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

Oreifej, Omar; Shah, Mubarak

9783319041834: Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications

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ISBN 10 : 3319041835 ISBN 13 : 9783319041834

Editeur : Springer International Publishing AG, 2014

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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 International Publishing AG
Date d'�dition2014
ISBN 10 3319041835
ISBN 13 9783319041834
ReliureReli�
Langueanglais
Nombre de pages114
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Robust Subspace Estimation Using Low-Rank Optimization : Theory and Applications

Mubarak Shah, Omar Oreifej

Edit� par Springer International Publishing, 2014

ISBN 10 : 3319041835 ISBN 13 : 9783319041834

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Robust Subspace Estimation Using Low-Rank Optimization

Omar Oreifej|Mubarak Shah

Edit� par Springer International Publishing, 2014

ISBN 10 : 3319041835 ISBN 13 : 9783319041834

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Gebunden. 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 4496903

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

Mubarak Shah

Edit� par Springer International Publishing, 2014

ISBN 10 : 3319041835 ISBN 13 : 9783319041834

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Buch. 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 9783319041834

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Robust Subspace Estimation Using Low-Rank Optimization

Mubarak Shah

Edit� par Springer International Publishing Apr 2014, 2014

ISBN 10 : 3319041835 ISBN 13 : 9783319041834

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Buch. 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. 120 pp. Englisch. N� de r�f. du vendeur 9783319041834

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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, 2014

ISBN 10 : 3319041835 ISBN 13 : 9783319041834

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Robust Subspace Estimation Using Low-Rank Optimization

Mubarak Shah

Edit� par Springer International Publishing, Springer International Publishing Apr 2014, 2014

ISBN 10 : 3319041835 ISBN 13 : 9783319041834

Neuf Couverture rigide

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Buch. Etat : Neu. 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 authorsdiscuss 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 120 pp. Englisch. N� de r�f. du vendeur 9783319041834

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