Intensity-based bi- and trinocular stereo vision: Bayesian decisions and regularizing assumptions

Methods to amplify and regularize the symmetric approach proposed before solving the ill-posed problem of the computational stereo vision are discussed. Under this approach epipolar profiles of a continuous optical surface are reconstructed from stereo images by using the dynamic programming (DP) implementation of a simple Bayesian MAP-decision which takes into account the symmetry of images, interdependent allowable distortions of the images, possible occlusions of surface parts, etc. Here we show that for this problem compound decisions can also be implemented by the same DP techniques. The regularization of MAP-decision in the binocular case and a scheme of symmetric trinocular stereo is also proposed. Some experimental results with the real images are presented