Multiscale texture segmentation with vector-valued nonlinear diffusions on arbitrary graphs
Xiaogang Dong, L. Pollak · 2005
We propose a novel family of nonlinear diffusion equations and apply it to the problem of texture segmentation. This family can be viewed as an extension of stabilized inverse diffusion equations (SIDEs) which were proposed for restoration, enhancement, and segmentation of scalar-valued signals and images in I. Pollak et al. (2000). Our new diffusion equations can process vector-valued images defined on arbitrary graphs. In addition, we introduce novel ways of utilizing the shape information during the diffusion process. We demonstrate the effectiveness of our methods by showing that they outperform state-of-the-art algorithms on a large number of texture segmentation tasks.