基于信息论中最大熵原理，提出了一种新的基于模糊划分的边缘检测算法，并介绍了模糊概率和用条件概率与条件熵来定义模糊划分熵的概念以及模糊划分的原理。该算法是利用自然划分以及梯度图像模糊划分的关系，在条件概率与模糊划分熵的基础上，通过最大模糊熵原则来实现图像分割中最优阈值的自动提取，以实现图像的边缘检测。通过不同类型测试图像的边缘检测结果比较表明，该算法用于边缘检测能获得很好的效果。

Image processing has to deal with much information of an image. Maximum entropy theorem of information theory is one of the useful tools to treat with this kind of information. Based upon the maximum fuzzy partition entropy principle, a novel approach for edge detection is presented. After the concept and the principle of the fuzzy probability and fuzzy partition are introduced briefly, a definition of fuzzy partition entropy is proposed. Using the relation of the probability partition and the fuzzy 2-partition of the image gradient, the algorithm is based on conditional probabilities and fuzzy partition. First, a gradient operator is performed and the gradient image is produced. Second, the problem of edge detection is to find a fuzzy partition of the gradient image, which is considered as being composed of edge region and smooth region, and the automatic optimal threshold is searched from gray-level histogram through maximizing the entropy of fuzzy partition. At last, an edge-enhancing procedure is executed on the edge image. The experiment is conducted on various test images and the results show that the proposed approach has better performances than some classical edge detection methods based on gradient do.