DocumentCode :
2340260
Title :
Ant colony optimization for image segmentation
Author :
Wang, Xiao-Nian ; Feng, Yuan-Jing ; Feng, Zu-Ren
Author_Institution :
Syst. Eng. Inst., Xi´´an Jiao Tong Univ., China
Volume :
9
fYear :
2005
fDate :
18-21 Aug. 2005
Firstpage :
5355
Abstract :
It is found that the multistage decision algorithm for image segmentation with active contour model (ACM) is similar to ant colony optimization (ACO). By means of constructing solution space and heuristic information, a new algorithm based on ACM is proposed in the paper, which uses ACO to search for the best path in a constrained region. This algorithm that provides a new approach to obtain precise contour, is proved to be convergent with probability one, and will reach the best feasible boundary with minimum energy function value. Moreover, this algorithm can also be used to solve other revised ACM problems. The simulation results show that the proposed approach is more effective than the genetic algorithm in literature (Mishraa et al., 2003).
Keywords :
genetic algorithms; image segmentation; active contour model; ant colony optimization; constrained region; genetic algorithm; heuristic information; image segmentation; multistage decision algorithm; Active contours; Ant colony optimization; Computational modeling; Equations; Genetic algorithms; Greedy algorithms; Image converters; Image segmentation; Optimization methods; Systems engineering and theory; Active Contour Model; Ant Colony Optimization; Image segment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
Type :
conf
DOI :
10.1109/ICMLC.2005.1527890
Filename :
1527890
Link To Document :
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