Convex set-based estimation of image flows

We introduce a set-based approach for estimating image motion based on an optical flow constraint and a finite number of arbitrary differential constraints describing physically plausible vector fields. Compared to related variational estimation approaches, our approach strictly satisfies each separate constraint and becomes not more involved in the presence of higher-order differential operators. The approach is implemented using established subgradient projection schemes onto the set of feasible solutions. Our approach is particularly suited if quantitative prior knowledge about structural flow properties is available, and for the regularized estimation of highly non-rigid image motion.