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conference proceedings - ieee International conference on Systems, Man and Cybernetics 2020年 2020-October卷 ii-iii页

作者： Gabbar, Hossam A. Trajkovic, Ljiljana Ontario Tech University Canada Simon Fraser University Canada

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conference on computer Vision and pattern recognition (CVPR)

作者： Y. Keller A. Averbuch Department of Computer Science Technion-Israel Institute of Technology Haifa Israel Department of Computer Science School of Mathematical Sciences Tel-Aviv University Tel-Aviv Israel

This paper presents an implicit similarity-based approach to registration of significantly dissimilar images, acquired by sensors at different modalities. The proposed algorithm introduces a robust matching criterion by aligning the locations of gradient maxima. The alignment is formulated as a parametric variational optimization problem, which is solved iteratively by considering the intensities of a single image. The location of the maxima of the second image's gradient are used as initialization. We are able to robustly estimate affine and projective global motions using 'coarse to fine' processing. even when the images are characterized by complex space varying intensity transformations. Finally, we present the registration of real images, which were taken by multi-sensor and multi-modality using affine and projective motion models.

关键词： image registration image sensors Robustness Magnetic resonance imaging Gradient methods computer science Sensor phenomena and characterization Remote sensing Biomedical imaging Computed tomography

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Polydioptric camera design and 3D motion estimation

Polydioptric camera design and 3D motion estimation

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conference on computer Vision and pattern recognition (CVPR)

作者： J. Neumann C. Fermuller Y. Aloimonos Computer Vision Laboratory University of Maryland College Park MD USA

Most cameras used in computer vision applications are still based on the pinhole principle inspired by our own eyes. It has been found though that this is not necessarily the optimal image formation principle for processing.visual information using a machine. We describe how to find the optimal camera for 3D motion estimation by analyzing the structure of the space formed by the light rays passing through a volume of space. Every camera corresponds to a sampling pattern in light ray space, thus the question of camera design can be rephrased as finding the optimal sampling pattern with regard to a given task. This framework suggests that large field-of-view multi-perspective (polydioptric) cameras are the optimal image sensors for 3D motion estimation. We conclude by proposing design principles for polydioptric cameras and describe an algorithm for such a camera that estimates its 3D motion in a scene independent and robust manner.

关键词： Cameras Motion estimation image sampling computer vision Application software Eyes image motion analysis image sensors Algorithm design and analysis Layout

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proceedings - ieee computer society'S INTERNATIONAL computer SOFTWARE & APPLICATIONS conference (COMPSAC), 3RD, 1979.

Proceedings - IEEE Computer Society's International Computer...

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proceedings - ieee computer society's International computer Software and Applications conference 1979年

作者： Anon

This conference proceedings contains 156 papers, of which 3 are given in abstract form only; 145 papers are indexed separately. The topics covered are: software development methodology; storage structure and file design; pattern recognition software; telecommunications software; computer-aided circuit design; software requirements and specifications; schema and view design; data base design; manufacturing software; automatic visual inspection; diagnostic and therapeutic systems; query description and processing. robots; biomedical image processing. distributed processing. software testing; computer graphics; data base management; microcomputer software; and Petri nets and other computation models.

关键词： computer PROGRAMMING

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View invariants for human action recognition

View invariants for human action recognition

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conference on computer Vision and pattern recognition (CVPR)

作者： V. Parameswaran R. Chellappa Center for Automation Research University of Maryland College Park MD USA

This paper presents two approaches for the representation and recognition of human action in video, aiming for view-point invariance. The paper first presents new results using a 2D approach presented earlier. Inherent limitations of the 2D approach are discussed and a new 3D approach that builds on recent work on 3D model-based invariants, is presented. Each action is represented as a unique curve in a 3D invariance space, surrounded by an acceptance volume ('action volume'). Given a video sequence, 2D quantities from the image are calculated and matched against candidate action volumes in a probabilistic framework. The theory is presented followed by results on arbitrary projections of motion-capture data which demonstrate a high degree of tolerance to viewpoint change.

关键词： Humans Video sequences Gunshot detection systems Motion analysis Automation Educational institutions Motion detection Machine learning Cameras image processing

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CANADIAN society FOR CIVIL ENGINEERING, conference, 1979.

Proceedings - IEEE Computer Society Conference on Pattern Re...

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proceedings - ieee computer society conference on pattern recognition and image processing 1979年

作者： Anon

proceedings include 55 papers that deal with the computer application in structural analysis and design; construction methods and equipment; municipal engineering; hydrotechnical construction and hydraulic structure; ... 详细信息

关键词： CIVIL ENGINEERING

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Simultaneous image classification and restoration using a variational approach

Simultaneous image classification and restoration using a va...

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conference on computer Vision and pattern recognition (CVPR)

作者： C. Samson L. Blanc-Feraud J. Zerubia G. Aubert Ariana project Sophia-Antipolis France Laboratoire J.A. Dieudonné Université de Nice Sophia-Antipolis Nice France

Herein, we present a variational model devoted to image classification coupled with an edge-preserving regularization process. In the last decade, the variational approach has proven its efficiency in the field of edge-preserving restoration. In this paper, we add a classification capability which contributes to provide images compound of homogeneous regions with regularized boundaries. The soundness of this model is based on the works developed on the phase transition theory in mechanics. The proposed algorithm is fast, easy to implement and efficient. We compare our results on both synthetic and satellite images with the ones obtained by a stochastic model using a Potts regularization.

关键词： image classification image restoration Satellites Stochastic processes Acoustic noise image processing image edge detection Active shape model image segmentation Active contours

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Self-Awareness for Autonomous Systems

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proceedings OF THE ieee 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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3D trajectory recovery for tracking multiple objects and trajectory guided recognition of actions

3D trajectory recovery for tracking multiple objects and tra...

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conference on computer Vision and pattern recognition (CVPR)

作者： R. Rosales S. Sclaroff Computer Science Department Boston University USA

A mechanism is proposed that integrates low-level (image processing., mid-level (recursive 3D trajectory estimation), and high level (action recognition) processes. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliable segment and maintain the tracking of moving objects before, during, and after occlusion. The 3D trajectory, occlusion, and segmentation information are utilized in extracting stabilized views of the moving object. Trajectory-guided recognition (TGR) is proposed as a new and efficient method for adaptive classification of action. The TGR approach is demonstrated using "motion history images" that are then recognized via a mixture of Gaussian classifier. The system was tested in recognizing various dynamic human outdoor activities; e.g., running, walking, roller blading, and cycling. Experiments with synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.

关键词： Trajectory Recursive estimation image recognition image processing Cameras Motion estimation Feedback Maintenance image segmentation Data mining

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Linear subspaces for illumination robust face recognition

Linear subspaces for illumination robust face recognition

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conference on computer Vision and pattern recognition (CVPR)

作者： A.U. Batur M.H.I.I.I. Hayes Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)0769512720

In this paper, we present a segmented linear subspace model for face recognition that is robust under varying illumination conditions. The algorithm generalizes the 3D illumination subspace model by segmenting the image into regions that have surface normals whose directions are close to each other. This segmentation is performed using a K-means clustering algorithm and requires only a few training images under different illuminations. When the linear subspace model is applied to the segmented image, recognition is robust to attached and cast shadows, and the recognition rate is equal to that of computationally more complex systems that require constructing the 3D surface of the face.

关键词： Lighting Robustness Face recognition image segmentation image recognition Clustering algorithms Light sources Signal processing image processing Shadow mapping

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