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dc.contributor.authorStraková, Erika
dc.contributor.authorLukáš, Dalibor
dc.contributor.authorBobovský, Zdenko
dc.contributor.authorKot, Tomáš
dc.contributor.authorMihola, Milan
dc.contributor.authorNovák, Petr
dc.date.accessioned2021-05-13T07:56:56Z
dc.date.available2021-05-13T07:56:56Z
dc.date.issued2021
dc.identifier.citationApplied Sciences. 2021, vol. 11, issue 5, art. no. 2268.cs
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10084/143067
dc.description.abstractWhile repairing industrial machines or vehicles, recognition of components is a critical and time-consuming task for a human. In this paper, we propose to automatize this task. We start with a Principal Component Analysis (PCA), which fits the scanned point cloud with an ellipsoid by computing the eigenvalues and eigenvectors of a 3-by-3 covariant matrix. In case there is a dominant eigenvalue, the point cloud is decomposed into two clusters to which the PCA is applied recursively. In case the matching is not unique, we continue to distinguish among several candidates. We decompose the point cloud into planar and cylindrical primitives and assign mutual features such as distance or angle to them. Finally, we refine the matching by comparing the matrices of mutual features of the primitives. This is a more computationally demanding but very robust method. We demonstrate the efficiency and robustness of the proposed methodology on a collection of 29 real scans and a database of 389 STL (Standard Triangle Language) models. As many as 27 scans are uniquely matched to their counterparts from the database, while in the remaining two cases, there is only one additional candidate besides the correct model. The overall computational time is about 10 min in MATLAB.cs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesApplied Sciencescs
dc.relation.urihttps://doi.org/10.3390/app11052268cs
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectprinciple component analysiscs
dc.subjectpoint cloudscs
dc.subject3-dimensional object recognitioncs
dc.titleMatching point clouds with STL models by using the principle component analysis and a decomposition into geometric primitivescs
dc.typearticlecs
dc.identifier.doi10.3390/app11052268
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume11cs
dc.description.issue5cs
dc.description.firstpageart. no. 2268cs
dc.identifier.wos000627986900001


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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.