Estimation of image motion fields: Bayesian formulation and stochastic solution

Presents a probabilistic formulation for motion estimation in images and a stochastic algorithm for minimization of the associated objective function. It is shown that motion estimation, an ill-posed problem, can be regularized by means of a Bayesian estimation approach. The unknown motion field is modeled as a two-dimensional vector Markov random field with a certain neighbourhood system. The posterior distribution of the motion field given image observations is then a Gibbs distribution. Maximization of this a posteriori probability to obtain the MAP estimate of the motion field is achieved by simulated annealing. Results of the estimation procedure applied to television sequences with natural motion are presented