Handbook of Robust Low-Rank and Sparse Matrix Decomposition

<p><strong>Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing</strong> 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.</p><p></p><p>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.</p><p></p><p>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.</p>