Handbook of Robust Low-Rank and Sparse Matrix Decomposition : Applications in Image and Video Processing book cover
1st Edition

Handbook of Robust Low-Rank and Sparse Matrix Decomposition
Applications in Image and Video Processing

ISBN 9781498724623
Published May 27, 2016 by Chapman and Hall/CRC
520 Pages 34 Color & 149 B/W Illustrations

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Book Description

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques.

Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance.

With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.

Table of Contents

Robust Principal Component Analysis
Robust Principal Component Analysis via Decomposition into Low-Rank and Sparse Matrices: An Overview
Thierry Bouwmans and El-Hadi Zahzah
Algorithms for Stable PCA
Necdet Serhat Aybat
Dual Smoothing and Value Function Techniques for Variational Matrix Decomposition
Aleksandr Aravkin and Stephen Becker
Robust Principal Component Analysis Based on Low-Rank and Block-Sparse Matrix Decomposition
Qiuwei Li, Gongguo Tang, and Arye Nehorai
Robust PCA by Controlling Sparsity in Model Residuals
Gonzalo Mateos and Georgios B. Giannakis

Robust Matrix Factorization
Unifying Nuclear Norm and Bilinear Factorization Methods
Ricardo Cabral, Fernando De la Torre, Joao Paulo Costeira, and Alexandre Bernardino
Robust Nonnegative Matrix Factorization under Separability Assumption
Abhishek Kumar and Vikas Sindhwani
Robust Matrix Completion through Nonconvex Approaches and Efficient Algorithms
Yuning Yang, Yunlong Feng, and J.A.K. Suykens
Factorized Robust Matrix Completion
Hassan Mansour, Dong Tian, and Anthony Vetro

Robust Subspace Learning and Tracking
Online (Recursive) Robust Principal Components Analysis
Namrata Vaswani, Chenlu Qiu, Brian Lois, and Jinchun Zhan
Incremental Methods for Robust Local Subspace Estimation
Paul Rodriguez and Brendt Wohlberg
Robust Orthonormal Subspace Learning (ROSL) for Efficient Low-Rank Recovery
Xianbiao Shu, Fatih Porikli, and Narendra Ahuja
A Unified View of Nonconvex Heuristic Approach for Low-Rank and Sparse Structure Learning
Yue Deng, Feng Bao, and Qionghai Dai

Applications in Image and Video Processing
A Variational Approach for Sparse Component Estimation and Low-Rank Matrix Recovery
Zhaofu Chen, Rafael Molina, and Aggelos K. Katsaggelos
Recovering Low-Rank and Sparse Matrices with Missing and Grossly Corrupted Observations
Fanhua Shang, Yuanyuan Liu, James Cheng, and Hong Cheng
Applications of Low-Rank and Sparse Matrix Decompositions in Hyperspectral Video Processing
Jen-Mei Chang and Torin Gerhart
Low Rank plus Sparse Spatiotemporal MRI: Acceleration, Background Suppression, and Motion Learning
Ricardo Otazo, Emmanuel Candes, and Daniel K. Sodickson

Applications in Background/Foreground Separation for Video Surveillance
LRSLibrary: Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos
Andrews Sobral, Thierry Bouwmans, and El-hadi Zahzah
Dynamic Mode Decomposition for Robust PCA with Applications to Foreground/Background Subtraction in Video Streams and Multi-Resolution Analysis
Jake Nathan Kutz, Xing Fu, Steven L. Brunton, and Jacob Grosek
Stochastic RPCA for Background/Foreground Separation
Sajid Javed, Seon Ho Oh, Thierry Bouwmans, and Soon Ki Jung
Bayesian Sparse Estimation for Background/Foreground Separation
Shinichi Nakajima, Masashi Sugiyama, and S. Derin Babacan


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Thierry Bouwmans is an associate professor at the University of La Rochelle. He is the author of more than 30 papers on background modeling and foreground detection and is the creator and administrator of the Background Subtraction website and DLAM website. He has also served as a reviewer for numerous international conferences and journals. His research interests focus on the detection of moving objects in challenging environments.

Necdet Serhat Aybat is an assistant professor in the Department of Industrial and Manufacturing Engineering at Pennsylvania State University. He received his PhD in operations research from Columbia University. His research focuses on developing fast first-order algorithms for large-scale convex optimization problems from diverse application areas, such as compressed sensing, matrix completion, convex regression, and distributed optimization.

El-hadi Zahzah is an associate professor at the University of La Rochelle. He is the author of more than 60 papers on fuzzy logic, expert systems, image analysis, spatio-temporal modeling, and background modeling and foreground detection. His research interests focus on the spatio-temporal relations and detection of moving objects in challenging environments.