Overview
- Provides a comprehensive summary of the state-of-the-art methods and applications of Low-Rank Optimization
- Reviews the latest approaches in a wide range of computer vision problems, including: Scene Reconstruction, Video Denoising, Activity Recognition, and Background Subtraction
- Involves a self-complete and detailed description of the methods and theories which makes it ideal for graduate students looking for a comprehensive resource in this area
- Includes supplementary material: sn.pub/extras
Part of the book series: The International Series in Video Computing (VICO, volume 12)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
- Activity recognition
- complex event recognition
- computer vision
- image processing
- low-rank optimization
- machine learning
- motion decomposition
- motion estimation
- particle advection
- principal component analysis
- robust subspace estimation
- seeing through water
- sparse representation
- turbulence mitigation
- video denoising
About this book
Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book, the authors discuss fundamental formulations and extensions for low-rank optimization-based subspace estimation and representation. By minimizing the rank of the matrix containing observations drawn from images, the authors demonstrate how to solve four fundamental computer vision problems, including video denosing, background subtraction, motion estimation, and activity recognition.
Authors and Affiliations
Bibliographic Information
Book Title: Robust Subspace Estimation Using Low-Rank Optimization
Book Subtitle: Theory and Applications
Authors: Omar Oreifej, Mubarak Shah
Series Title: The International Series in Video Computing
DOI: https://doi.org/10.1007/978-3-319-04184-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Hardcover ISBN: 978-3-319-04183-4Published: 03 April 2014
Softcover ISBN: 978-3-319-35248-0Published: 23 August 2016
eBook ISBN: 978-3-319-04184-1Published: 24 March 2014
Series ISSN: 1571-5205
Edition Number: 1
Number of Pages: VI, 114
Number of Illustrations: 2 b/w illustrations, 39 illustrations in colour
Topics: Computer Imaging, Vision, Pattern Recognition and Graphics