Neural model for feature matching in stereo vision
Shengrui Wang, Denis J. M. Poussart, Simon Gagné · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
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.