| Abstract |
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In this paper, joint conditional probability is localized to
better capture the local properties of a neighborhood for
image segmentation. A new local spatial likelihood is defined
for a neighborhood, which gives rise to local spatial
posterior associated with the defined local prior. The proposed
method associates a novel nonparametric approach
for estimating the underlying distributions and is compared
with a parametric approach. Both approaches segment images
by maximizing the local spatial posterior function.
The results indicate that the spatially localized posterior
function overcomes the inherent errors of general posterior
function and gives remarkable robustness against heavy
noises.
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 Additional Information
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Citation:
EunSang Bak, Kayvan Najarian,
"Robust Segmentation Using Parametric and Nonparametric Local Spatial Posteriors,"
itcc,
p. 626,
International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 1,
2004
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