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 multilabel partitions that can handle multiple motions.
The work by Ayvaci and Soatto REF defines an energy functional on a superpixel graph which is optimized using efficient graph cuts.
Motion segmentation with occlusions on the superpixel graph
2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops
2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops
Computer Science