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CVPR
2008
IEEE

Shape priors in variational image segmentation: Convexity, Lipschitz continuity and globally optimal solutions

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Shape priors in variational image segmentation: Convexity, Lipschitz continuity and globally optimal solutions
In this work, we introduce a novel implicit representation of shape which is based on assigning to each pixel a probability that this pixel is inside the shape. This probabilistic representation of shape resolves two important drawbacks of alternative implicit shape representations such as the level set method: Firstly, the space of shapes is convex in the sense that arbitrary convex combinations of a set of shapes again correspond to a valid shape. Secondly, we
Daniel Cremers, Frank R. Schmidt, Frank Barthel
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Daniel Cremers, Frank R. Schmidt, Frank Barthel
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