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Knot detection in coarse resolution CT images of logs
University of British Columbia.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.ORCID iD: 0000-0003-4530-0536
University of British Columbia.
Number of Authors: 3
2017 (English)In: International Wood Machining Seminar (IWMS-23), 2017Conference paper, Published paper (Refereed)
Abstract [en]

The use of X-ray computed tomography (CT) scanning of logs in sawmill is becoming a reality in the last few years, usually with rather costly and complex machines resembling medical scanners. However, a scanning solution has been developed that is less costly and more robust, and therefore more suited for sawmill needs. The rather coarse data from this machine has not been fully evaluated regarding possibilities to detect internal features such as knots. In this study, a knot detection algorithm developed for medical scanners was applied to images from a coarse resolution scanner, from four different logs of various species, and with different image resolution. The objective was to see if it was possible to detect knots automatically in the images. If so, the aim was to calculate the knot detection rate and the accuracy of detected knot size and position. These numbers were calculated compared to manually measured reference knots. This resulted in a knot detection rate of about 53 % overall, and a well detected knot position, but poorly detected knot size. It is possible to observe a certain difference between species and reconstruction resolution, however the material is too small to draw any definite conclusions. As a preliminary study, it provides input for further investigation on knot detection in coarse resolution X-ray CT images. Future work involves scanning more logs to get more data, and to pinpoint the resolution needed for accurate knot detection using the current algorithm.

Place, publisher, year, edition, pages
2017.
National Category
Wood Science Other Mechanical Engineering
Research subject
Wood Science and Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-59964OAI: oai:DiVA.org:ltu-59964DiVA: diva2:1040090
Conference
23rd International Wood Machining Seminar, Warsaw, Poland, 28-31 May 2017
Available from: 2016-10-26 Created: 2016-10-26 Last updated: 2017-10-19Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
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  • de-DE
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  • nn-NB
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Output format
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