| 基于Type-2模糊聚类的图像分割算法 |
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| 引用本文： | 周晚辉,刘文萍.基于Type-2模糊聚类的图像分割算法[J].计算机工程,2010,36(24):211-213. |
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| 作者姓名： | 周晚辉 刘文萍 |
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| 作者单位： | (北京林业大学信息学院，北京 100083) |
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| 基金项目： | 国家"973"计划基金资助项目，国家自然科学基金资助项目，北京林业大学科技创新计划基金资助项目 |
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| 摘 要： | 模糊C均值算法是图像分割的常用方法，但该算法对噪声非常敏感。为此，提出一种新算法，在模糊C均值算法基础上引进Type-2模糊理论，以提高算法的分割准确性和鲁棒性。该算法对模糊C均值算法中每一个样本的隶属度进行分段线性拉伸，利用拉伸的结果作为一个新的隶属度函数，并用该函数对图像进行分割。实验结果表明，该算法准确性较高，且具有良好的抗噪能力。
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| 关 键 词： | 图像分析 图像分割 模糊聚类 二型模糊 隶属函数 |
Image Segmentation Algorithm Based on Type-2 Fuzzy Clustering |
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| ZHOU Wan-hui,LIU Wen-ping.Image Segmentation Algorithm Based on Type-2 Fuzzy Clustering[J].Computer Engineering,2010,36(24):211-213. |
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| Authors: | ZHOU Wan-hui LIU Wen-ping |
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| Affiliation: | (College of Information, Beijing Forestry University, Beijing 100083, China) |
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| Abstract: | The Fuzzy C-Means(FCM) algorithm is one of the most popular image segmentation methods, but the FCM is sensitive to noise. A new image segmentation algorithm is proposed aiming to improve the segmentation precision and robustness of the FCM algorithm by introducing the Type-2 fuzzy theory. A piecewise-linear stretching method is applied to the membership values for each pixel. These membership values are derived using the FCM algorithm. The result of stretching defines a new membership function, which is used for image segmentation. Experimental results show the algorithm has higher image segmentation accuracy and better noise immune ability. |
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| Keywords: | image analysis image segmentation fuzzy clustering Type-2 fuzzy membership function |
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