Optical flow estimation with adaptive convolution kernel prior on discrete framework

We present a new energy model for optical flow estimation on discrete MRF framework. The proposed model yields discrete analog to the prevailing model with diffusion tensor-based regularizer, which has been optimized by variational approach. Inspired from the fact that the regularization process works as a convolution kernel filtering, we formulate the difference between original flow and filtered flow as a smoothness prior. Then the discrete framework enables us to employ a robust penalizer less concerning convexity and differentiability of the energy function. In addition, we provide a new kernel design based on the bilateral filter, adaptively controlling intensity variance according to the local statistics. The proposed kernel simultaneously addresses over-segmentation and over-smoothing problems, which is hard to achieve by tuning parameters. Involving a complex graph structure with large label sets, this work also presents a strategy to efficiently reduce memory requirement and computational time to a tolerable state. Experimental result shows the proposed method yields plausible results on the various data sets including large displacement and textured region.