Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing

ISBN-13: 9780367574789 / Angielski / Miękka / 2020 / 552 str.

Thierry Bouwmans; Necdet Serhat Aybat; El-Hadi Zahzah

Opis

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 proble

Spis treści

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.

O autorze

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.