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Informationen zum Autor 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. Klappentext This handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It offers a framework for computer vision applications, including image processing and video surveillance, and describes many methods and algorithms to tackle different formulation problems. It also presents an introduction for beginners that reviews various decompositions, loss functions, optimization problems, and solvers. Software demos, datasets, and codes are available online. Zusammenfassung 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. Inhaltsverzeichnis Robust Principal Component Analysis. Robust Matrix Factorization. Robust Subspace Learning and Tracking. Applications in Image and Video Processing. Applications in Background/Foreground Separation for Video Surveillance. Index. ...
