Most image segmentation techniques efficiently segment images with prominent
edges, but are less efficient for some images with low frequencies and overlapping
regions of homogeneous intensities. A recently proposed selective segmentation model
often works well, but not for such challenging images. In this paper, we introduce a
new model using the coefficient of variation as a fidelity term, and our test results show
it performs much better in these challenging cases.