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Detection of annual rings in wood
Linköping University, Department of Science and Technology.
2008 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This report describes an annual line detection algorithm for the WoodEye quality control system. The goal with the algorithm is to find the positions of annual lines on the four surfaces of a board. The purpose is to use this result to find the inner annual ring structure of the board. The work was done using image processing techniques to analyze images collected with WoodEye. The report gives the reader an insight in the requirements of quality control systems in the woodworking industry and the benefits of automated quality control versus manual inspection. The appearance and formation of annual lines are explained on a detailed level to provide insight on how the problem should be approached. A comparison between annual rings and fingerprints are made to see if ideas from this area of pattern recognition can be adapted to annual line detection. This comparison together with a study of existing methods led to the implementation of a fingerprint enhancement method. This method became a central part of the annual line detection algorithm. The annual line detection algorithm consists of two main steps; enhancing the edges of the annual rings, and tracking along the edges to form lines. Different solutions for components of the algorithm were tested to compare performance. The final algorithm was tested with different input images to find if the annual line detection algorithm works best with images from a grayscale or an RGB camera.

Place, publisher, year, edition, pages
2008. , 57 p.
Keyword [en]
image processing, image analysis, annual rings, annual lines, inner structure, wood, boards, pattern, detection, enhancement, linetracing, linetrace, trace, tracing, tracking, direction, growth direction, ALD, double angle, pith, scanner, woodeye, automation, quality control
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Science
URN: urn:nbn:se:liu:diva-15804ISRN: LiU-ITN-TEK-A--08/122--SEOAI: diva2:127411
Subject / course
Master of Science in Media Technology and Engineering
Available from: 2009-02-02 Created: 2008-12-05 Last updated: 2011-10-26Bibliographically approved

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Jonsson, Christian
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