图像分割中的交叉熵和模糊散度算法

本文将交叉熵和模糊散度应用于图像分割中，提出了中最优灰度值选取算法，其一是基于均匀分布假设的最小交叉熵算法，其二是利用后难概率的最大类间交叉熵算法，其三是类间最大模糊散度的改进算法，其四是最小模糊散度算法，针对图像阈什化分割的要求，在后两种算法中构造一种新的模糊录改度函数，本文采用均匀测试和开头测试比较各算法的性能，利用多种类型测试 是到的分割结果，显示了所筛算法的有效性和通用性。

lmage Segmentation Algorithms Based on Cross Entropy and Fuzzy Divergence

Cross entropy measures information discrepancy between two probability distributions.Induced by cross entropy,fuzzy divergence measures dissimilarity between two fuzzy sets,as fruit of both information theory and fuzzy set theory,In this paper,in the light of different criteria we present four new algorithms of optimal gray scale threshold selection for image segmentation,integrating cross entropy and fuzzy divergence with image histogram.The first algorithm is based on minimum cross entropy with the hypothesis of uniform probability distribution. The second algorithm maximizes between classcross entropy using posterior probability.The third one is a modified version of existing method based on maximum betweenclass fuzzy divergence.The last one is a minimum fuzzy divergence algorithm.According to the requirement of image thresholding,we construct a new fuzzy membership function in the last two algorithms.The effectiveness and generality of our new algorithms are shown by applying them to various test images and by evaluating the results with uniformity measure and shape measure.