Object-based estimation of dense motion fields

Motion estimation belongs to key techniques in image sequence processing. Segmentation of the motion fields such that, ideally, each independently moving object uniquely corresponds to one region, is one of the essential elements in object-based image processing. This paper is concerned with unsupervised simultaneous estimation of dense motion fields and their segmentations. It is based on a stochastic model relating image intensities to motion information. Based on the analysis of natural images, a region-based model of motion-compensated prediction error is proposed. In each region the error is modeled by a white stationary generalized Gaussian random process. The motion field and its segmentation are themselves modeled by a compound Gibbs/Markov random field accounting for statistical bindings in spatial direction and along the direction of motion trajectories. The a posteriori distribution of the motion field for a given image sequence is formulated as an objective function, such that its maximization results in the MAP estimate. A deterministic multiscale relaxation technique with regular structure is employed for optimization of the objective function. Simulation results are in a good agreement with human perception for both the motion fields and their segmentations