curved outline of an object while avoiding any background noise. • It works for 2D images, but similar techniques can be applied to 3D images. • Also known as snakes.
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).
each iteration to determine where the object is (and to adjust the current contour appropriately). Advantages • Existing edge detection algorithms can be used.
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.
find the curve, minimize the fitting term by moving the curve C closer to the boundary of the image in multiple iterations. Summary of “Active Contours Without Edges”
the Mumford-Shah functional is used to help segment the image into multiple objects before the fitting term is minimized. Summary of “Active Contours Without 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”
in iterations until the solution is stationary. • This is the default method used in MATLAB’ s activecontour function. 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 ◦ [2] V. Caselles, R. Kimmel, G. Sapiro, Geodesic active contours. International Journal of Computer Vision, Volume 22, Issue 1, pp. 61--‐79, 1997.