Markov random fields and block matching for multiresolution motion estimation

For medical application, we are interested in estimation of optical flow on the face and particularly on area around the eyes. Among the methods of optical flow estimation, gradient estimation and block matching are the main ones. However the gradient based approach can only be applied for small displacement. Generally the process of block matching gives only good result if the searching strategy is judiciously selected. Our approach is based on a Markov random field model combined with an algorithm of block matching in a multiresolution scheme. The multiresolution approach leads to detect a large range of displacement amplitude. The large displacements are detected on the coarse scales and the small ones will be detected successively on finer scales in a coarse scales and the small ones will be detected successively on finer scales. The tracking of motion is achieved by a block matching algorithm. This method gives the otpical flow whatever the amplitude of the motion is, if included in the range defined by the multiresolution approach. The result show clearly the complementary of Markov random fields estimation and block matching across the scales.