Generalized Flooding and Multicue PDE-Based Image Segmentation
Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. Moreover, the increasing demands of image analysis tasks in terms of segmentation results' quality introduce the necessity of employing multiple cues for improving image segmentation results. In this paper, we attempt 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 emphasize on the overall segmentation procedure, and 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 well-known morphological paradigm of watershed transform segmentation, which exploits intensity contrast and region size criteria, we investigate its partial differential equation (PDE) formulation, and we extend it in order to satisfy various flooding criteria, thus making it applicable to a wider range of images. Going a step further, we introduce a segmentation scheme that couples contrast criteria in flooding with texture information. The modeling of the proposed scheme is done via PDEs and the efficient incorporation of the available contrast and texture information, is done by selecting an appropriate cartoon-texture image decomposition scheme. The proposed coupled segmentation scheme is driven by two separate image components: artoon U (for contrast information) and texture component. The performance of the proposed segmentation scheme is demonstrated through a complete set of experimental results and substantiated using quantitative and qualitative criteria.