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Wood fingerprint recognition using knot neighborhood K-plet descriptors
Luleå University of Technology, Sweden.
RISE., SP – Sveriges Tekniska Forskningsinstitut, SP Sveriges tekniska forskningsinstitut, SP – Sveriges Tekniska Forskningsinstitut / Förädling och processer (TRf). Luleå University of Technology, Sweden.
Luleå University of Technology, Sweden.
Luleå University of Technology, Sweden.
2015 (engelsk)Inngår i: Wood Science and Technology, ISSN 0043-7719, E-ISSN 1432-5225, Vol. 49, nr 1, s. 7-20Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
2015. Vol. 49, nr 1, s. 7-20
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URN: urn:nbn:se:ri:diva-6919DOI: 10.1007/s00226-014-0679-3Scopus ID: 2-s2.0-84938410671Lokal ID: 30860OAI: oai:DiVA.org:ri-6919DiVA, id: diva2:964761
Tilgjengelig fra: 2016-09-08 Laget: 2016-09-08 Sist oppdatert: 2020-12-01bibliografisk kontrollert

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