Chapter

Scale-Space Theories in Computer Vision

Volume 1682 of the series Lecture Notes in Computer Science pp 141-151

Date:

An Active Contour Model without Edges

  • Tony ChanAffiliated withDepartment of Mathematics, University of California, Los Angeles
  • , Luminita VeseAffiliated withDepartment of Mathematics, University of California, Los Angeles

Abstract

In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gra- dient. The model is a combination between more classical active contour models using mean curvature motion techniques, and the Mumford-Shah model for segmentation. 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 is instead related to a particular segmentation of the image. Finally, we will present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable.