基于Curvelet变换和支持向量机的磁瓦表面缺陷识别方法  

Defect Detection on Magnetic Tile Surfaces Based on Fast Discrete Curvelet Transformand Support Vector Machine

作　　者：蒋红海[1] 殷国富[1] 刘培勇[1] 尹湘云[1] 

机构地区：[1]四川大学制造科学与工程学院,四川成都610065

出　　处：《四川大学学报（工程科学版）》2012年第3期147-152,共6页Journal of Sichuan University (Engineering Science Edition)

基　　金：国家科技支撑计划课题资助项目(2006BAF01A07);四川省高新技术产业重大关键技术资助项目(2010GZ0051)

摘　　要：针对磁瓦表面缺陷对比度低、自动识别困难的问题,作者提出了一种对磁瓦图像应用快速离散Curvelet变换(FDCT)提取特征,并用支持向量机(SVM)分类器进行分类的磁瓦微小缺陷自动识别方法。该方法首先对磁瓦图像做分块处理,并对各分块图像应用FDCT,计算分解系数的l2范数,获得磁瓦不同方向的纹理频域特征;然后以归一化的分解系数l2范数作为支持向量机分类器的特征向量,对图像做出分类。对不同缺陷占比的图像进行实验测试,结果显示,当缺陷部分占分块图像的比例在1/64以上时正确识别率大于83%。Difficulties exist in automatically inspecting surface defects because of the low intensity image contrast.To overcome these difficulties,a textures analysis method for detecting defects on the magnetic tile surfaces was described.In this methodology the original image was divided into several equal sized squares,and decomposed based on a fast discrete curvelet transform（FDCT） at different scales and orientations.Then the l2 norms on the curvelet coefficients were calculated as the feature vector for support vector machine（SVM） classifier.The experimental results showed that the defects retrieval accuracy achieved 83% when defects accounted for more than 1/64 of magnetic tile image.

关 键 词：CURVELET变换 表面缺陷 纹理 支持向量机 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]