二维模糊划分最大熵图像分割算法
Image segmentation through maximizing fuzzy partition entropy of 2-D histogram
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摘要: 该文提出了一种通过最大化二维直方图模糊划分熵分割灰度图像的新算法。首先介绍了模糊划分的原理,提出用条件概率与条件熵定义模糊划分熵。随后利用多维三角模定义了非相关模糊子集的广义直积,给出构造多维模糊划分的方法,并根据最大熵原理设计了一种基于二维直方图模糊划分熵分割灰度图像的新算法。对几例真实目标图像的对比分割实验结果表明该文方法性能优越。
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关键词:
- 图像处理; 图像分割; 模糊划分; 熵
Abstract: In this paper a novel method is presented to segment gray level image through maximizing the fuzzy partition entropy of two-dimensional histogram. After the concept of fuzzy partition is briefly introduced first, a new definition of fuzzy partition entropy based on condition probability and condition entropy is presented. Then, the multi-dimensional triangular-norm is applied to construct generalized Cartesian product of non-interactive fuzzy sets, and also an approach for generating multi-dimensional fuzzy partition is presented. Finally, a new method for segmenting gray level image through maximizing the fuzzy partition entropy of two-dimensional histogram is put forward. Experiments are conducted on real object pictures, and the results show that the approach presented herein performs better than some classical threshold selection methods do. -
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