Unsupervised segmentation incorporating colour, texture, and motion

Abstract: In this paper we integrate colour, texture, and motion into a segmentation process. The segmentation consists of two steps, which both combine the given information: a pre-segmentation step based on nonlinear diffusion for improving the quality of the features, and a variational framework for vector-valued data using a level set approach and a statistical model to describe the interior and the complement of a region. For the nonlinear diffusion we apply a novel diffusivity closely related to the total variation diffusivity, but being strictly edge enhancing. A multi-scale implementation is used in order to obtain more robust results. In several experiments we demonstrate the usefulness of integrating many kinds of information. Good results are obtained for both object segmentation and tracking of multiple objects.