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。
In this paper a novel method is presented to segment gray level image through maximizing the fuzzy partition entropy of two-dimensional histogram.
该方法采用直觉模糊包含度和直觉模糊划分熵来评价直觉模糊聚类的有效性。
The technique includes two important evaluation factors: intuitionistic fuzzy inclusion degree and intuitionistic fuzzy division entropy.
首先介绍了模糊概率、用条件概率与条件熵定义模糊划分熵的概念以及模糊划分的原理。
Firstly, a definition of fuzzy partition entropy is proposed after introducing the concepts of fuzzy probability and fuzzy partition.
基于模糊划分熵的图像阈值化分割方法因具有良好的分割性能已成为图像分割广泛应用的方法之一。
Image thresholding segmentation method based on fuzzy partition entropy has good segmentation performance and becomes a kind of extensive application method in image segmentation field.
提出了一种基于混沌蚁群算法优化二维模糊划分最大熵的红外图像分割方法。
An approach for infrared image segmentation based on fuzzy partition maximum entropy of two-dimensional histogram optimized by Chaos ant colony algorithm is proposed.
提出了一种基于混沌蚁群算法优化二维模糊划分最大熵的红外图像分割方法。
An approach for infrared image segmentation based on fuzzy partition maximum entropy of two-dimensional histogram optimized by Chaos ant colony algorithm is proposed.
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