A Mean Field EM-algorithm for Coherent Occlusion Handling in MAP-Estimation Prob

This paper presents a generative model based approach to deal with occlusions in vision problems which can be formulated as MAP-estimation problems. The approach is generic and targets applications in diverse domains like model-based object recognition, depth-from-stereo and image registration. It relies on a probabilistic imaging model, in which visible regions and occlusions are generated by two separate processes. The partitioning into visible and occluded regions is made explicit by the introduction of an hidden binary visibility map, which, to account for the coherent nature of occlusions, is modelled as a Markov Random Field. Inference is made tractable by a mean field EMalgorithm, which alternates between estimation of visibility and optimisation of model parameters. We demonstrate the effectiveness of the approach with two examples. First, in a N-view stereo experiment, we compute a dense depth map of a scene which is contaminated by multiple occluding objects. Finally, in a 2D-face recognition experiment, we try to identify people from partially occluded facial images.