基于最大模糊熵和遗传算法的图像分割

为了分割照度不均匀的网格图像,提出了一种基于最大模糊熵和遗传算法的阈值分割方法。基于模糊集合理论,根据像素灰度值把原始图像中的像素分为黑和亮两个模糊集,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,实现图像分割。由于穷举法搜索模糊参数的最优组合存在计算复杂度高、占用存储空间大等缺点,因此采用了遗传算法确定最优阈值。为了验证该方法的有效性,对其进行了图像分割实验,并与最大类间方差法、迭代法和一维最大熵法进行了比较。实验结果表明,该方法能够自动、有效地选取阈值,分割效果优于其它三种算法,并能保留原始图像的主要特征。

Image segmentation based on maximum fuzzy entropy and genetic algorithm

A threshold segmentation method based on maximum fuzzy entropy and genetic algorithm is proposed to segment a grid image with nonuniform illumination.Based on the fuzzy set theory,the method can classify the pixels of an image into two parts,namely,dark and bright part by their gray levels,define the fuzzy region,and search the optimal combination of the fuzzy parameters by the maximum fuzzy entropy principle.But it has a high computational complexity,and occupies large memory size.A genetic algorithm is implemented to search the optimal combination of the fuzzy parameters,which finally determine the threshold.In order to validate the proposed method,it is tested and compared with the Otsu's method,the iteration method and the one-dimensional maximum entropy method.Experimental results show that the proposed method can select the threshold automatically and efficiently,and has an advantage of reservation of the main features of the original image.