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Prior Knowledge, Level Set Representations & Visual Grouping

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Abstract

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.

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Authors and Affiliations

  1. Siemens Corporate Research, 755 College Road East, Princeton, NJ, 08540, USA

    Mikael Rousson

  2. M.A.S Ecole Centrale de Paris, Grande Voie des Vignes, 92295, Chatenay-Malabry, France

    Nikos Paragios

Authors
  1. Mikael Rousson
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  2. Nikos Paragios
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Correspondence to Mikael Rousson.

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Rousson, M., Paragios, N. Prior Knowledge, Level Set Representations & Visual Grouping. Int J Comput Vis 76, 231–243 (2008). https://doi.org/10.1007/s11263-007-0054-z

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