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模糊散度
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  相似匹配句对
     The Image Segmentation Based on Fuzzy Divergence
     基于模糊散度的图象分割
短句来源
     Entropy,Distance Measure and Divergence Measure
     模糊熵,距离测度和散度测度(英文)
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     The fuzzy analytic hierarchy process
     模糊层次分析法
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     Fuzzy Hydrology
     模糊水文学
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     lmage Segmentation Algorithms Based on Cross Entropy and Fuzzy Divergence
     图像分割中的交叉熵和模糊散度算法
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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...

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.

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.

Two algorithms for segmenting FMI (formation microresistivity imager) log are described.One is based on the threshold value of fuzzy divergence between the original image and its segmented image;the other is based on the segmenting transition area. They are applied to segmenting FMI images in the well T245 of the central uplift, Tarim basin The application results show that these algorithms are effective in segmenting FMI images.

为了从地层微电阻率扫描成像测井 (FMI) 图像中分割出主要反映裂缝、孔洞的子图像, 介绍了两种图像分割算法: 一种是基于图像间模糊散度的阈值化算法, 另一种是基于过渡区的分割算法。并分别用这两种分割算法对塔里木盆地中央隆起TZ45 井FMI图像进行分割。研究结果表明, 这两种算法对FMI成像测井资料获得了较好的分割效果

A new thresholding algorithm and its multi threshold extension are presented to improve the performance of image segmentation. The algorithms are based on computing the fuzzy divergence between original image and its segmented version. The optimal threshold is searched through a minimum fuzzy divergence criterion. A new fuzzy membership function is defined in the light of requirements of image thresholding which can overcome the influence on segmentation caused by the classical S function. Compared with...

A new thresholding algorithm and its multi threshold extension are presented to improve the performance of image segmentation. The algorithms are based on computing the fuzzy divergence between original image and its segmented version. The optimal threshold is searched through a minimum fuzzy divergence criterion. A new fuzzy membership function is defined in the light of requirements of image thresholding which can overcome the influence on segmentation caused by the classical S function. Compared with several traditional thresholding methods by applying them to various test images, the effectiveness and generality of our new algorithms are shown.

为提高灰度图像分割的效果,提出了一种新的基于图像间模糊散度的阈值化算法及它在多阈值选择中的推广算法。算法采用模糊集合分别表达分割前后的图像,通过最小模糊散度准则实现图像分割中最优阈值的自动提取。算法针对图像阈值化分割的要求构造了一种新的模糊隶属度函数,克服了传统S-函数带宽对分割效果的影响。将其与多种经典的阈值化分割算法一起,对不同类型的测试图像进行分割比较的结果表明,新算法有很好的通用性和有效性。

 
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