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使用模糊竞争Hopfield网络进行图像分割
引用本文:张星明,李凤森. 使用模糊竞争Hopfield网络进行图像分割[J]. 软件学报, 2000, 11(7): 953-956
作者姓名:张星明  李凤森
作者单位:中国科学院计算技术研究所CAD开放研究实验室,北京,100080
基金项目:本文研究得到国家摼盼鍞科技攻关项目基金(No.956010)资助.
摘    要:针对传统自组织竞争学习方法的不足,将模糊竞争学习引入竞争Hopfield网络中,由此设计了一个用于图像分割的模糊竞争Hopfield网络,通过将图像空间映射到灰度特征空间,实现灰度特征集的模糊聚类,进而实现图像分割.实验结果表明:对于二值分割,与Ostu方法相比,此算法在分割效果和对噪声的自适应能力方面具有明显的优点.对于多类分割,此算法比目前的FCM(fuzzy C mean)算法的处理速度要快.

关 键 词:图像分割,灰度特征,模糊竞争,Hopfield神经网络.
收稿时间:1999-03-16
修稿时间::

Using Fuzzy Competitive Hopfield Neural Network for Image Segmentation
ZHANG Xing-ming and ZHANG Xing-ming. Using Fuzzy Competitive Hopfield Neural Network for Image Segmentation[J]. Journal of Software, 2000, 11(7): 953-956
Authors:ZHANG Xing-ming and ZHANG Xing-ming
Affiliation:CAD Laboratory Institute of Computing Technology The Chinese Academy of Sciences Beijing 100080
Abstract:In this paper, based on the defect of self-organizing learning method, a fuzzy competitive learning method is proposed, and a fuzzy competitive Hopfield neural network for color image segmentation is designed based on competitive Hopfield neural network. The fuzzy clustering on gray feature set can be realized by means of mapping image space into gray feature space, then the color image segmentation can be done. The experiment results indicate that the algorithm is of better effect and adaptive ability to noise than Ostu method for binary segmentation, and shows higher processing speed than FCM (fuzzy C mean) algorithms for multi-class segmentation.
Keywords:Image segmentation   gray feature   fuzzy competition   Hopfield neural network.
本文献已被 CNKI 维普 万方数据 等数据库收录!
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