Local constraint integration in a connectionist model of stereo vision

The authors´ approach to stereo vision involves: (1) designing a general-purpose low-level matching algorithm: (2) testing it on a wide range of stereo pairs; and (3) adding mechanisms to the low-level algorithm that solve any remaining problems without interfering with its successful behavior. They begin by building the general support algorithm (GSA), a low-level matching algorithm that integrates the influence of a number of a locally defined constraints cooperatively and in parallel using only positive constraint influences (except for uniqueness). The constraints include uniqueness, coarse-to-fine and fine-to-coarse multiresolution, detailed match, figural continuity, and the disparity gradient. The GSA is implemented in a connectionist network. When tested on a wide-range of natural and synthetic images it produces a high percentage (97%) of correct matching decisions. The errors arise in situations that can not be identified using locally-defined constraints. These include partially occluded periodic regions, occlusions, and significant structural differences between the images