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Proceedings Paper

Neural model for feature matching in stereo vision
Author(s): Shengrui Wang; Denis Poussart; Simon Gagne
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Paper Abstract

The aim of this paper is to propose a neural network architecture as an approach to the feature matching problem in stereo vision. The model is based on the principle of shunting feedback competitive equations studied in depth by Grossberg and his colleagues. Psychophysical constraints utilized in the early computational models ofMarr-Poggio-Grimson Pollard-Mayhew- Frisby and Prazdny serve as basis for the architecture design of our network and for the selection of candidate matches. Competition and cooperation take place among the candidate matches and provide a strong and natural disambiguation power. 1.

Paper Details

Date Published: 1 February 1991
PDF: 12 pages
Proc. SPIE 1382, Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods, (1 February 1991); doi: 10.1117/12.25196
Show Author Affiliations
Shengrui Wang, Laval Univ. (Canada)
Denis Poussart, Laval Univ. (Canada)
Simon Gagne, Laval Univ. (Canada)

Published in SPIE Proceedings Vol. 1382:
Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods
David P. Casasent, Editor(s)

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