Probabilistic regularisation and symmetry in binocular dynamic programming stereo

Conventional binocular dynamic programming stereo is based on matching images of a given stereopair in order to obtain Bayesian or maximum likelihood estimates of hidden Markov models of epipolar terrain profiles. Because of partial occlusions and homogeneous textures, this problem is ill-posed and has to be regularised for getting a unique solution. Regularised matching involves usually heuristic weights of occluded points to make them comparable to binocularly visible points. An alternative way of regularisation is based on explicit Markov models of the profiles that allow to uniquely determine transition probabilities for the binocularly visible and occluded points. A desired profile maximises the likelihood ratio that relates the model derived from a stereopair to a purely random model. Transition probabilities for this latter act as the regularising parameters. Experiments with natural and artificial stereopairs outline a specific area in the parameter space where the reconstructed terrains more closely correspond to visual perception. 1 Center for Image Technology and Robotics Tamaki Campus, The University of Auckland, Auckland, New Zealand. [email protected] You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the CITR Tamaki web site under terms that include this permission. All other rights are reserved by the author(s). Probabilistic Regularisation and Symmetry in Binocular Dynamic Programming Stereo