Probabilistic regularisation and symmetry in binocular dynamic programming stereo

Authors:
Georgy Gimel'farb
Affiliations:
Centre for Image Technology and Robotics, Department of Computer Science, The University of Auckland, Tamaki Campus, Private Bag 92019, Auckland 1, New Zealand
Venue:
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Year:
2002

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Abstract

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.