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Shape priors in variational image segmentation: Convexity, lipschitz continuity and globally optimal solutions (2008)
| Content Provider | CiteSeerX |
|---|---|
| Author | Cremers, Daniel Schmidt, Frank R. Barthel, Frank |
| Description | 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 prove that the introduction of shape priors into variational image segmentation leads to functionals which are convex with respect to shape deformations. For a large class of commonly considered (spatially continuous) functionals, we prove that – under mild regularity assumptions – segmentation and tracking with statistical shape priors can be performed in a globally optimal manner. In experiments on tracking a walking person through a cluttered scene we demonstrate the advantage of global versus local optimality. 1. In IEEE Int. Conf. on Comp. Vision and Patt. Recog |
| File Format | |
| Language | English |
| Publisher Date | 2008-01-01 |
| Access Restriction | Open |
| Subject Keyword | Alternative Implicit Shape Representation Probabilistic Representation Optimal Manner Arbitrary Convex Combination Optimal Solution Important Drawback Large Class Statistical Shape Prior Novel Implicit Representation Shape Prior Valid Shape Variational Image Segmentation Cluttered Scene Shape Resolve Lipschitz Continuity Mild Regularity Assumption Segmentation Global Versus Local Optimality |
| Content Type | Text |
| Resource Type | Article |
