Guiding ziplock snakes with a priori information

In this paper, we present a method to combine a grammatical model that encodes a priori shape information with the ziplock snakes presented by Neuenschwander et al. (1997). A competing mechanism is adopted to take advantage of the shape models without inducing excessive computation. The resulting model-based ziplock snakes have many advantages over the original model: they can accurately locate contour features, produce more refined results, and deal with multiple contours, missing image cues, and noise.