Guiding ziplock snakes with a priori information

Active contour models, or snakes, are effective and robust in contour extraction. In most papers on snakes, an initialization close to the desired contour is assumed to be provided, which is inappropriate in many cases. The ziplock snake model presented by Neuenschwander et al. (1997), however, needs only two user-supplied endpoints. The optimization process for a ziplock snake starts from the two endpoints and progresses towards the center of the snake. In this paper, we present a method to combine a grammatical model that encodes a priori shape information with the ziplock snakes. 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