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

Oreifej, Omar; Shah, Mubarak

 
9783319352480: Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications: 12 (The International Series in Video Computing)
Tapa blanda
ISBN 10: 3319352482 ISBN 13: 9783319352480
Editorial: Springer, 2016

Sinopsis

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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Reseña del editor

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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2016
Idioma
Inglés
ISBN 10 
3319352482
ISBN 13 
9783319352480
Encuadernación
Tapa blanda
Número de páginas
120
Contacto del fabricante
no disponible
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Otras ediciones populares con el mismo título

9783319041834: Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications: 12 (The International Series in Video Computing, 12)

Edición Destacada

ISBN 10:  3319041835 ISBN 13:  9783319041834
Editorial: Springer, 2014
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