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Automated knot detection for high speed computed tomography on Pinus sylvestris L. and Picea abies (L.) Karst. using ellipse fitting in concentric surfaces
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Träteknologi.
SP Technical Research Institute of Sweden, Skellefteå.
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Träteknologi.
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Träteknologi.
2013 (engelsk)Inngår i: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 96, s. 238-245Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

High speed industrial computed tomography (CT) scanning of sawlogs is new to the sawmill industry and therefore there are no properly evaluated algorithms for detecting knots in such images. This article presents an algorithm that detects knots in CT images of logs by segmenting the knots with variable thresholds on cylindrical shells of the CT images. The knots are fitted to ellipses and matched between several cylindrical shells. Parameterized knots are constructed using regression models from the matched knot ellipses. The algorithm was tested on a variety of Scandinavian Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) with a knot detection rate of 88–94% and generating about 1% falsely detected knots.

sted, utgiver, år, opplag, sider
2013. Vol. 96, s. 238-245
HSV kategori
Forskningsprogram
Träteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-8418DOI: 10.1016/j.compag.2013.06.003ISI: 000323795700022Scopus ID: 2-s2.0-84880384633Lokal ID: 6ee1639c-6afa-4b91-8c86-8608b761e1ebOAI: oai:DiVA.org:ltu-8418DiVA, id: diva2:981356
Merknad
Validerad; 2013; 20130702 (andbra)Tilgjengelig fra: 2016-09-29 Laget: 2016-09-29 Sist oppdatert: 2018-07-10bibliografisk kontrollert

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Johansson, ErikSkog, JohanFredriksson, Magnus

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