Robust Subspace Estimation Using Low-Rank Optimization

Theory and Applications

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, General Computing
Cover of the book Robust Subspace Estimation Using Low-Rank Optimization by Omar Oreifej, Mubarak Shah, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Omar Oreifej, Mubarak Shah ISBN: 9783319041841
Publisher: Springer International Publishing Publication: March 24, 2014
Imprint: Springer Language: English
Author: Omar Oreifej, Mubarak Shah
ISBN: 9783319041841
Publisher: Springer International Publishing
Publication: March 24, 2014
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Trusted Computing Platforms by Omar Oreifej, Mubarak Shah
Cover of the book The Semantic Web: ESWC 2015 Satellite Events by Omar Oreifej, Mubarak Shah
Cover of the book Proof Patterns by Omar Oreifej, Mubarak Shah
Cover of the book Astrophysical Black Holes by Omar Oreifej, Mubarak Shah
Cover of the book Photocatalytic Semiconductors by Omar Oreifej, Mubarak Shah
Cover of the book Education Technology Policies in the Middle East by Omar Oreifej, Mubarak Shah
Cover of the book Mathematical Methods for Curves and Surfaces by Omar Oreifej, Mubarak Shah
Cover of the book Regulation of Heat Shock Protein Responses by Omar Oreifej, Mubarak Shah
Cover of the book The Quality of Society by Omar Oreifej, Mubarak Shah
Cover of the book New Trends in Medical and Service Robots by Omar Oreifej, Mubarak Shah
Cover of the book Homogeneous Turbulence Dynamics by Omar Oreifej, Mubarak Shah
Cover of the book Policing in Russia by Omar Oreifej, Mubarak Shah
Cover of the book Multidisciplinary Design of Sharing Services by Omar Oreifej, Mubarak Shah
Cover of the book Aniridia by Omar Oreifej, Mubarak Shah
Cover of the book Virtual and Remote Control Tower by Omar Oreifej, Mubarak Shah
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy