The effect of different thresholding methods in RGB imaging

Jari Miettinen*, Heikki Ailisto

*Corresponding author for this work

    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
    Country/TerritoryUnited States
    CityBoston, MA
    Period29/10/0131/10/01

    Keywords

    • Color
    • Detection
    • Machine vision
    • Threshold
    • Wood

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