Learning to Solve Random-Dot Stereograms of Dense and Transparent
Surfaces with Recurrent Backpropagation
Ning Qian and Terrence J. Sejnowski, Proceedings of the 1988
Connectionist Models Summer School , Morgan Kaufmann, 1989, 435-443.
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Abstract
Binocular depth perception, or stereopsis, depends on matching
corresponding points in two images taken from two vantage points. In
random-dot stereograms the features to be matched are individual
pixels. We have used recurrent backpropagation learning algorithm of
Pineda (1987) to construct network models with lateral and feedback
connections that can solve the correspondence problem for random-dot
stereograms. The network learned the uniqueness and continuity
constraints originally proposed by Marr and Poggio (1976) from a
training set of dense random-dot stereograms. We also constructed
networks that can solve sparse random-dot stereograms of transparent
surfaces. The success of the learning algorithm depended on taking
advantage of translation invariance and restrictions on the range of
interactions.
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