A New Multilevel Thresholding Method Using Swarm Intelligence Algorithm for Image Segmentation

• 作者: Ramanujam Kayalvizhi Sathya P. Duraisamy
• 来源期刊: 智能学习系统与应用(英文)
• 学科: 工学
• 关键词: Image SEGMENTATION MULTILEVEL THRESHOLDING PARTICLE SWARM Optimization
• 年，卷（期）: 2010,（3）
• 页码范围: 126-138
• 页数: 13页
• 分类号: TP39

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).