Optical flow robust estimation in a hybrid multi-resolution MRF framework

We propose in this paper a robust multi-resolution technique to estimate dense velocity field from image sequences. It couples a Gaussian pyramidal down-sampling decomposition together with a multi-grid approach. At each pyramid level, bilinear interpolation and efficient warping techniques are performed to generate a residual images. The displacement field is computed in a Markov random field (MRF) framework. We compare two different methods to minimize the Gibbs energy: a modified iterative conditional mode (ICM) and a graph-cut algorithm extended to multi-grid scheme. We validate and demonstrate the robustness of our approach on synthetic and real images for fluid experiment applications.