The problem of image segmentation is known to become particularly challenging
in the case of partial occlusion of the object(s) of interest, background
clutter, and the presence of strong noise. To overcome this problem, the
present paper introduces a novel approach segmentation through the use of
"weak" shape priors. Specifically, in the proposed method, an segmenting active
contour is constrained to converge to a configuration at which its geometric
parameters attain their empirical probability densities closely matching the
corresponding model densities that are learned based on training samples. It is
shown through numerical experiments that the proposed shape modeling can be
regarded 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, the
priors provide sufficient constraints to regularize the convergence of
segmentation, while requiring substantially smaller training sets to yield less
biased results as compared to the case of PCA-based regularization methods. The
main advantages of the proposed technique over some existing alternatives is
demonstrated in a series of experiments.Comment: 27 pages, 8 figure