An efficient translation-rotation template matching using pre-computed scores of rotated templates

Abstract

Printed Circuit Board Assembly (PCBA) quality inspection is an essential stage in PCBA manufacturing. In this paper, we focus on development of efficient template matching algorithms used in Automated Optical Inspection (AOI) for PCBA quality inspection in post-reflow process. In our work, a pre-computed set of Normalized Cross Correlation (NCC) scores from rotated templates to the original template are used to eliminate unnecessary calculation and to estimate rotation angles of scene object images. Two models called multiple band and piecewise linear are implemented and compared to find suitable rotation angles of candidate locations. Since the technique follows traditional systematic window sliding, existing efficient implementation techniques of template matching can be directly applied. Unlike other rotation invariant methods, accurate rotation angles can be directly obtained from the technique. Experimental results show excellent performance for PCBA quality inspection applications.