Suthep Madarasmi, Daniel Kersten, Ting-Chuen Pong
The classical computational model for stereo vision incorporates a uniqueness inhibition constraint to enforce a one-to-one feature match, thereby sacrificing the ability to handle transparency. Crit(cid:173) ics of the model disregard the uniqueness constraint and argue that the smoothness constraint can provide the excitation support required for transparency computation. However, this modifica(cid:173) tion fails in neighborhoods with sparse features. We propose a Bayesian approach to stereo vision with priors favoring cohesive over transparent surfaces. The disparity and its segmentation into a multi-layer "depth planes" representation are simultaneously com(cid:173) puted. The smoothness constraint propagates support within each layer, providing mutual excitation for non-neighboring transparent or partially occluded regions. Test results for various random-dot and other stereograms are presented.