The effect of different thresholding methods in RGB imaging

Jari Miettinen, Heikki Ailisto

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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 pine-wood. 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.

Original languageEnglish
Title of host publicationIntelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision
PublisherInternational Society for Optics and Photonics SPIE
Pages459-465
Number of pages7
ISBN (Print)081944300X
DOIs
Publication statusPublished - 1 Dec 2001
MoE publication typeA4 Article in a conference publication
EventIntelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision - Boston, MA, United States
Duration: 29 Oct 200131 Oct 2001

Publication series

SeriesProceedings of SPIE
Volume4572
ISSN0277-786X

Conference

ConferenceIntelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision
CountryUnited States
CityBoston, MA
Period29/10/0131/10/01

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Keywords

  • Color
  • Detection
  • Machine vision
  • Threshold
  • Wood

Cite this

Miettinen, J., & Ailisto, H. (2001). The effect of different thresholding methods in RGB imaging. In Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision (pp. 459-465). International Society for Optics and Photonics SPIE. Proceedings of SPIE, Vol.. 4572 https://doi.org/10.1117/12.444215