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Tensor voting [electronic resource] : a perceptual organization approach to computer vision and machine learning / Philippos Mordohai and Gérard Medioni

Author
Mordohai, Philippos
Published
San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, [2006]
Copyright Date
©2006
Edition
1st ed.
Physical Description
1 electronic document (ix, 126 pages) : digital file
Additional Creators
Medioni, Gérard
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Restrictions on Access
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Available for subscribers only.
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.
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.
Subject(s)
ISBN
1598291017 (electronic bk.)
9781598291018 (electronic bk.)
1598291009 (paper)
9781598291001 (paper)
Related Titles
Synthesis digital library of engineering and computer science
Note
Part of: Synthesis digital library of engineering and computer science.
Series from website.
Title from PDF t.p. (viewed on Oct. 10, 2008).
Bibliography Note
Includes bibliographical references (pages 115-123).
Technical Details
Mode of access: World Wide Web.
System requirements: PDF reader.
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View MARC record | catkey: 7058275