Object-based motion computation

Abstract

This contribution addresses the simultaneous estimation of dense motion fields and their segmentation from image sequences. Weak constraints incorporated by a stochastic model relate the image sequence to the motion field and its segmentation. Following the analysis of the error signal of motion compensated prediction, a segment-wise stationary generalized Gaussian model is introduced. The motion field and its segmentation are themselves modeled by a compound Gibbs random field accounting for spatio-temporal statistical bindings where the temporal bindings are directed along the motion trajectories. A Bayesian objective function is expressed according to the model. The estimates are calculated simultaneously by multiscale optimization of this objective function and ML-estimation of model parameters. Simulation results demonstrate the performance of the proposed scheme for motion as well as for disparity estimation.