International Journal of Computer Vision

, Volume 22, Issue 1, pp 61–79 | Cite as

Geodesic Active Contours

  • Vicent Caselles
  • Ron Kimmel
  • Guillermo Sapiro


A novel scheme for the detection of object boundaries is presented. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both interior and exterior boundaries. The proposed approach is based on the relation between active contours and the computation of geodesics or minimal distance curves. The minimal distance curve lays in a Riemannian space whose metric is defined by the image content. This geodesic approach for object segmentation allows to connect classical “snakes” based on energy minimization and geometric active contours based on the theory of curve evolution. Previous models of geometric active contours are improved, allowing stable boundary detection when their gradients suffer from large variations, including gaps. Formal results concerning existence, uniqueness, stability, and correctness of the evolution are presented as well. The scheme was implemented using an efficient algorithm for curve evolution. Experimental results of applying the scheme to real images including objects with holes and medical data imagery demonstrate its power. The results may be extended to 3D object segmentation as well.

dynamic contours variational problems differential geometry Riemannian geometry geodesics curve evolution topology free boundary detection 


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Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • Vicent Caselles
    • 1
  • Ron Kimmel
    • 2
  • Guillermo Sapiro
    • 3
  1. 1.Department of Mathematics and InformaticsUniversity of Illes BalearsPalma de MallorcaSpain
  2. 2.Department of Electrical Engineering, Technion, I.I.T.HaifaIsrael
  3. 3.Hewlett-Packard LabsPalo Alto

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