robust subspace estimation using von shah mubarak (5 Ergebnisse)

Hardcover. Zustand: Brand New. 2014 edition. 114 pages. 9.25x6.25x0.50 inches. In Stock.

Paperback. Zustand: Brand New. reprint edition. 120 pages. 9.25x6.10x0.29 inches. In Stock.

Buch. Zustand: Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 120 pp. Englisch.

Buch. Zustand: 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.

Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.