Multiresolution Markov random field and multigrid algorithm for a discontinuity-preserving estimation of the optical flow

摘要

In this paper we address the intricate issue of recovering (long range) velocity fields between consecutive frames of an image sequence. Within the Bayesian estimation framework, we design a global objective function to be minimized. This energy function is classically composed of two terms. The first one reinforces the fragile modeling of the optical flow constraint equation making use of robust estimators, while the second (a priori) term incorporates a discontinuity preserving smoothness constraint. A multiresolution definition of this differential estimation method aims at accessing long range displacements in a coarse-to- fine incremental way. As for the associated successive minimizations, they are processed through a very efficient deterministic multigrid relaxation algorithm.