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E-BOOK

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Author	Mordohai, Philippos.
Title	Tensor voting : a perceptual organization approach to computer vision and machine learning / Philippos Mordohai and G�rard Medioni.
Edition	First edition.
OCLC	200609IVM008
ISBN	1598291017 (electronic bk.)
	9781598291018 (electronic bk.)
	1598291009 (paper)
	9781598291001 (paper)
ISBN/ISSN	10.2200/S00049ED1V01Y200609IVM008 doi
Publisher	San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, [2006]
	�2006
Description	1 electronic document (ix, 126 pages) : digital file.
LC Subject heading/s	Computer vision.
	Machine learning.
	Three-dimensional imaging.
SUBJECT	Perceptual organization.
	Computer vision.
	Machine learning.
	Tensor voting.
	Stereo vision.
	Dimensionality estimation.
	Manifold learning.
	Function approximation.
	Figure completion.
System details note	Mode of access: World Wide Web.
	System requirements: PDF reader.
Bibliography	Includes bibliographical references (pages 115-123).
Contents	Introduction -- Tensor voting -- Stereo vision from a perceptual organization perspective -- Tensor voting in ND -- Dimensionality estimation manifold learning and function approximation -- Boundary inference -- Figure completion -- Conclusions -- References.
Restrictions	Abstract freely available; full-text restricted to subscribers or individual document purchasers.
	Available for subscribers only.
	Access may be restricted to authorized users only.
	Unlimited user license access
NOTE	Compendex.
	Google book search.
	INSPEC.
Subject summary	This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organization methodology applicable in situations that may seem heterogeneous initially. We show how several problems can be posed as the organization of the inputs into salient perceptual structures, which are inferred via tensor voting. The work presented here extends the original tensor voting framework with the addition of boundary inference capabilities; a novel re-formulation of the framework applicable to high-dimensional spaces and the development of algorithms for computer vision and machine learning problems. We show complete analysis for some problems, while we briefly outline our approach for other applications and provide pointers to relevant sources.
NOTE	Google scholar.
General note	Part of: Synthesis digital library of engineering and computer science.
	Series from website.
	Title from PDF t.p. (viewed on Oct. 10, 2008).

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Summary