Modulation-feature based textured image segmentation using curve evolution
In this paper we incorporate recent results from AM-FM models for texture analysis into the variational model of image segmentation and examine the potential benefits of using the combination of these two approaches for texture segmentation. Using the dominant components analysis (DCA) technique we obtain a low-dimensional, yet rich texture feature vector that proves to be useful for texture segmentation. We use an unsupervised scheme for texture segmentation, where only the number of regions is known a-priori. Experimental results on both synthetic and challenging real-world images demonstrate the potential of the proposed combination.