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dc.contributor.authorKryl, Martin
dc.contributor.authorDanys, Lukáš
dc.contributor.authorJaroš, René
dc.contributor.authorMartinek, Pavel Radek
dc.contributor.authorKodytek, Pavel
dc.contributor.authorBilík, Petr
dc.date.accessioned2021-02-26T12:03:16Z
dc.date.available2021-02-26T12:03:16Z
dc.date.issued2020
dc.identifier.citationJournal of Sensors. 2020, vol. 2020, art. no. 3217126.cs
dc.identifier.issn1687-725X
dc.identifier.issn1687-7268
dc.identifier.urihttp://hdl.handle.net/10084/142889
dc.description.abstractForestry is an undoubtedly crucial part of today's industry; thus, automation of certain visual tasks could lead to a significant increase in productivity and reduction of labor costs. Eye fatigue or lack of attention during manual visual inspections can lead to falsely categorized wood, thus leading to major loss of earnings. These mistakes could be eliminated using automated vision inspection systems. This article focuses on the comparison of researched methodologies related to wood type classification and wood defect detection/identification; hence, readers with an intention of building a similar vision-based system have summarized review to build upon.cs
dc.language.isoencs
dc.publisherHindawics
dc.relation.ispartofseriesJournal of Sensorscs
dc.relation.urihttp://doi.org/10.1155/2020/3217126cs
dc.rightsCopyright © 2020 Martin Kryl et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.titleWood recognition and quality imaging inspection systemscs
dc.typearticlecs
dc.identifier.doi10.1155/2020/3217126
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume2020cs
dc.description.firstpageart. no. 3217126cs
dc.identifier.wos000576138100002


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Copyright © 2020 Martin Kryl et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as Copyright © 2020 Martin Kryl et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.