基于二维最大熵原理和改进遗传算法的图像阈值分割  

Image Thresholding Segmentation Based on 2D Maximum Entropy Principle and Improved Genetic Algorithm

作　　者：李宏言[1] 盛利元[1] 陈良款[1] 李更强[1] 

机构地区：[1]中南大学物理科学与技术学院,湖南长沙410083

出　　处：《计算机与现代化》2007年第2期34-37,共4页Computer and Modernization

摘　　要：传统的最佳直方图熵法对低信噪比图像的分割效果不理想,而二维最大熵法充分利用了像素的灰度分布信息和像素间的空间相关信息,提高了阈值分割的抗噪性能。本文对二维最大熵法进行了详细阐述和推导,对遗传算法进行了一系列改进,提出了一种新的类抛物线型变异算子。实验结果表明,二维最大熵法具有较强的鲁棒性,同时改进的遗传算法能够快速收敛到最佳分割阈值,其优化效果十分明显。The traditional segmentation method based on maximum histogram entropy can net obtain ideal result for lower signal-noise-rate images. But 2D maximum entropy method can improve the anti-noise ability by fully using the information of gray distribution and spacial correlation of neighbouring pixels. In this paper, a 2D maximum entropy method is expounded detailedly, genetic algorithm is improved, and especially a new parabola-kind mutation operator is proposed. The result of experiment shows that 2D magnum entropy method is of higher robustness, and the improved genetic algorithm can converge the optimum threshold quickly and has good optimum effect.

关 键 词：阈值分割 二维最大熵法 改进遗传算法 类抛物线型变异算子 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]