J. Chang and T. Chou
Iterative graph cuts for image segmentation with a nonlinear statistical shape prior.
Journal of mathematical imaging and visionVol. 49 No. 1
(2014) pp. 87-97
AbstractShape-based regularization has proven to be a useful method for delineating objects within noisy images where one has prior knowledge of the shape of the targeted object. When a collection of possible shapes is available, the specification of a shape prior using kernel density estimation is a natural technique. Unfortunately, energy functionals arising from kernel density estimation are of a form that makes them impossible to directly minimize using efficient optimization algorithms such as graph cuts. Our main contribution is to show how one may recast the energy functional into a form that is minimizable iteratively and efficiently using graph cuts.