Showing papers by "Thomas Brox published in 2003"

Active unsupervised texture segmentation on a diffusion based feature space

Abstract: We propose a novel and efficient approach for active unsupervised texture segmentation. First, we show how we can extract a small set of good features for texture segmentation based on the structure tensor and nonlinear diffusion. Then, we propose a variational framework that incorporates these features in a level set based unsupervised segmentation process that adaptively takes into account their estimated statistical information inside and outside the region to segment. The approach has been tested on various textured images, and its performance is favorably compared to recent studies.

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.

Equivalence results for TV diffusion and TV regularisation

Abstract: It has been stressed that regularisation methods and diffusion processes approximate each other. In this paper we identify a situation where both processes are even identical: the space-discrete 1-D case of total variation (TV) denoising. This equivalence is proved by deriving identical analytical solutions for both processes. The temporal evolution confirms that space-discrete TV methods implement a region merging strategy with finite extinction time. Between two merging events, only extremal segments move. Their speed is inversely proportional to their size. Our results stress the distinguished nature of TV denoising. Furthermore, they enable a mutual transfer of all theoretical and algorithmic achievements between both techniques.