A Probabilistic Interpretation of Geometric Active Contour Segmentation

Abstract

Active contours or snakes are widely used for segmentation and tracking. These techniques require the minimization of an energy function, which is typically a linear combination of a data-fit term and regularization terms. This energy function can be tailored to the intrinsic object and image features. This can be done by either modifying the actual terms or by changing the weighting parameters of the terms. There is, however, no sure way to set these terms and weighting parameters optimally for a given application. Although heuristic techniques exist for parameter estimation, often trial and error is used. In this paper, we propose a probabilistic interpretation to segmentation. This approach results in a generalization of state of the art active contour segmentation. In the proposed framework all parameters have a statistical interpretation, thus avoiding ad hoc parameter settings.