Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications - Couverture souple

Oreifej, Omar; Shah, Mubarak

 
9783319352480: Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications
Couverture souple
ISBN 10 : 3319352482 ISBN 13 : 9783319352480
Editeur : Springer, 2016

Synopsis

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.

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Éditeur
Springer
Date d'édition
2016
Langue
anglais
ISBN 10 
3319352482
ISBN 13 
9783319352480
Reliure
Broché
Nombre de pages
120
Coordonnées du fabricant
non disponible
Personne responsable
CMN Printpool Inh. Mathis Neumann
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Hamburg
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9783319041834: Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications

Edition présentée

ISBN 10 :  3319041835 ISBN 13 :  9783319041834
Editeur : Springer International Publishin..., 2014
Couverture rigide