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首页> 外文期刊>Applied Soft Computing >Robust fuzzy c-means clustering algorithm with adaptive spatial & intensity constraint and membership linking for noise image segmentation
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Robust fuzzy c-means clustering algorithm with adaptive spatial & intensity constraint and membership linking for noise image segmentation

机译：具有自适应空间和强度约束和噪声图像分割的隶属关系的鲁棒模糊C型聚类算法

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摘要

The fuzzy C-means (FCM) clustering method is proven to be an efficient method to segment images. However, the FCM method is not robustness and less accurate for noise images. In this paper, a modified FCM method named FCM_SICM for noise image segmentation is proposed. Firstly, fast bilateral filter is used to acquire local spatial & intensity information; secondly, absolute difference image between the original image and the bilateral filtered image is employed and the reciprocal of the difference image and the difference image itself constrain conventional FCM as well as the local spatial & intensity information respectively; finally, membership linking is achieved by summing all membership degrees calculated from previous iteration within every cluster in squared logarithmic form as the denominator of objective function. Experiments show that this proposed method achieves superior segmentation performance in terms of segmentation accuracy (SA), average intersection-overunion (mIoU), E-measure and number of iteration steps on mixed noise images compared with several state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved.
机译：模糊C-Manial（FCM）聚类方法被证明是分段图像的有效方法。但是，FCM方法不是鲁棒性，噪声图像的稳健性并不准确。本文提出了一种名为FCM_SICM的修改的FCM方法，用于噪声图像分割。首先，使用快速双侧过滤器来获取局部空间和强度信息;其次，采用原始图像和双侧滤波图像之间的绝对差异图像，并且分别限制传统的FCM以及差异图像本身的倒数分别限制局部空间和强度信息;最后，通过在每个集群中以方形对数形式的每个群集中计算的所有成员程度求和为目标函数的分母来实现成员资格链接。实验表明，这种提出的方​​法在分割精度（SA），平均交叉口（MIOU），电子测量和混合噪声图像上的迭代步骤的速度和迭代步骤的卓越分割性能实现了卓越的分割性能。与多种最先进的方法相比。 （c）2020 Elsevier B.V.保留所有权利。

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