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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Yes, you can access Handbook of Robust Low-Rank and Sparse Matrix Decomposition by Thierry Bouwmans, Necdet Serhat Aybat, El-hadi Zahzah in PDF and/or ePUB format, as well as other popular books in Computer Science & Programming Games. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Chapman and Hall/CRC

Year

2016

ISBN

9781315353531

Edition

Topic

Computer Science

Subtopic

Programming Games

Index

Computer Science

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the Editors
List of Contributors
Part I: Robust Principal Component Analysis
Part II: Robust Matrix Factorization
Part III: Robust Subspace Learning and Tracking
Part IV: Applications in Image and Video Processing
Part V: Applications in Background/Foreground Separation for Video Surveillance
Index