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.
Our research is partly funded by the projects IMAVIS HPMT-CT-2000-00040 within the framework of the Marie Curie Fellowship Training Sites Programme as well as the European project Cogvisys numbered 3E010361, and the projects WE 2602/1-1 and SO 363/9-1 of the Deutsche Forschungsgemeinschaft (DFG). This is gratefully acknowledged.
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Brox, T., Rousson, M., Deriche, R., Weickert, J. (2003). Unsupervised Segmentation Incorporating Colour, Texture, and Motion. In: Petkov, N., Westenberg, M.A. (eds) Computer Analysis of Images and Patterns. CAIP 2003. Lecture Notes in Computer Science, vol 2756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45179-2_44
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DOI: https://doi.org/10.1007/978-3-540-45179-2_44
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