Robust subspace estimation using low-rank optimization : theory and applications /

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Bibliographic Details
Author / Creator:	Oreifej, Omar, author.
Imprint:	Cham ; New York : Springer, [2014]
Description:	1 online resource.
Language:	English
Series:	The International Series in Video Computing, 1571-5205 ; 12 International series in video computing ; 12.
Subject:	Computer vision. Trajectory optimization. Computer vision. Trajectory optimization. Electronic books.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11084115

Hidden Bibliographic Details
Other authors / contributors:	Shah, Mubarak, author.
ISBN:	9783319041841 3319041843 9783319041834 3319041835
Digital file characteristics:	text file PDF
Notes:	Includes bibliographical references. English. Print version record.
Summary:	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 façade. 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.
Other form:	Print version: Robust Subspace Estimation Using Low-rank Optimization 9783319041834
Standard no.:	10.1007/978-3-319-04184-1

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