Wood Fingerprints : Recognition of Sawn Wood Products
Abstract: This thesis deals with wood fingerprints and presents ways to track sawn wood products through an industrial process using cameras. The possibility to identify individual wood products comes from the biological variation of the trees, where the genetic code, environment and breakdown process creates a unique appearance for every board. This application has much of the same challenges as are found in human biometrics applications.The vision for the future is to be able to utilize existing imaging sensors in the production line to track individual products through a disordered and diverging product flow. The flow speed in wood industries is usually very high and with a high degree of automation. Wood fingerprints combined with automated inspection makes it possible to tailor subsequent processing steps for each product and can bring the operators vital feedback on process parameters.The motivation for this work comes from the wood industry wanting to keep track of products without invasive methods such as bar code stickers or painted labels. In the project Hol-i-Wood Patching Robot, an automatic scanner- and robot system is being developed, where there is a need to keep track of the shuttering panels that are going to be mended by several automatic robot systems. In this thesis, two different strategies to recognize previously scanned sawn wood products are presented. The first approach uses feature detectors to find matching features between two images. This approach proved to be robust even when subjected to moderate geometric- and radiometric image distortions. The recognition accuracy reached 100% when matching high quality scans of Scots pine boards that have more than 20 knots.The second approach uses local knot neighborhood geometry to find point matches between images. The recognition accuracy reached above 99% when matching simulated Scots pine panels with realistically added noise to the knot positions, whilst more than 85% of the knots were found.Both presented approaches proved to be viable options for recognition of sawn wood products. In order to improve the recognition methods further, a larger dataset needs to be acquired and a method to calibrate parameter settings needs to be developed.
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