Segmented snake for contour detection

Abstract The active contour model, called snake, has been proved to be an effective method in contour detection. This method has been successfully employed in the areas of object recognition, computer vision, computer graphics and biomedical images. However, this model suffers from a great limitation, that is, it is difficult to locate concave parts of an object. In view of such a limitation, a segmented snake is designed and proposed in this paper. The basic idea of the proposed method is to convert the global optimization of a closed snake curve into local optimization on a number of open snake curves. The segmented snake algorithm consists of two steps. In the first step, the original snake model is adopted to locate the initial contour near the object boundary. In the second step, a recursive split-and-merge procedure is developed to determine the final object contour. The proposed method is able to locate all convex, concave and high curvature parts of an object accurately. A number of images are selected to evaluate the capability of the proposed algorithm and the results are encouraging.

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