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Robust Subspace Estimation Using by Oreifej Omar (25 results)
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Condition: As New. Unread book in perfect condition.
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Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications (The International Series in Video Computing, 12)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications (The International Series in Video Computing, 12)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Robust Subspace Estimation Using Low-Rank Optimization : Theory and Applications
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Robust Subspace Estimation Using Low-rank Optimization : Theory and Applications
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Condition: New. pp. 112.
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Robust Subspace Estimation Using Low-Rank Optimization : Theory and Applications
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications
Published by Springer-Verlag New York Inc, 2014
ISBN 10: 3319041835 ISBN 13: 9783319041834
Language: English
Hardcover. Condition: Brand New. 2014 edition. 114 pages. 9.25x6.25x0.50 inches. In Stock.
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Robust Subspace Estimation Using Low-rank Optimization: Theory and Applications
Published by Springer-Verlag New York Inc, 2016
ISBN 10: 3319352482 ISBN 13: 9783319352480
Language: English
Paperback. Condition: Brand New. reprint edition. 120 pages. 9.25x6.10x0.29 inches. In Stock.
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Robust Subspace Estimation Using Low-Rank Optimization : Theory and Applications
Published by Springer, Berlin, Springer International Publishing, Springer, 2016
ISBN 10: 3319352482 ISBN 13: 9783319352480
Language: English
Taschenbuch. Condition: 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.
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Taschenbuch. Condition: Neu. Robust Subspace Estimation Using Low-Rank Optimization | Theory and Applications | Omar Oreifej (u. a.) | Taschenbuch | Previously published in hardcover | vi | Englisch | 2016 | Springer | EAN 9783319352480 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Robust Subspace Estimation Using Low-rank Optimization : Theory and Applications
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 136.36
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Paperback. Condition: Like New. Like New. book.
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Condition: As New. Unread book in perfect condition.
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Robust Subspace Estimation Using Low-Rank Optimization : Theory and Applications
Published by Springer International Publishing, 2014
ISBN 10: 3319041835 ISBN 13: 9783319041834
Language: English
Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.
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Condition: new. Questo è un articolo print on demand.
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Robust Subspace Estimation Using Low-Rank Optimization
Published by Berlin Springer International Publishing Springer Aug 2016, 2016
ISBN 10: 3319352482 ISBN 13: 9783319352480
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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.
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Condition: New. Print on Demand pp. 112.
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Condition: New. PRINT ON DEMAND pp. 112.
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Robust Subspace Estimation Using Low-Rank Optimization
Published by Springer International Publishing, 2014
ISBN 10: 3319041835 ISBN 13: 9783319041834
Language: English
Gebunden. Condition: 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.
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Robust Subspace Estimation Using Low-Rank Optimization
Published by Springer International Publishing, 2016
ISBN 10: 3319352482 ISBN 13: 9783319352480
Language: English
Condition: 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.
