Automatic Knot Detection in Coarse-Resolution Cone-Beam Computed Tomography Images of Softwood Logs
Document identifier: oai:DiVA.org:ltu-76189
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10.13073/FPJ-D-19-00008Keyword: Engineering and Technology,
Mechanical Engineering,
Other Mechanical Engineering,
Teknik och teknologier,
Maskinteknik,
Annan maskinteknik,
Träteknik,
Wood Science and EngineeringPublication year: 2019Relevant Sustainable Development Goals (SDGs):
The SDG label(s) above have been assigned by OSDG.aiAbstract: X-ray computed tomography (CT) scanning of sawmill logs is associated with costly and complex machines. An alternative scanning solution was developed, but its data have not been evaluated regarding detection of internal features. In this exploratory study, a knot detection algorithm was applied to images of four logs to evaluate its performance in terms of knot position and size. The results were a detection rate of 67 percent, accurate position, and inaccurate size. Although the sample size was small, it was concluded that automatic knot detection in coarse resolution CT images of softwoods is feasible, albeit for knots of sufficient size.
Authors
Magnus Fredriksson
Luleå tekniska universitet; Träteknik
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Julie Cool
Univ British Columbia, Dept Wood Sci, Fac Forestry, Vancouver, BC, Canada
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Avramidis Stavros
Univ British Columbia, Dept Wood Sci, Fac Forestry, Vancouver, BC, Canada. Forest Prod Soc, Peachtree Corners, GA USA
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>>
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header:
identifier: oai:DiVA.org:ltu-76189
datestamp: 2021-04-19T13:09:51Z
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recordCreationDate: 2019-10-01
identifier:
http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76189
10.13073/FPJ-D-19-00008
2-s2.0-85072637863
titleInfo:
@attributes:
lang: eng
title: Automatic Knot Detection in Coarse-Resolution Cone-Beam Computed Tomography Images of Softwood Logs
abstract: X-ray computed tomography (CT) scanning of sawmill logs is associated with costly and complex machines. An alternative scanning solution was developed but its data have not been evaluated regarding detection of internal features. In this exploratory study a knot detection algorithm was applied to images of four logs to evaluate its performance in terms of knot position and size. The results were a detection rate of 67 percent accurate position and inaccurate size. Although the sample size was small it was concluded that automatic knot detection in coarse resolution CT images of softwoods is feasible albeit for knots of sufficient size.
subject:
@attributes:
lang: eng
authority: uka.se
topic:
Engineering and Technology
Mechanical Engineering
Other Mechanical Engineering
@attributes:
lang: swe
authority: uka.se
topic:
Teknik och teknologier
Maskinteknik
Annan maskinteknik
@attributes:
lang: swe
authority: ltu
topic: Träteknik
genre: Research subject
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lang: eng
authority: ltu
topic: Wood Science and Engineering
genre: Research subject
language:
languageTerm: eng
genre:
publication/journal-article
ref
note:
Published
3
Validerad;2019;Nivå 2;2019-10-01 (johcin)
name:
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authority: ltu
namePart:
Fredriksson
Magnus
1984-
role:
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affiliation:
Luleå tekniska universitet
Träteknik
nameIdentifier:
magfre
0000-0003-4530-0536
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Cool
Julie
role:
roleTerm: aut
affiliation: Univ British Columbia Dept Wood Sci Fac Forestry Vancouver BC Canada
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Stavros
Avramidis
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roleTerm: aut
affiliation: Univ British Columbia Dept Wood Sci Fac Forestry Vancouver BC Canada. Forest Prod Soc Peachtree Corners GA USA
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dateIssued: 2019
publisher: Forest Products Society
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title: Forest products journal
identifier: 0015-7473
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type: volume
number: 69
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type: issue
number: 3
extent:
start: 185
end: 187
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