四川大学学报：工程科学版2012，Vol.44Issue(3)：147-152,6.
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
摘要
Abstract
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Key words
Curvelet transform/surface defects/textures/support vector machines分类
信息技术与安全科学引用本文复制引用
蒋红海,殷国富,刘培勇,尹湘云..基于Curvelet变换和支持向量机的磁瓦表面缺陷识别方法[J].四川大学学报：工程科学版,2012,44(3):147-152,6.基金项目
国家科技支撑计划课题资助项目 （）
四川省高新技术产业重大关键技术资助项目 （）
