Motion Segmentation with Accurate Boundaries - A Tensor Voting Approach

Producing an accurate motion flow field is very difficult at motion boundaries. We present a novel, noniterative approach for segmentation from image motion, based on two voting processes, in different dimensional spaces. By expressing the motion layers as surfaces in a 4-D space, a voting process is first used to enforce the smoothness of motion and determine an estimation of pixel velocities, motion regions and boundaries. The boundary estimation is then combined with intensity information from the original images in order to locally define a boundary tensor field. The correct boundary is inferred by a 2-D voting process within this field, that enforces the smoothness of boundaries. Finally, correct velocities are computed for the pixels near boundaries, as they are reassigned to different regions. We demonstrate our contribution by analyzing several image sequences, containing multiple types of motion.