quality. An integrated model-based system has been developed which makes use of a blackboard architecture and multiple knowledge sources. A model definition interface allows quantitative models, of feature appearance and location, to be built from examples as well as more qualitative modelling constructs. Visual task definition and blackboard control modules allow task-specific knowledge sources to act on information available to the blackboard in a hypothesise and test reasoning cycle. Further knowledge-based modules include object selection, location hypothesis, intelligent segmentation, and constraint propagation systems. Alternative solutions to given tasks are permitted.

Image-based tracking system for vibration measurement of a rotating object using a laser scanning vibrometer

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

Kim, Dongkyu, E-mail: akein@gist.ac.kr; Khalil, Hossam; Jo, Youngjoon

2016-06-28

An image-based tracking system using laser scanning vibrometer is developed for vibration measurement of a rotating object. The proposed system unlike a conventional one can be used where the position or velocity sensor such as an encoder cannot be attached to an object. An image processing algorithm is introduced to detect a landmark and laser beam based on their colors. Then, through using feedback control system, the laser beam can track a rotating object.

a New Object-Based Framework to Detect Shodows in High-Resolution Satellite Imagery Over Urban Areas

NASA Astrophysics Data System (ADS)

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

2015-12-01

In this paper a new object-based framework to detect shadow areas in high resolution satellite images is proposed. To produce shadow map in pixel level state of the art supervised machine learning algorithms are employed. Automatic ground truth generation based on Otsu thresholding on shadow and non-shadow indices is used to train the classifiers. It is followed by segmenting the image scene and create image objects. To detect shadow objects, a majority voting on pixel-based shadow detection result is designed. GeoEye-1 multi-spectral image over an urban area in Qom city of Iran is used in the experiments. Results shows the superiority of our proposed method over traditional pixel-based, visually and quantitatively.

Characterization of fiber diameter using image analysis

NASA Astrophysics Data System (ADS)

Baheti, S.; Tunak, M.

2017-10-01

Due to high surface area and porosity, the applications of nanofibers have increased in recent years. In the production process, determination of average fiber diameter and fiber orientation is crucial for quality assessment. The objective of present study was to compare the relative performance of different methods discussed in literature for estimation of fiber diameter. In this work, the existing automated fiber diameter analysis software packages available in literature were developed and validated based on simulated images of known fiber diameter. Finally, all methods were compared for their reliable and accurate estimation of fiber diameter in electro spun nanofiber membranes based on obtained mean and standard deviation.

An efficient direct method for image registration of flat objects

NASA Astrophysics Data System (ADS)

Nikolaev, Dmitry; Tihonkih, Dmitrii; Makovetskii, Artyom; Voronin, Sergei

2017-09-01

Image alignment of rigid surfaces is a rapidly developing area of research and has many practical applications. Alignment methods can be roughly divided into two types: feature-based methods and direct methods. Known SURF and SIFT algorithms are examples of the feature-based methods. Direct methods refer to those that exploit the pixel intensities without resorting to image features and image-based deformations are general direct method to align images of deformable objects in 3D space. Nevertheless, it is not good for the registration of images of 3D rigid objects since the underlying structure cannot be directly evaluated. In the article, we propose a model that is suitable for image alignment of rigid flat objects under various illumination models. The brightness consistency assumptions used for reconstruction of optimal geometrical transformation. Computer simulation results are provided to illustrate the performance of the proposed algorithm for computing of an accordance between pixels of two images.

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

Object detection from images obtained through underwater turbulence medium

NASA Astrophysics Data System (ADS)

Furhad, Md. Hasan; Tahtali, Murat; Lambert, Andrew

2017-09-01

Imaging through underwater experiences severe distortions due to random fluctuations of temperature and salinity in water, which produces underwater turbulence through diffraction limited blur. Lights reflecting from objects perturb and attenuate contrast, making the recognition of objects of interest difficult. Thus, the information available for detecting underwater objects of interest becomes a challenging task as they have inherent confusion among the background, foreground and other image properties. In this paper, a saliency-based approach is proposed to detect the objects acquired through an underwater turbulent medium. This approach has drawn attention among a wide range of computer vision applications, such as image retrieval, artificial intelligence, neuro-imaging and object detection. The image is first processed through a deblurring filter. Next, a saliency technique is used on the image for object detection. In this step, a saliency map that highlights the target regions is generated and then a graph-based model is proposed to extract these target regions for object detection.

Binary partition tree analysis based on region evolution and its application to tree simplification.

PubMed

Lu, Huihai; Woods, John C; Ghanbari, Mohammed

2007-04-01

Pyramid image representations via tree structures are recognized methods for region-based image analysis. Binary partition trees can be applied which document the merging process with small details found at the bottom levels and larger ones close to the root. Hindsight of the merging process is stored within the tree structure and provides the change histories of an image property from the leaf to the root node. In this work, the change histories are modelled by evolvement functions and their second order statistics are analyzed by using a knee function. Knee values show the reluctancy of each merge. We have systematically formulated these findings to provide a novel framework for binary partition tree analysis, where tree simplification is demonstrated. Based on an evolvement function, for each upward path in a tree, the tree node associated with the first reluctant merge is considered as a pruning candidate. The result is a simplified version providing a reduced solution space and still complying with the definition of a binary tree. The experiments show that image details are preserved whilst the number of nodes is dramatically reduced. An image filtering tool also results which preserves object boundaries and has applications for segmentation.

Software Method for Computed Tomography Cylinder Data Unwrapping, Re-slicing, and Analysis

NASA Technical Reports Server (NTRS)

Roth, Don J.

2013-01-01

A software method has been developed that is applicable for analyzing cylindrical and partially cylindrical objects inspected using computed tomography (CT). This method involves unwrapping and re-slicing data so that the CT data from the cylindrical object can be viewed as a series of 2D sheets (or flattened onion skins ) in addition to a series of top view slices and 3D volume rendering. The advantages of viewing the data in this fashion are as follows: (1) the use of standard and specialized image processing and analysis methods is facilitated having 2D array data versus a volume rendering; (2) accurate lateral dimensional analysis of flaws is possible in the unwrapped sheets versus volume rendering; (3) flaws in the part jump out at the inspector with the proper contrast expansion settings in the unwrapped sheets; and (4) it is much easier for the inspector to locate flaws in the unwrapped sheets versus top view slices for very thin cylinders. The method is fully automated and requires no input from the user except proper voxel dimension from the CT experiment and wall thickness of the part. The software is available in 32-bit and 64-bit versions, and can be used with binary data (8- and 16-bit) and BMP type CT image sets. The software has memory (RAM) and hard-drive based modes. The advantage of the (64-bit) RAM-based mode is speed (and is very practical for users of 64-bit Windows operating systems and computers having 16 GB or more RAM). The advantage of the hard-drive based analysis is one can work with essentially unlimited-sized data sets. Separate windows are spawned for the unwrapped/re-sliced data view and any image processing interactive capability. Individual unwrapped images and un -wrapped image series can be saved in common image formats. More information is available at http://www.grc.nasa.gov/WWW/OptInstr/ NDE_CT_CylinderUnwrapper.html.

Accuracy of lung nodule density on HRCT: analysis by PSF-based image simulation.

PubMed

Ohno, Ken; Ohkubo, Masaki; Marasinghe, Janaka C; Murao, Kohei; Matsumoto, Toru; Wada, Shinichi

2012-11-08

A computed tomography (CT) image simulation technique based on the point spread function (PSF) was applied to analyze the accuracy of CT-based clinical evaluations of lung nodule density. The PSF of the CT system was measured and used to perform the lung nodule image simulation. Then, the simulated image was resampled at intervals equal to the pixel size and the slice interval found in clinical high-resolution CT (HRCT) images. On those images, the nodule density was measured by placing a region of interest (ROI) commonly used for routine clinical practice, and comparing the measured value with the true value (a known density of object function used in the image simulation). It was quantitatively determined that the measured nodule density depended on the nodule diameter and the image reconstruction parameters (kernel and slice thickness). In addition, the measured density fluctuated, depending on the offset between the nodule center and the image voxel center. This fluctuation was reduced by decreasing the slice interval (i.e., with the use of overlapping reconstruction), leading to a stable density evaluation. Our proposed method of PSF-based image simulation accompanied with resampling enables a quantitative analysis of the accuracy of CT-based evaluations of lung nodule density. These results could potentially reveal clinical misreadings in diagnosis, and lead to more accurate and precise density evaluations. They would also be of value for determining the optimum scan and reconstruction parameters, such as image reconstruction kernels and slice thicknesses/intervals.

Scene analysis for effective visual search in rough three-dimensional-modeling scenes

NASA Astrophysics Data System (ADS)

Wang, Qi; Hu, Xiaopeng

2016-11-01

Visual search is a fundamental technology in the computer vision community. It is difficult to find an object in complex scenes when there exist similar distracters in the background. We propose a target search method in rough three-dimensional-modeling scenes based on a vision salience theory and camera imaging model. We give the definition of salience of objects (or features) and explain the way that salience measurements of objects are calculated. Also, we present one type of search path that guides to the target through salience objects. Along the search path, when the previous objects are localized, the search region of each subsequent object decreases, which is calculated through imaging model and an optimization method. The experimental results indicate that the proposed method is capable of resolving the ambiguities resulting from distracters containing similar visual features with the target, leading to an improvement of search speed by over 50%.

Semantics and technologies in modern design of interior stairs

NASA Astrophysics Data System (ADS)

Kukhta, M.; Sokolov, A.; Pelevin, E.

2015-10-01

Use of metal in the design of interior stairs presents new features for shaping, and can be implemented using different technologies. The article discusses the features of design and production technologies of forged metal spiral staircase considering the image semantics based on the historical and cultural heritage. To achieve the objective was applied structural- semantic method (to identify the organization of structure and semantic features of the artistic image), engineering methods (to justify the construction of the object), anthropometry method and ergonomics (to provide usability), methods of comparative analysis (to reveale the features of the way the ladder in different periods of culture). According to the research results are as follows. Was revealed the semantics influence on the design of interior staircase that is based on the World Tree image. Also was suggested rational calculation of steps to ensure the required strength. And finally was presented technology, providing the realization of the artistic image. In the practical part of the work is presented version of forged staircase.

A novel lobster-eye imaging system based on Schmidt-type objective for X-ray-backscattering inspection

NASA Astrophysics Data System (ADS)

Xu, Jie; Wang, Xin; Zhan, Qi; Huang, Shengling; Chen, Yifan; Mu, Baozhong

2016-07-01

This paper presents a novel lobster-eye imaging system for X-ray-backscattering inspection. The system was designed by modifying the Schmidt geometry into a treble-lens structure in order to reduce the resolution difference between the vertical and horizontal directions, as indicated by ray-tracing simulations. The lobster-eye X-ray imaging system is capable of operating over a wide range of photon energies up to 100 keV. In addition, the optics of the lobster-eye X-ray imaging system was tested to verify that they meet the requirements. X-ray-backscattering imaging experiments were performed in which T-shaped polymethyl-methacrylate objects were imaged by the lobster-eye X-ray imaging system based on both the double-lens and treble-lens Schmidt objectives. The results show similar resolution of the treble-lens Schmidt objective in both the vertical and horizontal directions. Moreover, imaging experiments were performed using a second treble-lens Schmidt objective with higher resolution. The results show that for a field of view of over 200 mm and with a 500 mm object distance, this lobster-eye X-ray imaging system based on a treble-lens Schmidt objective offers a spatial resolution of approximately 3 mm.

High contrast imaging through adaptive transmittance control in the focal plane

NASA Astrophysics Data System (ADS)

Dhadwal, Harbans S.; Rastegar, Jahangir; Feng, Dake

2016-05-01

High contrast imaging, in the presence of a bright background, is a challenging problem encountered in diverse applications ranging from the daily chore of driving into a sun-drenched scene to in vivo use of biomedical imaging in various types of keyhole surgeries. Imaging in the presence of bright sources saturates the vision system, resulting in loss of scene fidelity, corresponding to low image contrast and reduced resolution. The problem is exacerbated in retro-reflective imaging systems where the light sources illuminating the object are unavoidably strong, typically masking the object features. This manuscript presents a novel theoretical framework, based on nonlinear analysis and adaptive focal plane transmittance, to selectively remove object domain sources of background light from the image plane, resulting in local and global increases in image contrast. The background signal can either be of a global specular nature, giving rise to parallel illumination from the entire object surface or can be represented by a mosaic of randomly orientated, small specular surfaces. The latter is more representative of real world practical imaging systems. Thus, the background signal comprises of groups of oblique rays corresponding to distributions of the mosaic surfaces. Through the imaging system, light from group of like surfaces, converges to a localized spot in the focal plane of the lens and then diverges to cast a localized bright spot in the image plane. Thus, transmittance of a spatial light modulator, positioned in the focal plane, can be adaptively controlled to block a particular source of background light. Consequently, the image plane intensity is entirely due to the object features. Experimental image data is presented to verify the efficacy of the methodology.

Models of formation and some algorithms of hyperspectral image processing

NASA Astrophysics Data System (ADS)

Achmetov, R. N.; Stratilatov, N. R.; Yudakov, A. A.; Vezenov, V. I.; Eremeev, V. V.

2014-12-01

Algorithms and information technologies for processing Earth hyperspectral imagery are presented. Several new approaches are discussed. Peculiar properties of processing the hyperspectral imagery, such as multifold signal-to-noise reduction, atmospheric distortions, access to spectral characteristics of every image point, and high dimensionality of data, were studied. Different measures of similarity between individual hyperspectral image points and the effect of additive uncorrelated noise on these measures were analyzed. It was shown that these measures are substantially affected by noise, and a new measure free of this disadvantage was proposed. The problem of detecting the observed scene object boundaries, based on comparing the spectral characteristics of image points, is considered. It was shown that contours are processed much better when spectral characteristics are used instead of energy brightness. A statistical approach to the correction of atmospheric distortions, which makes it possible to solve the stated problem based on analysis of a distorted image in contrast to analytical multiparametric models, was proposed. Several algorithms used to integrate spectral zonal images with data from other survey systems, which make it possible to image observed scene objects with a higher quality, are considered. Quality characteristics of hyperspectral data processing were proposed and studied.

From static electric images to electric flow: towards dynamic perceptual cues in active electroreception.

PubMed

Hofmann, Volker; Sanguinetti-Scheck, Juan I; Gómez-Sena, Leonel; Engelmann, Jacob

2013-01-01

Active electroreception is an ancestral trait found in many aquatic vertebrates and has evolved independently in two teleost lineages, the Gymnotiformes and the Mormyriformes. Unique to these so-called weakly electric fish is their ability to actively generate electrical currents in the water and sense the electrical properties of the environment. How natural behavior contributes to this sensory system has been of interest to neuroethologists since the pioneering works of Lissmann. Here we report on a mutual modeling and experimental study of the stimuli available during active electrolocation of Gnathonemus petersii (Mormyridae). We show the validity of the model (I) by demonstrating that localized spatial patterns of object induced modulations in the electric field (electric images) are comparable to experimentally mapped 2-dimensional electric images and (II) by replicating earlier key findings showing that a normalized metric of electric image width provides an unambiguous cue for distance estimation. We then show that electric images and the distance metric vary systematically when an object is moved along the trunk. These potential ambiguities with regard to localization lead us to a spatiotemporal analysis of electric images. We introduce a new temporal metric for distance estimation that is based on the normalized spatial properties of electrical images. Finally, based on a survey of exploratory behavior, we show how objects situated at the tail, a region previously neglected, cast global electric images that extend over the whole sensory epithelium of the animals. Copyright © 2012 Elsevier Ltd. All rights reserved.

Visual wetness perception based on image color statistics.

PubMed

Sawayama, Masataka; Adelson, Edward H; Nishida, Shin'ya

2017-05-01

Color vision provides humans and animals with the abilities to discriminate colors based on the wavelength composition of light and to determine the location and identity of objects of interest in cluttered scenes (e.g., ripe fruit among foliage). However, we argue that color vision can inform us about much more than color alone. Since a trichromatic image carries more information about the optical properties of a scene than a monochromatic image does, color can help us recognize complex material qualities. Here we show that human vision uses color statistics of an image for the perception of an ecologically important surface condition (i.e., wetness). Psychophysical experiments showed that overall enhancement of chromatic saturation, combined with a luminance tone change that increases the darkness and glossiness of the image, tended to make dry scenes look wetter. Theoretical analysis along with image analysis of real objects indicated that our image transformation, which we call the wetness enhancing transformation, is consistent with actual optical changes produced by surface wetting. Furthermore, we found that the wetness enhancing transformation operator was more effective for the images with many colors (large hue entropy) than for those with few colors (small hue entropy). The hue entropy may be used to separate surface wetness from other surface states having similar optical properties. While surface wetness and surface color might seem to be independent, there are higher order color statistics that can influence wetness judgments, in accord with the ecological statistics. The present findings indicate that the visual system uses color image statistics in an elegant way to help estimate the complex physical status of a scene.

Digital 3D Microstructure Analysis of Concrete using X-Ray Micro Computed Tomography SkyScan 1173: A Preliminary Study

NASA Astrophysics Data System (ADS)

Latief, F. D. E.; Mohammad, I. H.; Rarasati, A. D.

2017-11-01

Digital imaging of a concrete sample using high resolution tomographic imaging by means of X-Ray Micro Computed Tomography (μ-CT) has been conducted to assess the characteristic of the sample’s structure. A standard procedure of image acquisition, reconstruction, image processing of the method using a particular scanning device i.e., the Bruker SkyScan 1173 High Energy Micro-CT are elaborated. A qualitative and a quantitative analysis were briefly performed on the sample to deliver some basic ideas of the capability of the system and the bundled software package. Calculation of total VOI volume, object volume, percent of object volume, total VOI surface, object surface, object surface/volume ratio, object surface density, structure thickness, structure separation, total porosity were conducted and analysed. This paper should serve as a brief description of how the device can produce the preferred image quality as well as the ability of the bundled software packages to help in performing qualitative and quantitative analysis.

Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover

NASA Astrophysics Data System (ADS)

de Oliveira Silveira, Eduarda Martiniano; de Menezes, Michele Duarte; Acerbi Júnior, Fausto Weimar; Castro Nunes Santos Terra, Marcela; de Mello, José Márcio

2017-07-01

Accurate mapping and monitoring of savanna and semiarid woodland biomes are needed to support the selection of areas of conservation, to provide sustainable land use, and to improve the understanding of vegetation. The potential of geostatistical features, derived from medium spatial resolution satellite imagery, to characterize contrasted landscape vegetation cover and improve object-based image classification is studied. The study site in Brazil includes cerrado sensu stricto, deciduous forest, and palm swamp vegetation cover. Sentinel 2 and Landsat 8 images were acquired and divided into objects, for each of which a semivariogram was calculated using near-infrared (NIR) and normalized difference vegetation index (NDVI) to extract the set of geostatistical features. The features selected by principal component analysis were used as input data to train a random forest algorithm. Tests were conducted, combining spectral and geostatistical features. Change detection evaluation was performed using a confusion matrix and its accuracies. The semivariogram curves were efficient to characterize spatial heterogeneity, with similar results using NIR and NDVI from Sentinel 2 and Landsat 8. Accuracy was significantly greater when combining geostatistical features with spectral data, suggesting that this method can improve image classification results.

Heuristic Analysis Model of Nitrided Layers’ Formation Consisting of the Image Processing and Analysis and Elements of Artificial Intelligence

PubMed Central

Wójcicki, Tomasz; Nowicki, Michał

2016-01-01

The article presents a selected area of research and development concerning the methods of material analysis based on the automatic image recognition of the investigated metallographic sections. The objectives of the analyses of the materials for gas nitriding technology are described. The methods of the preparation of nitrided layers, the steps of the process and the construction and operation of devices for gas nitriding are given. We discuss the possibility of using the methods of digital images processing in the analysis of the materials, as well as their essential task groups: improving the quality of the images, segmentation, morphological transformations and image recognition. The developed analysis model of the nitrided layers formation, covering image processing and analysis techniques, as well as selected methods of artificial intelligence are presented. The model is divided into stages, which are formalized in order to better reproduce their actions. The validation of the presented method is performed. The advantages and limitations of the developed solution, as well as the possibilities of its practical use, are listed. PMID:28773389

Thermal image analysis using the serpentine method

NASA Astrophysics Data System (ADS)

Koprowski, Robert; Wilczyński, Sławomir

2018-03-01

Thermal imaging is an increasingly widespread alternative to other imaging methods. As a supplementary method in diagnostics, it can be used both statically and with dynamic temperature changes. The paper proposes a new image analysis method that allows for the acquisition of new diagnostic information as well as object segmentation. The proposed serpentine analysis uses known and new methods of image analysis and processing proposed by the authors. Affine transformations of an image and subsequent Fourier analysis provide a new diagnostic quality. The method is fully repeatable and automatic and independent of inter-individual variability in patients. The segmentation results are by 10% better than those obtained from the watershed method and the hybrid segmentation method based on the Canny detector. The first and second harmonics of serpentine analysis enable to determine the type of temperature changes in the region of interest (gradient, number of heat sources etc.). The presented serpentine method provides new quantitative information on thermal imaging and more. Since it allows for image segmentation and designation of contact points of two and more heat sources (local minimum), it can be used to support medical diagnostics in many areas of medicine.

Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island

NASA Astrophysics Data System (ADS)

Heleno, S.; Matias, M.; Pina, P.; Sousa, A. J.

2015-09-01

A method for semi-automatic landslide detection, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a Support Vector Machine classifier on a GeoEye-1 multispectral image, sensed 3 days after the major damaging landslide event that occurred in Madeira island (20 February 2010), with a pre-event LIDAR Digital Elevation Model. The testing is developed in a 15 km2-wide study area, where 95 % of the landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier east facing-slopes.

Graph-based layout analysis for PDF documents

NASA Astrophysics Data System (ADS)

Xu, Canhui; Tang, Zhi; Tao, Xin; Li, Yun; Shi, Cao

2013-03-01

To increase the flexibility and enrich the reading experience of e-book on small portable screens, a graph based method is proposed to perform layout analysis on Portable Document Format (PDF) documents. Digital born document has its inherent advantages like representing texts and fractional images in explicit form, which can be straightforwardly exploited. To integrate traditional image-based document analysis and the inherent meta-data provided by PDF parser, the page primitives including text, image and path elements are processed to produce text and non text layer for respective analysis. Graph-based method is developed in superpixel representation level, and page text elements corresponding to vertices are used to construct an undirected graph. Euclidean distance between adjacent vertices is applied in a top-down manner to cut the graph tree formed by Kruskal's algorithm. And edge orientation is then used in a bottom-up manner to extract text lines from each sub tree. On the other hand, non-textual objects are segmented by connected component analysis. For each segmented text and non-text composite, a 13-dimensional feature vector is extracted for labelling purpose. The experimental results on selected pages from PDF books are presented.

CellProfiler Tracer: exploring and validating high-throughput, time-lapse microscopy image data.

PubMed

Bray, Mark-Anthony; Carpenter, Anne E

2015-11-04

Time-lapse analysis of cellular images is an important and growing need in biology. Algorithms for cell tracking are widely available; what researchers have been missing is a single open-source software package to visualize standard tracking output (from software like CellProfiler) in a way that allows convenient assessment of track quality, especially for researchers tuning tracking parameters for high-content time-lapse experiments. This makes quality assessment and algorithm adjustment a substantial challenge, particularly when dealing with hundreds of time-lapse movies collected in a high-throughput manner. We present CellProfiler Tracer, a free and open-source tool that complements the object tracking functionality of the CellProfiler biological image analysis package. Tracer allows multi-parametric morphological data to be visualized on object tracks, providing visualizations that have already been validated within the scientific community for time-lapse experiments, and combining them with simple graph-based measures for highlighting possible tracking artifacts. CellProfiler Tracer is a useful, free tool for inspection and quality control of object tracking data, available from http://www.cellprofiler.org/tracer/.

An object-based classification method for automatic detection of lunar impact craters from topographic data

NASA Astrophysics Data System (ADS)

Vamshi, Gasiganti T.; Martha, Tapas R.; Vinod Kumar, K.

2016-05-01

Identification of impact craters is a primary requirement to study past geological processes such as impact history. They are also used as proxies for measuring relative ages of various planetary or satellite bodies and help to understand the evolution of planetary surfaces. In this paper, we present a new method using object-based image analysis (OBIA) technique to detect impact craters of wide range of sizes from topographic data. Multiresolution image segmentation of digital terrain models (DTMs) available from the NASA's LRO mission was carried out to create objects. Subsequently, objects were classified into impact craters using shape and morphometric criteria resulting in 95% detection accuracy. The methodology developed in a training area in parts of Mare Imbrium in the form of a knowledge-based ruleset when applied in another area, detected impact craters with 90% accuracy. The minimum and maximum sizes (diameters) of impact craters detected in parts of Mare Imbrium by our method are 29 m and 1.5 km, respectively. Diameters of automatically detected impact craters show good correlation (R2 > 0.85) with the diameters of manually detected impact craters.

Marginal Fisher analysis and its variants for human gait recognition and content- based image retrieval.

PubMed

Xu, Dong; Yan, Shuicheng; Tao, Dacheng; Lin, Stephen; Zhang, Hong-Jiang

2007-11-01

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for human gait recognition and content-based image retrieval (CBIR). In this paper, we present extensions of our recently proposed marginal Fisher analysis (MFA) to address these problems. For human gait recognition, we first present a direct application of MFA, then inspired by recent advances in matrix and tensor-based dimensionality reduction algorithms, we present matrix-based MFA for directly handling 2-D input in the form of gray-level averaged images. For CBIR, we deal with the relevance feedback problem by extending MFA to marginal biased analysis, in which within-class compactness is characterized only by the distances between each positive sample and its neighboring positive samples. In addition, we present a new technique to acquire a direct optimal solution for MFA without resorting to objective function modification as done in many previous algorithms. We conduct comprehensive experiments on the USF HumanID gait database and the Corel image retrieval database. Experimental results demonstrate that MFA and its extensions outperform related algorithms in both applications.

Fuzzy connectedness and object definition

NASA Astrophysics Data System (ADS)

Udupa, Jayaram K.; Samarasekera, Supun

1995-04-01

Approaches to object information extraction from images should attempt to use the fact that images are fuzzy. In past image segmentation research, the notion of `hanging togetherness' of image elements specified by their fuzzy connectedness has been lacking. We present a theory of fuzzy objects for n-dimensional digital spaces based on a notion of fuzzy connectedness of image elements. Although our definitions lead to problems of enormous combinatorial complexity, the theoretical results allow us to reduce this dramatically. We demonstrate the utility of the theory and algorithms in image segmentation based on several practical examples.

A Method of Face Detection with Bayesian Probability

NASA Astrophysics Data System (ADS)

Sarker, Goutam

2010-10-01

The objective of face detection is to identify all images which contain a face, irrespective of its orientation, illumination conditions etc. This is a hard problem, because the faces are highly variable in size, shape lighting conditions etc. Many methods have been designed and developed to detect faces in a single image. The present paper is based on one `Appearance Based Method' which relies on learning the facial and non facial features from image examples. This in its turn is based on statistical analysis of examples and counter examples of facial images and employs Bayesian Conditional Classification Rule to detect the probability of belongingness of a face (or non-face) within an image frame. The detection rate of the present system is very high and thereby the number of false positive and false negative detection is substantially low.

Distribution quantification on dermoscopy images for computer-assisted diagnosis of cutaneous melanomas.

PubMed

Liu, Zhao; Sun, Jiuai; Smith, Lyndon; Smith, Melvyn; Warr, Robert

2012-05-01

Computerised analysis on skin lesion images has been reported to be helpful in achieving objective and reproducible diagnosis of melanoma. In particular, asymmetry in shape, colour and structure reflects the irregular growth of melanin under the skin and is of great importance for diagnosing the malignancy of skin lesions. This paper proposes a novel asymmetry analysis based on a newly developed pigmentation elevation model and the global point signatures (GPSs). Specifically, the pigmentation elevation model was first constructed by computer-based analysis of dermoscopy images, for the identification of melanin and haemoglobin. Asymmetry of skin lesions was then assessed through quantifying distributions of the pigmentation elevation model using the GPSs, derived from a Laplace-Beltrami operator. This new approach allows quantifying the shape and pigmentation distributions of cutaneous lesions simultaneously. Algorithm performance was tested on 351 dermoscopy images, including 88 malignant melanomas and 263 benign naevi, employing a support vector machine (SVM) with tenfold cross-validation strategy. Competitive diagnostic results were achieved using the proposed asymmetry descriptor only, presenting 86.36 % sensitivity, 82.13 % specificity and overall 83.43 % accuracy, respectively. In addition, the proposed GPS-based asymmetry analysis enables working on dermoscopy images from different databases and is approved to be inherently robust to the external imaging variations. These advantages suggested that the proposed method has good potential for follow-up treatment.

Image acquisition with immersion objective lenses using electrons emitted with several tenths of an electron volt energies: towards high spatial resolution ESCA analysis.

PubMed

Bernheim, M

2006-03-01

This study aims to evaluate the spatial resolution achievable with photoelectrons in order to perform localised UPS or XPS analyses on various heterogeneous samples. This investigation is intentionally restricted to direct image acquisition by immersion objective lenses, involving electrons ejected with initial energies of several tenths of an electron-volt. In order to characterise the contribution of all optical elements, analytical investigations were associated to numerical simulations based on SIMION 7 software. The acquisition of high-quality images implies a simultaneous reduction in spherical and chromatic aberrations by a narrow aperture stop placed at the output pupil of the objective. With such limitations in useful emission angles, it is shown that monochromatic electron beams build images with a resolution of about 1 nm, especially for the acceleration bias mode where the focussing electrode is biased at a positive high voltage. Even energy dispersed electron beams, limited by a 4 eV band pass spectrometer, can produce images convenient for highly localised ESCA analyses (resolution 3 nm), where the objective lens is associated with an aperture stop of 30 microm in diameter without using acceleration voltages above 5000 V.

Enhancement of low visibility aerial images using histogram truncation and an explicit Retinex representation for balancing contrast and color consistency

NASA Astrophysics Data System (ADS)

Liu, Changjiang; Cheng, Irene; Zhang, Yi; Basu, Anup

2017-06-01

This paper presents an improved multi-scale Retinex (MSR) based enhancement for ariel images under low visibility. For traditional multi-scale Retinex, three scales are commonly employed, which limits its application scenarios. We extend our research to a general purpose enhanced method, and design an MSR with more than three scales. Based on the mathematical analysis and deductions, an explicit multi-scale representation is proposed that balances image contrast and color consistency. In addition, a histogram truncation technique is introduced as a post-processing strategy to remap the multi-scale Retinex output to the dynamic range of the display. Analysis of experimental results and comparisons with existing algorithms demonstrate the effectiveness and generality of the proposed method. Results on image quality assessment proves the accuracy of the proposed method with respect to both objective and subjective criteria.

Optical Observation, Image-processing, and Detection of Space Debris in Geosynchronous Earth Orbit

NASA Astrophysics Data System (ADS)

Oda, H.; Yanagisawa, T.; Kurosaki, H.; Tagawa, M.

2014-09-01

We report on optical observations and an efficient detection method of space debris in the geosynchronous Earth orbit (GEO). We operate our new Australia Remote Observatory (ARO) where an 18 cm optical telescope with a charged-coupled device (CCD) camera covering a 3.14-degree field of view is used for GEO debris survey, and analyse datasets of successive CCD images using the line detection method (Yanagisawa and Nakajima 2005). In our operation, the exposure time of each CCD image is set to be 3 seconds (or 5 seconds), and the time interval of CCD shutter open is about 4.7 seconds (or 6.7 seconds). In the line detection method, a sufficient number of sample objects are taken from each image based on their shape and intensity, which includes not only faint signals but also background noise (we take 500 sample objects from each image in this paper). Then we search a sequence of sample objects aligning in a straight line in the successive images to exclude the noise sample. We succeed in detecting faint signals (down to about 1.8 sigma of background noise) by applying the line detection method to 18 CCD images. As a result, we detected about 300 GEO objects up to magnitude of 15.5 among 5 nights data. We also calculate orbits of objects detected using the Simplified General Perturbations Satellite Orbit Model 4(SGP4), and identify the objects listed in the two-line-element (TLE) data catalogue publicly provided by the U.S. Strategic Command (USSTRATCOM). We found that a certain amount of our detections are new objects that are not contained in the catalogue. We conclude that our ARO and detection method posse a high efficiency detection of GEO objects despite the use of comparatively-inexpensive observation and analysis system. We also describe the image-processing specialized for the detection of GEO objects (not for usual astronomical objects like stars) in this paper.

Comparison of segmentation algorithms for fluorescence microscopy images of cells.

PubMed

Dima, Alden A; Elliott, John T; Filliben, James J; Halter, Michael; Peskin, Adele; Bernal, Javier; Kociolek, Marcin; Brady, Mary C; Tang, Hai C; Plant, Anne L

2011-07-01

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability. Published 2011 Wiley-Liss, Inc.

Research Instruments

NASA Technical Reports Server (NTRS)

1992-01-01

The GENETI-SCANNER, newest product of Perceptive Scientific Instruments, Inc. (PSI), rapidly scans slides, locates, digitizes, measures and classifies specific objects and events in research and diagnostic applications. Founded by former NASA employees, PSI's primary product line is based on NASA image processing technology. The instruments karyotype - a process employed in analysis and classification of chromosomes - using a video camera mounted on a microscope. Images are digitized, enabling chromosome image enhancement. The system enables karyotyping to be done significantly faster, increasing productivity and lowering costs. Product is no longer being manufactured.

The Land-Use and Land-Cover Change Analysis in Beijing Huairou in Last Ten Years

NASA Astrophysics Data System (ADS)

Zhao, Q.; Liu, G.; Tu, J.; Wang, Z.

2018-04-01

With eCognition software, the sample-based object-oriented classification method is used. Remote sensing images in Huairou district of Beijing had been classified using remote sensing images of last ten years. According to the results of image processing, the land use types in Huairou district of Beijing were analyzed in the past ten years, and the changes of land use types in Huairou district were obtained, and the reasons for its occurrence were analyzed.

Diagnostic support for glaucoma using retinal images: a hybrid image analysis and data mining approach.

PubMed

Yu, Jin; Abidi, Syed Sibte Raza; Artes, Paul; McIntyre, Andy; Heywood, Malcolm

2005-01-01

The availability of modern imaging techniques such as Confocal Scanning Laser Tomography (CSLT) for capturing high-quality optic nerve images offer the potential for developing automatic and objective methods for diagnosing glaucoma. We present a hybrid approach that features the analysis of CSLT images using moment methods to derive abstract image defining features. The features are then used to train classifers for automatically distinguishing CSLT images of normal and glaucoma patient. As a first, in this paper, we present investigations in feature subset selction methods for reducing the relatively large input space produced by the moment methods. We use neural networks and support vector machines to determine a sub-set of moments that offer high classification accuracy. We demonstratee the efficacy of our methods to discriminate between healthy and glaucomatous optic disks based on shape information automatically derived from optic disk topography and reflectance images.

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments.

PubMed

Al-Nawashi, Malek; Al-Hazaimeh, Obaida M; Saraee, Mohamad

2017-01-01

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function. Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e., human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups: normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval. Finally, a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention.

Automatic textual annotation of video news based on semantic visual object extraction

NASA Astrophysics Data System (ADS)

Boujemaa, Nozha; Fleuret, Francois; Gouet, Valerie; Sahbi, Hichem

2003-12-01

In this paper, we present our work for automatic generation of textual metadata based on visual content analysis of video news. We present two methods for semantic object detection and recognition from a cross modal image-text thesaurus. These thesaurus represent a supervised association between models and semantic labels. This paper is concerned with two semantic objects: faces and Tv logos. In the first part, we present our work for efficient face detection and recogniton with automatic name generation. This method allows us also to suggest the textual annotation of shots close-up estimation. On the other hand, we were interested to automatically detect and recognize different Tv logos present on incoming different news from different Tv Channels. This work was done jointly with the French Tv Channel TF1 within the "MediaWorks" project that consists on an hybrid text-image indexing and retrieval plateform for video news.

Chapter 17: Bioimage Informatics for Systems Pharmacology

PubMed Central

Li, Fuhai; Yin, Zheng; Jin, Guangxu; Zhao, Hong; Wong, Stephen T. C.

2013-01-01

Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies. PMID:23633943

An automatic method to detect and track the glottal gap from high speed videoendoscopic images.

PubMed

Andrade-Miranda, Gustavo; Godino-Llorente, Juan I; Moro-Velázquez, Laureano; Gómez-García, Jorge Andrés

2015-10-29

The image-based analysis of the vocal folds vibration plays an important role in the diagnosis of voice disorders. The analysis is based not only on the direct observation of the video sequences, but also in an objective characterization of the phonation process by means of features extracted from the recorded images. However, such analysis is based on a previous accurate identification of the glottal gap, which is the most challenging step for a further automatic assessment of the vocal folds vibration. In this work, a complete framework to automatically segment and track the glottal area (or glottal gap) is proposed. The algorithm identifies a region of interest that is adapted along time, and combine active contours and watershed transform for the final delineation of the glottis and also an automatic procedure for synthesize different videokymograms is proposed. Thanks to the ROI implementation, our technique is robust to the camera shifting and also the objective test proved the effectiveness and performance of the approach in the most challenging scenarios that it is when exist an inappropriate closure of the vocal folds. The novelties of the proposed algorithm relies on the used of temporal information for identify an adaptive ROI and the use of watershed merging combined with active contours for the glottis delimitation. Additionally, an automatic procedure for synthesize multiline VKG by the identification of the glottal main axis is developed.

Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

NASA Astrophysics Data System (ADS)

Novelli, Antonio; Aguilar, Manuel A.; Nemmaoui, Abderrahim; Aguilar, Fernando J.; Tarantino, Eufemia

2016-10-01

This paper shows the first comparison between data from Sentinel-2 (S2) Multi Spectral Instrument (MSI) and Landsat 8 (L8) Operational Land Imager (OLI) headed up to greenhouse detection. Two closely related in time scenes, one for each sensor, were classified by using Object Based Image Analysis and Random Forest (RF). The RF input consisted of several object-based features computed from spectral bands and including mean values, spectral indices and textural features. S2 and L8 data comparisons were also extended using a common segmentation dataset extracted form VHR World-View 2 (WV2) imagery to test differences only due to their specific spectral contribution. The best band combinations to perform segmentation were found through a modified version of the Euclidian Distance 2 index. Four different RF classifications schemes were considered achieving 89.1%, 91.3%, 90.9% and 93.4% as the best overall accuracies respectively, evaluated over the whole study area.

Local multifractal detrended fluctuation analysis for non-stationary image's texture segmentation

NASA Astrophysics Data System (ADS)

Wang, Fang; Li, Zong-shou; Li, Jin-wei

2014-12-01

Feature extraction plays a great important role in image processing and pattern recognition. As a power tool, multifractal theory is recently employed for this job. However, traditional multifractal methods are proposed to analyze the objects with stationary measure and cannot for non-stationary measure. The works of this paper is twofold. First, the definition of stationary image and 2D image feature detection methods are proposed. Second, a novel feature extraction scheme for non-stationary image is proposed by local multifractal detrended fluctuation analysis (Local MF-DFA), which is based on 2D MF-DFA. A set of new multifractal descriptors, called local generalized Hurst exponent (Lhq) is defined to characterize the local scaling properties of textures. To test the proposed method, both the novel texture descriptor and other two multifractal indicators, namely, local Hölder coefficients based on capacity measure and multifractal dimension Dq based on multifractal differential box-counting (MDBC) method, are compared in segmentation experiments. The first experiment indicates that the segmentation results obtained by the proposed Lhq are better than the MDBC-based Dq slightly and superior to the local Hölder coefficients significantly. The results in the second experiment demonstrate that the Lhq can distinguish the texture images more effectively and provide more robust segmentations than the MDBC-based Dq significantly.

Object-based connectedness facilitates matching.

PubMed

Koning, Arno; van Lier, Rob

2003-10-01

In two matching tasks, participants had to match two images of object pairs. Image-based (IB) connectedness refers to connectedness between the objects in an image. Object-based (OB) connectedness refers to connectedness between the interpreted objects. In Experiment 1, a monocular depth cue (shadow) was used to distinguish different relation types between object pairs. Three relation types were created: IB/OB-connected objects, IB/OB-disconnected objects, and IB-connected/OB-disconnected objects. It was found that IB/OB-connected objects were matched faster than IB/OB-disconnected objects. Objects that were IB-connected/OB-disconnected were matched equally to IB/OB-disconnected objects. In Experiment 2, stereoscopic presentation was used. With relation types comparable to those in Experiment 1, it was again found that OB connectedness determined speed of matching, rather than IB connectedness. We conclude that matching of projections of three-dimensional objects depends more on OB connectedness than on IB connectedness.

Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating

NASA Astrophysics Data System (ADS)

Matikainen, Leena; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Puttonen, Eetu; Ahokas, Eero

2017-06-01

During the last 20 years, airborne laser scanning (ALS), often combined with passive multispectral information from aerial images, has shown its high feasibility for automated mapping processes. The main benefits have been achieved in the mapping of elevated objects such as buildings and trees. Recently, the first multispectral airborne laser scanners have been launched, and active multispectral information is for the first time available for 3D ALS point clouds from a single sensor. This article discusses the potential of this new technology in map updating, especially in automated object-based land cover classification and change detection in a suburban area. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from an object-based random forests analysis suggest that the multispectral ALS data are very useful for land cover classification, considering both elevated classes and ground-level classes. The overall accuracy of the land cover classification results with six classes was 96% compared with validation points. The classes under study included building, tree, asphalt, gravel, rocky area and low vegetation. Compared to classification of single-channel data, the main improvements were achieved for ground-level classes. According to feature importance analyses, multispectral intensity features based on several channels were more useful than those based on one channel. Automatic change detection for buildings and roads was also demonstrated by utilising the new multispectral ALS data in combination with old map vectors. In change detection of buildings, an old digital surface model (DSM) based on single-channel ALS data was also used. Overall, our analyses suggest that the new data have high potential for further increasing the automation level in mapping. Unlike passive aerial imaging commonly used in mapping, the multispectral ALS technology is independent of external illumination conditions, and there are no shadows on intensity images produced from the data. These are significant advantages in developing automated classification and change detection procedures.

[Application of Fourier transform profilometry in 3D-surface reconstruction].

PubMed

Shi, Bi'er; Lu, Kuan; Wang, Yingting; Li, Zhen'an; Bai, Jing

2011-08-01

With the improvement of system frame and reconstruction methods in fluorescent molecules tomography (FMT), the FMT technology has been widely used as an important experimental tool in biomedical research. It is necessary to get the 3D-surface profile of the experimental object as the boundary constraints of FMT reconstruction algorithms. We proposed a new 3D-surface reconstruction method based on Fourier transform profilometry (FTP) method under the blue-purple light condition. The slice images were reconstructed using proper image processing methods, frequency spectrum analysis and filtering. The results of experiment showed that the method properly reconstructed the 3D-surface of objects and has the mm-level accuracy. Compared to other methods, this one is simple and fast. Besides its well-reconstructed, the proposed method could help monitor the behavior of the object during the experiment to ensure the correspondence of the imaging process. Furthermore, the method chooses blue-purple light section as its light source to avoid the interference towards fluorescence imaging.

Integrated image data and medical record management for rare disease registries. A general framework and its instantiation to theGerman Calciphylaxis Registry.

PubMed

Deserno, Thomas M; Haak, Daniel; Brandenburg, Vincent; Deserno, Verena; Classen, Christoph; Specht, Paula

2014-12-01

Especially for investigator-initiated research at universities and academic institutions, Internet-based rare disease registries (RDR) are required that integrate electronic data capture (EDC) with automatic image analysis or manual image annotation. We propose a modular framework merging alpha-numerical and binary data capture. In concordance with the Office of Rare Diseases Research recommendations, a requirement analysis was performed based on several RDR databases currently hosted at Uniklinik RWTH Aachen, Germany. With respect to the study management tool that is already successfully operating at the Clinical Trial Center Aachen, the Google Web Toolkit was chosen with Hibernate and Gilead connecting a MySQL database management system. Image and signal data integration and processing is supported by Apache Commons FileUpload-Library and ImageJ-based Java code, respectively. As a proof of concept, the framework is instantiated to the German Calciphylaxis Registry. The framework is composed of five mandatory core modules: (1) Data Core, (2) EDC, (3) Access Control, (4) Audit Trail, and (5) Terminology as well as six optional modules: (6) Binary Large Object (BLOB), (7) BLOB Analysis, (8) Standard Operation Procedure, (9) Communication, (10) Pseudonymization, and (11) Biorepository. Modules 1-7 are implemented in the German Calciphylaxis Registry. The proposed RDR framework is easily instantiated and directly integrates image management and analysis. As open source software, it may assist improved data collection and analysis of rare diseases in near future.

Stripe nonuniformity correction for infrared imaging system based on single image optimization

NASA Astrophysics Data System (ADS)

Hua, Weiping; Zhao, Jufeng; Cui, Guangmang; Gong, Xiaoli; Ge, Peng; Zhang, Jiang; Xu, Zhihai

2018-06-01

Infrared imaging is often disturbed by stripe nonuniformity noise. Scene-based correction method can effectively reduce the impact of stripe noise. In this paper, a stripe nonuniformity correction method based on differential constraint is proposed. Firstly, the gray distribution of stripe nonuniformity is analyzed and the penalty function is constructed by the difference of horizontal gradient and vertical gradient. With the weight function, the penalty function is optimized to obtain the corrected image. Comparing with other single-frame approaches, experiments show that the proposed method performs better in both subjective and objective analysis, and does less damage to edge and detail. Meanwhile, the proposed method runs faster. We have also discussed the differences between the proposed idea and multi-frame methods. Our method is finally well applied in hardware system.

An Intelligent Architecture Based on Field Programmable Gate Arrays Designed to Detect Moving Objects by Using Principal Component Analysis

PubMed Central

Bravo, Ignacio; Mazo, Manuel; Lázaro, José L.; Gardel, Alfredo; Jiménez, Pedro; Pizarro, Daniel

2010-01-01

This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices. PMID:22163406

An intelligent architecture based on Field Programmable Gate Arrays designed to detect moving objects by using Principal Component Analysis.

PubMed

Bravo, Ignacio; Mazo, Manuel; Lázaro, José L; Gardel, Alfredo; Jiménez, Pedro; Pizarro, Daniel

2010-01-01

This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices.

Comparative Performance Analysis of Intel Xeon Phi, GPU, and CPU: A Case Study from Microscopy Image Analysis

PubMed Central

Teodoro, George; Kurc, Tahsin; Kong, Jun; Cooper, Lee; Saltz, Joel

2014-01-01

We study and characterize the performance of operations in an important class of applications on GPUs and Many Integrated Core (MIC) architectures. Our work is motivated by applications that analyze low-dimensional spatial datasets captured by high resolution sensors, such as image datasets obtained from whole slide tissue specimens using microscopy scanners. Common operations in these applications involve the detection and extraction of objects (object segmentation), the computation of features of each extracted object (feature computation), and characterization of objects based on these features (object classification). In this work, we have identify the data access and computation patterns of operations in the object segmentation and feature computation categories. We systematically implement and evaluate the performance of these operations on modern CPUs, GPUs, and MIC systems for a microscopy image analysis application. Our results show that the performance on a MIC of operations that perform regular data access is comparable or sometimes better than that on a GPU. On the other hand, GPUs are significantly more efficient than MICs for operations that access data irregularly. This is a result of the low performance of MICs when it comes to random data access. We also have examined the coordinated use of MICs and CPUs. Our experiments show that using a performance aware task strategy for scheduling application operations improves performance about 1.29× over a first-come-first-served strategy. This allows applications to obtain high performance efficiency on CPU-MIC systems - the example application attained an efficiency of 84% on 192 nodes (3072 CPU cores and 192 MICs). PMID:25419088

IMAGE EXPLORER: Astronomical Image Analysis on an HTML5-based Web Application

NASA Astrophysics Data System (ADS)

Gopu, A.; Hayashi, S.; Young, M. D.

2014-05-01

Large datasets produced by recent astronomical imagers cause the traditional paradigm for basic visual analysis - typically downloading one's entire image dataset and using desktop clients like DS9, Aladin, etc. - to not scale, despite advances in desktop computing power and storage. This paper describes Image Explorer, a web framework that offers several of the basic visualization and analysis functionality commonly provided by tools like DS9, on any HTML5 capable web browser on various platforms. It uses a combination of the modern HTML5 canvas, JavaScript, and several layers of lossless PNG tiles producted from the FITS image data. Astronomers are able to rapidly and simultaneously open up several images on their web-browser, adjust the intensity min/max cutoff or its scaling function, and zoom level, apply color-maps, view position and FITS header information, execute typically used data reduction codes on the corresponding FITS data using the FRIAA framework, and overlay tiles for source catalog objects, etc.

Application of Composite Small Calibration Objects in Traffic Accident Scene Photogrammetry

PubMed Central

Chen, Qiang; Xu, Hongguo; Tan, Lidong

2015-01-01

In order to address the difficulty of arranging large calibration objects and the low measurement accuracy of small calibration objects in traffic accident scene photogrammetry, a photogrammetric method based on a composite of small calibration objects is proposed. Several small calibration objects are placed around the traffic accident scene, and the coordinate system of the composite calibration object is given based on one of them. By maintaining the relative position and coplanar relationship of the small calibration objects, the local coordinate system of each small calibration object is transformed into the coordinate system of the composite calibration object. The two-dimensional direct linear transformation method is improved based on minimizing the reprojection error of the calibration points of all objects. A rectified image is obtained using the nonlinear optimization method. The increased accuracy of traffic accident scene photogrammetry using a composite small calibration object is demonstrated through the analysis of field experiments and case studies. PMID:26011052

Digital imaging and image analysis applied to numerical applications in forensic hair examination.

PubMed

Brooks, Elizabeth; Comber, Bruce; McNaught, Ian; Robertson, James

2011-03-01

A method that provides objective data to complement the hair analysts' microscopic observations, which is non-destructive, would be of obvious benefit in the forensic examination of hairs. This paper reports on the use of objective colour measurement and image analysis techniques of auto-montaged images. Brown Caucasian telogen scalp hairs were chosen as a stern test of the utility of these approaches. The results show the value of using auto-montaged images and the potential for the use of objective numerical measures of colour and pigmentation to complement microscopic observations. 2010. Published by Elsevier Ireland Ltd. All rights reserved.

Recent development of feature extraction and classification multispectral/hyperspectral images: a systematic literature review

NASA Astrophysics Data System (ADS)

Setiyoko, A.; Dharma, I. G. W. S.; Haryanto, T.

2017-01-01

Multispectral data and hyperspectral data acquired from satellite sensor have the ability in detecting various objects on the earth ranging from low scale to high scale modeling. These data are increasingly being used to produce geospatial information for rapid analysis by running feature extraction or classification process. Applying the most suited model for this data mining is still challenging because there are issues regarding accuracy and computational cost. This research aim is to develop a better understanding regarding object feature extraction and classification applied for satellite image by systematically reviewing related recent research projects. A method used in this research is based on PRISMA statement. After deriving important points from trusted sources, pixel based and texture-based feature extraction techniques are promising technique to be analyzed more in recent development of feature extraction and classification.

Fast algorithm of low power image reformation for OLED display

NASA Astrophysics Data System (ADS)

Lee, Myungwoo; Kim, Taewhan

2014-04-01

We propose a fast algorithm of low-power image reformation for organic light-emitting diode (OLED) display. The proposed algorithm scales the image histogram in a way to reduce power consumption in OLED display by remapping the gray levels of the pixels in the image based on the fast analysis of the histogram of the input image while maintaining contrast of the image. The key idea is that a large number of gray levels are never used in the images and these gray levels can be effectively exploited to reduce power consumption. On the other hand, to maintain the image contrast the gray level remapping is performed by taking into account the object size in the image to which each gray level is applied, that is, reforming little for the gray levels in the objects of large size. Through experiments with 24 Kodak images, it is shown that our proposed algorithm is able to reduce the power consumption by 10% even with 9% contrast enhancement. Our algorithm runs in a linear time so that it can be applied to moving pictures with high resolution.

Automatic Topography Using High Precision Digital Moire Methods

NASA Astrophysics Data System (ADS)

Yatagai, T.; Idesawa, M.; Saito, S.

1983-07-01

Three types of moire topographic methods using digital techniques are proposed. Deformed gratings obtained by projecting a reference grating onto an object under test are subjected to digital analysis. The electronic analysis procedures of deformed gratings described here enable us to distinguish between depression and elevation of the object, so that automatic measurement of 3-D shapes and automatic moire fringe interpolation are performed. Based on the digital moire methods, we have developed a practical measurement system, with a linear photodiode array on a micro-stage as a scanning image sensor. Examples of fringe analysis in medical applications are presented.

Pixel-based flood mapping from SAR imagery: a comparison of approaches

NASA Astrophysics Data System (ADS)

Landuyt, Lisa; Van Wesemael, Alexandra; Van Coillie, Frieke M. B.; Verhoest, Niko E. C.

2017-04-01

Due to their all-weather, day and night capabilities, SAR sensors have been shown to be particularly suitable for flood mapping applications. Thus, they can provide spatially-distributed flood extent data which are valuable for calibrating, validating and updating flood inundation models. These models are an invaluable tool for water managers, to take appropriate measures in times of high water levels. Image analysis approaches to delineate flood extent on SAR imagery are numerous. They can be classified into two categories, i.e. pixel-based and object-based approaches. Pixel-based approaches, e.g. thresholding, are abundant and in general computationally inexpensive. However, large discrepancies between these techniques exist and often subjective user intervention is needed. Object-based approaches require more processing but allow for the integration of additional object characteristics, like contextual information and object geometry, and thus have significant potential to provide an improved classification result. As means of benchmark, a selection of pixel-based techniques is applied on a ERS-2 SAR image of the 2006 flood event of River Dee, United Kingdom. This selection comprises Otsu thresholding, Kittler & Illingworth thresholding, the Fine To Coarse segmentation algorithm and active contour modelling. The different classification results are evaluated and compared by means of several accuracy measures, including binary performance measures.

Non-sky polarization-based dehazing algorithm for non-specular objects using polarization difference and global scene feature.

PubMed

Qu, Yufu; Zou, Zhaofan

2017-10-16

Photographic images taken in foggy or hazy weather (hazy images) exhibit poor visibility and detail because of scattering and attenuation of light caused by suspended particles, and therefore, image dehazing has attracted considerable research attention. The current polarization-based dehazing algorithms strongly rely on the presence of a "sky area", and thus, the selection of model parameters is susceptible to external interference of high-brightness objects and strong light sources. In addition, the noise of the restored image is large. In order to solve these problems, we propose a polarization-based dehazing algorithm that does not rely on the sky area ("non-sky"). First, a linear polarizer is used to collect three polarized images. The maximum- and minimum-intensity images are then obtained by calculation, assuming the polarization of light emanating from objects is negligible in most scenarios involving non-specular objects. Subsequently, the polarization difference of the two images is used to determine a sky area and calculate the infinite atmospheric light value. Next, using the global features of the image, and based on the assumption that the airlight and object radiance are irrelevant, the degree of polarization of the airlight (DPA) is calculated by solving for the optimal solution of the correlation coefficient equation between airlight and object radiance; the optimal solution is obtained by setting the right-hand side of the equation to zero. Then, the hazy image is subjected to dehazing. Subsequently, a filtering denoising algorithm, which combines the polarization difference information and block-matching and 3D (BM3D) filtering, is designed to filter the image smoothly. Our experimental results show that the proposed polarization-based dehazing algorithm does not depend on whether the image includes a sky area and does not require complex models. Moreover, the dehazing image except specular object scenarios is superior to those obtained by Tarel, Fattal, Ren, and Berman based on the criteria of no-reference quality assessment (NRQA), blind/referenceless image spatial quality evaluator (BRISQUE), blind anistropic quality index (AQI), and e.

Thread concept for automatic task parallelization in image analysis

NASA Astrophysics Data System (ADS)

Lueckenhaus, Maximilian; Eckstein, Wolfgang

1998-09-01

Parallel processing of image analysis tasks is an essential method to speed up image processing and helps to exploit the full capacity of distributed systems. However, writing parallel code is a difficult and time-consuming process and often leads to an architecture-dependent program that has to be re-implemented when changing the hardware. Therefore it is highly desirable to do the parallelization automatically. For this we have developed a special kind of thread concept for image analysis tasks. Threads derivated from one subtask may share objects and run in the same context but may process different threads of execution and work on different data in parallel. In this paper we describe the basics of our thread concept and show how it can be used as basis of an automatic task parallelization to speed up image processing. We further illustrate the design and implementation of an agent-based system that uses image analysis threads for generating and processing parallel programs by taking into account the available hardware. The tests made with our system prototype show that the thread concept combined with the agent paradigm is suitable to speed up image processing by an automatic parallelization of image analysis tasks.

Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text.

PubMed

Anderson, Andrew James; Bruni, Elia; Lopopolo, Alessandro; Poesio, Massimo; Baroni, Marco

2015-10-15

Embodiment theory predicts that mental imagery of object words recruits neural circuits involved in object perception. The degree of visual imagery present in routine thought and how it is encoded in the brain is largely unknown. We test whether fMRI activity patterns elicited by participants reading objects' names include embodied visual-object representations, and whether we can decode the representations using novel computational image-based semantic models. We first apply the image models in conjunction with text-based semantic models to test predictions of visual-specificity of semantic representations in different brain regions. Representational similarity analysis confirms that fMRI structure within ventral-temporal and lateral-occipital regions correlates most strongly with the image models and conversely text models correlate better with posterior-parietal/lateral-temporal/inferior-frontal regions. We use an unsupervised decoding algorithm that exploits commonalities in representational similarity structure found within both image model and brain data sets to classify embodied visual representations with high accuracy (8/10) and then extend it to exploit model combinations to robustly decode different brain regions in parallel. By capturing latent visual-semantic structure our models provide a route into analyzing neural representations derived from past perceptual experience rather than stimulus-driven brain activity. Our results also verify the benefit of combining multimodal data to model human-like semantic representations. Copyright © 2015 Elsevier Inc. All rights reserved.

Optimization of propagation-based x-ray phase-contrast tomography for breast cancer imaging

NASA Astrophysics Data System (ADS)

Baran, P.; Pacile, S.; Nesterets, Y. I.; Mayo, S. C.; Dullin, C.; Dreossi, D.; Arfelli, F.; Thompson, D.; Lockie, D.; McCormack, M.; Taba, S. T.; Brun, F.; Pinamonti, M.; Nickson, C.; Hall, C.; Dimmock, M.; Zanconati, F.; Cholewa, M.; Quiney, H.; Brennan, P. C.; Tromba, G.; Gureyev, T. E.

2017-03-01

The aim of this study was to optimise the experimental protocol and data analysis for in-vivo breast cancer x-ray imaging. Results are presented of the experiment at the SYRMEP beamline of Elettra Synchrotron using the propagation-based phase-contrast mammographic tomography method, which incorporates not only absorption, but also x-ray phase information. In this study the images of breast tissue samples, of a size corresponding to a full human breast, with radiologically acceptable x-ray doses were obtained, and the degree of improvement of the image quality (from the diagnostic point of view) achievable using propagation-based phase-contrast image acquisition protocols with proper incorporation of x-ray phase retrieval into the reconstruction pipeline was investigated. Parameters such as the x-ray energy, sample-to-detector distance and data processing methods were tested, evaluated and optimized with respect to the estimated diagnostic value using a mastectomy sample with a malignant lesion. The results of quantitative evaluation of images were obtained by means of radiological assessment carried out by 13 experienced specialists. A comparative analysis was performed between the x-ray and the histological images of the specimen. The results of the analysis indicate that, within the investigated range of parameters, both the objective image quality characteristics and the subjective radiological scores of propagation-based phase-contrast images of breast tissues monotonically increase with the strength of phase contrast which in turn is directly proportional to the product of the radiation wavelength and the sample-to-detector distance. The outcomes of this study serve to define the practical imaging conditions and the CT reconstruction procedures appropriate for low-dose phase-contrast mammographic imaging of live patients at specially designed synchrotron beamlines.

Objective determination of image end-members in spectral mixture analysis of AVIRIS data

NASA Technical Reports Server (NTRS)

Tompkins, Stefanie; Mustard, John F.; Pieters, Carle M.; Forsyth, Donald W.

1993-01-01

Spectral mixture analysis has been shown to be a powerful, multifaceted tool for analysis of multi- and hyper-spectral data. Applications of AVIRIS data have ranged from mapping soils and bedrock to ecosystem studies. During the first phase of the approach, a set of end-members are selected from an image cube (image end-members) that best account for its spectral variance within a constrained, linear least squares mixing model. These image end-members are usually selected using a priori knowledge and successive trial and error solutions to refine the total number and physical location of the end-members. However, in many situations a more objective method of determining these essential components is desired. We approach the problem of image end-member determination objectively by using the inherent variance of the data. Unlike purely statistical methods such as factor analysis, this approach derives solutions that conform to a physically realistic model.

Cellular neural network-based hybrid approach toward automatic image registration

NASA Astrophysics Data System (ADS)

Arun, Pattathal VijayaKumar; Katiyar, Sunil Kumar

2013-01-01

Image registration is a key component of various image processing operations that involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however, inability to properly model object shape as well as contextual information has limited the attainable accuracy. A framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as vector machines, cellular neural network (CNN), scale invariant feature transform (SIFT), coreset, and cellular automata is proposed. CNN has been found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using coreset optimization. The salient features of this work are cellular neural network approach-based SIFT feature point optimization, adaptive resampling, and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. This system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. This methodology is also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

Computer analysis of gallbladder ultrasonic images towards recognition of pathological lesions

NASA Astrophysics Data System (ADS)

Ogiela, M. R.; Bodzioch, S.

2011-06-01

This paper presents a new approach to gallbladder ultrasonic image processing and analysis towards automatic detection and interpretation of disease symptoms on processed US images. First, in this paper, there is presented a new heuristic method of filtering gallbladder contours from images. A major stage in this filtration is to segment and section off areas occupied by the said organ. This paper provides for an inventive algorithm for the holistic extraction of gallbladder image contours, based on rank filtration, as well as on the analysis of line profile sections on tested organs. The second part concerns detecting the most important lesion symptoms of the gallbladder. Automating a process of diagnosis always comes down to developing algorithms used to analyze the object of such diagnosis and verify the occurrence of symptoms related to given affection. The methodology of computer analysis of US gallbladder images presented here is clearly utilitarian in nature and after standardising can be used as a technique for supporting the diagnostics of selected gallbladder disorders using the images of this organ.

Improvement and Extension of Shape Evaluation Criteria in Multi-Scale Image Segmentation

NASA Astrophysics Data System (ADS)

Sakamoto, M.; Honda, Y.; Kondo, A.

2016-06-01

From the last decade, the multi-scale image segmentation is getting a particular interest and practically being used for object-based image analysis. In this study, we have addressed the issues on multi-scale image segmentation, especially, in improving the performances for validity of merging and variety of derived region's shape. Firstly, we have introduced constraints on the application of spectral criterion which could suppress excessive merging between dissimilar regions. Secondly, we have extended the evaluation for smoothness criterion by modifying the definition on the extent of the object, which was brought for controlling the shape's diversity. Thirdly, we have developed new shape criterion called aspect ratio. This criterion helps to improve the reproducibility on the shape of object to be matched to the actual objectives of interest. This criterion provides constraint on the aspect ratio in the bounding box of object by keeping properties controlled with conventional shape criteria. These improvements and extensions lead to more accurate, flexible, and diverse segmentation results according to the shape characteristics of the target of interest. Furthermore, we also investigated a technique for quantitative and automatic parameterization in multi-scale image segmentation. This approach is achieved by comparing segmentation result with training area specified in advance by considering the maximization of the average area in derived objects or satisfying the evaluation index called F-measure. Thus, it has been possible to automate the parameterization that suited the objectives especially in the view point of shape's reproducibility.

Multi-energy method of digital radiography for imaging of biological objects

NASA Astrophysics Data System (ADS)

Ryzhikov, V. D.; Naydenov, S. V.; Opolonin, O. D.; Volkov, V. G.; Smith, C. F.

2016-03-01

This work has been dedicated to the search for a new possibility to use multi-energy digital radiography (MER) for medical applications. Our work has included both theoretical and experimental investigations of 2-energy (2E) and 3- energy (3D) radiography for imaging the structure of biological objects. Using special simulation methods and digital analysis based on the X-ray interaction energy dependence for each element of importance to medical applications in the X-ray range of energy up to 150 keV, we have implemented a quasi-linear approximation for the energy dependence of the X-ray linear mass absorption coefficient μm (E) that permits us to determine the intrinsic structure of the biological objects. Our measurements utilize multiple X-ray tube voltages (50, 100, and 150 kV) with Al and Cu filters of different thicknesses to achieve 3-energy X-ray examination of objects. By doing so, we are able to achieve significantly improved imaging quality of the structure of the subject biological objects. To reconstruct and visualize the final images, we use both two-dimensional (2D) and three-dimensional (3D) palettes of identification. The result is a 2E and/or 3E representation of the object with color coding of each pixel according to the data outputs. Following the experimental measurements and post-processing, we produce a 3D image of the biological object - in the case of our trials, fragments or parts of chicken and turkey.

Application of syntactic methods of pattern recognition for data mining and knowledge discovery in medicine

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-04-01

This paper presents and discusses possibilities of application of selected algorithms belonging to the group of syntactic methods of patten recognition used to analyze and extract features of shapes and to diagnose morphological lesions seen on selected medical images. This method is particularly useful for specialist morphological analysis of shapes of selected organs of abdominal cavity conducted to diagnose disease symptoms occurring in the main pancreatic ducts, upper segments of ureters and renal pelvis. Analysis of the correct morphology of these organs is possible with the application of the sequential and tree method belonging to the group of syntactic methods of pattern recognition. The objective of this analysis is to support early diagnosis of disease lesions, mainly characteristic for carcinoma and pancreatitis, based on examinations of ERCP images and a diagnosis of morphological lesions in ureters as well as renal pelvis based on an analysis of urograms. In the analysis of ERCP images the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis, while in the case of kidney radiogram analysis the aim is to diagnose local irregularities of ureter lumen and to examine the morphology of renal pelvis and renal calyxes. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of description of features of shapes and context-free sequential attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing of width diagrams of the examined structures. Additionally, in order to support the analysis of the correct structure of renal pelvis a method using the tree grammar for syntactic pattern recognition to define its correct morphological shapes has been presented.

Burn severity estimation using GeoEye imagery, object-based image analysis (OBIA), and Composite Burn Index (CBI) measurements

NASA Astrophysics Data System (ADS)

Dragozi, E.; Gitas, Ioannis Z.; Stavrakoudis, Dimitris G.; Minakou, C.

2015-06-01

Forest fires greatly influence the stability and functions of the forest ecosystems. The ever increasing need for accurate and detailed information regarding post-fire effects (burn severity) has led to several studies on the matter. In this study the combined use of Very High Resolution (VHR) satellite data (GeoEye), Objectbased image analysis (OBIA) and Composite Burn Index (CBI) measurements in estimating burn severity, at two different time points (2011 and 2012) is assessed. The accuracy of the produced maps was assessed and changes in burn severity between the two dates were detected using the post classification comparison approach. It was found that the produced burn severity map for 2011 was approximately 10% more accurate than that of 2012. This was mainly attributed to the increased heterogeneity of the study area in the second year, which led to an increased number of mixed class objects and consequently made it more difficult to spectrally discriminate between the severity classes. Following the post-classification analysis, the severity class changes were mainly attributed to the trees' ability to survive severe fire damage and sprout new leaves. Moreover, the results of the study suggest that when classifying CBI-based burn severity using VHR imagery it would be preferable to use images captured soon after the fire.

Objective quality assessment for multiexposure multifocus image fusion.

PubMed

Hassen, Rania; Wang, Zhou; Salama, Magdy M A

2015-09-01

There has been a growing interest in image fusion technologies, but how to objectively evaluate the quality of fused images has not been fully understood. Here, we propose a method for objective quality assessment of multiexposure multifocus image fusion based on the evaluation of three key factors of fused image quality: 1) contrast preservation; 2) sharpness; and 3) structure preservation. Subjective experiments are conducted to create an image fusion database, based on which, performance evaluation shows that the proposed fusion quality index correlates well with subjective scores, and gives a significant improvement over the existing fusion quality measures.

a Method for the Registration of Hemispherical Photographs and Tls Intensity Images

NASA Astrophysics Data System (ADS)

Schmidt, A.; Schilling, A.; Maas, H.-G.

2012-07-01

Terrestrial laser scanners generate dense and accurate 3D point clouds with minimal effort, which represent the geometry of real objects, while image data contains texture information of object surfaces. Based on the complementary characteristics of both data sets, a combination is very appealing for many applications, including forest-related tasks. In the scope of our research project, independent data sets of a plain birch stand have been taken by a full-spherical laser scanner and a hemispherical digital camera. Previously, both kinds of data sets have been considered separately: Individual trees were successfully extracted from large 3D point clouds, and so-called forest inventory parameters could be determined. Additionally, a simplified tree topology representation was retrieved. From hemispherical images, leaf area index (LAI) values, as a very relevant parameter for describing a stand, have been computed. The objective of our approach is to merge a 3D point cloud with image data in a way that RGB values are assigned to each 3D point. So far, segmentation and classification of TLS point clouds in forestry applications was mainly based on geometrical aspects of the data set. However, a 3D point cloud with colour information provides valuable cues exceeding simple statistical evaluation of geometrical object features and thus may facilitate the analysis of the scan data significantly.

Prospects for phenological monitoring in an arid southwestern U.S. rangeland using field observations with hyperspatial and moderate resolution imagery

NASA Astrophysics Data System (ADS)

Browning, D. M.; Laliberte, A. S.; Rango, A.; Herrick, J. E.

2009-12-01

Relating field observations of plant phenological events to remotely sensed depictions of land surface phenology remains a challenge to the vertical integration of data from disparate sources. This research conducted at the Jornada Basin Long-Term Ecological Research site in southern New Mexico capitalizes on legacy datasets pertaining to reproductive phenology and biomass and hyperspatial imagery. Large amounts of exposed bare soil and modest cover from shrubs and grasses in these arid and semi-arid ecosystems challenge the integration of field observations of phenology and remotely sensed data to monitor changes in land surface phenology. Drawing on established field protocols for reproductive phenology, hyperspatial imagery (4 cm), and object-based image analysis, we explore the utility of two approaches to scale detailed observations (i.e., field and 4 cm imagery) to the extent of long-term field plots (50 x 50m) and moderate resolution Landsat Thematic Mapper (TM) imagery (30 x 30m). Very high resolution color-infrared imagery was collected June 2007 across 15 LTER study sites that transect five distinct vegetation communities along a continuum of grass to shrub dominance. We examined two methods for scaling spectral vegetation indices (SVI) at 4 cm resolution: pixel averaging and object-based integration. Pixel averaging yields the mean SVI value for all pixels within the plot or TM pixel. Alternatively, the object-based method is based on a weighted average of SVI values that correspond to discrete image objects (e.g., individual shrubs or grass patches). Object-based image analysis of 4 cm imagery provides a detailed depiction of ground cover and allows us to extract species-specific contributions to upscaled SVI values. The ability to discern species- or functional-group contributions to remotely sensed signals of vegetation greenness can greatly enhance the design of field sampling protocols for phenological research. Furthermore, imagery from unmanned aerial vehicles (UAV) is a cost-effective and increasingly available resource and generation of UAV mosaics has been accomplished so that larger study areas can be addressed. This technology can provide a robust basis for scaling relationships for phenology-based research applications.

Surface-region context in optimal multi-object graph-based segmentation: robust delineation of pulmonary tumors.

PubMed

Song, Qi; Chen, Mingqing; Bai, Junjie; Sonka, Milan; Wu, Xiaodong

2011-01-01

Multi-object segmentation with mutual interaction is a challenging task in medical image analysis. We report a novel solution to a segmentation problem, in which target objects of arbitrary shape mutually interact with terrain-like surfaces, which widely exists in the medical imaging field. The approach incorporates context information used during simultaneous segmentation of multiple objects. The object-surface interaction information is encoded by adding weighted inter-graph arcs to our graph model. A globally optimal solution is achieved by solving a single maximum flow problem in a low-order polynomial time. The performance of the method was evaluated in robust delineation of lung tumors in megavoltage cone-beam CT images in comparison with an expert-defined independent standard. The evaluation showed that our method generated highly accurate tumor segmentations. Compared with the conventional graph-cut method, our new approach provided significantly better results (p < 0.001). The Dice coefficient obtained by the conventional graph-cut approach (0.76 +/- 0.10) was improved to 0.84 +/- 0.05 when employing our new method for pulmonary tumor segmentation.

Blind guidance system based on laser triangulation

NASA Astrophysics Data System (ADS)

Wu, Jih-Huah; Wang, Jinner-Der; Fang, Wei; Lee, Yun-Parn; Shan, Yi-Chia; Kao, Hai-Ko; Ma, Shih-Hsin; Jiang, Joe-Air

2012-05-01

We propose a new guidance system for the blind. An optical triangulation method is used in the system. The main components of the proposed system comprise of a notebook computer, a camera, and two laser modules. The track image of the light beam on the ground or on the object is captured by the camera and then the image is sent to the notebook computer for further processing and analysis. Using a developed signal-processing algorithm, our system can determine the object width and the distance between the object and the blind person through the calculation of the light line positions on the image. A series of feasibility tests of the developed blind guidance system were conducted. The experimental results show that the distance between the test object and the blind can be measured with a standard deviation of less than 8.5% within the range of 40 and 130 cm, while the test object width can be measured with a standard deviation of less than 4.5% within the range of 40 and 130 cm. The application potential of the designed system to the blind guidance can be expected.

General Approach for Rock Classification Based on Digital Image Analysis of Electrical Borehole Wall Images

NASA Astrophysics Data System (ADS)

Linek, M.; Jungmann, M.; Berlage, T.; Clauser, C.

2005-12-01

Within the Ocean Drilling Program (ODP), image logging tools have been routinely deployed such as the Formation MicroScanner (FMS) or the Resistivity-At-Bit (RAB) tools. Both logging methods are based on resistivity measurements at the borehole wall and therefore are sensitive to conductivity contrasts, which are mapped in color scale images. These images are commonly used to study the structure of the sedimentary rocks and the oceanic crust (petrologic fabric, fractures, veins, etc.). So far, mapping of lithology from electrical images is purely based on visual inspection and subjective interpretation. We apply digital image analysis on electrical borehole wall images in order to develop a method, which augments objective rock identification. We focus on supervised textural pattern recognition which studies the spatial gray level distribution with respect to certain rock types. FMS image intervals of rock classes known from core data are taken in order to train textural characteristics for each class. A so-called gray level co-occurrence matrix is computed by counting the occurrence of a pair of gray levels that are a certain distant apart. Once the matrix for an image interval is computed, we calculate the image contrast, homogeneity, energy, and entropy. We assign characteristic textural features to different rock types by reducing the image information into a small set of descriptive features. Once a discriminating set of texture features for each rock type is found, we are able to discriminate the entire FMS images regarding the trained rock type classification. A rock classification based on texture features enables quantitative lithology mapping and is characterized by a high repeatability, in contrast to a purely visual subjective image interpretation. We show examples for the rock classification between breccias, pillows, massive units, and horizontally bedded tuffs based on ODP image data.

The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel; Ershov, Maksim; Strotov, Valery

2016-10-01

This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

The Analysis of Object-Based Change Detection in Mining Area: a Case Study with Pingshuo Coal Mine

NASA Astrophysics Data System (ADS)

Zhang, M.; Zhou, W.; Li, Y.

2017-09-01

Accurate information on mining land use and land cover change are crucial for monitoring and environmental change studies. In this paper, RapidEye Remote Sensing Image (Map 2012) and SPOT7 Remote Sensing Image (Map 2015) in Pingshuo Mining Area are selected to monitor changes combined with object-based classification and change vector analysis method, we also used R in highresolution remote sensing image for mining land classification, and found the feasibility and the flexibility of open source software. The results show that (1) the classification of reclaimed mining land has higher precision, the overall accuracy and kappa coefficient of the classification of the change region map were 86.67 % and 89.44 %. It's obvious that object-based classification and change vector analysis which has a great significance to improve the monitoring accuracy can be used to monitor mining land, especially reclaiming mining land; (2) the vegetation area changed from 46 % to 40 % accounted for the proportion of the total area from 2012 to 2015, and most of them were transformed into the arable land. The sum of arable land and vegetation area increased from 51 % to 70 %; meanwhile, build-up land has a certain degree of increase, part of the water area was transformed into arable land, but the extent of the two changes is not obvious. The result illustrated the transformation of reclaimed mining area, at the same time, there is still some land convert to mining land, and it shows the mine is still operating, mining land use and land cover are the dynamic procedure.

Digital Correction of Motion Artifacts in Microscopy Image Sequences Collected from Living Animals Using Rigid and Non-Rigid Registration

PubMed Central

Lorenz, Kevin S.; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.

2013-01-01

Digital image analysis is a fundamental component of quantitative microscopy. However, intravital microscopy presents many challenges for digital image analysis. In general, microscopy volumes are inherently anisotropic, suffer from decreasing contrast with tissue depth, lack object edge detail, and characteristically have low signal levels. Intravital microscopy introduces the additional problem of motion artifacts, resulting from respiratory motion and heartbeat from specimens imaged in vivo. This paper describes an image registration technique for use with sequences of intravital microscopy images collected in time-series or in 3D volumes. Our registration method involves both rigid and non-rigid components. The rigid registration component corrects global image translations, while the non-rigid component manipulates a uniform grid of control points defined by B-splines. Each control point is optimized by minimizing a cost function consisting of two parts: a term to define image similarity, and a term to ensure deformation grid smoothness. Experimental results indicate that this approach is promising based on the analysis of several image volumes collected from the kidney, lung, and salivary gland of living rodents. PMID:22092443

Remote Safety Monitoring for Elderly Persons Based on Omni-Vision Analysis

PubMed Central

Xiang, Yun; Tang, Yi-ping; Ma, Bao-qing; Yan, Hang-chen; Jiang, Jun; Tian, Xu-yuan

2015-01-01

Remote monitoring service for elderly persons is important as the aged populations in most developed countries continue growing. To monitor the safety and health of the elderly population, we propose a novel omni-directional vision sensor based system, which can detect and track object motion, recognize human posture, and analyze human behavior automatically. In this work, we have made the following contributions: (1) we develop a remote safety monitoring system which can provide real-time and automatic health care for the elderly persons and (2) we design a novel motion history or energy images based algorithm for motion object tracking. Our system can accurately and efficiently collect, analyze, and transfer elderly activity information and provide health care in real-time. Experimental results show that our technique can improve the data analysis efficiency by 58.5% for object tracking. Moreover, for the human posture recognition application, the success rate can reach 98.6% on average. PMID:25978761

From Phenomena to Objects: Segmentation of Fuzzy Objects and its Application to Oceanic Eddies

NASA Astrophysics Data System (ADS)

Wu, Qingling

A challenging image analysis problem that has received limited attention to date is the isolation of fuzzy objects---i.e. those with inherently indeterminate boundaries---from continuous field data. This dissertation seeks to bridge the gap between, on the one hand, the recognized need for Object-Based Image Analysis of fuzzy remotely sensed features, and on the other, the optimization of existing image segmentation techniques for the extraction of more discretely bounded features. Using mesoscale oceanic eddies as a case study of a fuzzy object class evident in Sea Surface Height Anomaly (SSHA) imagery, the dissertation demonstrates firstly, that the widely used region-growing and watershed segmentation techniques can be optimized and made comparable in the absence of ground truth data using the principle of parsimony. However, they both have significant shortcomings, with the region growing procedure creating contour polygons that do not follow the shape of eddies while the watershed technique frequently subdivides eddies or groups together separate eddy objects. Secondly, it was determined that these problems can be remedied by using a novel Non-Euclidian Voronoi (NEV) tessellation technique. NEV is effective in isolating the extrema associated with eddies in SSHA data while using a non-Euclidian cost-distance based procedure (based on cumulative gradients in ocean height) to define the boundaries between fuzzy objects. Using this procedure as the first stage in isolating candidate eddy objects, a novel "region-shrinking" multicriteria eddy identification algorithm was developed that includes consideration of shape and vorticity. Eddies identified by this region-shrinking technique compare favorably with those identified by existing techniques, while simplifying and improving existing automated eddy detection algorithms. However, it also tends to find a larger number of eddies as a result of its ability to separate what other techniques identify as connected eddies. The research presented here is of significance not only to eddy research in oceanography, but also to other areas of Earth System Science for which the automated detection of features lacking rigid boundary definitions is of importance.

Object-based locust habitat mapping using high-resolution multispectral satellite data in the southern Aral Sea basin

NASA Astrophysics Data System (ADS)

Navratil, Peter; Wilps, Hans

2013-01-01

Three different object-based image classification techniques are applied to high-resolution satellite data for the mapping of the habitats of Asian migratory locust (Locusta migratoria migratoria) in the southern Aral Sea basin, Uzbekistan. A set of panchromatic and multispectral Système Pour l'Observation de la Terre-5 satellite images was spectrally enhanced by normalized difference vegetation index and tasseled cap transformation and segmented into image objects, which were then classified by three different classification approaches: a rule-based hierarchical fuzzy threshold (HFT) classification method was compared to a supervised nearest neighbor classifier and classification tree analysis by the quick, unbiased, efficient statistical trees algorithm. Special emphasis was laid on the discrimination of locust feeding and breeding habitats due to the significance of this discrimination for practical locust control. Field data on vegetation and land cover, collected at the time of satellite image acquisition, was used to evaluate classification accuracy. The results show that a robust HFT classifier outperformed the two automated procedures by 13% overall accuracy. The classification method allowed a reliable discrimination of locust feeding and breeding habitats, which is of significant importance for the application of the resulting data for an economically and environmentally sound control of locust pests because exact spatial knowledge on the habitat types allows a more effective surveying and use of pesticides.

Studying the Sky/Planets Can Drown You in Images: Machine Learning Solutions at JPL/Caltech

NASA Technical Reports Server (NTRS)

Fayyad, U. M.

1995-01-01

JPL is working to develop a domain-independent system capable of small-scale object recognition in large image databases for science analysis. Two applications discussed are the cataloging of three billion sky objects in the Sky Image Cataloging and Analysis Tool (SKICAT) and the detection of possibly one million small volcanoes visible in the Magellan synthetic aperture radar images of Venus (JPL Adaptive Recognition Tool, JARTool).

Medical image computing for computer-supported diagnostics and therapy. Advances and perspectives.

PubMed

Handels, H; Ehrhardt, J

2009-01-01

Medical image computing has become one of the most challenging fields in medical informatics. In image-based diagnostics of the future software assistance will become more and more important, and image analysis systems integrating advanced image computing methods are needed to extract quantitative image parameters to characterize the state and changes of image structures of interest (e.g. tumors, organs, vessels, bones etc.) in a reproducible and objective way. Furthermore, in the field of software-assisted and navigated surgery medical image computing methods play a key role and have opened up new perspectives for patient treatment. However, further developments are needed to increase the grade of automation, accuracy, reproducibility and robustness. Moreover, the systems developed have to be integrated into the clinical workflow. For the development of advanced image computing systems methods of different scientific fields have to be adapted and used in combination. The principal methodologies in medical image computing are the following: image segmentation, image registration, image analysis for quantification and computer assisted image interpretation, modeling and simulation as well as visualization and virtual reality. Especially, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients and will gain importance in diagnostic and therapy of the future. From a methodical point of view the authors identify the following future trends and perspectives in medical image computing: development of optimized application-specific systems and integration into the clinical workflow, enhanced computational models for image analysis and virtual reality training systems, integration of different image computing methods, further integration of multimodal image data and biosignals and advanced methods for 4D medical image computing. The development of image analysis systems for diagnostic support or operation planning is a complex interdisciplinary process. Image computing methods enable new insights into the patient's image data and have the future potential to improve medical diagnostics and patient treatment.

Detection of potato beetle damage using remote sensing from small unmanned aircraft systems

NASA Astrophysics Data System (ADS)

Hunt, E. Raymond; Rondon, Silvia I.

2017-04-01

Colorado potato beetle (CPB) adults and larvae devour leaves of potato and other solanaceous crops and weeds, and may quickly develop resistance to pesticides. With early detection of CPB damage, more options are available for precision integrated pest management, which reduces the amount of pesticides applied in a field. Remote sensing with small unmanned aircraft systems (sUAS) has potential for CPB detection because low flight altitudes allow image acquisition at very high spatial resolution. A five-band multispectral sensor and up-looking incident light sensor were mounted on a six-rotor sUAS, which was flown at altitudes of 60 and 30 m in June 2014. Plants went from visibly undamaged to having some damage in just 1 day. Whole-plot normalized difference vegetation index (NDVI) and the number of pixels classified as damaged (0.70≤NDVI≤0.80) were not correlated with visible CPB damage ranked from least to most. Area of CPB damage estimated using object-based image analysis was highly correlated to the visual ranking of damage. Furthermore, plant height calculated using structure-from-motion point clouds was related to CPB damage, but this method required extensive operator intervention for success. Object-based image analysis has potential for early detection based on high spatial resolution sUAS remote sensing.

Proton radiography for inline treatment planning and positioning verification of small animals.

PubMed

Müller, Johannes; Neubert, Christian; von Neubeck, Cläre; Baumann, Michael; Krause, Mechthild; Enghardt, Wolfgang; Bütof, Rebecca; Dietrich, Antje; Lühr, Armin

2017-11-01

As proton therapy becomes increasingly well established, there is a need for high-quality clinically relevant in vivo data to gain better insight into the radiobiological effects of proton irradiation on both healthy and tumor tissue. This requires the development of easily applicable setups that allow for efficient, fractionated, image-guided proton irradiation of small animals, the most widely used pre-clinical model. Here, a method is proposed to perform dual-energy proton radiography for inline positioning verification and treatment planning. Dual-energy proton radiography exploits the differential enhancement of object features in two successively measured two-dimensional (2D) dose distributions at two different proton energies. The two raw images show structures that are dominated by energy absorption (absorption mode) or scattering (scattering mode) of protons in the object, respectively. Data post-processing allowed for the separation of both signal contributions in the respective images. The images were evaluated regarding recognizable object details and feasibility of rigid registration to acquired planar X-ray scans. Robust, automated rigid registration of proton radiography and planar X-ray images in scattering mode could be reliably achieved with the animal bedding unit used as registration landmark. Distinguishable external and internal features of the imaged mouse included the outer body contour, the skull with substructures, the lung, abdominal structures and the hind legs. Image analysis based on the combined information of both imaging modes allowed image enhancement and calculation of 2D water-equivalent path length (WEPL) maps of the object along the beam direction. Fractionated irradiation of exposed target volumes (e.g., subcutaneous tumor model or brain) can be realized with the suggested method being used for daily positioning and range determination. Robust registration of X-ray and proton radiography images allows for the irradiation of tumor entities that require conventional computed tomography (CT)-based planning, such as orthotopic lung or brain tumors, similar to conventional patient treatment.

A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping

PubMed Central

Chuang, Yung-Chung Matt; Shiu, Yi-Shiang

2016-01-01

Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply. PMID:27128915

A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping.

PubMed

Chuang, Yung-Chung Matt; Shiu, Yi-Shiang

2016-04-26

Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply.

Vision based obstacle detection and grouping for helicopter guidance

NASA Technical Reports Server (NTRS)

Sridhar, Banavar; Chatterji, Gano

1993-01-01

Electro-optical sensors can be used to compute range to objects in the flight path of a helicopter. The computation is based on the optical flow/motion at different points in the image. The motion algorithms provide a sparse set of ranges to discrete features in the image sequence as a function of azimuth and elevation. For obstacle avoidance guidance and display purposes, these discrete set of ranges, varying from a few hundreds to several thousands, need to be grouped into sets which correspond to objects in the real world. This paper presents a new method for object segmentation based on clustering the sparse range information provided by motion algorithms together with the spatial relation provided by the static image. The range values are initially grouped into clusters based on depth. Subsequently, the clusters are modified by using the K-means algorithm in the inertial horizontal plane and the minimum spanning tree algorithms in the image plane. The object grouping allows interpolation within a group and enables the creation of dense range maps. Researchers in robotics have used densely scanned sequence of laser range images to build three-dimensional representation of the outside world. Thus, modeling techniques developed for dense range images can be extended to sparse range images. The paper presents object segmentation results for a sequence of flight images.

On the usefulness of gradient information in multi-objective deformable image registration using a B-spline-based dual-dynamic transformation model: comparison of three optimization algorithms

NASA Astrophysics Data System (ADS)

Pirpinia, Kleopatra; Bosman, Peter A. N.; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

2015-03-01

The use of gradient information is well-known to be highly useful in single-objective optimization-based image registration methods. However, its usefulness has not yet been investigated for deformable image registration from a multi-objective optimization perspective. To this end, within a previously introduced multi-objective optimization framework, we use a smooth B-spline-based dual-dynamic transformation model that allows us to derive gradient information analytically, while still being able to account for large deformations. Within the multi-objective framework, we previously employed a powerful evolutionary algorithm (EA) that computes and advances multiple outcomes at once, resulting in a set of solutions (a so-called Pareto front) that represents efficient trade-offs between the objectives. With the addition of the B-spline-based transformation model, we studied the usefulness of gradient information in multiobjective deformable image registration using three different optimization algorithms: the (gradient-less) EA, a gradientonly algorithm, and a hybridization of these two. We evaluated the algorithms to register highly deformed images: 2D MRI slices of the breast in prone and supine positions. Results demonstrate that gradient-based multi-objective optimization significantly speeds up optimization in the initial stages of optimization. However, allowing sufficient computational resources, better results could still be obtained with the EA. Ultimately, the hybrid EA found the best overall approximation of the optimal Pareto front, further indicating that adding gradient-based optimization for multiobjective optimization-based deformable image registration can indeed be beneficial

How to Determine the Centre of Mass of Bodies from Image Modelling

ERIC Educational Resources Information Center

Dias, Marco Adriano; Carvalho, Paulo Simeão; Rodrigues, Marcelo

2016-01-01

Image modelling is a recent technique in physics education that includes digital tools for image treatment and analysis, such as digital stroboscopic photography (DSP) and video analysis software. It is commonly used to analyse the motion of objects. In this work we show how to determine the position of the centre of mass (CM) of objects with…

Research on autonomous identification of airport targets based on Gabor filtering and Radon transform

NASA Astrophysics Data System (ADS)

Yi, Juan; Du, Qingyu; Zhang, Hong jiang; Zhang, Yao lei

2017-11-01

Target recognition is a leading key technology in intelligent image processing and application development at present, with the enhancement of computer processing ability, autonomous target recognition algorithm, gradually improve intelligence, and showed good adaptability. Taking the airport target as the research object, analysis the airport layout characteristics, construction of knowledge model, Gabor filter and Radon transform based on the target recognition algorithm of independent design, image processing and feature extraction of the airport, the algorithm was verified, and achieved better recognition results.

Global Contrast Based Salient Region Detection.

PubMed

Cheng, Ming-Ming; Mitra, Niloy J; Huang, Xiaolei; Torr, Philip H S; Hu, Shi-Min

2015-03-01

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

Rapid Extraction of Landslide and Spatial Distribution Analysis after Jiuzhaigou Ms7.0 Earthquake Based on Uav Images

NASA Astrophysics Data System (ADS)

Jiao, Q. S.; Luo, Y.; Shen, W. H.; Li, Q.; Wang, X.

2018-04-01

Jiuzhaigou earthquake led to the collapse of the mountains and formed lots of landslides in Jiuzhaigou scenic spot and surrounding roads which caused road blockage and serious ecological damage. Due to the urgency of the rescue, the authors carried unmanned aerial vehicle (UAV) and entered the disaster area as early as August 9 to obtain the aerial images near the epicenter. On the basis of summarizing the earthquake landslides characteristics in aerial images, by using the object-oriented analysis method, landslides image objects were obtained by multi-scale segmentation, and the feature rule set of each level was automatically built by SEaTH (Separability and Thresholds) algorithm to realize the rapid landslide extraction. Compared with visual interpretation, object-oriented automatic landslides extraction method achieved an accuracy of 94.3 %. The spatial distribution of the earthquake landslide had a significant positive correlation with slope and relief and had a negative correlation with the roughness, but no obvious correlation with the aspect. The relationship between the landslide and the aspect was not found and the probable reason may be that the distance between the study area and the seismogenic fault was too far away. This work provided technical support for the earthquake field emergency, earthquake landslide prediction and disaster loss assessment.

A Variational Level Set Approach Based on Local Entropy for Image Segmentation and Bias Field Correction.

PubMed

Tang, Jian; Jiang, Xiaoliang

2017-01-01

Image segmentation has always been a considerable challenge in image analysis and understanding due to the intensity inhomogeneity, which is also commonly known as bias field. In this paper, we present a novel region-based approach based on local entropy for segmenting images and estimating the bias field simultaneously. Firstly, a local Gaussian distribution fitting (LGDF) energy function is defined as a weighted energy integral, where the weight is local entropy derived from a grey level distribution of local image. The means of this objective function have a multiplicative factor that estimates the bias field in the transformed domain. Then, the bias field prior is fully used. Therefore, our model can estimate the bias field more accurately. Finally, minimization of this energy function with a level set regularization term, image segmentation, and bias field estimation can be achieved. Experiments on images of various modalities demonstrated the superior performance of the proposed method when compared with other state-of-the-art approaches.

Assessment of Multiresolution Segmentation for Extracting Greenhouses from WORLDVIEW-2 Imagery

NASA Astrophysics Data System (ADS)

Aguilar, M. A.; Aguilar, F. J.; García Lorca, A.; Guirado, E.; Betlej, M.; Cichon, P.; Nemmaoui, A.; Vallario, A.; Parente, C.

2016-06-01

The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In this way, object based image analysis (OBIA) approach has been proved as the best option when working with VHR satellite imagery. OBIA considers spectral, geometric, textural and topological attributes associated with meaningful image objects. Thus, the first step of OBIA, referred to as segmentation, is to delineate objects of interest. Determination of an optimal segmentation is crucial for a good performance of the second stage in OBIA, the classification process. The main goal of this work is to assess the multiresolution segmentation algorithm provided by eCognition software for delineating greenhouses from WorldView- 2 multispectral orthoimages. Specifically, the focus is on finding the optimal parameters of the multiresolution segmentation approach (i.e., Scale, Shape and Compactness) for plastic greenhouses. The optimum Scale parameter estimation was based on the idea of local variance of object heterogeneity within a scene (ESP2 tool). Moreover, different segmentation results were attained by using different combinations of Shape and Compactness values. Assessment of segmentation quality based on the discrepancy between reference polygons and corresponding image segments was carried out to identify the optimal setting of multiresolution segmentation parameters. Three discrepancy indices were used: Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR) and Euclidean Distance 2 (ED2).

CT Image Sequence Analysis for Object Recognition - A Rule-Based 3-D Computer Vision System

Treesearch

Dongping Zhu; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman

1991-01-01

Research is now underway to create a vision system for hardwood log inspection using a knowledge-based approach. In this paper, we present a rule-based, 3-D vision system for locating and identifying wood defects using topological, geometric, and statistical attributes. A number of different features can be derived from the 3-D input scenes. These features and evidence...

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms.

PubMed

Reena Benjamin, J; Jayasree, T

2018-02-01

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms. A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details. The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations. In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

Extending a prototype knowledge- and object-based image analysis model to coarser spatial resolution imagery: an example from the Missouri River

USGS Publications Warehouse

Strong, Laurence L.

2012-01-01

A prototype knowledge- and object-based image analysis model was developed to inventory and map least tern and piping plover habitat on the Missouri River, USA. The model has been used to inventory the state of sandbars annually for 4 segments of the Missouri River since 2006 using QuickBird imagery. Interpretation of the state of sandbars is difficult when images for the segment are acquired at different river stages and different states of vegetation phenology and canopy cover. Concurrent QuickBird and RapidEye images were classified using the model and the spatial correspondence of classes in the land cover and sandbar maps were analysed for the spatial extent of the images and at nest locations for both bird species. Omission and commission errors were low for unvegetated land cover classes used for nesting by both bird species and for land cover types with continuous vegetation cover and water. Errors were larger for land cover classes characterized by a mixture of sand and vegetation. Sandbar classification decisions are made using information on land cover class proportions and disagreement between sandbar classes was resolved using fuzzy membership possibilities. Regression analysis of area for a paired sample of 47 sandbars indicated an average positive bias, 1.15 ha, for RapidEye that did not vary with sandbar size. RapidEye has potential to reduce temporal uncertainty about least tern and piping plover habitat but would not be suitable for mapping sandbar erosion, and characterization of sandbar shapes or vegetation patches at fine spatial resolution.

Extending a prototype knowledge and object based image analysis model to coarser spatial resolution imagery: an example from the Missouri River

USGS Publications Warehouse

Strong, Laurence L.

2012-01-01

A prototype knowledge- and object-based image analysis model was developed to inventory and map least tern and piping plover habitat on the Missouri River, USA. The model has been used to inventory the state of sandbars annually for 4 segments of the Missouri River since 2006 using QuickBird imagery. Interpretation of the state of sandbars is difficult when images for the segment are acquired at different river stages and different states of vegetation phenology and canopy cover. Concurrent QuickBird and RapidEye images were classified using the model and the spatial correspondence of classes in the land cover and sandbar maps were analysed for the spatial extent of the images and at nest locations for both bird species. Omission and commission errors were low for unvegetated land cover classes used for nesting by both bird species and for land cover types with continuous vegetation cover and water. Errors were larger for land cover classes characterized by a mixture of sand and vegetation. Sandbar classification decisions are made using information on land cover class proportions and disagreement between sandbar classes was resolved using fuzzy membership possibilities. Regression analysis of area for a paired sample of 47 sandbars indicated an average positive bias, 1.15 ha, for RapidEye that did not vary with sandbar size. RapidEye has potential to reduce temporal uncertainty about least tern and piping plover habitat but would not be suitable for mapping sandbar erosion, and characterization of sandbar shapes or vegetation patches at fine spatial resolution.

Object Segmentation and Ground Truth in 3D Embryonic Imaging.

PubMed

Rajasekaran, Bhavna; Uriu, Koichiro; Valentin, Guillaume; Tinevez, Jean-Yves; Oates, Andrew C

2016-01-01

Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets.

Object Segmentation and Ground Truth in 3D Embryonic Imaging

PubMed Central

Rajasekaran, Bhavna; Uriu, Koichiro; Valentin, Guillaume; Tinevez, Jean-Yves; Oates, Andrew C.

2016-01-01

Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets. PMID:27332860

Mapping landslide source and transport areas in VHR images with Object-Based Analysis and Support Vector Machines

NASA Astrophysics Data System (ADS)

Heleno, Sandra; Matias, Magda; Pina, Pedro

2015-04-01

Visual interpretation of satellite imagery remains extremely demanding in terms of resources and time, especially when dealing with numerous multi-scale landslides affecting wide areas, such as is the case of rainfall-induced shallow landslides. Applying automated methods can contribute to more efficient landslide mapping and updating of existing inventories, and in recent years the number and variety of approaches is rapidly increasing. Very High Resolution (VHR) images, acquired by space-borne sensors with sub-metric precision, such as Ikonos, Quickbird, Geoeye and Worldview, are increasingly being considered as the best option for landslide mapping, but these new levels of spatial detail also present new challenges to state of the art image analysis tools, asking for automated methods specifically suited to map landslide events on VHR optical images. In this work we develop and test a methodology for semi-automatic landslide recognition and mapping of landslide source and transport areas. The method combines object-based image analysis and a Support Vector Machine supervised learning algorithm, and was tested using a GeoEye-1 multispectral image, sensed 3 days after a damaging landslide event in Madeira Island, together with a pre-event LiDAR DEM. Our approach has proved successful in the recognition of landslides on a 15 Km2-wide study area, with 81 out of 85 landslides detected in its validation regions. The classifier also showed reasonable performance (false positive rate 60% and false positive rate below 36% in both validation regions) in the internal mapping of landslide source and transport areas, in particular in the sunnier east-facing slopes. In the less illuminated areas the classifier is still able to accurately map the source areas, but performs poorly in the mapping of landslide transport areas.

A comparison of autonomous techniques for multispectral image analysis and classification

NASA Astrophysics Data System (ADS)

Valdiviezo-N., Juan C.; Urcid, Gonzalo; Toxqui-Quitl, Carina; Padilla-Vivanco, Alfonso

2012-10-01

Multispectral imaging has given place to important applications related to classification and identification of objects from a scene. Because of multispectral instruments can be used to estimate the reflectance of materials in the scene, these techniques constitute fundamental tools for materials analysis and quality control. During the last years, a variety of algorithms has been developed to work with multispectral data, whose main purpose has been to perform the correct classification of the objects in the scene. The present study introduces a brief review of some classical as well as a novel technique that have been used for such purposes. The use of principal component analysis and K-means clustering techniques as important classification algorithms is here discussed. Moreover, a recent method based on the min-W and max-M lattice auto-associative memories, that was proposed for endmember determination in hyperspectral imagery, is introduced as a classification method. Besides a discussion of their mathematical foundation, we emphasize their main characteristics and the results achieved for two exemplar images conformed by objects similar in appearance, but spectrally different. The classification results state that the first components computed from principal component analysis can be used to highlight areas with different spectral characteristics. In addition, the use of lattice auto-associative memories provides good results for materials classification even in the cases where some spectral similarities appears in their spectral responses.

A segmentation algorithm based on image projection for complex text layout

NASA Astrophysics Data System (ADS)

Zhu, Wangsheng; Chen, Qin; Wei, Chuanyi; Li, Ziyang

2017-10-01

Segmentation algorithm is an important part of layout analysis, considering the efficiency advantage of the top-down approach and the particularity of the object, a breakdown of projection layout segmentation algorithm. Firstly, the algorithm will algorithm first partitions the text image, and divided into several columns, then for each column scanning projection, the text image is divided into several sub regions through multiple projection. The experimental results show that, this method inherits the projection itself and rapid calculation speed, but also can avoid the effect of arc image information page segmentation, and also can accurate segmentation of the text image layout is complex.

Detecting Slums from Quick Bird Data in Pune Using AN Object Oriented Approach

NASA Astrophysics Data System (ADS)

Shekhar, S.

2012-07-01

We have been witnessing a gradual and steady transformation from a pre dominantly rural society to an urban society in India and by 2030, it will have more people living in urban than rural areas. Slums formed an integral part of Indian urbanisation as most of the Indian cities lack in basic needs of an acceptable life. Many efforts are being taken to improve their conditions. To carry out slum renewal programs and monitor its implementation, slum settlements should be recorded to obtain an adequate spatial data base. This can be only achieved through the analysis of remote sensing data with very high spatial resolution. Regarding the occurrences of settlement areas in the remote sensing data pixel-based approach on a high resolution image is unable to represent the heterogeneity of complex urban environments. Hence there is a need for sophisticated method and data for slum analysis. An attempt has been made to detect and discriminate the slums of Pune city by describing typical characteristics of these settlements, by using eCognition software from quick bird data on the basis of object oriented approach. Based on multi resolution segmentation, initial objects were created and further depend on texture, geometry and contextual characteristics of the image objects, they were classified into slums and non-slums. The developed rule base allowed the description of knowledge about phenomena clearly and easily using fuzzy membership functions and the described knowledge stored in the classification rule base led to the best classification with more than 80% accuracy.

Infrared and visible image fusion with spectral graph wavelet transform.

PubMed

Yan, Xiang; Qin, Hanlin; Li, Jia; Zhou, Huixin; Zong, Jing-guo

2015-09-01

Infrared and visible image fusion technique is a popular topic in image analysis because it can integrate complementary information and obtain reliable and accurate description of scenes. Multiscale transform theory as a signal representation method is widely used in image fusion. In this paper, a novel infrared and visible image fusion method is proposed based on spectral graph wavelet transform (SGWT) and bilateral filter. The main novelty of this study is that SGWT is used for image fusion. On the one hand, source images are decomposed by SGWT in its transform domain. The proposed approach not only effectively preserves the details of different source images, but also excellently represents the irregular areas of the source images. On the other hand, a novel weighted average method based on bilateral filter is proposed to fuse low- and high-frequency subbands by taking advantage of spatial consistency of natural images. Experimental results demonstrate that the proposed method outperforms seven recently proposed image fusion methods in terms of both visual effect and objective evaluation metrics.

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

NASA Astrophysics Data System (ADS)

Cheng, Gong; Han, Junwei; Zhou, Peicheng; Guo, Lei

2014-12-01

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.

Automatic Adviser on Mobile Objects Status Identification and Classification

NASA Astrophysics Data System (ADS)

Shabelnikov, A. N.; Liabakh, N. N.; Gibner, Ya M.; Saryan, A. S.

2018-05-01

A mobile object status identification task is defined within the image discrimination theory. It is proposed to classify objects into three classes: object operation status; its maintenance is required and object should be removed from the production process. Two methods were developed to construct the separating boundaries between the designated classes: a) using statistical information on the research objects executed movement, b) basing on regulatory documents and expert commentary. Automatic Adviser operation simulation and the operation results analysis complex were synthesized. Research results are commented using a specific example of cuts rolling from the hump yard. The work was supported by Russian Fundamental Research Fund, project No. 17-20-01040.

Improved Object Localization Using Accurate Distance Estimation in Wireless Multimedia Sensor Networks

PubMed Central

Ur Rehman, Yasar Abbas; Tariq, Muhammad; Khan, Omar Usman

2015-01-01

Object localization plays a key role in many popular applications of Wireless Multimedia Sensor Networks (WMSN) and as a result, it has acquired a significant status for the research community. A significant body of research performs this task without considering node orientation, object geometry and environmental variations. As a result, the localized object does not reflect the real world scenarios. In this paper, a novel object localization scheme for WMSN has been proposed that utilizes range free localization, computer vision, and principle component analysis based algorithms. The proposed approach provides the best possible approximation of distance between a wmsn sink and an object, and the orientation of the object using image based information. Simulation results report 99% efficiency and an error ratio of 0.01 (around 1 ft) when compared to other popular techniques. PMID:26528919

VizieR Online Data Catalog: OGLE II SMC eclipsing binaries (Wyrzykowski+, 2004)

NASA Astrophysics Data System (ADS)

Wyrzykowski, L.; Udalski, A.; Kubiak, M.; Szymanski, M. K.; Zebrun, K.; Soszinski, I.; Wozniak, P. R.; Pietrzynski, G.; Szewczyk, O.

2009-03-01

We present new version of the OGLE-II catalog of eclipsing binary stars detected in the Small Magellanic Cloud, based on Difference Image Analysis catalog of variable stars in the Magellanic Clouds containing data collected from 1997 to 2000. We found 1351 eclipsing binary stars in the central 2.4 square degree area of the SMC. 455 stars are newly discovered objects, not found in the previous release of the catalog. The eclipsing objects were selected with the automatic search algorithm based on the artificial neural network. The full catalog with individual photometry is accessible from the OGLE INTERNET archive, at ftp://sirius.astrouw.edu.pl/ogle/ogle2/var_stars/smc/ecl . Regular observations of the SMC fields started on June 26, 1997 and covered about 2.4 square degrees of central parts of the SMC. Reductions of the photometric data collected up to the end of May 2000 were performed with the Difference Image Analysis (DIA) package. (1 data file).

Hyperspectral imaging simulation of object under sea-sky background

NASA Astrophysics Data System (ADS)

Wang, Biao; Lin, Jia-xuan; Gao, Wei; Yue, Hui

2016-10-01

Remote sensing image simulation plays an important role in spaceborne/airborne load demonstration and algorithm development. Hyperspectral imaging is valuable in marine monitoring, search and rescue. On the demand of spectral imaging of objects under the complex sea scene, physics based simulation method of spectral image of object under sea scene is proposed. On the development of an imaging simulation model considering object, background, atmosphere conditions, sensor, it is able to examine the influence of wind speed, atmosphere conditions and other environment factors change on spectral image quality under complex sea scene. Firstly, the sea scattering model is established based on the Philips sea spectral model, the rough surface scattering theory and the water volume scattering characteristics. The measured bi directional reflectance distribution function (BRDF) data of objects is fit to the statistical model. MODTRAN software is used to obtain solar illumination on the sea, sky brightness, the atmosphere transmittance from sea to sensor and atmosphere backscattered radiance, and Monte Carlo ray tracing method is used to calculate the sea surface object composite scattering and spectral image. Finally, the object spectrum is acquired by the space transformation, radiation degradation and adding the noise. The model connects the spectrum image with the environmental parameters, the object parameters, and the sensor parameters, which provide a tool for the load demonstration and algorithm development.

Streak detection and analysis pipeline for optical images

NASA Astrophysics Data System (ADS)

Virtanen, J.; Granvik, M.; Torppa, J.; Muinonen, K.; Poikonen, J.; Lehti, J.; Säntti, T.; Komulainen, T.; Flohrer, T.

2014-07-01

We describe a novel data processing and analysis pipeline for optical observations of moving objects, either of natural (asteroids, meteors) or artificial origin (satellites, space debris). The monitoring of the space object populations requires reliable acquisition of observational data to support the development and validation of population models, and to build and maintain catalogues of orbital elements. The orbital catalogues are, in turn, needed for the assessment of close approaches (for asteroids, with the Earth; for satellites, with each other) and for the support of contingency situations or launches. For both types of populations, there is also increasing interest to detect fainter objects corresponding to the small end of the size distribution. We focus on the low signal-to-noise (SNR) detection of objects with high angular velocities, resulting in long and faint object trails, or streaks, in the optical images. The currently available, mature image processing algorithms for detection and astrometric reduction of optical data cover objects that cross the sensor field-of-view comparably slowly, and, particularly for satellites, within a rather narrow, predefined range of angular velocities. By applying specific tracking techniques, the objects appear point-like or as short trails in the exposures. However, the general survey scenario is always a 'track-before-detect' problem, resulting in streaks of arbitrary lengths. Although some considerations for low-SNR processing of streak-like features are available in the current image processing and computer vision literature, algorithms are not readily available yet. In the ESA-funded StreakDet (Streak detection and astrometric reduction) project, we develop and evaluate an automated processing pipeline applicable to single images (as compared to consecutive frames of the same field) obtained with any observing scenario, including space-based surveys and both low- and high-altitude populations. The algorithmic flow starts from the segmentation of the acquired image (i.e., the extraction of all sources), followed by the astrometric and photometric characterization of the candidate streaks, and ends with orbital validation of the detected streaks. For the low-SNR extraction of objects, we put forward an approach which does not rely on a priori information, such as the object velocities, a typical assumption in earlier implementations. Our algorithm is based on local grayscale mean difference evaluation, followed by a threshold operation and spatial filtering of black-and-white (1-bit) data to remove stars and other non-streak features. For long streaks, the challenge is to extract position information and related registered epochs with sufficient precision. Moreover, satellite streaks can show up in complex morphologies because of their fast, and often irregular lightcurve variations. A central concept of the pipeline is streak classification which guides the actual characterization process by aiming to identify the interesting sources and to filter out the uninteresting ones, as well as by allowing the tailoring of algorithms for specific streak classes (e.g. PSF fitting for point-like vs. long, disintegrated streaks). Finally, to validate the single-image detections, the processing is finalized by orbital analysis using our statistical inverse methods (see, Muinonen et al., this conference), resulting in preliminary orbital classification (e.g., Earth-bound vs. non-Earth-bound orbits) for the detected streaks.

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.

Scheduler for monitoring objects orbiting earth using satellite-based telescopes

DOEpatents

Olivier, Scot S; Pertica, Alexander J; Riot, Vincent J; De Vries, Willem H; Bauman, Brian J; Nikolaev, Sergei; Henderson, John R; Phillion, Donald W

2015-04-28

An ephemeris refinement system includes satellites with imaging devices in earth orbit to make observations of space-based objects ("target objects") and a ground-based controller that controls the scheduling of the satellites to make the observations of the target objects and refines orbital models of the target objects. The ground-based controller determines when the target objects of interest will be near enough to a satellite for that satellite to collect an image of the target object based on an initial orbital model for the target objects. The ground-based controller directs the schedules to be uploaded to the satellites, and the satellites make observations as scheduled and download the observations to the ground-based controller. The ground-based controller then refines the initial orbital models of the target objects based on the locations of the target objects that are derived from the observations.

Monitoring objects orbiting earth using satellite-based telescopes

DOEpatents

Olivier, Scot S.; Pertica, Alexander J.; Riot, Vincent J.; De Vries, Willem H.; Bauman, Brian J.; Nikolaev, Sergei; Henderson, John R.; Phillion, Donald W.

2015-06-30

An ephemeris refinement system includes satellites with imaging devices in earth orbit to make observations of space-based objects ("target objects") and a ground-based controller that controls the scheduling of the satellites to make the observations of the target objects and refines orbital models of the target objects. The ground-based controller determines when the target objects of interest will be near enough to a satellite for that satellite to collect an image of the target object based on an initial orbital model for the target objects. The ground-based controller directs the schedules to be uploaded to the satellites, and the satellites make observations as scheduled and download the observations to the ground-based controller. The ground-based controller then refines the initial orbital models of the target objects based on the locations of the target objects that are derived from the observations.

Object-based Forest Fire Analysis for Pedrógão Grande Fire Using Landsat 8 OLI and Sentinel-2A Imagery

NASA Astrophysics Data System (ADS)

Tonbul, H.; Kavzoglu, T.

2017-12-01

Forest fires are among the most important natural disasters with the damage to the natural habitat and human-life. Mapping damaged forest fires is crucial for assessing ecological effects caused by fire, monitoring land cover changes and modeling atmospheric and climatic effects of fire. In this context, satellite data provides a great advantage to users by providing a rapid process of detecting burning areas and determining the severity of fire damage. Especially, Mediterranean ecosystems countries sets the suitable conditions for the forest fires. In this study, the determination of burnt areas of forest fire in Pedrógão Grande region of Portugal occurred in June 2017 was carried out using Landsat 8 OLI and Sentinel-2A satellite images. The Pedrógão Grande fire was one of the largest fires in Portugal, more than 60 people was killed and thousands of hectares were ravaged. In this study, four pairs of pre-fire and post-fire top of atmosphere (TOA) and atmospherically corrected images were utilized. The red and near infrared (NIR) spectral bands of pre-fire and post-fire images were stacked and multiresolution segmentation algorithm was applied. In the segmentation processes, the image objects were generated with estimated optimum homogeneity criteria. Using eCognition software, rule sets have been created to distinguish unburned areas from burned areas. In constructing the rule sets, NDVI threshold values were determined pre- and post-fire and areas where vegetation loss was detected using the NDVI difference image. The results showed that both satellite images yielded successful results for burned area discrimination with a very high degree of consistency in terms of spatial overlap and total burned area (over 93%). Object based image analysis (OBIA) was found highly effective in delineation of burnt areas.

Identification of the critical depth-of-cut through a 2D image of the cutting region resulting from taper cutting of brittle materials

NASA Astrophysics Data System (ADS)

Gu, Wen; Zhu, Zhiwei; Zhu, Wu-Le; Lu, Leyao; To, Suet; Xiao, Gaobo

2018-05-01

An automatic identification method for obtaining the critical depth-of-cut (DoC) of brittle materials with nanometric accuracy and sub-nanometric uncertainty is proposed in this paper. With this method, a two-dimensional (2D) microscopic image of the taper cutting region is captured and further processed by image analysis to extract the margin of generated micro-cracks in the imaging plane. Meanwhile, an analytical model is formulated to describe the theoretical curve of the projected cutting points on the imaging plane with respect to a specified DoC during the whole cutting process. By adopting differential evolution algorithm-based minimization, the critical DoC can be identified by minimizing the deviation between the extracted margin and the theoretical curve. The proposed method is demonstrated through both numerical simulation and experimental analysis. Compared with conventional 2D- and 3D-microscopic-image-based methods, determination of the critical DoC in this study uses the envelope profile rather than the onset point of the generated cracks, providing a more objective approach with smaller uncertainty.

Cardiac imaging: working towards fully-automated machine analysis & interpretation.

PubMed

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-03-01

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

A benchmark for comparison of dental radiography analysis algorithms.

PubMed

Wang, Ching-Wei; Huang, Cheng-Ta; Lee, Jia-Hong; Li, Chung-Hsing; Chang, Sheng-Wei; Siao, Ming-Jhih; Lai, Tat-Ming; Ibragimov, Bulat; Vrtovec, Tomaž; Ronneberger, Olaf; Fischer, Philipp; Cootes, Tim F; Lindner, Claudia

2016-07-01

Dental radiography plays an important role in clinical diagnosis, treatment and surgery. In recent years, efforts have been made on developing computerized dental X-ray image analysis systems for clinical usages. A novel framework for objective evaluation of automatic dental radiography analysis algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2015 Bitewing Radiography Caries Detection Challenge and Cephalometric X-ray Image Analysis Challenge. In this article, we present the datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. The main contributions of the challenge include the creation of the dental anatomy data repository of bitewing radiographs, the creation of the anatomical abnormality classification data repository of cephalometric radiographs, and the definition of objective quantitative evaluation for comparison and ranking of the algorithms. With this benchmark, seven automatic methods for analysing cephalometric X-ray image and two automatic methods for detecting bitewing radiography caries have been compared, and detailed quantitative evaluation results are presented in this paper. Based on the quantitative evaluation results, we believe automatic dental radiography analysis is still a challenging and unsolved problem. The datasets and the evaluation software will be made available to the research community, further encouraging future developments in this field. (http://www-o.ntust.edu.tw/~cweiwang/ISBI2015/). Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

IDL Object Oriented Software for Hinode/XRT Image Analysis

NASA Astrophysics Data System (ADS)

Higgins, P. A.; Gallagher, P. T.

2008-09-01

We have developed a set of object oriented IDL routines that enable users to search, download and analyse images from the X-Ray Telescope (XRT) on-board Hinode. In this paper, we give specific examples of how the object can be used and how multi-instrument data analysis can be performed. The XRT object is a highly versatile and powerful IDL object, which will prove to be a useful tool for solar researchers. This software utilizes the generic Framework object available within the GEN branch of SolarSoft.

Fast and objective detection and analysis of structures in downhole images

NASA Astrophysics Data System (ADS)

Wedge, Daniel; Holden, Eun-Jung; Dentith, Mike; Spadaccini, Nick

2017-09-01

Downhole acoustic and optical televiewer images, and formation microimager (FMI) logs are important datasets for structural and geotechnical analyses for the mineral and petroleum industries. Within these data, dipping planar structures appear as sinusoids, often in incomplete form and in abundance. Their detection is a labour intensive and hence expensive task and as such is a significant bottleneck in data processing as companies may have hundreds of kilometres of logs to process each year. We present an image analysis system that harnesses the power of automated image analysis and provides an interactive user interface to support the analysis of televiewer images by users with different objectives. Our algorithm rapidly produces repeatable, objective results. We have embedded it in an interactive workflow to complement geologists' intuition and experience in interpreting data to improve efficiency and assist, rather than replace the geologist. The main contributions include a new image quality assessment technique for highlighting image areas most suited to automated structure detection and for detecting boundaries of geological zones, and a novel sinusoid detection algorithm for detecting and selecting sinusoids with given confidence levels. Further tools are provided to perform rapid analysis of and further detection of structures e.g. as limited to specific orientations.

Method of assessing heterogeneity in images

DOEpatents

Jacob, Richard E.; Carson, James P.

2016-08-23

A method of assessing heterogeneity in images is disclosed. 3D images of an object are acquired. The acquired images may be filtered and masked. Iterative decomposition is performed on the masked images to obtain image subdivisions that are relatively homogeneous. Comparative analysis, such as variogram analysis or correlogram analysis, is performed of the decomposed images to determine spatial relationships between regions of the images that are relatively homogeneous.

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

NASA Astrophysics Data System (ADS)

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

2013-04-01

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

Directly manipulated free-form deformation image registration.

PubMed

Tustison, Nicholas J; Avants, Brian B; Gee, James C

2009-03-01

Previous contributions to both the research and open source software communities detailed a generalization of a fast scalar field fitting technique for cubic B-splines based on the work originally proposed by Lee . One advantage of our proposed generalized B-spline fitting approach is its immediate application to a class of nonrigid registration techniques frequently employed in medical image analysis. Specifically, these registration techniques fall under the rubric of free-form deformation (FFD) approaches in which the object to be registered is embedded within a B-spline object. The deformation of the B-spline object describes the transformation of the image registration solution. Representative of this class of techniques, and often cited within the relevant community, is the formulation of Rueckert who employed cubic splines with normalized mutual information to study breast deformation. Similar techniques from various groups provided incremental novelty in the form of disparate explicit regularization terms, as well as the employment of various image metrics and tailored optimization methods. For several algorithms, the underlying gradient-based optimization retained the essential characteristics of Rueckert's original contribution. The contribution which we provide in this paper is two-fold: 1) the observation that the generic FFD framework is intrinsically susceptible to problematic energy topographies and 2) that the standard gradient used in FFD image registration can be modified to a well-understood preconditioned form which substantially improves performance. This is demonstrated with theoretical discussion and comparative evaluation experimentation.

Mapping ecological states in a complex environment

NASA Astrophysics Data System (ADS)

Steele, C. M.; Bestelmeyer, B.; Burkett, L. M.; Ayers, E.; Romig, K.; Slaughter, A.

2013-12-01

The vegetation of northern Chihuahuan Desert rangelands is sparse, heterogeneous and for most of the year, consists of a large proportion of non-photosynthetic material. The soils in this area are spectrally bright and variable in their reflectance properties. Both factors provide challenges to the application of remote sensing for estimating canopy variables (e.g., leaf area index, biomass, percentage canopy cover, primary production). Additionally, with reference to current paradigms of rangeland health assessment, remotely-sensed estimates of canopy variables have limited practical use to the rangeland manager if they are not placed in the context of ecological site and ecological state. To address these challenges, we created a multifactor classification system based on the USDA-NRCS ecological site schema and associated state-and-transition models to map ecological states on desert rangelands in southern New Mexico. Applying this system using per-pixel image processing techniques and multispectral, remotely sensed imagery raised other challenges. Per-pixel image classification relies upon the spectral information in each pixel alone, there is no reference to the spatial context of the pixel and its relationship with its neighbors. Ecological state classes may have direct relevance to managers but the non-unique spectral properties of different ecological state classes in our study area means that per-pixel classification of multispectral data performs poorly in discriminating between different ecological states. We found that image interpreters who are familiar with the landscape and its associated ecological site descriptions perform better than per-pixel classification techniques in assigning ecological states. However, two important issues affect manual classification methods: subjectivity of interpretation and reproducibility of results. An alternative to per-pixel classification and manual interpretation is object-based image analysis. Object-based image analysis provides a platform for classification that more closely resembles human recognition of objects within a remotely sensed image. The analysis presented here compares multiple thematic maps created for test locations on the USDA-ARS Jornada Experimental Range ranch. Three study sites in different pastures, each 300 ha in size, were selected for comparison on the basis of their ecological site type (';Clayey', ';Sandy' and a combination of both) and the degree of complexity of vegetation cover. Thematic maps were produced for each study site using (i) manual interpretation of digital aerial photography (by five independent interpreters); (ii) object-oriented, decision-tree classification of fine and moderate spatial resolution imagery (Quickbird; Landsat Thematic Mapper) and (iii) ground survey. To identify areas of uncertainty, we compared agreement in location, areal extent and class assignation between 5 independently produced, manually-digitized ecological state maps and with the map created from ground survey. Location, areal extent and class assignation of the map produced by object-oriented classification was also assessed with reference to the ground survey map.

AKARI North Ecliptic Pole Deep Survey. Revision of the catalogue via a new image analysis

NASA Astrophysics Data System (ADS)

Murata, K.; Matsuhara, H.; Wada, T.; Arimatsu, K.; Oi, N.; Takagi, T.; Oyabu, S.; Goto, T.; Ohyama, Y.; Malkan, M.; Pearson, C.; Małek, K.; Solarz, A.

2013-11-01

Context. We present the revised near- to mid-infrared catalogue of the AKARI North Ecliptic Pole deep survey. The survey has the unique advantage of continuous filter coverage from 2 to 24 μm over nine photometric bands, but the initial version of the survey catalogue leaves room for improvement in the image analysis stage; the original images are strongly contaminated by the behaviour of the detector and the optical system. Aims: The purpose of this study is to devise new image analysis methods and to improve the detection limit and reliability of the source extraction. Methods: We removed the scattered light and stray light from the Earth limb, and corrected for artificial patterns in the images by creating appropriate templates. We also removed any artificial sources due to bright sources by using their properties or masked them out visually. In addition, for the mid-infrared source extraction, we created detection images by stacking all six bands. This reduced the sky noise and enabled us to detect fainter sources more reliably. For the near-infrared source catalogue, we considered only objects with counterparts from ground-based catalogues to avoid fake sources. For our ground-based catalogues, we used catalogues based on the CFHT/MegaCam z' band, CFHT/WIRCam Ks band and Subaru/Scam z' band. Objects with multiple counterparts were all listed in the catalogue with a merged flag for the AKARI flux. Results: The detection limits of all mid-infrared bands were improved by ~20%, and the total number of detected objects was increased by ~2000 compared with the previous version of the catalogue; it now has 9560 objects. The 5σ detection limits in our catalogue are 11, 9, 10, 30, 34, 57, 87, 93, and 256 μJy in the N2, N3, N4, S7, S9W, S11, L15, L18W, and L24 bands, respectively. The astrometric accuracies of these band detections are 0.48, 0.52, 0.55, 0.99, 0.95, 1.1, 1.2, 1.3, and 1.6 arcsec, respectively. The false-detection rate of all nine bands was decreased to less than 0.3%. In total, 27 770 objects are listed in the catalogue, 11 349 of which have mid-infrared fluxes. The catalogue is available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/559/A132 or at the ISAS/JAXA observers page, http://www.ir.isas.jaxa.jp/ASTRO-F/Observation/

PlenoPatch: Patch-Based Plenoptic Image Manipulation.

PubMed

Zhang, Fang-Lue; Wang, Jue; Shechtman, Eli; Zhou, Zi-Ye; Shi, Jia-Xin; Hu, Shi-Min

2017-05-01

Patch-based image synthesis methods have been successfully applied for various editing tasks on still images, videos and stereo pairs. In this work we extend patch-based synthesis to plenoptic images captured by consumer-level lenselet-based devices for interactive, efficient light field editing. In our method the light field is represented as a set of images captured from different viewpoints. We decompose the central view into different depth layers, and present it to the user for specifying the editing goals. Given an editing task, our method performs patch-based image synthesis on all affected layers of the central view, and then propagates the edits to all other views. Interaction is done through a conventional 2D image editing user interface that is familiar to novice users. Our method correctly handles object boundary occlusion with semi-transparency, thus can generate more realistic results than previous methods. We demonstrate compelling results on a wide range of applications such as hole-filling, object reshuffling and resizing, changing object depth, light field upscaling and parallax magnification.

An image processing and analysis tool for identifying and analysing complex plant root systems in 3D soil using non-destructive analysis: Root1.

PubMed

Flavel, Richard J; Guppy, Chris N; Rabbi, Sheikh M R; Young, Iain M

2017-01-01

The objective of this study was to develop a flexible and free image processing and analysis solution, based on the Public Domain ImageJ platform, for the segmentation and analysis of complex biological plant root systems in soil from x-ray tomography 3D images. Contrasting root architectures from wheat, barley and chickpea root systems were grown in soil and scanned using a high resolution micro-tomography system. A macro (Root1) was developed that reliably identified with good to high accuracy complex root systems (10% overestimation for chickpea, 1% underestimation for wheat, 8% underestimation for barley) and provided analysis of root length and angle. In-built flexibility allowed the user interaction to (a) amend any aspect of the macro to account for specific user preferences, and (b) take account of computational limitations of the platform. The platform is free, flexible and accurate in analysing root system metrics.

TH-E-BRF-02: 4D-CT Ventilation Image-Based IMRT Plans Are Dosimetrically Comparable to SPECT Ventilation Image-Based Plans

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

Kida, S; University of Tokyo Hospital, Bunkyo, Tokyo; Bal, M

Purpose: An emerging lung ventilation imaging method based on 4D-CT can be used in radiotherapy to selectively avoid irradiating highly-functional lung regions, which may reduce pulmonary toxicity. Efforts to validate 4DCT ventilation imaging have been focused on comparison with other imaging modalities including SPECT and xenon CT. The purpose of this study was to compare 4D-CT ventilation image-based functional IMRT plans with SPECT ventilation image-based plans as reference. Methods: 4D-CT and SPECT ventilation scans were acquired for five thoracic cancer patients in an IRB-approved prospective clinical trial. The ventilation images were created by quantitative analysis of regional volume changes (amore » surrogate for ventilation) using deformable image registration of the 4D-CT images. A pair of 4D-CT ventilation and SPECT ventilation image-based IMRT plans was created for each patient. Regional ventilation information was incorporated into lung dose-volume objectives for IMRT optimization by assigning different weights on a voxel-by-voxel basis. The objectives and constraints of the other structures in the plan were kept identical. The differences in the dose-volume metrics have been evaluated and tested by a paired t-test. SPECT ventilation was used to calculate the lung functional dose-volume metrics (i.e., mean dose, V20 and effective dose) for both 4D-CT ventilation image-based and SPECT ventilation image-based plans. Results: Overall there were no statistically significant differences in any dose-volume metrics between the 4D-CT and SPECT ventilation imagebased plans. For example, the average functional mean lung dose of the 4D-CT plans was 26.1±9.15 (Gy), which was comparable to 25.2±8.60 (Gy) of the SPECT plans (p = 0.89). For other critical organs and PTV, nonsignificant differences were found as well. Conclusion: This study has demonstrated that 4D-CT ventilation image-based functional IMRT plans are dosimetrically comparable to SPECT ventilation image-based plans, providing evidence to use 4D-CT ventilation imaging for clinical applications. Supported in part by Free to Breathe Young Investigator Research Grant and NIH/NCI R01 CA 093626. The authors thank Philips Radiation Oncology Systems for the Pinnacle3 treatment planning systems.« less

Imaging, object detection, and change detection with a polarized multistatic GPR array

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

Beer, N. Reginald; Paglieroni, David W.

A polarized detection system performs imaging, object detection, and change detection factoring in the orientation of an object relative to the orientation of transceivers. The polarized detection system may operate on one of several modes of operation based on whether the imaging, object detection, or change detection is performed separately for each transceiver orientation. In combined change mode, the polarized detection system performs imaging, object detection, and change detection separately for each transceiver orientation, and then combines changes across polarizations. In combined object mode, the polarized detection system performs imaging and object detection separately for each transceiver orientation, and thenmore » combines objects across polarizations and performs change detection on the result. In combined image mode, the polarized detection system performs imaging separately for each transceiver orientation, and then combines images across polarizations and performs object detection followed by change detection on the result.« less

The Research on Dryland Crop Classification Based on the Fusion of SENTINEL-1A SAR and Optical Images

NASA Astrophysics Data System (ADS)

Liu, F.; Chen, T.; He, J.; Wen, Q.; Yu, F.; Gu, X.; Wang, Z.

2018-04-01

In recent years, the quick upgrading and improvement of SAR sensors provide beneficial complements for the traditional optical remote sensing in the aspects of theory, technology and data. In this paper, Sentinel-1A SAR data and GF-1 optical data were selected for image fusion, and more emphases were put on the dryland crop classification under a complex crop planting structure, regarding corn and cotton as the research objects. Considering the differences among various data fusion methods, the principal component analysis (PCA), Gram-Schmidt (GS), Brovey and wavelet transform (WT) methods were compared with each other, and the GS and Brovey methods were proved to be more applicable in the study area. Then, the classification was conducted based on the object-oriented technique process. And for the GS, Brovey fusion images and GF-1 optical image, the nearest neighbour algorithm was adopted to realize the supervised classification with the same training samples. Based on the sample plots in the study area, the accuracy assessment was conducted subsequently. The values of overall accuracy and kappa coefficient of fusion images were all higher than those of GF-1 optical image, and GS method performed better than Brovey method. In particular, the overall accuracy of GS fusion image was 79.8 %, and the Kappa coefficient was 0.644. Thus, the results showed that GS and Brovey fusion images were superior to optical images for dryland crop classification. This study suggests that the fusion of SAR and optical images is reliable for dryland crop classification under a complex crop planting structure.

A Model-Based Approach for Microvasculature Structure Distortion Correction in Two-Photon Fluorescence Microscopy Images

PubMed Central

Dao, Lam; Glancy, Brian; Lucotte, Bertrand; Chang, Lin-Ching; Balaban, Robert S; Hsu, Li-Yueh

2015-01-01

SUMMARY This paper investigates a post-processing approach to correct spatial distortion in two-photon fluorescence microscopy images for vascular network reconstruction. It is aimed at in vivo imaging of large field-of-view, deep-tissue studies of vascular structures. Based on simple geometric modeling of the object-of-interest, a distortion function is directly estimated from the image volume by deconvolution analysis. Such distortion function is then applied to sub volumes of the image stack to adaptively adjust for spatially varying distortion and reduce the image blurring through blind deconvolution. The proposed technique was first evaluated in phantom imaging of fluorescent microspheres that are comparable in size to the underlying capillary vascular structures. The effectiveness of restoring three-dimensional spherical geometry of the microspheres using the estimated distortion function was compared with empirically measured point-spread function. Next, the proposed approach was applied to in vivo vascular imaging of mouse skeletal muscle to reduce the image distortion of the capillary structures. We show that the proposed method effectively improve the image quality and reduce spatially varying distortion that occurs in large field-of-view deep-tissue vascular dataset. The proposed method will help in qualitative interpretation and quantitative analysis of vascular structures from fluorescence microscopy images. PMID:26224257

Point Analysis in Java applied to histological images of the perforant pathway: a user's account.

PubMed

Scorcioni, Ruggero; Wright, Susan N; Patrick Card, J; Ascoli, Giorgio A; Barrionuevo, Germán

2008-01-01

The freeware Java tool Point Analysis in Java (PAJ), created to perform 3D point analysis, was tested in an independent laboratory setting. The input data consisted of images of the hippocampal perforant pathway from serial immunocytochemical localizations of the rat brain in multiple views at different resolutions. The low magnification set (x2 objective) comprised the entire perforant pathway, while the high magnification set (x100 objective) allowed the identification of individual fibers. A preliminary stereological study revealed a striking linear relationship between the fiber count at high magnification and the optical density at low magnification. PAJ enabled fast analysis for down-sampled data sets and a friendly interface with automated plot drawings. Noted strengths included the multi-platform support as well as the free availability of the source code, conducive to a broad user base and maximum flexibility for ad hoc requirements. PAJ has great potential to extend its usability by (a) improving its graphical user interface, (b) increasing its input size limit, (c) improving response time for large data sets, and (d) potentially being integrated with other Java graphical tools such as ImageJ.

Wavelet-based polarimetry analysis

NASA Astrophysics Data System (ADS)

Ezekiel, Soundararajan; Harrity, Kyle; Farag, Waleed; Alford, Mark; Ferris, David; Blasch, Erik

2014-06-01

Wavelet transformation has become a cutting edge and promising approach in the field of image and signal processing. A wavelet is a waveform of effectively limited duration that has an average value of zero. Wavelet analysis is done by breaking up the signal into shifted and scaled versions of the original signal. The key advantage of a wavelet is that it is capable of revealing smaller changes, trends, and breakdown points that are not revealed by other techniques such as Fourier analysis. The phenomenon of polarization has been studied for quite some time and is a very useful tool for target detection and tracking. Long Wave Infrared (LWIR) polarization is beneficial for detecting camouflaged objects and is a useful approach when identifying and distinguishing manmade objects from natural clutter. In addition, the Stokes Polarization Parameters, which are calculated from 0°, 45°, 90°, 135° right circular, and left circular intensity measurements, provide spatial orientations of target features and suppress natural features. In this paper, we propose a wavelet-based polarimetry analysis (WPA) method to analyze Long Wave Infrared Polarimetry Imagery to discriminate targets such as dismounts and vehicles from background clutter. These parameters can be used for image thresholding and segmentation. Experimental results show the wavelet-based polarimetry analysis is efficient and can be used in a wide range of applications such as change detection, shape extraction, target recognition, and feature-aided tracking.

EIT image regularization by a new Multi-Objective Simulated Annealing algorithm.

PubMed

Castro Martins, Thiago; Sales Guerra Tsuzuki, Marcos

2015-01-01

Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.

Subpixel Mapping of Hyperspectral Image Based on Linear Subpixel Feature Detection and Object Optimization

NASA Astrophysics Data System (ADS)

Liu, Zhaoxin; Zhao, Liaoying; Li, Xiaorun; Chen, Shuhan

2018-04-01

Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

Fourier Ptychographic Microscopy for Rapid, High-Resolution Imaging of Circulating Tumor Cells Enriched by Microfiltration.

PubMed

Williams, Anthony; Chung, Jaebum; Yang, Changhuei; Cote, Richard J

2017-01-01

Examining the hematogenous compartment for evidence of metastasis has increased significantly within the oncology research community in recent years, due to the development of technologies aimed at the enrichment of circulating tumor cells (CTCs), the subpopulation of primary tumor cells that gain access to the circulatory system and are responsible for colonization at distant sites. In contrast to other technologies, filtration-based CTC enrichment, which exploits differences in size between larger tumor cells and surrounding smaller, non-tumor blood cells, has the potential to improve CTC characterization through isolation of tumor cell populations with greater molecular heterogeneity. However, microscopic analysis of uneven filtration surfaces containing CTCs is laborious, time-consuming, and inconsistent, preventing widespread use of filtration-based enrichment technologies. Here, integrated with a microfiltration-based CTC and rare cell enrichment device we have previously described, we present a protocol for Fourier Ptychographic Microscopy (FPM), a method that, unlike many automated imaging platforms, produces high-speed, high-resolution images that can be digitally refocused, allowing users to observe objects of interest present on multiple focal planes within the same image frame. The development of a cost-effective and high-throughput CTC analysis system for filtration-based enrichment technologies could have profound clinical implications for improved CTC detection and analysis.

A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT

PubMed Central

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A.; Pan, Xiaochuan

2010-01-01

Purpose: Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. Methods: The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack–Noo-formula-based filteredbackprojection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. Results: The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. Conclusions: They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories. PMID:20175463

A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT.

PubMed

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A; Pan, Xiaochuan

2010-01-01

Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack-Noo-formula-based filteredback-projection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories.

Efficient Spatiotemporal Clutter Rejection and Nonlinear Filtering-based Dim Resolved and Unresolved Object Tracking Algorithms

NASA Astrophysics Data System (ADS)

Tartakovsky, A.; Tong, M.; Brown, A. P.; Agh, C.

2013-09-01

We develop efficient spatiotemporal image processing algorithms for rejection of non-stationary clutter and tracking of multiple dim objects using non-linear track-before-detect methods. For clutter suppression, we include an innovative image alignment (registration) algorithm. The images are assumed to contain elements of the same scene, but taken at different angles, from different locations, and at different times, with substantial clutter non-stationarity. These challenges are typical for space-based and surface-based IR/EO moving sensors, e.g., highly elliptical orbit or low earth orbit scenarios. The algorithm assumes that the images are related via a planar homography, also known as the projective transformation. The parameters are estimated in an iterative manner, at each step adjusting the parameter vector so as to achieve improved alignment of the images. Operating in the parameter space rather than in the coordinate space is a new idea, which makes the algorithm more robust with respect to noise as well as to large inter-frame disturbances, while operating at real-time rates. For dim object tracking, we include new advancements to a particle non-linear filtering-based track-before-detect (TrbD) algorithm. The new TrbD algorithm includes both real-time full image search for resolved objects not yet in track and joint super-resolution and tracking of individual objects in closely spaced object (CSO) clusters. The real-time full image search provides near-optimal detection and tracking of multiple extremely dim, maneuvering objects/clusters. The super-resolution and tracking CSO TrbD algorithm provides efficient near-optimal estimation of the number of unresolved objects in a CSO cluster, as well as the locations, velocities, accelerations, and intensities of the individual objects. We demonstrate that the algorithm is able to accurately estimate the number of CSO objects and their locations when the initial uncertainty on the number of objects is large. We demonstrate performance of the TrbD algorithm both for satellite-based and surface-based EO/IR surveillance scenarios.

An image-processing methodology for extracting bloodstain pattern features.

PubMed

Arthur, Ravishka M; Humburg, Philomena J; Hoogenboom, Jerry; Baiker, Martin; Taylor, Michael C; de Bruin, Karla G

2017-08-01

There is a growing trend in forensic science to develop methods to make forensic pattern comparison tasks more objective. This has generally involved the application of suitable image-processing methods to provide numerical data for identification or comparison. This paper outlines a unique image-processing methodology that can be utilised by analysts to generate reliable pattern data that will assist them in forming objective conclusions about a pattern. A range of features were defined and extracted from a laboratory-generated impact spatter pattern. These features were based in part on bloodstain properties commonly used in the analysis of spatter bloodstain patterns. The values of these features were consistent with properties reported qualitatively for such patterns. The image-processing method developed shows considerable promise as a way to establish measurable discriminating pattern criteria that are lacking in current bloodstain pattern taxonomies. Copyright © 2017 Elsevier B.V. All rights reserved.

Objective Measurement of Erythema in Psoriasis using Digital Color Photography with Color Calibration

PubMed Central

Raina, Abhay; Hennessy, Ricky; Rains, Michael; Allred, James; Hirshburg, Jason M; Diven, Dayna; Markey, Mia K.

2016-01-01

Background Traditional metrics for evaluating the severity of psoriasis are subjective, which complicates efforts to measure effective treatments in clinical trials. Methods We collected images of psoriasis plaques and calibrated the coloration of the images according to an included color card. Features were extracted from the images and used to train a linear discriminant analysis classifier with cross-validation to automatically classify the degree of erythema. The results were tested against numerical scores obtained by a panel of dermatologists using a standard rating system. Results Quantitative measures of erythema based on the digital color images showed good agreement with subjective assessment of erythema severity (κ = 0.4203). The color calibration process improved the agreement from κ = 0.2364 to κ = 0.4203. Conclusions We propose a method for the objective measurement of the psoriasis severity parameter of erythema and show that the calibration process improved the results. PMID:26517973

Intelligent Luminance Control of Lighting Systems Based on Imaging Sensor Feedback

PubMed Central

Liu, Haoting; Zhou, Qianxiang; Yang, Jin; Jiang, Ting; Liu, Zhizhen; Li, Jie

2017-01-01

An imaging sensor-based intelligent Light Emitting Diode (LED) lighting system for desk use is proposed. In contrast to the traditional intelligent lighting system, such as the photosensitive resistance sensor-based or the infrared sensor-based system, the imaging sensor can realize a finer perception of the environmental light; thus it can guide a more precise lighting control. Before this system works, first lots of typical imaging lighting data of the desk application are accumulated. Second, a series of subjective and objective Lighting Effect Evaluation Metrics (LEEMs) are defined and assessed for these datasets above. Then the cluster benchmarks of these objective LEEMs can be obtained. Third, both a single LEEM-based control and a multiple LEEMs-based control are developed to realize a kind of optimal luminance tuning. When this system works, first it captures the lighting image using a wearable camera. Then it computes the objective LEEMs of the captured image and compares them with the cluster benchmarks of the objective LEEMs. Finally, the single LEEM-based or the multiple LEEMs-based control can be implemented to get a kind of optimal lighting effect. Many experiment results have shown the proposed system can tune the LED lamp automatically according to environment luminance changes. PMID:28208781

Intelligent Luminance Control of Lighting Systems Based on Imaging Sensor Feedback.

PubMed

Liu, Haoting; Zhou, Qianxiang; Yang, Jin; Jiang, Ting; Liu, Zhizhen; Li, Jie

2017-02-09

An imaging sensor-based intelligent Light Emitting Diode (LED) lighting system for desk use is proposed. In contrast to the traditional intelligent lighting system, such as the photosensitive resistance sensor-based or the infrared sensor-based system, the imaging sensor can realize a finer perception of the environmental light; thus it can guide a more precise lighting control. Before this system works, first lots of typical imaging lighting data of the desk application are accumulated. Second, a series of subjective and objective Lighting Effect Evaluation Metrics (LEEMs) are defined and assessed for these datasets above. Then the cluster benchmarks of these objective LEEMs can be obtained. Third, both a single LEEM-based control and a multiple LEEMs-based control are developed to realize a kind of optimal luminance tuning. When this system works, first it captures the lighting image using a wearable camera. Then it computes the objective LEEMs of the captured image and compares them with the cluster benchmarks of the objective LEEMs. Finally, the single LEEM-based or the multiple LEEMs-based control can be implemented to get a kind of optimal lighting effect. Many experiment results have shown the proposed system can tune the LED lamp automatically according to environment luminance changes.

An object-based image analysis of pinyon and juniper woodlands treated to reduce fuels

USDA-ARS?s Scientific Manuscript database

Mechanical and prescribed fire treatments are commonly used to reduce fuel loads and maintain or restore sagebrush steppe rangelands across the Great Basin where pinyon (Pinus) and juniper (Juniperus) trees are encroaching and infilling. Geospatial technologies, particularly remote sensing, could p...

Parallel algorithm of real-time infrared image restoration based on total variation theory

NASA Astrophysics Data System (ADS)

Zhu, Ran; Li, Miao; Long, Yunli; Zeng, Yaoyuan; An, Wei

2015-10-01

Image restoration is a necessary preprocessing step for infrared remote sensing applications. Traditional methods allow us to remove the noise but penalize too much the gradients corresponding to edges. Image restoration techniques based on variational approaches can solve this over-smoothing problem for the merits of their well-defined mathematical modeling of the restore procedure. The total variation (TV) of infrared image is introduced as a L1 regularization term added to the objective energy functional. It converts the restoration process to an optimization problem of functional involving a fidelity term to the image data plus a regularization term. Infrared image restoration technology with TV-L1 model exploits the remote sensing data obtained sufficiently and preserves information at edges caused by clouds. Numerical implementation algorithm is presented in detail. Analysis indicates that the structure of this algorithm can be easily implemented in parallelization. Therefore a parallel implementation of the TV-L1 filter based on multicore architecture with shared memory is proposed for infrared real-time remote sensing systems. Massive computation of image data is performed in parallel by cooperating threads running simultaneously on multiple cores. Several groups of synthetic infrared image data are used to validate the feasibility and effectiveness of the proposed parallel algorithm. Quantitative analysis of measuring the restored image quality compared to input image is presented. Experiment results show that the TV-L1 filter can restore the varying background image reasonably, and that its performance can achieve the requirement of real-time image processing.

Rigorous accuracy assessment for 3D reconstruction using time-series Dual Fluoroscopy (DF) image pairs

NASA Astrophysics Data System (ADS)

Al-Durgham, Kaleel; Lichti, Derek D.; Kuntze, Gregor; Ronsky, Janet

2017-06-01

High-speed biplanar videoradiography, or clinically referred to as dual fluoroscopy (DF), imaging systems are being used increasingly for skeletal kinematics analysis. Typically, a DF system comprises two X-ray sources, two image intensifiers and two high-speed video cameras. The combination of these elements provides time-series image pairs of articulating bones of a joint, which permits the measurement of bony rotation and translation in 3D at high temporal resolution (e.g., 120-250 Hz). Assessment of the accuracy of 3D measurements derived from DF imaging has been the subject of recent research efforts by several groups, however with methodological limitations. This paper presents a novel and simple accuracy assessment procedure based on using precise photogrammetric tools. We address the fundamental photogrammetry principles for the accuracy evaluation of an imaging system. Bundle adjustment with selfcalibration is used for the estimation of the system parameters. The bundle adjustment calibration uses an appropriate sensor model and applies free-network constraints and relative orientation stability constraints for a precise estimation of the system parameters. A photogrammetric intersection of time-series image pairs is used for the 3D reconstruction of a rotating planar object. A point-based registration method is used to combine the 3D coordinates from the intersection and independently surveyed coordinates. The final DF accuracy measure is reported as the distance between 3D coordinates from image intersection and the independently surveyed coordinates. The accuracy assessment procedure is designed to evaluate the accuracy over the full DF image format and a wide range of object rotation. Experiment of reconstruction of a rotating planar object reported an average positional error of 0.44 +/- 0.2 mm in the derived 3D coordinates (minimum 0.05 and maximum 1.2 mm).

Image processing and machine learning in the morphological analysis of blood cells.

PubMed

Rodellar, J; Alférez, S; Acevedo, A; Molina, A; Merino, A

2018-05-01

This review focuses on how image processing and machine learning can be useful for the morphological characterization and automatic recognition of cell images captured from peripheral blood smears. The basics of the 3 core elements (segmentation, quantitative features, and classification) are outlined, and recent literature is discussed. Although red blood cells are a significant part of this context, this study focuses on malignant lymphoid cells and blast cells. There is no doubt that these technologies may help the cytologist to perform efficient, objective, and fast morphological analysis of blood cells. They may also help in the interpretation of some morphological features and may serve as learning and survey tools. Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies. © 2018 John Wiley & Sons Ltd.

Integrating DICOM structure reporting (SR) into the medical imaging informatics data grid

NASA Astrophysics Data System (ADS)

Lee, Jasper; Le, Anh; Liu, Brent

2008-03-01

The Medical Imaging Informatics (MI2) Data Grid developed at the USC Image Processing and Informatics Laboratory enables medical images to be shared securely between multiple imaging centers. Current applications include an imaging-based clinical trial setting where multiple field sites perform image acquisition and a centralized radiology core performs image analysis, often using computer-aided diagnosis tools (CAD) that generate a DICOM-SR to report their findings and measurements. As more and more CAD tools are being developed in the radiology field, the generated DICOM Structure Reports (SR) holding key radiological findings and measurements that are not part of the DICOM image need to be integrated into the existing Medical Imaging Informatics Data Grid with the corresponding imaging studies. We will discuss the significance and method involved in adapting DICOM-SR into the Medical Imaging Informatics Data Grid. The result is a MI2 Data Grid repository from which users can send and receive DICOM-SR objects based on the imaging-based clinical trial application. The services required to extract and categorize information from the structured reports will be discussed, and the workflow to store and retrieve a DICOM-SR file into the existing MI2 Data Grid will be shown.

High-speed Fourier ptychographic microscopy based on programmable annular illuminations.

PubMed

Sun, Jiasong; Zuo, Chao; Zhang, Jialin; Fan, Yao; Chen, Qian

2018-05-16

High-throughput quantitative phase imaging (QPI) is essential to cellular phenotypes characterization as it allows high-content cell analysis and avoids adverse effects of staining reagents on cellular viability and cell signaling. Among different approaches, Fourier ptychographic microscopy (FPM) is probably the most promising technique to realize high-throughput QPI by synthesizing a wide-field, high-resolution complex image from multiple angle-variably illuminated, low-resolution images. However, the large dataset requirement in conventional FPM significantly limits its imaging speed, resulting in low temporal throughput. Moreover, the underlying theoretical mechanism as well as optimum illumination scheme for high-accuracy phase imaging in FPM remains unclear. Herein, we report a high-speed FPM technique based on programmable annular illuminations (AIFPM). The optical-transfer-function (OTF) analysis of FPM reveals that the low-frequency phase information can only be correctly recovered if the LEDs are precisely located at the edge of the objective numerical aperture (NA) in the frequency space. By using only 4 low-resolution images corresponding to 4 tilted illuminations matching a 10×, 0.4 NA objective, we present the high-speed imaging results of in vitro Hela cells mitosis and apoptosis at a frame rate of 25 Hz with a full-pitch resolution of 655 nm at a wavelength of 525 nm (effective NA = 0.8) across a wide field-of-view (FOV) of 1.77 mm 2 , corresponding to a space-bandwidth-time product of 411 megapixels per second. Our work reveals an important capability of FPM towards high-speed high-throughput imaging of in vitro live cells, achieving video-rate QPI performance across a wide range of scales, both spatial and temporal.

High-speed digital phonoscopy images analyzed by Nyquist plots

NASA Astrophysics Data System (ADS)

Yan, Yuling

2012-02-01

Vocal-fold vibration is a key dynamic event in voice production, and the vibratory characteristics of the vocal fold correlate closely with voice quality and health condition. Laryngeal imaging provides direct means to observe the vocal fold vibration; in the past, however, available modalities were either too slow or impractical to resolve the actual vocal fold vibrations. This limitation has now been overcome by high-speed digital imaging (HSDI) (or high-speed digital phonoscopy), which records images of the vibrating vocal folds at a rate of 2000 frames per second or higher- fast enough to resolve a specific, sustained phonatory vocal fold vibration. The subsequent image-based functional analysis of voice is essential to better understanding the mechanism underlying voice production, as well as assisting the clinical diagnosis of voice disorders. Our primary objective is to develop a comprehensive analytical platform for voice analysis using the HSDI recordings. So far, we have developed various analytical approaches for the HSDI-based voice analyses. These include Nyquist plots and associated analysese that are used along with FFT and Spectrogram in the analysis of the HSDI data representing normal voice and specific voice pathologies.

Quantitative metrics for assessment of chemical image quality and spatial resolution

DOE PAGES

Kertesz, Vilmos; Cahill, John F.; Van Berkel, Gary J.

2016-02-28

Rationale: Currently objective/quantitative descriptions of the quality and spatial resolution of mass spectrometry derived chemical images are not standardized. Development of these standardized metrics is required to objectively describe chemical imaging capabilities of existing and/or new mass spectrometry imaging technologies. Such metrics would allow unbiased judgment of intra-laboratory advancement and/or inter-laboratory comparison for these technologies if used together with standardized surfaces. Methods: We developed two image metrics, viz., chemical image contrast (ChemIC) based on signal-to-noise related statistical measures on chemical image pixels and corrected resolving power factor (cRPF) constructed from statistical analysis of mass-to-charge chronograms across features of interest inmore » an image. These metrics, quantifying chemical image quality and spatial resolution, respectively, were used to evaluate chemical images of a model photoresist patterned surface collected using a laser ablation/liquid vortex capture mass spectrometry imaging system under different instrument operational parameters. Results: The calculated ChemIC and cRPF metrics determined in an unbiased fashion the relative ranking of chemical image quality obtained with the laser ablation/liquid vortex capture mass spectrometry imaging system. These rankings were used to show that both chemical image contrast and spatial resolution deteriorated with increasing surface scan speed, increased lane spacing and decreasing size of surface features. Conclusions: ChemIC and cRPF, respectively, were developed and successfully applied for the objective description of chemical image quality and spatial resolution of chemical images collected from model surfaces using a laser ablation/liquid vortex capture mass spectrometry imaging system.« less

Quantitative metrics for assessment of chemical image quality and spatial resolution

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

Kertesz, Vilmos; Cahill, John F.; Van Berkel, Gary J.

Rationale: Currently objective/quantitative descriptions of the quality and spatial resolution of mass spectrometry derived chemical images are not standardized. Development of these standardized metrics is required to objectively describe chemical imaging capabilities of existing and/or new mass spectrometry imaging technologies. Such metrics would allow unbiased judgment of intra-laboratory advancement and/or inter-laboratory comparison for these technologies if used together with standardized surfaces. Methods: We developed two image metrics, viz., chemical image contrast (ChemIC) based on signal-to-noise related statistical measures on chemical image pixels and corrected resolving power factor (cRPF) constructed from statistical analysis of mass-to-charge chronograms across features of interest inmore » an image. These metrics, quantifying chemical image quality and spatial resolution, respectively, were used to evaluate chemical images of a model photoresist patterned surface collected using a laser ablation/liquid vortex capture mass spectrometry imaging system under different instrument operational parameters. Results: The calculated ChemIC and cRPF metrics determined in an unbiased fashion the relative ranking of chemical image quality obtained with the laser ablation/liquid vortex capture mass spectrometry imaging system. These rankings were used to show that both chemical image contrast and spatial resolution deteriorated with increasing surface scan speed, increased lane spacing and decreasing size of surface features. Conclusions: ChemIC and cRPF, respectively, were developed and successfully applied for the objective description of chemical image quality and spatial resolution of chemical images collected from model surfaces using a laser ablation/liquid vortex capture mass spectrometry imaging system.« less

Computer-aided target tracking in motion analysis studies

NASA Astrophysics Data System (ADS)

Burdick, Dominic C.; Marcuse, M. L.; Mislan, J. D.

1990-08-01

Motion analysis studies require the precise tracking of reference objects in sequential scenes. In a typical situation, events of interest are captured at high frame rates using special cameras, and selected objects or targets are tracked on a frame by frame basis to provide necessary data for motion reconstruction. Tracking is usually done using manual methods which are slow and prone to error. A computer based image analysis system has been developed that performs tracking automatically. The objective of this work was to eliminate the bottleneck due to manual methods in high volume tracking applications such as the analysis of crash test films for the automotive industry. The system has proven to be successful in tracking standard fiducial targets and other objects in crash test scenes. Over 95 percent of target positions which could be located using manual methods can be tracked by the system, with a significant improvement in throughput over manual methods. Future work will focus on the tracking of clusters of targets and on tracking deformable objects such as airbags.