Occlusion and visible background and foreground areas in stereo: A Bayesian approach

Efficient techniques are introduced in this paper for the identification of the occlusion and visible background and foreground areas in a noisy stere oscopic image pair. Three different Bayes decision methods are tested for t his purpose. The first, and simplest, uses three hypotheses for the formula tion of the Bayes decision rules, adopting the right image as a reference. After performing a dual-Bayes decision test having each time as a different image of the stereo pair as reference, consistency checking is added to th ese tests to form the second method. Finally, four compound hypotheses are used in the third method, which is the most accurate but also the more deta iled and computationally involved of the three. Experimental results illust rating the performance of the proposed techniques are presented and evaluat ed.