摘要
针对带钢表面缺陷的识别和分类技术,本文采用一种将不变矩与主成分分析法相结合的特征提取方法。首先,对每幅缺陷图像提取22维不变矩特征向量,满足对图像平移、尺度及旋转变化都不敏感;然后,为了提高分类器的效率,应用主成分分析法对特征向量进行空间降维处理,得到4维特征向量;最后,将特征向量作为BP神经网络的输入,对网络进行权值和阈值训练,达到缺陷分类的目的。实验结果表明,该方法对带钢表面缺陷的平均正确识别率可达到85%以上。
A method of feature extraction which is composed of invariable moment functions and Principal Component Analysis （PCA） is presented in order to recognize and classify the surface defects of strips. First, a 22-dimensional eigenvector which was invariable was extracted from images when the image was translated, scaled and rotated. And then, in order to improve the efficiency of classification, PCA was applied to reduce the dimension of the eigenvector. As a result, the 4-dimensional eigenvector was obtained. Finally, using these eigenvectors as input, weights and thresholds of the BP neural network were trained for the purpose of defect classification. Experimental results show that the average efficiency of the correct identification can reach 85%, and it＇s fit for the application for detection of surface defects of strips,
出处
《光电工程》
EI
CAS
CSCD
北大核心
2008年第7期90-94,共5页
Opto-Electronic Engineering
基金
长江学者和创新团队发展资助(IRT0423)
作者简介
张媛（1983-），女（汉族），辽宁沈阳人，硕士研究生，主要研究工作是机器视觉。E-mail：zhangyuan8384@yahoo.com．cn
