Wood fingerprint recognition using knot neighborhood K-plet descriptors
2015 (English)In: Wood Science and Technology, ISSN 0043-7719, E-ISSN 1432-5225, Vol. 49, no 1, 7-20 p.Article in journal (Refereed) Published
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
Place, publisher, year, edition, pages
2015. Vol. 49, no 1, 7-20 p.
Research subject Wood Technology; Signal Processing
IdentifiersURN: urn:nbn:se:ltu:diva-7733DOI: 10.1007/s00226-014-0679-3Local ID: 6266a684-7c00-49d2-ac80-b65cd350c889OAI: oai:DiVA.org:ltu-7733DiVA: diva2:980623
Validerad; 2015; Nivå 2; 20140429 (erikjo)2016-09-292016-09-29Bibliographically approved