Generalized Watershed and PDEs for Geometric- Textural Segmentation

In this paper we approach the segmentation problem by attempting to incorporate cues such as intensity contrast, region size and texture in the segmentation procedure and derive improved results compared to using individual cues separately. We propose efficient simplification operators and feature extraction schemes, capable of quantifying important characteristics like geometrical complexity, rate of change in local contrast variations and orientation, that eventually favor the final segmentation result. Based on the morphological paradigm of watershed transform we investigate and extend its PDE formulation in order to satisfy various flooding criteria, and couple them with texture information thus making it applicable to a wider range of images.