A Novel Algorithm for Active Contour Models: Adaptive Snakes

We present a novel algorithm to optimize deformable contour models. It overcomes an inherent problem of traditional snakes using a dynamic intervention in the evolution of the curve so that leads to better fidelity in contour detection. In practice, the main difference from the traditional GVF snakes is the definition of a feature merit function. The proposed method solves the problem of points bunching together on strong portions of the contour and achieves fuller matching of concavities with external forces. We also show with a set of wellparameterized images that the proposed solution leads to better convergence and matching.