steel surface defect detection and localization based on SVD and two-side compressive measurements
This paper proposes a method for defect detection and localization based on singular value decomposition and two-side compressive measurements.First,the feasibility of the singular value decomposition for defect detection and localization is analyzed,then the invariance of the geometrical structure of the rows or columns of the raw data and the compressive data is justified,so the energy and pattern contained in the raw data can be transferred into the compressive data and kept in the singular values and singular vectors.On this basis,the proposed defect detection algorithm based on the singular values of compressive data and the proposed defect localization algorithm based on the singular vectors are given without reconstruction of images.Simulation results show that the proposed method based on compressive measurements has a good performance.
defect detection compressed sensing singular value decomposition random projection
Jingli Gao Chenglin Wen Meiqin Liu
College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China College of Electrical Engineering,Henan University of Technology,Zhengzhou 450001,China
国际会议
长沙
英文
1401-1406
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）