Abstract

An adaptive connectionist model was implemented to estimate structure from stereo by associating patterns of activity caused by the covariation of the left and right eye images to the surface structure of the environment. Neurallike elements tuned to different disparities and to different resolutions formed the units of the system. Artificial surfaces were computer synthesized and then illuminated to create left and right eye images. Pairs of image and depth maps were taught to the system by linear association and Widrow-Hoff error correction techniques. Extracting depth information about the environment from 2-D stereo image pairs has generally been considered as a two-part process. First, points on the left and right retinas that correspond to a single point in the world are matched, and second, the depth of the point is computed by the disparity information given by this correspondence. Our implementation implicitly incorporates both steps. The system was tested with stereo pairs that had not previously been learned and was able to reconstruct the depth maps with reasonable accuracy.

© 1986 Optical Society of America