An adaptive snake algorithm for contour detection

Contour based techniques have proved to be an effective approach in object recognition. Active contour models (also called snakes), which optimize/minimize an energy function, have become popular for boundary detection. A snake is confused by a highly convex boundary. We present a novel adaptive algorithm to solve this problem. For every point in the initial position, the energy of its neighboring points is calculated by a greedy algorithm. If the target contour is not included in its neighbors, we can increase the radius of its neighbors and calculate the energy of all the points again until the target contour is included. The target contour can be obtained by iterating once. In addition, the convergent radius is increased. It can be applied to objects of high convexity. Comparative experiments indicate the validity of this method.