期刊名称：Journal of Intelligent Learning Systems and Applications
印刷版ISSN：2150-8402
电子版ISSN：2150-8410
出版年度：2010
卷号：2
期号：3
页码：126-138
DOI：10.4236/jilsa.2010.23016
出版社：Scientific Research Publishing
摘要：Thresholding is a popular image segmentation method that converts gray-level image into binary image. The selection of optimum thresholds has remained a challenge over decades. In order to determine thresholds, most methods analyze the histogram of the image. The optimal thresholds are often found by either minimizing or maximizing an objective function with respect to the values of the thresholds. In this paper, a new intelligence algorithm, particle swarm opti-mization (PSO), is presented for multilevel thresholding in image segmentation. This algorithm is used to maximize the Kapur’s and Otsu’s objective functions. The performance of the PSO has been tested on ten sample images and it is found to be superior as compared with genetic algorithm (GA).
