Title :
Automatic threshold selection based on ant colony optimization algorithm
Author :
Ye, Zhiwei ; Zheng, Zhaobao ; Yu, Xin ; Ning, Xiaogang
Author_Institution :
Sch. of Remote Sensing & Inf. Eng., Wuhan Univ.
Abstract :
Image segmentation is a long-term difficult problem, which hasn´t been fully solved. Threshold is one of the most popular algorithms. Ant colony optimization algorithm (ACO) was recently proposed algorithm, which has been successfully applied to solve many combinatorial optimization problems. On the analysis of Ostu, we are aware that threshold selection can be viewed as a combinatorial optimization problem. Thus, we introduce a new method to select image threshold automatically based on ACO algorithm. The performance of this algorithm is compared with Ostu, and experimental results show that ACO algorithm can reveal very encouraging results in terms of the quality of solution found and the processing time required
Keywords :
combinatorial mathematics; image segmentation; optimisation; ant colony optimization algorithm; automatic threshold selection; combinatorial optimization problem; image segmentation; Algorithm design and analysis; Ant colony optimization; Character recognition; Computer errors; Computer vision; Image analysis; Image processing; Image segmentation; Insects; Remote sensing; Ant colony algorithm; between-class square error; image segmentation; threshold;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614730