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

@article{Cremers2006KernelDE,
  title={Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation},
  author={Daniel Cremers and Stanley Osher and Stefano Soatto},
  journal={International Journal of Computer Vision},
  year={2006},
  volume={69},
  pages={335-351}
}
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… CONTINUE READING
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