Active Contours without Edges

Luminita Vese


In this talk, I will present a new model for active contours to detect objects in a given image. The model is based on techniques of curve evolution, Mumford-Shah functional for segmentation, and the level set method of S. Osher and J. Sethian. The model can detect objects whose boundaries are not necessarily defined by gradient. We minimize an energy which can be seen as a particular case of the so-called minimal partition problem. In the level set formulation, the problem becomes a ``mean-curvature flow''-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but it is instead related to a particular segmentation of the image. Finally, I will present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. We will also see that interior contours are automatically detected.

Maintained by Alexandre R.J. FRANÇOIS