AM-FM image segmentation

We introduce a new modulation domain texture segmentation algorithm. The approach begins by constructing a dominant component AM-FM image model, where the dominant amplitude and frequency modulations are used as segmentation features. Statistical clustering is applied in this feature space to compute an initial segmentation which is then refined by morphological filtering and connected components labeling. The algorithm, which consistently delivers correct pixel classification rates exceeding 94%, is only partially unsupervised at present since the desired number of regions must be known a priori. Our future work is focused on developing strategies to make the approach fully unsupervised.