基于克隆选择算法的最大模糊熵图像分割
Image Segmentation Based on Maximum Fuzzy Entropy and Clone Selection Algorithm
-
摘要: 为了分割照度不均匀的图像,提出了一种基于最大模糊熵和免疫克隆选择算法的阈值分割方法.该方法利用最大模糊熵准则确定模糊区间的范围,寻找模糊参数的最优组合,确定区分目标和背景的最佳阈值,实现图像分割.为了验证该方法的有效性,对其进行了图像分割实验,并与遗传算法进行了比较.实验结果表明,该算法能够自动、有效地选取阈值,分割效果优于遗传算法,并能保留原始图像的主要特征.Abstract: A threshold segmentation method based on maximum fuzzy entropy and immune clone selection algorithm is proposed to segment an image with nonuniform illumination.According to the maximum fuzzy entropy principle,the optimal combination of the fuzzy parameters is searched,and the optimal threshold is determined to distinguish the object and background.In order to validate the proposed method,it is tested and compared with genetic algorithm.Experimental results show that the proposed method gives better performance,and can select the threshold automatically and efficiently, and has an advantage of reservation of the main features of the original image.
下载:
