International Journal of Computer Vision

, Volume 76, Issue 3, pp 231–243 | Cite as

Prior Knowledge, Level Set Representations & Visual Grouping

  • Mikael RoussonEmail author
  • Nikos Paragios


In this paper, we propose a level set method for shape-driven object extraction. We introduce a voxel-wise probabilistic level set formulation to account for prior knowledge. To this end, objects are represented in an implicit form. Constraints on the segmentation process are imposed by seeking a projection to the image plane of the prior model modulo a similarity transformation. The optimization of a statistical metric between the evolving contour and the model leads to motion equations that evolve the contour toward the desired image properties while recovering the pose of the object in the new image. Upon convergence, a solution that is similarity invariant with respect to the model and the corresponding transformation are recovered. Promising experimental results demonstrate the potential of such an approach.


Level set method Distance transforms Curve propagation Similarity transformation Pose estimation Object extraction 


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Siemens Corporate ResearchPrincetonUSA
  2. 2.M.A.S Ecole Centrale de ParisChatenay-MalabryFrance

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