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

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.

Robust Principal Component AnalysisRobust Principal Component Analysis via Decomposition into Low-Rank and Sparse Matrices: An Overview Thierry Bouwmans and El-Hadi ZahzahAlgorithms 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 NehoraiRobust PCA by Controlling Sparsity in Model Residuals Gonzalo Mateos and Georgios B. Giannakis Robust Matrix FactorizationUnifying Nuclear Norm and Bilinear Factorization Methods Ricardo Cabral, Fernando De la Torre, Joao Paulo Costeira, and Alexandre Bernardino Robust Nonnegative Matrix Factorization under Separability AssumptionAbhishek 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 TrackingOnline (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 ProcessingA Variational Approach for Sparse Component Estimation and Low-Rank Matrix Recovery Zhaofu Chen, Rafael Molina, and Aggelos K. KatsaggelosRecovering 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 SurveillanceLRSLibrary: Low-Rank and Sparse Tools for Background Modeling and Subtraction in VideosAndrews 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 SeparationShinichi Nakajima, Masashi Sugiyama, and S. Derin Babacan Index

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.