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.

Table of Contents

Part I: Robust Principal Component Analysis
Chapter 1: Robust Principal Component Analysis via Decomposition into Low-Rank and Sparse Matrices: An Overview
Chapter 2: Algorithms for Stable PCA
Chapter 3: Dual Smoothing and Value Function Techniques for Variational Matrix Decomposition
Chapter 4: Robust Principal Component Analysis Based on Low-Rank and Block-Sparse Matrix Decomposition
Chapter 5: Robust PCA by Controlling Sparsity in Model Residuals

Part II: Robust Matrix Factorization
Chapter 6: Unifying Nuclear Norm and Bilinear Factorization Methods
Chapter 7: Robust Non-Negative Matrix Factorization under Separability Assumption
Chapter 8: Robust Matrix Completion through Nonconvex Approaches and Efficient Algorithms
Chapter 9: Factorized Robust Matrix Completion
Chapter 10: Online (Recursive) Robust Principal Components Analysis
Chapter 11: Incremental Methods for Robust Local Subspace Estimation
Chapter 12: Robust Orthonormal Subspace Learning (ROSL) for Efficient Low-Rank Recovery
Chapter 13: A Unified View of Nonconvex Heuristic Approach for Low-Rank and Sparse Structure Learning
Chapter 14: A Variational Approach for Sparse Component Estimation and Low-Rank Matrix Recovery
Chapter 15: Recovering Low-Rank and Sparse Matrices with Missing and Grossly Corrupted Observations
Chapter 16: Applications of Low-Rank and Sparse Matrix Decompositions in Hyperspectral Video Processing
Chapter 17: Low-Rank plus Sparse Spatiotemporal MRI: Acceleration, Background Suppression, and Motion Learning
Chapter 18: LRSLibrary: Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos
Chapter 19: Dynamic Mode Decomposition for Robust PCA with Applications to Foreground/Background Subtraction in Video Streams and Multi-Resolution Analysis
Chapter 20: Stochastic RPCA for Background/Foreground Separation
Chapter 21: Bayesian Sparse Estimation for Background/Foreground Separation