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Special Section Guest Editorial: Advances in High-Dimensional Medical Image processing

JOURNAL OF MEDICAL IMAGING 2022年第5期9卷 052401页

作者： Isgum, Ivana Landman, Bennett A. Vrtovec, Tomaz Univ Amsterdam Amsterdam UMC Locat Amsterdam Netherlands Vanderbilt Univ Nashville TN USA Univ Ljubljana Fac Elect Engn Ljubljana Slovenia

Guest Editors Ivana Išgum, Bennett A. Landman, and Tomaž Vrtovec introduce the JMI Special Section on Advances in High-Dimensional Medical Image processing.

关键词： Medical imaging Image processing Image segmentation Computed tomography Magnetic resonance imaging Image analysis Image fusion Neuroimaging Positron emission tomography visual process modeling

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Special Section Guest Editorial: Advanced Image processing and Applications for Smart Agricultural Systems

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Journal of Electronic Imaging 2023年第5期32卷

作者： Li, Yang Ercişli, Sezai Shihezi University College of Mechanical and Electrical Engineering Shihezi China Ataturk University Department of Horticulture Agricultural Faculty Erzurum Turkey

Guest editors Yang Li and Sezai Ercisli introduce the Special Section on Advanced Image processing and Applications for Smart Agricultural Systems.

关键词： Agriculture Image processing Deep learning Lithium Object detection Electrical engineering Image quality System identification Unmanned aerial vehicles visual process modeling

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Special Section Guest Editorial:Perceptually Driven visual Information Analysis

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Journal of Electronic Imaging 2017年第6期25卷

作者： Larabi, Mohamed-Chaker Lee, Sanghoon El Hassouni, Mohammed Morain-Nicolier, Frédéric Jennane, Rachid Université de Poitiers XLIM-SIC UMR 6172 CNRS Boulevard Marie et Pierre Curie BP 30179 Futuroscope86962 France Yonsei University Multidimensional Insight Lab C703 MDI Lab 3rd Engineering Building Seodaemun-gu120-749 Korea Republic of Mohammed v University of Rabat LRIT URAC 29 Laboratory Faculty of Sciences Avenue Ibn Battouta Rabat Morocco Université de Reims CRESTIC 9 rue de Québec Troyes Cedex10026 France Université d'Orléans I3MTO Laboratory 8 rue Léonard de Vinci OrléansF-45072 France

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EXTRACTION OF EDGE-BASED AND REGION-BASED FEATURES FOR OBJECT RECOGNITION 12

EXTRACTION OF EDGE-BASED AND REGION-BASED FEATURES FOR OBJEC...

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12th Conference on Intelligent Robots and Computer Vision: Algorithms and Techniques

作者： COUTTS, B RAVI, S HU, GZ SHRIKHANDE, N CENT MICHIGAN UNIV DEPT COMP SCIMT PLEASANTMI 48858

ISBN: (纸本)0819413208

One of the central problems of computer vision is object recognition. A catalogue of model objects is described as a set of features such as edges and surfaces. The same features are extracted from the scene and matched against the models for object recognition. Edges and surfaces extracted from the scenes are often noisy and imperfect. In this paper algorithms are described for improving low level edge and surface features. Existing edge extraction algorithms are applied to the intensity image to obtain edge features. Initial edges are traced by following directions of the current contour. These are improved by using corresponding depth and intensity information for decision making at branch points. Surface fitting routines are applied to the range image to obtain planar surface patches. An algorithm of region growing is developed that starts with a coarse segmentation and uses quadric surface fitting to iteratively merge adjacent regions into quadric surfaces based on approximate orthogonal distance regression. Surface information obtained is returned to the edge extraction routine to detect and remove fake edges. This process repeats until no more merging or edge improvement can take place. Both synthetic (with Gaussian noise) and real images containing multiple object scenes have been tested using the merging criteria. Results appeared quite encouraging.

关键词： Edge detection Object recognition Image segmentation Algorithm development Feature extraction Detection and tracking algorithms Image processing algorithms and systems Computer vision technology Machine vision visual process modeling

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DIVERGENCE AS A MEASURE OF visual RECOGNITION - BIAS AND ERRORS CAUSED BY SMALL SAMPLES

DIVERGENCE AS A MEASURE OF VISUAL RECOGNITION - BIAS AND ERR...

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CONF ON visual COMMUNICATIONS AND IMAGE processING : IMAGE processING

作者： LAU, M OKAGAKI, T Univ. of Minnesota Medical School (United States)

ISBN: (纸本)0819407437

Total divergence has been shown to be a useful measure of performance of visual recognition processes. Measurement and comparison of divergences using samples require statistical processing and sample size becomes an important factor in practical applications when sample sizes are small. We recently completed a numerical study on the applicability of total divergence to a medical Pap test process in small samples. This study suggested that in order to keep measurement biases within +/- 0.5 decits, 100 - 200 samples of cytology- histology pairs were required in the best classifications of 3, 4, and 5 category-states. At these sample sizes, measurement errors (standard errors) were also contained within +/- 0.5 decits. This study also confirmed that previously reported, over-estimated propagated errors in small samples were in fact over-estimation, and that their use for testing a null hypothesis was valid. The number of samples with indefinable statistics due to a zero denominator can be as high as 30% when the sample sizes were 500 for 3, 4, and 5 category-state classifications. Biases due to small samples were positive for most category-states except for the optimum 3 category-states, in which bias changed to negative (bias inversion), and observed errors of total divergence paradoxically decreased as N decreased (error-sample paradox) for a small sample size (N < 700).

关键词： Error analysis visualization Image processing Monte Carlo methods Classification systems visual communications Image classification Tissues Matrices visual process modeling

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Introducing the Special Series on 2D and 3D Imaging: Perspectives in Human and Model Observer Performance

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JOURNAL OF MEDICAL IMAGING 2020年第5期7卷 051201页

作者： Mello-Thoms, Claudia Abbey, Craig K. Krupinski, Elizabeth A. Univ Iowa Dept Radiol Iowa City IA 52242 USA Univ Calif Santa Barbara Dept Psychol & Brain Sci Santa Barbara CA 93106 USA Emory Univ Dept Radiol & Imaging Sci Atlanta GA 30322 USA

Guest editors Claudia Mello-Thoms, Craig Abbey, and Elizabeth A. Krupinski introduce the Special Series on 2D and 3D Imaging: Perspectives in Human and Model Observer Performance.

关键词： Medical imaging Image processing Performance modeling 3D image processing 3D modeling Stereoscopy Medical research visualization Radiology visual process modeling

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Special Section Guest Editorial: Synthetic Aperture Radar Imaging Technology in Deep Learning: New Trends and Viewpoints

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JOURNAL OF ELECTRONIC IMAGING 2023年第2期32卷

作者： Shankar, Achyut Zhang, Li Hu, Yu Chen Singh, Prabhishek Univ Warwick Warwick England Univ London Egham Dept Comp Sci Royal Holoway London England Providence Univ Taichung Taiwan Bennett Univ Sch Comp Sci Engn & Technol Greater Noida Uttar Pradesh India

Guest Editors Achyut Shankar, Li Zhang, Yu Chen Hu, and Prabhishek Singh introduce the Special Section on Synthetic Aperture Radar Imaging Technology in Deep Learning: New Trends and Viewpoints.

关键词： Synthetic aperture radar Deep learning Image processing Feature extraction Imaging technologies Image classification Image sensors Remote sensing Scattering visual process modeling

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Polygon-based bounding volume as a spatio-temporal data model for video content access

Polygon-based bounding volume as a spatio-temporal data mode...

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Conference on International Multimedia Management Systems

作者： Song, JJ Park, YC Kim, PK Kim, KS Golshani, F Panchanathan, S Arizona State Univ Dept Comp Sci & Engn Media Lab Tempe AZ 85287 USA

ISBN: (纸本)0819438758

Indexing, retrieval and delivery of visual and spatio-temporal properties of video objects requires efficient data models and sound operations on the model are mandatory. However, most object-based video data models address only a single aspect of those properties. In this paper, we present an efficient video object representation method that captures the visual, spatial and temporal properties of objects in a video in the form of an unified abstracted data type. The proposed data type is a polygon mesh, named video object mesh, which is defined in a spatio-temporal domain. Based on the application needs, a contour of an object is modeled with a polygonal contour. With the contour and color information of the object, content-based triangularization is performed. A video object in a frame is modeled with two dimensional-polygon mesh. Each vertex in the mesh, color information is embedded for further use. By using motion analysis, a corresponding vertex in the adjacent frame is identified connected to the vertex that is being analyzed. These processes are continued until a video object disappears. The result of these processes is a three dimensional polygon mesh that models location variant motion and location invariant motion that that can not be captured by traditional trajectory based motion model. The proposed model is also useful camera motion analysis. Since a surface shape of a video object mesh has partial information of camera motion.

关键词： Video Data modeling Motion models 3D modeling Motion analysis Cameras visualization Video processing visual process modeling

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Quantifying image quality in graphics: Perspective on subjective and objective metrics and their performance

Quantifying image quality in graphics: Perspective on subjec...

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Conference on Human Vision and Electronic Imaging XVIII

作者： Mantiuk, Rafal K. Bangor Univ Sch Comp Sci Bangor Gwynedd Wales

ISBN: (纸本)9780819494245

We explore three problems related to quality assessment in computer graphics: the design of efficient user studies;the scene-referred metrics for comparing high-dynamic-range images;and the comparison of metric performance for the database of computer graphics distortions. This paper summarizes the most important observations from investigating these problems and gives a high level perspective on the problem of quality assessment in graphics.

关键词： Image quality visualization Data modeling visual process modeling High dynamic range imaging Computer graphics Visibility Spatial frequencies Light scattering Error analysis

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A Multiple Feature Based Particle Filter Using Mutual Information Maximization

A Multiple Feature Based Particle Filter Using Mutual Inform...

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Conference on Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques

作者： Hong, Kihyun Han, Kyuseo Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9780819484154

In designing a tracking algorithm, utilizing several different features, e. g., color histogram, gradient histogram and other object descriptors, is preferable to increase robustness of tracking performance. In this paper, we propose a multiple feature fusion framework to improve the tracking by assigning appropriate weights to individual features. The feature weights are optimally obtained by a waterfilling procedure that maximizes mutual information between target object features and query features. Especially, in this paper, we focus on a particle filter tracking implementation of the multiple feature fusion framework. Our experiments show that object tracking with multiple features outperforms single feature based tracking methods and illustrates that the proposed optimal feature weighting increases robustness of multiple-feature based tracking performance.

关键词： Particle filters Detection and tracking algorithms Particles process modeling Algorithm development Signal to noise ratio visual process modeling Computer vision technology Dynamical systems Machine vision

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