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Efficient kernel density estimation of shape and intensity priors for level set segmentation (bibtex)
Efficient kernel density estimation of shape and intensity priors for level set segmentation (bibtex)
by D. Cremers and M. Rousson
Reference:
Efficient kernel density estimation of shape and intensity priors for level set segmentation (D. Cremers and M. Rousson), Chapter in Parametric and Geometric Deformable Models: An application in Biomaterials and Medical Imagery (J. S. Suri, A. Farag, eds.), Springer, 2007. 
Bibtex Entry:
@incollection{Cremers-Rousson-07,
 author = {D. Cremers and M. Rousson},
 title = {Efficient kernel density estimation of shape and intensity priors
    for level set segmentation},
 booktitle = {Parametric and {G}eometric {D}eformable {M}odels: {A}n application
    in {B}iomaterials and {M}edical {I}magery},
 publisher = {Springer},
 year = {2007},
 editor = {J. S. Suri and A. Farag},
 month = {May},
 titleurl = {cremers_rousson07.pdf},
 topic = {Segmentation, Shape Priors, Medical Image Analysis, Statistics},
 keywords = {shape-priors, medical imaging},
}
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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:
CVG Group DVL Group SRL Group