Direct estimate of motion parameters by means of Markov random fields

Motion estimation in image sequences is undoubtedly one of the most studied problems because for many applications, going from video coding to pattern recognition, motion estimation is a fundamental tool. A new methodology which, by minimizing a specific potential function, determines for each image pixel its motion parameter set is presented. The approach is based on MRFs (Markov random fields) acting on a first-order neighborhood for each selected point and on a simple motion model that accounts for rotations and translations. Experimental results on synthetic and real world sequences have demonstrated the good performance of the adopted technique and moreover a quantitative and qualitative comparison with another well-known approach has confirmed the goodness of the proposed algorithm.