Improved Normalized Cross-Correlation for Defect Detection in Printed-Circuit Boards

Normalized cross-correlation (NCC) is used in many machine vision applications for industrial inspection. However, the high computational cost of NCC impedes real-time inspection. In this paper, we propose a modified low-complexity NCC scheme to discover the location of missing integrated circuits (ICs) in automatic printed-circuit board (PCB) inspection. In the proposed scheme, 2-D sub-images in the matching process are converted into 1-D feature descriptors. The running sub-images are scanned vertically and horizontally, augmented with spatial statistical measures, and then transformed by the discrete cosine transform (DCT) to create the feature descriptors. Experimental results of template matching on a 32-image dataset of PC and mobile phone PCBs show that the proposed scheme is more efficient and outperforms the traditional NCC and other schemes based on 1-D representations. Also, signal-to-noise measures show the robustness of the proposed technique against noise.