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Maximum Variance Image Segmentation Based on Improved Genetic Algorithm
Haier International Training Center, Qingdao, China July 30-August 01
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SNPD.2007.252Eighth ACIS International Conference ...
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Chun-mei Wang, Su-zhen Wang, Chong-ming Zhang, Jun-zhong Zou, "Maximum Variance Image Segmentation Based on Improved Genetic Algorithm," Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on, vol. 2, pp. 491-494, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007.
 
 
BibTex
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@article{ 10.1109/SNPD.2007.252,
author = {Chun-mei Wang and Su-zhen Wang and Chong-ming Zhang and Jun-zhong Zou},
title = {Maximum Variance Image Segmentation Based on Improved Genetic Algorithm},
journal ={Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on},
volume = {2},
year = {2007},
isbn = {0-7695-2909-7},
pages = {491-494},
doi = {http://doi.ieeecomputersociety.org/10.1109/SNPD.2007.252},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
TI - Maximum Variance Image Segmentation Based on Improved Genetic Algorithm
SN - 0-7695-2909-7
SP491
EP494
A1 - Chun-mei Wang,
A1 - Su-zhen Wang,
A1 - Chong-ming Zhang,
A1 - Jun-zhong Zou,
PY - 2007
KW - null
VL - 2
JA - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
ER -
 
Chun-mei Wang, East China University of Science and Technology, China; Shanghai Normal University, China
Su-zhen Wang, Shandong University of Science and Technology, China
Chong-ming Zhang, Shanghai Normal University, China
Jun-zhong Zou, East China University of Science and Technology, China
An image segmentation method based on the OTSU and improved genetic algorithm (GA) is presented. The OTSU is taken as evaluation function and the segmentation problem is turned to the optimization problem. That is, GA efficiently searches the segmentation parameter space in order to obtain the optimal threshold. On the other hand, to overcome some limitation of GA, elite reinsertion is applied. The experimental results indicate that the method can not only obtain a better result, but also shorten the processing time.
Citation:
Chun-mei Wang, Su-zhen Wang, Chong-ming Zhang, Jun-zhong Zou, "Maximum Variance Image Segmentation Based on Improved Genetic Algorithm," snpd, vol. 2, pp.491-494, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007
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