二維模糊劃分最大熵圖像分割算法
Image segmentation through maximizing fuzzy partition entropy of 2-D histogram
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摘要: 該文提出了一種通過最大化二維直方圖模糊劃分熵分割灰度圖像的新算法。首先介紹了模糊劃分的原理,提出用條件概率與條件熵定義模糊劃分熵。隨后利用多維三角模定義了非相關模糊子集的廣義直積,給出構(gòu)造多維模糊劃分的方法,并根據(jù)最大熵原理設計了一種基于二維直方圖模糊劃分熵分割灰度圖像的新算法。對幾例真實目標圖像的對比分割實驗結(jié)果表明該文方法性能優(yōu)越。
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關鍵詞:
- 圖像處理; 圖像分割; 模糊劃分; 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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