Ant colony optimization for image segmentation

被引：0

作者：

Wang, XN ^{[1
]}

Feng, YJ ^{[1
]}

Feng, ZR ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Syst Engn Inst, Xian 710049, Peoples R China

来源：

PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9 | 2005年

关键词：

active contour model; ant colony optimization; image segment;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 [11].

引用

页码：5355 / 5360

页数：6

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