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Proceedings Paper

Neural network modeling of new energy function for stereo matching
Author(s): Jun Jae Lee; Seok Je Cho; Yeong-Ho Ha
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Paper Abstract

In vision research, most problems can be modeled as minimizing an energy function. Particularly, stereo matching can be viewed as one of the optimization problems in which the constraints must be satisfied simultaneously. Neural networks have been demonstrated to be very effective in computing these problems. In this paper, an approach to solve the stereo matching problem using the neural network with a new energy function is presented. The new energy function is derived not only to satisfy three constraints of similarity, smoothness, and uniqueness, but also to ensure Hopfield's convergence rules of symmetrical interconnection strength without self-feedback. Experimental results shows good stereo matching for sparse random dot stereograms and real images.

Paper Details

Date Published: 1 March 1992
PDF: 10 pages
Proc. SPIE 1608, Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods, (1 March 1992); doi: 10.1117/12.135114
Show Author Affiliations
Jun Jae Lee, Kyungpook National Univ. (South Korea)
Seok Je Cho, Kyungpook National Univ. (South Korea)
Yeong-Ho Ha, Kyungpook National Univ. (South Korea)

Published in SPIE Proceedings Vol. 1608:
Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods
David P. Casasent, Editor(s)

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