of vertices) at some distance from the object. 2. Minimize the energy iteratively, which gradually moves the points of the snake closer to the contour of the object. 3. Stop when the energy is minimized (the snake’s contour matches the object’s contour).
based on a stopping edge detection function. Instead, the image is segmented by minimizing an energy. Advantages • Works for images with and without gradients. • Supports finding smooth edges and even disconnected edges.
be used to solve the specific case of the minimal partition problem that results from segmenting the image with this technique. The technique described in this paper uses the level set method to translate the Mumford-Shah model to segment the image. Summary of “Active Contours Without Edges”
F. Chan, L. A. Vese, Active contours without edges. IEEE Transactions on Image Processing, Volume 10, Issue 2, pp. 266--‐277, 2001 ◦  V. Caselles, R. Kimmel, G. Sapiro, Geodesic active contours. International Journal of Computer Vision, Volume 22, Issue 1, pp. 61--‐79, 1997.