lmage Segmentation Algorithms Based on Cross Entropy and Fuzzy Divergence

XUE Jing  hao; ZHANG Yu  jin; LIN Xing  gang

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lmage Segmentation Algorithms Based on Cross Entropy and Fuzzy Divergence

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- lmage Segmentation Algorithms Based on Cross Entropy and Fuzzy Divergence
- Acta Electronica Sinica Issue 10, Pages: 131-134(1999)
- 作者机构：
  
  清华大学电子工程系!北京100084
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN911.73
- Published：1999
- 稿件说明：
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[1]薛景浩,章毓晋,林行刚.图像分割中的交叉熵和模糊散度算法[J].电子学报,1999(10):131-134.

XUE Jing hao, ZHANG Yu jin, LIN Xing gang. lmage Segmentation Algorithms Based on Cross Entropy and Fuzzy Divergence[J]. Acta Electronica Sinica, 1999, (10): 131-134.
[1]薛景浩,章毓晋,林行刚.图像分割中的交叉熵和模糊散度算法[J].电子学报,1999(10):131-134. DOI：

XUE Jing hao, ZHANG Yu jin, LIN Xing gang. lmage Segmentation Algorithms Based on Cross Entropy and Fuzzy Divergence[J]. Acta Electronica Sinica, 1999, (10): 131-134. DOI：

摘要

本文将交叉熵和模糊散度应用于图像分割中，提出了四种最优灰度阈值选取算法．其一是基于均匀分布假设的最小交叉熵算法，其二是利用后验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．针对图像阈值化分割的要求，在后两种算法中构造了一种新的模糊隶属度函数．本文采用均匀测度和形状测度比较各算法的性能．利用多种类型测试图像得到的分割结果，显示了所提算法的有效性和通用性．

Abstract

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.

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