Abstract
In the wood industry, there is a wish to recognize and track wood products through production chains. Traceability would facilitate improved process control and extraction of quality measures of various production steps. In this paper, a novel wood surface recognition system that uses scale and rotationally invariant feature descriptors called K-plets is described and evaluated. The idea behind these descriptors is to use information of how knots are positioned in relation to each other. The performance and robustness of the proposed system were tested on 212 wood panel images with varying levels of normally distributed errors applied to the knot positions. The results showed that the proposed method is able to successfully identify 99–100 % of all panel images with knot positional error levels that can be expected in practical applications.
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Acknowledgments
This work has been part of the Hol-i-Wood Patching Robot project and received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 284573. The Hol-i-Wood PR is a collaboration between Luleå University of Technology (LTU), TU Wien and TU München, as well as industrial partners: MiCROTEC, Springer, TTTech and LIP-BLED.
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Pahlberg, T., Johansson, E., Hagman, O. et al. Wood fingerprint recognition using knot neighborhood K-plet descriptors. Wood Sci Technol 49, 7–20 (2015). https://doi.org/10.1007/s00226-014-0679-3
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DOI: https://doi.org/10.1007/s00226-014-0679-3