Structure from stereo by associative learning of the constraints

A computational model of structure from stereo that develops smoothness constraints naturally by associative learning of a large number of example mappings from disparity data to surface depth data is proposed. Banks of disparity-selective graded response units at all spatial locations in the visual field were the input data. These cells responded to matches of luminance change at convergent, divergent, or zero offsets in the left and right 'retina' samples. Surfaces were created by means of a pseudo-Markov process. From these surfaces, shaded marked and ummarked surfaces were created, along with random-dot versions of the same surfaces. Learning of these example shaded and shaded marked surfaces allowed the system to solve stereo mappings both for the surfaces it had learned and for surfaces it had not learned but which had been created by the same pseudo-Markov process. Further, the model was able to solve some random-dot versions of the surfaces when the surfaces had been learned as shaded marked surfaces.