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

Handbook of Robust Low-Rank and Sparse Matrix Decomposition

Applications in Image and Video Processing, 1st Edition

Edited by Thierry Bouwmans, Necdet Serhat Aybat, El-hadi Zahzah

Chapman and Hall/CRC

520 pages | 34 Color Illus. | 149 B/W Illus.

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Hardback: 9781498724623
pub: 2016-05-27
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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


About the Editors

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.

Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Machine Theory
MATHEMATICS / Graphic Methods