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

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Daniel Cremers, Frank R. Schmidt, Frank Barthel
• Keywords: Computer Vision, CVPR 2008, Implicit Shape Representations, Valid Shape