Tensor voting a perceptual organization approach to computer vision and machine learning /

• Main Author: Mordohai, Philippos.
• Other Authors: Medioni, Gérard.
• Format: Electronic
• Language: English
• Published: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, c2006.
• Edition: 1st ed.
• Series: Synthesis lectures on image, video, and multimedia processing ; #8.
• Subjects: Computer vision.; Machine learning.; Three-dimensional imaging.
• Online Access: Abstract with links to full text
• Item Description: Part of: Synthesis digital library of engineering and computer science.; Series from website.; Title from PDF t.p. (viewed on Oct. 10, 2008).
• Physical Description: 1 electronic document (ix, 126 p.) : digital file.
• Format: Mode of access: World Wide Web.; System requirements: PDF reader.
• Bibliography: Includes bibliographical references (p. 115-123).
• ISBN: 1598291017 (electronic bk.); 9781598291018 (electronic bk.); 1598291009 (paper); 9781598291001 (paper)
• ISSN: 1559-8144
• Access: Abstract freely available; full-text restricted to subscribers or individual document purchasers.; Available for subscribers only.

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.