Active Contour Based Segmentation of 3D Surfaces

  • Matthias Krueger
  • Patrice Delmas
  • Georgy Gimel’farb
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5303)


Algorithms incorporating 3D information have proven to be superior to purely 2D approaches in many areas of computer vision including face biometrics and recognition. Still, the range of methods for feature extraction from 3D surfaces is limited. Very popular in 2D image analysis, active contours have been generalized to curved surfaces only recently. Current implementations require a global surface parametrisation. We show that a balloon force cannot be included properly in existing methods, making them unsuitable for applications with noisy data. To overcome this drawback we propose a new algorithm for evolving geodesic active contours on implicit surfaces. We also introduce a new narrowband scheme which results in linear computational complexity. The performance of our model is illustrated on various real and synthetic 3D surfaces.


Active Contour Active Contour Model Geodesic Curvature Implicit Surface Signed Distance Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Matthias Krueger
    • 1
  • Patrice Delmas
    • 1
  • Georgy Gimel’farb
    • 1
  1. 1.Dept. of Computer ScienceThe University of AucklandAucklandNew Zealand

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