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16th International Conference on Pattern Recognition (ICPR'02) - Volume 1
Multiple Objects Segmentation Based on Maximum-Likelihood Estimation and Optimum Entropy-Distribution (MLE-OED)
Quebec City, QC, Canada
August 11-August 15
ISBN: 0-7695-1695-X
ASCII Text
x 
 
Xie Jun, H. T. Tsui, Xia Deshen, "Multiple Objects Segmentation Based on Maximum-Likelihood Estimation and Optimum Entropy-Distribution (MLE-OED)," Pattern Recognition, International Conference on, vol. 1, pp. 10707, 16th International Conference on Pattern Recognition (ICPR'02) - Volume 1, 2002.
 
 
BibTex
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@article{ 10.1109/ICPR.2002.10014,
author = {Xie Jun and H. T. Tsui and Xia Deshen},
title = {Multiple Objects Segmentation Based on Maximum-Likelihood Estimation and Optimum Entropy-Distribution (MLE-OED)},
journal ={Pattern Recognition, International Conference on},
volume = {1},
year = {2002},
issn = {1051-4651},
pages = {10707},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2002.10014},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - Multiple Objects Segmentation Based on Maximum-Likelihood Estimation and Optimum Entropy-Distribution (MLE-OED)
SN - 1051-4651
SP
EP
A1 - Xie Jun,
A1 - H. T. Tsui,
A1 - Xia Deshen,
PY - 2002
KW - null
VL - 1
JA - Pattern Recognition, International Conference on
ER -
 
Xie Jun, Chinese University of Hong Kong
H. T. Tsui, Chinese University of Hong Kong
Xia Deshen, Nanjing University of Science & Technology
A new method based on MLE-OED is proposed for unsupervised image segmentation of multiple objects which have fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function proposed to implement image segmentation based on the image's local spatial information and global intensity distribution properties. The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method was validated by simulated and real examples. Its performance in the experiments is better than those of two popular methods.
Citation:
Xie Jun, H. T. Tsui, Xia Deshen, "Multiple Objects Segmentation Based on Maximum-Likelihood Estimation and Optimum Entropy-Distribution (MLE-OED)," icpr, vol. 1, pp.10707, 16th International Conference on Pattern Recognition (ICPR'02) - Volume 1, 2002
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