Integrating region and edge information for texture segmentation using a modified constraint satisfaction neural network

In this paper, we propose an approach for texture segmentation by integrating region and edge information. The algorithm uses a constraint satisfaction neural network for texture segmentation with additional edge constraints. Initial class probabilities and edge maps are computed using multi-channel, multi-resolution filters to obtain image segmented map and edge map. The complementary information of the segmented map and the edge map are iteratively updated using a modified CSNN to satisfy a set of constraints to obtain superior segmentation results.The proposed methodology is tested on simulated as well as natural textures and it produces satisfactory results. The proposed methodology is also tested on a synthetic aperture radar (SAR) image.