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85748 Garching info@vision.in.tum.de

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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 M. Rousson and D. Cremers
Reference:
Efficient kernel density estimation of shape and intensity priors for level set segmentation (M. Rousson and D. Cremers), In Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 1, 2005. 
Bibtex Entry:
@string{miccai="Medical Image Computing and Computer Assisted Intervention (MICCAI)"}
@inproceedings{Rousson-Cremers-05,
 author = {M. Rousson and D. Cremers},
 title = {Efficient kernel density estimation of shape and intensity priors
    for level set segmentation},
 booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)},
 year = {2005},
 volume = {1},
 pages = {757--764},
 keywords = {image-segmentation,shape,Parzen,parametric,2d cardiac ultrasound,3d ct prostate, medical imaging},
 titleurl = {rousson_cremers05.pdf},
 topic = {Shape, Segmentation, Statistics, Level Sets},
}
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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