基于信息论中最大熵原理，提出了一种2维直方图模糊划分Renyi熵分割算法。首先介绍了模糊划分的原理，由于Renyi熵是Shannon熵的广义形式，因此用模糊概率和条件概率来定义模糊划分Renyi熵。然后在向量空间内搜索最优参数组合，利用隶属函数实现图像分割。选用3幅不同类型的图像进行MATLAB仿真实验，结果表明该方法对噪声及杂散点等干扰的抑制性能较理想，且显著优于对比方法所得结果。

Based on the maximum entropy principle, a new segmentation approach of fuzzy partition Renyi entropy of two-dimensional histogram is proposed. First, the concept of fuzzy partition is introduced. In view of the Renyi entropy as a generalized form of Shannon entropy, fuzzy probability and conditional probability are used to define the fuzzy Renyi entropy. Then, in sample space the optimal pair of parameters is searched. Finally, image segmentation is realized using membership functions. Experiments are conducted on three real object pictures by MATLAB. Results show that the proposed approach does good control performance to noise and interference. And its better than other contrast methods.