基于粗糙集和模糊c均值的鲁棒图像分割算法

2010 Third International Symposium on Information Science and Engineering Pub Date : 2010-12-24 DOI:10.1109/ISISE.2010.122

Zhang Chao-quan, Liu Jian-sheng, Zou Wei-gang

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引用次数: 2

摘要

传统的模糊c均值(Fuzzy C-means, FCM)算法的图像分割只利用每个像素的灰度值，当图像被噪声破坏时，分割的精度会大大降低。为此，本文提出了一种基于粗糙集理论和模糊c均值聚类的图像分割方法。测试结果表明，该方法具有良好的分割性能。

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Robust Image Segmentation Algorithm Based on Rough Sets and Fuzzy C-Means

Image segmentation with the traditional Fuzzy C-means (FCM) algorithm only uses each pixel's gray value, when the image is corrupted by noises, the accuracy of segmentation will be greatly reduced. So, this paper proposed an image segmentation method which based on rough sets theory and fuzzy c-mean clustering. The test result shows that the method has a good segmentation performance.

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2010 Third International Symposium on Information Science and Engineering

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