Application of Shearlet transform to classification of surface defects for metals
作者：
Highlights：
• We developed a method called DST-KLPP which is effective in classification of surface defects of different metals.
• DST-KLPP was tested with samples of three typical metals, including slabs, hot-rolled steels and aluminum sheets.
• DST-KLPP provides higher classification rates than other methods, including Wavelet, Curvelet and Contourlet transform.
• DST-KLPP can recognize defects from complex backgrounds efficiently.
• DST-KLPP can recognize tiny defects from low-contrast images availably.
摘要
•We developed a method called DST-KLPP which is effective in classification of surface defects of different metals.•DST-KLPP was tested with samples of three typical metals, including slabs, hot-rolled steels and aluminum sheets.•DST-KLPP provides higher classification rates than other methods, including Wavelet, Curvelet and Contourlet transform.•DST-KLPP can recognize defects from complex backgrounds efficiently.•DST-KLPP can recognize tiny defects from low-contrast images availably.
论文关键词：Surface inspection,Feature extraction,Multi-scale geometric analysis,Shearlet transform,Kernel Locality Preserving Projections
论文评审过程：Received 15 April 2014, Revised 22 November 2014, Accepted 2 January 2015, Available online 29 January 2015.
论文官网地址：https://doi.org/10.1016/j.imavis.2015.01.001
