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http://dx.doi.org/10.5302/J.ICROS.2014.14.0028

An Enhanced Histogram Matching Method for Automatic Visual Defect Inspection robust to Illumination and Resolution  

Kang, Su-Min (Department of Electronic Engineering, Dankook University)
Park, Se-Hyuk (Department of Electronic Engineering, Dankook University)
Huh, Kyung-Moo (Department of Electronic Engineering, Dankook University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.20, no.10, 2014 , pp. 1030-1035 More about this Journal
Abstract
Machine vision inspection systems have replaced human inspectors in defect inspection fields for several decades. However, the inspection results of machine vision are often affected by small changes of illumination. When small changes of illumination appear in image histograms, the influence of illumination can be decreased by transformation of the histogram. In this paper, we propose an enhanced histogram matching algorithm which corrects distorted histograms by variations of illumination. We use the resolution resizing method for an optimal matching of input and reference histograms and reduction of quantization errors from the digitizing process. The proposed algorithm aims not only for improvement of the accuracy of defect detection, but also robustness against variations of illumination in machine vision inspection. The experimental results show that the proposed method maintains uniform inspection error rates under dramatic illumination changes whereas the conventional inspection method reveals inconsistent inspection results in the same illumination conditions.
Keywords
vision; inspection; illumination; resolution; histogram;
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