{"product_id":"handbook-of-robust-low-rank-and-sparse-matrix-decomposition-applications-in-image-and-video-processing-paperback","title":"Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eThierry Bouwmans\u003c\/b\u003e (Editor), \u003cb\u003eNecdet Serhat Aybat\u003c\/b\u003e (Editor), \u003cb\u003eEl-Hadi Zahzah\u003c\/b\u003e (Editor)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eHandbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing\u003c\/strong\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eDivided 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.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eWith 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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eThierry Bouwmans\u003c\/strong\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eNecdet Serhat Aybat\u003c\/strong\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eEl-hadi Zahzah\u003c\/strong\u003e 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.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 552\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.2 x 9.9 x 6.9 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 30, 2020\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":46593611169989,"sku":"9780367574789","price":114.81,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0757\/6718\/5605\/files\/Lt8GGF99Vb9780367574789.webp?v=1785074042","url":"https:\/\/selloorium.com\/products\/handbook-of-robust-low-rank-and-sparse-matrix-decomposition-applications-in-image-and-video-processing-paperback","provider":"Selloorium","version":"1.0","type":"link"}