Robust Active Contour Segmentation with an Efficient Global Optimizer

Conference Paper · August 2011with5 Reads
DOI: 10.1007/978-3-642-23687-7_18 · Source: DBLP
Conference: Advances Concepts for Intelligent Vision Systems - 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings

    Abstract

    Active contours or snakes are widely used for segmentation and tracking. Recently a new active contour model was proposed,
    combining edge and region information. The method has a convex energy function, thus becoming invariant to the initialization
    of the active contour. This method is promising, but has no regularization term. Therefore segmentation results of this method
    are highly dependent of the quality of the images. We propose a new active contour model which also uses region and edge information,
    but which has an extra regularization term. This work provides an efficient optimization scheme based on Split Bregman for
    the proposed active contour method. It is experimentally shown that the proposed method has significant better results in
    the presence of noise and clutter.