This paper presents a two-phase automatic snake algorithm for the segmentation of multiple
objects from noisy or cluttered backgrounds. Traditional snake algorithms are often limited in their
ability to process multiple objects and are required to have manually-drawn initial contours and fixed
weighting parameters. Our algorithm features two phases: (1) the active-points phase and (2) the
active-contours phase. In the first phase, grid points evenly distributed in the image are attracted and
moved to form clusters near object boundaries. These clustered active points are then analyzed to
obtain convex polygons as initial snake contours in the second phase, where a no-search movement
scheme with space-varying weighting parameters is employed. Both the kinetics of active points and
deformation of active contours accept our proposed adaptive gradient vector flow (AGVF) field as the
contracting forces. Experiments show the stability of the AGVF field and good performance of our
snake algorithm in segmenting multiple objects from noisy or cluttered backgrounds.
