<title>New method for segmentation and motion field estimation</title>

Abstract

In this paper we investigate a new approach to image sequence processing. In some applications of image processing (medical diagnosis, analysis of physical phenomena . . .) the segmented image and the motion field are both needed. It is physically coherent to suppose that these two kinds of data are linked together, and so it would be an improvement to take their mutual interaction into account : different areas make it possible to define motion boundaries and the motion field constitutes temporal information. This algorithm is based on the use of Markov Random Fields (M.R.F.) which yield good results in such domains. The use of MRF models in association with a Maximum A Posteriori (M.A.P.) criterion leads to the minimization of a hamiltonian which is a non-convex function in this case. In order to avoid local minima, we use a multigrid method to compute this minimum.