Tensor voting : a perceptual organization approach to computer vision and machine learning /
By: Mordohai, Philippos.
Contributor(s): Medioni, Gérard.
Material type:
BookSeries: Synthesis lectures on image, video, and multimedia processing: #8.Publisher: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, c2006Edition: 1st ed.Description: 1 electronic document (ix, 126 p.) : digital file.ISBN: 1598291017 (electronic bk.); 9781598291018 (electronic bk.); 1598291009 (paper); 9781598291001 (paper).Uniform titles: Synthesis digital library of engineering and computer science. Subject(s): Computer vision | Machine learning | Three-dimensional imaging | Perceptual organization | Computer vision | Machine learning | Tensor voting | Stereo vision | Dimensionality estimation | Manifold learning | Function approximation | Figure completionDDC classification: 006.3/7 Online resources: Abstract with links to resource | Abstract with links to full text | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBKE080 |
Mode of access: World Wide Web.
System requirements: PDF reader.
Part of: Synthesis digital library of engineering and computer science.
Series from website.
Includes bibliographical references (p. 115-123).
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.
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INSPEC
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.
Title from PDF t.p. (viewed on Oct. 10, 2008).
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