Multiresolution object detection and delineation
Computer Vision, Graphics, and Image Processing
Application of 'vision in the loop' for inspection of lace fabric
Real-Time Imaging - Special issue on real-time visual monitoring and inspection
Object Detection and Localization by Dynamic Template Warping
International Journal of Computer Vision
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The evaluation of normalized cross correlations for defect detection
Pattern Recognition Letters
Fast normalized cross correlation for defect detection
Pattern Recognition Letters
Defect detection in patterned wafers using anisotropic kernels
Machine Vision and Applications
Defect detection of IC wafer based on two-dimension wavelet transform
Microelectronics Journal
Detection of soldering defects in Printed Circuit Boards with Hierarchical Marked Point Processes
Pattern Recognition Letters
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Pattern matching has been used extensively for many machine vision applications such as optical character recognition, face detection, object detection, and defect detection. The normalized cross correlation (NCC) is the most commonly used technique in pattern matching. However, it is computationally intensive, sensitive to environmental changes such as lighting and shifting, and suffers from false alarms for a complicated image that contains partial uniform regions. In this paper, a pattern-matching scheme based on the quantile-quantile plot (Q-Q plot) is proposed for defect detection applications. In a Q-Q plot, the quantiles of an inspection image are plotted against the corresponding quantiles of the template image. The p-value of Chi-square test from the resulting Q-Q plot is then used as the quantitative measure of similarity between two compared images. The quantile representation transforms the 2D gray-level information into the 1D quantile one. It can therefore efficiently reduce the dimensionality of the data, and accelerate the computation. Experimental results have shown that the proposed pattern-matching scheme is computationally fast and is tolerable to minor displacement and process variation. The proposed similarity measure of p-value has excellent discrimination capability to detect subtle defects, compared with the traditional measure of NCC. With a proper normalization of the Q-Q plot, the p-value measure can be tolerable to moderate light changes. Experimental results from assembled PCB (printed circuit board) samples, IC wafers, and liquid crystal display (LCD) panels have shown the efficacy of the proposed pattern-matching scheme for defect detection.