A constraint learning feedback dynamic model for stereopsis

This paper presents a stereo matcher inspired by the earlier work of Marr and Poggio (1976). Two major extensions are introduced: the algorithm is extended to gray-level images, and the inhibitory/excitatory weights of the model are learned rather than set a priori according to "uniqueness" and "continuity" constraints. Gray level stereo pairs of real scenes with known disparity maps are used to train the model. The trained system is successfully tested on other gray level stereo pairs of real scenes as well as a set of random dot stereograms. Performance is compared to a recent stereo matching algorithm.<
>