Kenel-based shape regularization with application to image segmentation and tracking
Josh Chang (Mathematical Biosciences Institute, The Ohio State University)
(January 31, 2013 10:20 AM - 11:10 AM)
Shape-based regularization has proven to be a useful method for delineating objects from noisy images encountered in many applications when 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. This process transforms the problem of shape-regularized image segmentation into an optimization problem involving a nonlinear energy functional.
In this talk I will present a framework for minimization of this energy functional with application to segmentation of still images. I will then present an extension of this approach to the identification of traveling fronts in image sequences.