A Multiphase Dynamic Labeling Model for Variational Recognition-driven Image Segmentation |
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Authors: | Daniel Cremers Nir Sochen Christoph Schnörr |
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Affiliation: | (1) Department of Computer Science, University of Bonn, Germany;(2) Department of Applied Mathematics, Tel Aviv University, Israel;(3) Department of Mathematics and Computer Science, University of Mannheim, Germany |
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Abstract: | We propose a variational framework for the integration of multiple competing shape priors into level set based segmentation
schemes. By optimizing an appropriate cost functional with respect to both a level set function and a (vector-valued) labeling
function, we jointly generate a segmentation (by the level set function) and a recognition-driven partition of the image domain
(by the labeling function) which indicates where to enforce certain shape priors. Our framework fundamentally extends previous
work on shape priors in level set segmentation by directly addressing the central question of where to apply which prior. It allows for the seamless integration of numerous shape priors such that—while segmenting both multiple known and
unknown objects—the level set process may selectively use specific shape knowledge for simultaneously enhancing segmentation
and recognizing shape. |
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Keywords: | image segmentation shape priors variational methods level set methods dynamic labeling recognition modeling |
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