Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation

In this paper, we make two contributions to the field of level set based image segmentation. Firstly, we propose shape dissimilarity measures on the space of level set functions which are analytically invariant under the action of certain transformation groups. The invariance is obtained by an intrinsic registration of the evolving level set function. In contrast to existing approaches to invariance in the level set framework, this closed-form solution removes the need to iteratively optimize explicit pose parameters. The resulting shape gradient is more accurate in that it takes into account the effect of boundary variation on the object's pose. Secondly, based on these invariant shape dissimilarity measures, we propose a statistical shape prior which allows to accurately encode multiple fairly distinct training shapes. This prior constitutes an extension of kernel density estimators to the level set domain. In contrast to the commonly employed Gaussian distribution, such nonpara...

• Type: Journal
• Year: 2006
• Where: IJCV
• Authors: Daniel Cremers, Stanley Osher, Stefano Soatto
• Keywords: Density Estimators, IJCV 2006, Image Segmentation, Shape Dissimilarity