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Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

DC Field Value Language
dc.contributor.authorHwang, Sung-Wook-
dc.contributor.authorSugiyama, Junji-
dc.date.accessioned2021-07-15T06:12:40Z-
dc.date.available2021-07-15T15:35:34Z-
dc.date.issued2021-04-28-
dc.identifier.citationPlant Methods. 2021 Apr 28;17(1):47ko_KR
dc.identifier.issn1746-4811-
dc.identifier.urihttps://hdl.handle.net/10371/174711-
dc.description.abstractThe remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.ko_KR
dc.description.sponsorshipThis study was supported by Grants-in-Aid for Scientifc Research (Grant Number H1805485) from the Japan Society for the Promotion of Science.ko_KR
dc.language.isoenko_KR
dc.publisherBMCko_KR
dc.subjectConvolutional neural networks-
dc.subjectComputer vision-
dc.subjectDeep learning-
dc.subjectImage recognition-
dc.subjectMachine learning-
dc.subjectWood identifcation-
dc.subjectWood anatomy-
dc.titleComputer vision-based wood identification and its expansion and contribution potentials in wood science: A reviewko_KR
dc.typeArticleko_KR
dc.contributor.AlternativeAuthor황성욱-
dc.identifier.doi10.1186/s13007-021-00746-1-
dc.citation.journaltitlePlant Methodsko_KR
dc.language.rfc3066en-
dc.rights.holderThe Author(s)-
dc.date.updated2021-05-02T04:10:13Z-
dc.citation.number1ko_KR
dc.citation.startpage47ko_KR
dc.citation.volume17ko_KR
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