A quantile-quantile plot based pattern matching for defect detection

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 LCD (liquid crystal display) panels have shown the efficacy of the proposed pattern matching scheme for defect detection.