基于模糊理论的2维隶属划分Renyi熵分割算法

刘锁兰; 杨静宇

doi:10.11834/jig.20090220

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专辑

基于模糊理论的2维隶属划分Renyi熵分割算法
Segmentation Approach Based on Fuzzy Theory and Renyi Entropy of 2D Membership Partition
2009年14卷第2期页码：323-327
纸质出版：2009
DOI： 10.11834/jig.20090220
稿件说明：

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刘锁兰, 杨静宇. 基于模糊理论的2维隶属划分Renyi熵分割算法[J]. 中国图象图形学报, 2009,14(2):323-327. DOI： 10.11834/jig.20090220.

LIU Suolan, YANG Jingyu. Segmentation Approach Based on Fuzzy Theory and Renyi Entropy of 2D Membership Partition[J]. Journal of Image and Graphics, 2009, 14(2): 323-327. DOI： 10.11834/jig.20090220.

摘要

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。

Abstract

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.