Handbook of Robust Low-Rank and Sparse Matrix Decomposition (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
552
Utgivningsdatum
2020-06-30
Förlag
Chapman & Hall/CRC
Dimensioner
251 x 175 x 30 mm
Vikt
1339 g
Antal komponenter
1
ISBN
9780367574789

Handbook of Robust Low-Rank and Sparse Matrix Decomposition

Applications in Image and Video Processing

av Thierry Bouwmans, Necdet Serhat Aybat, El-Hadi Zahzah
Häftad,  Engelska, 2020-06-30
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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.
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  • Background Modeling and Foreground Detection for Video Surveillance

    Background Modeling and Foreground Detection for Video Surveillance

    Thierry Bouwmans, Fatih Porikli, Benjamin Hferlin, Antoine Vacavant

    Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes ...

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Övrig information

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.

Innehållsförteckning

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.

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