Denoising Maximum Entropy Image Segmentation Based on Improved Genetic Algorithm

摘要：

In this paper, we present denoising maximum entropy image segmentation on the basis of maximum histogram entropy and 2D maximum entropy.The approach is divided into two stages: before the segmentation, we first make an image dcspeckle processing, and in the process of seeking the threshold, particle swarm optimization genetic algorithm (PSOGA) is used to reduce computation time and improve solution accuracy by combining the standard genetic algorithm (GA) with particle swarm optimization (PSO).Through segmentation test and comparison, the results obtained by our method are short time-consuming and strong anti-noise ability.Computational results show that PSOGA has good global optimal scarehi capabilities and faster search speed.

关键词： image segmentation two-dimensional entropy particle swarm optimization genetic algorithm

作者: Hui-Yong Men Wen-Yong Wang Ya-Kun Zhan

作者单位: Ideal Information Technology Research Institute,Northeast Normal University Changchun,China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Education Technology and Computer(2011第三届教育技术与计算机国际会议 ICETC2011)

会议地点: 成都

会议语种:英文

页码: 290-293

在线出版日期: 2011-07-15（万方平台首次上网日期，不代表论文的发表时间）