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The problem of image segmentation is known to become particularly challengingin the case of partial occlusion of the object(s) of interest, backgroundclutter, and the presence of strong noise. To overcome this problem, thepresent paper introduces a novel approach segmentation through the use of"weak" shape priors. Specifically, in the proposed method, an segmenting activecontour is constrained to converge to a configuration at which its geometricparameters attain their empirical probability densities closely matching thecorresponding model densities that are learned based on training samples. It isshown through numerical experiments that the proposed shape modeling can beregarded as "weak" in the sense that it minimally influences the segmentation,which is allowed to be dominated by data-related forces. On the other hand, thepriors provide sufficient constraints to regularize the convergence ofsegmentation, while requiring substantially smaller training sets to yield lessbiased results as compared to the case of PCA-based regularization methods. Themain advantages of the proposed technique over some existing alternatives isdemonstrated in a series of experiments.

Image Segmentation Using Weak Shape Priors