Bayesian segmentation and motion estimation in video sequences using a Markov-Potts model

The segmentation of an image can be presented as an inverse ill-posed problem. The segmentation problem is presented as, knowing an observed image g, how to obtain an original image f in which a classification in statistically homogeneous regions must be established. The inversion technique we use is done in a Bayesian probabilistic framework. Prior hypothesis, made on different parameters of the problem leads to the expression of posterior probability laws from which we can estimate the searched, realistic, segmentation. We report here a new approach for the segmentation of video sequences based on recent works on the Bayesian segmentation and fusion [3, 4, 5]. In our Bayesian framework we make the assumption that the images follow a Potts-Markov random field model. The final implementation is done with Markov Chain Monte Carlo (MCMC) and Gibbs sampling algorithms. The originality here resides in the fact that the sequence segmentation is done sequentially from one frame to the next in a Markov Space "temporal-like" framework. The result is also used for a first approach of motion estimation.