Prior Knowledge, Level Set Representations & Visual Grouping

Mikaël Rousson 1 Nikos Paragios 2, 3
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
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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Submitted on : Saturday, December 14, 2013 - 11:36:02 AM
Last modification on : Thursday, February 7, 2019 - 5:29:11 PM

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Mikaël Rousson, Nikos Paragios. Prior Knowledge, Level Set Representations & Visual Grouping. International Journal of Computer Vision, Springer Verlag, 2008, 76 (3), pp.231-243. ⟨10.1007/s11263-007-0054-z⟩. ⟨hal-00918700⟩



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