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

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ISBN 10 : 3319352482 ISBN 13 : 9783319352480

Editeur : Springer, 2016

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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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�diteurSpringer
Date d'�dition2016
ISBN 10 3319352482
ISBN 13 9783319352480
ReliureBroch�
Langueanglais
Nombre de pages120
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Robust Subspace Estimation Using Low-Rank Optimization

Omar Oreifej|Mubarak Shah

Edit� par Springer International Publishing, 2016

ISBN 10 : 3319352482 ISBN 13 : 9783319352480

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a comprehensive summary of the state-of-the-art methods and applications of Low-Rank OptimizationReviews the latest approaches in a wide range of computer vision problems, including: Scene Reconstruction, Video Denoising, Activity Recogni. N� de r�f. du vendeur 458602722

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Robust Subspace Estimation Using Low-Rank Optimization

Omar Oreifej

Edit� par Berlin Springer International Publishing Springer Aug 2016, 2016

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 114 pp. Englisch. N� de r�f. du vendeur 9783319352480

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Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications (The International Series in Video Computing, 12)

Oreifej, Omar; Shah, Mubarak

Edit� par Springer, 2016

ISBN 10 : 3319352482 ISBN 13 : 9783319352480

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

Omar Oreifej

Edit� par Springer, Berlin, Springer International Publishing, Springer, 2016

ISBN 10 : 3319352482 ISBN 13 : 9783319352480

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N� de r�f. du vendeur 9783319352480

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