Low Rank Models In Visual Analysis

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Low Rank Models In Visual Analysis
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Author : Zhouchen Lin
language : en
Publisher: Academic Press
Release Date : 2017-06-06
Low Rank Models In Visual Analysis written by Zhouchen Lin and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-06 with Computers categories.
Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their application to visual analysis. It provides insight into the ideas behind the models and their algorithms, giving details of their formulation and deduction. The main applications included are video denoising, background modeling, image alignment and rectification, motion segmentation, image segmentation and image saliency detection. Readers will learn which Low-rank models are highly useful in practice (both linear and nonlinear models), how to solve low-rank models efficiently, and how to apply low-rank models to real problems. - Presents a self-contained, up-to-date introduction that covers underlying theory, algorithms and the state-of-the-art in current applications - Provides a full and clear explanation of the theory behind the models - Includes detailed proofs in the appendices
Low Rank And Sparse Modeling For Visual Analysis
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Author : Yun Fu
language : en
Publisher: Springer
Release Date : 2014-10-30
Low Rank And Sparse Modeling For Visual Analysis written by Yun Fu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-30 with Computers categories.
This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.
Low Rank And Sparse Modeling For Visual Analysis
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Author : Yun Fu
language : en
Publisher: Springer
Release Date : 2014-11-01
Low Rank And Sparse Modeling For Visual Analysis written by Yun Fu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-01 with Computers categories.
This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.
Deep Learning Through Sparse And Low Rank Modeling
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Author : Zhangyang Wang
language : en
Publisher: Academic Press
Release Date : 2019-04-11
Deep Learning Through Sparse And Low Rank Modeling written by Zhangyang Wang and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-11 with Computers categories.
Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. - Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks - Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models - Provides tactics on how to build and apply customized deep learning models for various applications
Cardiovascular And Coronary Artery Imaging
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Author : Ayman S. El-Baz
language : en
Publisher: Academic Press
Release Date : 2021-11-24
Cardiovascular And Coronary Artery Imaging written by Ayman S. El-Baz and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-24 with Computers categories.
Cardiovascular and Coronary Artery Imaging, Volume One covers state-of-the-art approaches for automated non-invasive systems in early cardiovascular disease diagnosis. The book includes several prominent imaging modalities, such as MRI, CT and PET technologies. A special emphasis is placed on automated imaging analysis techniques, which are important to biomedical imaging analysis of the cardiovascular system. This is a comprehensive, multi-contributed reference work that details the latest developments in spatial, temporal and functional cardiac imaging. - Takes an integrated approach to cardiovascular and coronary imaging, covering machine learning, deep learning and reinforcement learning approaches - Covers state-of-the-art approaches for automated non-invasive systems for early cardiovascular disease diagnosis - Provides a perspective on future cardiovascular imaging and highlights areas that still need improvement
Computer Vision For Assistive Healthcare
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Author : Leo Marco
language : en
Publisher: Academic Press
Release Date : 2018-05-15
Computer Vision For Assistive Healthcare written by Leo Marco and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-15 with Technology & Engineering categories.
Computer Vision for Assistive Healthcare describes how advanced computer vision techniques provide tools to support common human needs, such as mental functioning, personal mobility, sensory functions, daily living activities, image processing, pattern recognition, machine learning and how language processing and computer graphics cooperate with robotics to provide such tools. Users will learn about the emerging computer vision techniques for supporting mental functioning, algorithms for analyzing human behavior, and how smart interfaces and virtual reality tools lead to the development of advanced rehabilitation systems able to perform human action and activity recognition. In addition, the book covers the technology behind intelligent wheelchairs, how computer vision technologies have the potential to assist blind people, and about the computer vision-based solutions recently employed for safety and health monitoring. - Gives the state-of-the-art computer vision techniques and tools for assistive healthcare - Includes a broad range of topic areas, ranging from image processing, pattern recognition, machine learning to robotics, natural language processing and computer graphics - Presents a wide range of application areas, ranging from mobility, sensory substitution, and safety and security, to mental and physical rehabilitation and training - Written by leading researchers in this growing field of research - Describes the outstanding research challenges that still need to be tackled, giving researchers good indicators of research opportunities
Skeletonization
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Author : Punam K Saha
language : en
Publisher: Academic Press
Release Date : 2017-06-06
Skeletonization written by Punam K Saha and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-06 with Technology & Engineering categories.
Skeletonization: Theory, Methods and Applications is a comprehensive reference on skeletonization, written by the world's leading researchers in the field. The book presents theory, methods, algorithms and their evaluation, together with applications. Skeletonization is used in many image processing and computer vision applications such as shape recognition and analysis, shape decomposition and character recognition, as well as medical imaging for pulmonary, cardiac, mammographic applications. Part I includes theories and methods unique to skeletonization. Part II includes novel applications including skeleton-based characterization of human trabecular bone micro-architecture, image registration and correspondence establishment in anatomical structures, skeleton-based fast, fully automated generation of vessel tree structure for clinical evaluation of blood vessel systems. - Offers a complete picture of skeletonization and its application to image processing, computer vision, pattern recognition and biomedical engineering - Provides an in-depth presentation on various topics of skeletonization, including principles, theory, methods, algorithms, evaluation and real-life applications - Discusses distance-analysis, geometry, topology, scale and symmetry-analysis in the context of object understanding and analysis using medial axis and skeletonization
Group And Crowd Behavior For Computer Vision
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Author : Vittorio Murino
language : en
Publisher: Academic Press
Release Date : 2017-04-18
Group And Crowd Behavior For Computer Vision written by Vittorio Murino and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-18 with Computers categories.
Group and Crowd Behavior for Computer Vision provides a multidisciplinary perspective on how to solve the problem of group and crowd analysis and modeling, combining insights from the social sciences with technological ideas in computer vision and pattern recognition. The book answers many unresolved issues in group and crowd behavior, with Part One providing an introduction to the problems of analyzing groups and crowds that stresses that they should not be considered as completely diverse entities, but as an aggregation of people. Part Two focuses on features and representations with the aim of recognizing the presence of groups and crowds in image and video data. It discusses low level processing methods to individuate when and where a group or crowd is placed in the scene, spanning from the use of people detectors toward more ad-hoc strategies to individuate group and crowd formations. Part Three discusses methods for analyzing the behavior of groups and the crowd once they have been detected, showing how to extract semantic information, predicting/tracking the movement of a group, the formation or disaggregation of a group/crowd and the identification of different kinds of groups/crowds depending on their behavior. The final section focuses on identifying and promoting datasets for group/crowd analysis and modeling, presenting and discussing metrics for evaluating the pros and cons of the various models and methods. This book gives computer vision researcher techniques for segmentation and grouping, tracking and reasoning for solving group and crowd modeling and analysis, as well as more general problems in computer vision and machine learning. - Presents the first book to cover the topic of modeling and analysis of groups in computer vision - Discusses the topics of group and crowd modeling from a cross-disciplinary perspective, using social science anthropological theories translated into computer vision algorithms - Focuses on group and crowd analysis metrics - Discusses real industrial systems dealing with the problem of analyzing groups and crowds
Neural Information Processing
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Author : Derong Liu
language : en
Publisher: Springer
Release Date : 2017-11-07
Neural Information Processing written by Derong Liu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-07 with Computers categories.
The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.
Image Analysis And Recognition
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Author : Aurélio Campilho
language : en
Publisher: Springer
Release Date : 2014-10-09
Image Analysis And Recognition written by Aurélio Campilho and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-09 with Computers categories.
The two volumes LNCS 8814 and 8815 constitute the thoroughly refereed proceedings of the 11th International Conference on Image Analysis and Recognition, ICIAR 2014, held in Vilamoura, Portugal, in October 2014. The 107 revised full papers presented were carefully reviewed and selected from 177 submissions. The papers are organized in the following topical sections: image representation and models; sparse representation; image restoration and enhancement; feature detection and image segmentation; classification and learning methods; document image analysis; image and video retrieval; remote sensing; applications; action, gestures and audio-visual recognition; biometrics; medical image processing and analysis; medical image segmentation; computer-aided diagnosis; retinal image analysis; 3D imaging; motion analysis and tracking; and robot vision.