Graph Cuts Using a Riemannian Metric Induced by Tensor Voting

Abstract

In this paper, we present a new algorithm that combines the advantages of tensor voting into graph cuts. Tensor voting has been a popular tool for a number of early vision problems since it can use principles of perceptual grouping, which are not well considered in graph cuts. We attempt to encode the power of tensor voting into an energy minimization framework. For this, we assume that the tensor map obtained by tensor voting induces a Riemannian metric in image domain, and the metric is constructed according to the conventional ways of tensor interpretation. Finally, by embedding the induced Riemannian metric into the graph via edge weights, the graph cuts algorithm can have priors considering principles of perceptual grouping. The proposed method can be used in the labeling of occluded regions, object segmentation using only edge information, and boundary regularization.