Effect of different thresholding methods in RGB imaging

Jari Miettinen; Heikki J. Ailisto

doi:10.1117/12.444215

5 October 2001 Effect of different thresholding methods in RGB imaging

Jari Miettinen, Heikki J. Ailisto

Proceedings Volume 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision; (2001) https://doi.org/10.1117/12.444215
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States

Abstract

Typical surface inspection tasks using RGB vision require the analysis of tens of megabytes of image data per second, with low false alarm and error escape rates. Although automatic inspection systems have become more common on production lines, for example in sawmills, there are substantial needs to improve their performance and accuracy. Detection is one very important part of image flow before defect recognition. Detection is used in order to find suspicious regions of the image, containing possibly defective areas, since defect detection has to cope with very high data rates. It has to be based on relative simple methods. In this paper we describe the effect of different thresholding methods in RGB defect detection. Threshold values were calculated for R, G and B channels, difference channels R-G, R-B and G- B and for mean values from R, G and B channels. The analysis was performed for pinewood. Error escape rate and false alarm rates were used as evaluation criteria. In this paper, R and G channel thresholding methods were the best ones.

Citation Download Citation

Jari Miettinen and Heikki J. Ailisto "Effect of different thresholding methods in RGB imaging", Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); https://doi.org/10.1117/12.444215

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