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Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)
Maximum Variance Image Segmentation Based on Improved Genetic Algorithm
Haier International Training Center, Qingdao, China
July 30-August 01
ISBN: 0-7695-2909-7
| ASCII Text | x | ||
| Chun-mei Wang, Su-zhen Wang, Chong-ming Zhang, Jun-zhong Zou, "Maximum Variance Image Segmentation Based on Improved Genetic Algorithm," 2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, vol. 2, pp. 491-494, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007. | |||
| BibTex | x | ||
| @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 ={2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing}, 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 | x | ||
| TY - CONF JO - 2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 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 VL - 2 JA - 2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SNPD.2007.252
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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