COMPARISON OF STOCHASTIC AND DETERMINISTIC SOLUTION METHODS IN BAYESIAN-ESTIMATION OF 2D-MOTION

The estimation of 2D motion from spatio-temporally sampled image sequences is discussed, concentrating on the optimization aspect of the problem formulated through a Bayesian framework based on Markov random field (MRF) models. First, the Maximum A Posteriori Probability (MAP) formulation for motion estimation over discrete and continuous state spaces is reviewed along with the solution method using simulated annealing (SA). Then, instantaneous 'freezing' is applied to the stochastic algorithms resulting in well known deterministic methods. The stochastic algorithms are compared with their deterministic approximations over image sequences with natural data and synthetic as well as natural motion.