Robust Subspace Estimation Using by Oreifej Omar (28 results)
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Hardcover. Condition: Brand New. 2014 edition. 114 pages. 9.25x6.25x0.50 inches. In Stock.
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Published by Springer, Berlin, Springer International Publishing, Springer, 2016
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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 | 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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Language: English
Published by Berlin Springer International Publishing Springer Aug 2016, 2016
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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 increasi…ng 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. 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, Vid…eo Denoising, Activity Recogni.
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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, Vid…eo Denoising, Activity Recogni.
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 120 pp. Englisch.
