Using Eigenvalues of Covariance Matrices for Automated Visual Inspection of Microdrills

Abstract

This paper proposes a translation, rotation, and template-free automated visual inspection scheme that detects microdrill defects using the eigenvalues of covariance matrices. We first derived the colour images of microdrills and extracted the boundary of the first facets. Then, the smaller eigenvalues of the covariance matrices of given regions of support were calculated for boundary representation, and they were thresholded to separate the boundaries into segments. The least square linear regression method was used to fit the segments into linear equations. Eventually, the defects were detected by three inspection rules that measure five features of microdrills including: gap distance, parallel, and enclosed angles, accordingly. The proposed scheme was implemented in C++ with a graphical user interface environment. Fifteen microdrills, digitized without alignment, were used to verify the proposed inspection process. Experimental results show that the proposed scheme reliably achieves the inspection of microdrills.