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Wear Debris Identification Using Feature Extraction and Neural Network

Wear Debris Identification Using Feature Extraction and Neural Network
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摘要 A method and results of identification of wear debris using their morphological features are presented. The color images of wear debris were used as initial data. Each particle was characterized by a set of numerical parameters combined by its shape, color and surface texture features through a computer vision system. Those features were used as input vector of artificial neural network for wear debris identification. A radius basis function (RBF) network based model suitable for wear debris recognition was established, and its algorithm was presented in detail. Compared with traditional recognition methods, the RBF network model is faster in convergence, and higher in accuracy. A method and results of identification of wear debris using their morphological features are presented. The color images of wear debris were used as initial data. Each particle was characterized by a set of numerical parameters combined by its shape, color and surface texture features through a computer vision system. Those features were used as input vector of artificial neural network for wear debris identification. A radius basis function (RBF) network based model suitable for wear debris recognition was established, and its algorithm was presented in detail. Compared with traditional recognition methods, the RBF network model is faster in convergence, and higher in accuracy.
出处 《Journal of Donghua University(English Edition)》 EI CAS 2004年第4期42-45,共4页 东华大学学报(英文版)
基金 ThisworkwasfinanciallysupportedbytheNationalNaturalScienceFoundationofChina,No .5 0 175 0 69.
关键词 wear debris CHARACTERIZATION neural network pattern recognition. 损耗 图像处理 计算机视觉系统 神经网络 表征 模式识别 形态特征
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参考文献9

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