Title :
Maximum Variance Image Segmentation Based on Improved Genetic Algorithm
Author :
Wang Chun-mei ; Wang Su-zhen ; Zhang Chong-ming ; Zou Jun-zhong
Author_Institution :
East China Univ. of Sci. & Technol., Shanghai
fDate :
July 30 2007-Aug. 1 2007
Abstract :
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.
Keywords :
genetic algorithms; image segmentation; elite reinsertion; genetic algorithm; maximum variance image segmentation; optimal image threshold; optimization; Biological cells; Educational institutions; Genetic algorithms; Histograms; Image edge detection; Image segmentation; Pixel; Software engineering; Space technology; Target recognition;
Conference_Titel :
Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-0-7695-2909-7
DOI :
10.1109/SNPD.2007.252