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

Oreifej, Omar; Shah, Mubarak

ISBN 10: 3319041835 / ISBN 13: 9783319041834

Verlag: Springer, 2014

In. Bestandsnummer des Verkäufers ria9783319041834_new

Inhaltsangabe:

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.

Bibliografische Details

Titel: Robust Subspace Estimation Using Low-Rank ...
Verlag: Springer
Erscheinungsdatum: 2014
Einband: Hardcover
Zustand: New