Abstract
Motion estimation in image sequences is undoubtedly one
of the most studied research fields, given that motion estimation
is a basic tool for disparate applications, ranging from video
coding to pattern recognition. In this paper a new methodology
which, by minimizing a specific potential function, directly
determines for each image pixel the motion parameters of the
object the pixel belongs to is presented. The approach is based
on Markov random fields modelling, acting on a first-order
neighborhood of each point and on a simple motion model that
accounts for rotations and translations. Experimental results
both on synthetic (noiseless and noisy) and real world sequences
have been carried out and they demonstrate the good performance
of the adopted technique. Furthermore a quantitative and
qualitative comparison with other well-known approaches has
confirmed the goodness of the proposed methodology.