caa a22 4500 467914303 CHVBK 20180406152857.0 cr unu---uuuuu 170328e20060901xx s 000 0 eng 10.1007/s11263-006-7533-5 doi (NATIONALLICENCE)springer-10.1007/s11263-006-7533-5 Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation [Elektronische Daten] [Daniel Cremers, Stanley Osher, Stefano Soatto] 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 nonparametric density estimators are suited to model aribtrary distributions. We demonstrate the advantages of this multi-modal shape prior applied to the segmentation and tracking of a partially occluded walking person in a video sequence, and on the segmentation of the left ventricle in cardiac ultrasound images. We give quantitative results on segmentation accuracy and on the dependency of segmentation results on the number of training shapes. Springer Science + Business Media, LLC, 2006 image segmentation nationallicence shape priors nationallicence level set methods nationallicence Bayesian inference nationallicence alignment nationallicence kernel density estimation nationallicence Cremers Daniel Department of Computer Science, University of Bonn, Germany aut Osher Stanley Department of Mathematics, University of California, Los Angeles aut Soatto Stefano Department of Computer Science, University of California, Los Angeles aut International Journal of Computer Vision Kluwer Academic Publishers 69/3(2006-09-01), 335-351 0920-5691 69:3<335 2006 69 11263 https://doi.org/10.1007/s11263-006-7533-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11263-006-7533-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Cremers Daniel Department of Computer Science, University of Bonn, Germany aut NATIONALLICENCE 700 1- Osher Stanley Department of Mathematics, University of California, Los Angeles aut NATIONALLICENCE 700 1- Soatto Stefano Department of Computer Science, University of California, Los Angeles aut NATIONALLICENCE 773 0- International Journal of Computer Vision Kluwer Academic Publishers 69/3(2006-09-01), 335-351 0920-5691 69:3<335 2006 69 11263 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer