Chienchung Chang and Shankar Chatterjee,
Ranging Through Gabor Logons, A Consistent, Hierarchical Approach,
IEEE Transactions on Neural Networks,
4, 5, pp. 827-843, September 1993.
Chienchung Chang and Shankar Chatterjee,
Ranging Through Gabor Logons, A Consistent, Hierarchical Approach,
IEEE Transactions on Neural Networks,
4, 5, pp. 827-843, September 1993.In this work, the correspondence problem in stereo vision is handled by matching two sets of dense feature vectors. Inspired by biological evidence, these feature vectors are generated by a correlation between a bank of Gabor sensors and the intensity image. the sensors consist of two-dimensional Gabor filters at various scales (spatial frequencies)and orientations, which bear close resemblance to the receptive field profiles of simple VI cells in visual cortex. A hierarchical, stochastic relaxation method is then used to obtain the dense stereo disparities. Unlike traditional hierarchical methods for stereo, feature based hierarchical processing yields consistent disparities. To avoid false matchings due to static occlusion, a dual matching, based on the image geometry, is used.