A probabilistic formulation of image registration

This paper deals with the computation of dense image correspondences and the detection of occlusion. We propose a Bayesian approach to the image registration problem. The images are regarded as noisy measurements of an underlying 'true' image-function. Additionally, the image data is considered incomplete, in the sense that we do not know which pixels from a particular image are occluded in the other images. We describe an EM-algorithm, which iterates between estimating values for all hidden quantities, and optimizing the optical flow by differential techniques. The Bayesian way of describing the problem leads to more insight in existing differential approaches, and offers some natural extensions to them. The resulting system involves less parameters and gives an interpretation to the remaining ones. An important feature is the photometric detection of occluded pixels.