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

Shah, Mubarak; Oreifej, Omar

 
9783319041834: Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications: 12 (The International Series in Video Computing)
Tapa dura
ISBN 10: 3319041835 ISBN 13: 9783319041834
Editorial: Springer, 2014

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.

Reseña del editor

Recovering the low-rank structure of a linear subspace using a small set of corrupted examples has recently been made feasible through substantial advances in the area of matrix completion and nuclear-norm minimization. Such low-rank structures appear in certain conditions heavily in computer vision, for instance, in the frames of a video, the camera motion, and a picture of a building facade. In this book, we discuss several formulations and extensions of low-rank optimization, and demonstrate how recovering the underlying basis and detecting the corresponding outliers allow us to solve fundamental computer vision problems, including video denoising, background subtraction, action detection, and complex event recognition.

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Editorial
Springer
Año de publicación
2014
Idioma
Inglés
ISBN 10 
3319041835
ISBN 13 
9783319041834
Encuadernación
Tapa dura
Número de páginas
122
Contacto del fabricante
Springer-Verlag GmbH
customerservice@springernature.com

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9783319352480: Robust Subspace Estimation Using Low-Rank Optimization: Theory and Applications: 12 (The International Series in Video Computing)

Edición Destacada

ISBN 10:  3319352482 ISBN 13:  9783319352480
Editorial: Springer, 2016
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