采用遗传算法与最大模糊熵的双阈值图像分割

Image Segmentation by Double Thresholding with Maximum Fuzzy Entropy and Genetic Algorithm

提出了一(?)基于概率划分的最大模糊熵双阈值图像分割方法。文章将概率划分、模糊划分及最大模糊熵准 则结合起来,提出一种 概率划分的最大模糊熵准则。将图像分为暗、灰和亮三个部分,并分别采用S函数、Π函数 和Z函数来描述其模糊性。利用最大模糊熵准则确定图像的两个模糊区域带宽及其属性,进而确定图像的两个最佳分 割门限。由于需优化处理的参数较多,本文采用遗传算法对六个模糊参数进行组合寻优,并采用合适的编码方案以避免无 效染色体的产生。试验结果证明利用本文提出的最大模糊熵准则分割图像具有较好的效果,采用遗传算法也使本文的分割 算法速度大大提高,在较短的时间内能够得到满意的分割结果。

In the paper a three-level thresholding method for image segmentation based on probability partition and fuzzy partition and entropy theory is presented. The paper defines a new fuzzy entropy through probability analysis. The image is partition to three parts, which include dark, gray and white part, whose member functions of the fuzzy region are Z-function and H-function and S-function. The width and attribute of the fuzzy region can be decided by maximum fuzzy entropy. The procedure for finding the optimal combination of all the fuzzy parameter is implemented by a genetic algorithm with appropriate coding method to avoid useless chromosomes. The experiment results show that our proposed method gives better performance than other general methods with good real-time by using genetic algorithm.