Spatio-temporal Prior Shape Constraint for Level Set Segmentation

  • Timothée Bailloeul
  • Véronique Prinet
  • Bruno Serra
  • Philippe Marthon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3757)


This paper exposes a novel formulation of prior shape constraint incorporation for the level set segmentation of objects from corrupted images. Applicable to variational frameworks, the proposed scheme consists in weighting the prior shape constraint by a function of time and space to overcome local minima issues of the energy functional. Pose parameters which make the prior shape constraint invariant from global transformations are estimated by the downhill simplex algorithm, which is more tractable and robust than the traditional gradient descent. The proposed scheme is simple, easy to implement and can be generalized to any variational approach incorporating a single prior shape. Results illustrated with different kinds of images demonstrate the efficiency of the method.


Gradient Descent Active Contour Variational Framework Global Transformation Shape Constraint 
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 2005

Authors and Affiliations

  • Timothée Bailloeul
    • 1
    • 2
    • 3
  • Véronique Prinet
    • 1
  • Bruno Serra
    • 2
  • Philippe Marthon
    • 3
  1. 1.LIAMA, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Alcatel SpaceCannes La BoccaFrance
  3. 3.LIMA (IRIT)ToulouseFrance

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