论文部分内容阅读
图像分割是图像处理和分析的基础,通过分析遗传算法(Genetic Algorithm,GA)在图像分割中的应用优劣,提出利用模拟退火思想的改进遗传退火(Genetic Simulated Annealing Algorithm,GASA)的图像阈值分割算法,算法整个运行过程由冷却温度进度表控制,使用改进的最小误差公式代替遗传算法的适应度函数,将问题转化,从而求得灰度图像的一个最佳阈值。实验数据表明,基于改进遗传退火算法的最小误差图像分割方法能较好提高算法的全局搜索能力,避免遗传算法陷入局部最优,并且能更快速、更稳定收敛到最佳的分割阈值,得到更好的图像分割效果。
Image segmentation is the basis of image processing and analysis. By analyzing the advantages and disadvantages of Genetic Algorithm (GA) in image segmentation, this paper proposes a method of image segmentation based on Genetic Simulated Annealing Algorithm (GASA) The algorithm and algorithm are controlled by the cooling temperature schedule, and the improved minimum error formula is used instead of the fitness function of genetic algorithm to transform the problem so as to obtain an optimal threshold of the gray image. Experimental results show that the proposed method based on the improved genetic annealing algorithm can improve the global search ability of the algorithm and avoid the genetic algorithm getting into the local optimum, and can converge to the optimal segmentation threshold more quickly and stably. Good image segmentation effect.