Motion segmentation with occlusions on the superpixel graph

We present a motion segmentation algorithm that partitions the image plane into disjoint regions based on their parametric motion. It relies on a finer partitioning of the image domain into regions of uniform photometric properties, with motion segments made of unions of such ¿superpixels¿. We exploit recent advances in combinatorial graph optimization that yield computationally efficient estimates. The energy functional is built on a superpixel graph, and is iteratively minimized by computing a parametric motion model in closed-form, followed by a graph cut of the superpixel adjacency graph. It generalizes naturally to multi-label partitions that can handle multiple motions.