1. Segmentation. Usually, motion segmentation and image restoration are considered separately. We propose to solve the two problems in a coupled way, allowing the motion segmentation part to positively influence the restoration and vice-versa. To this end, a theoretically justified problem is proposed, studied on the space of bounded variations. A suitable numerical scheme is then derived, using Gamma-convergence and Geman-Reynolds Theorem.
2. Tracking. Considering these regions, a higher level of interpretation is approached establishing how these regions are related to each other. This problem presents many difficulties: severe occlusions, merging/splitting objects and defects in the detection. The method is based on a spatio-temporal (2D+t) representation of the moving regions. It uses a perceptual grouping approach, the Tensor Voting methodology, to enforce smoothness in the space and time of the tracked regions. Many examples will illustrate the talk.