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DC poleHodnotaJazyk
dc.contributor.authorPointner, Andreas
dc.contributor.authorPraschl, Christoph
dc.contributor.authorKrauss, Oliver
dc.contributor.authorSchuler, Andreas
dc.contributor.authorHelm, Emmanuel
dc.contributor.authorZwettler, Gerald
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-08-31T05:29:37Z
dc.date.available2021-08-31T05:29:37Z
dc.date.issued2021
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 11-20.en
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45005
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectshlukovánícs
dc.subjectextrakce obrysůcs
dc.subjectstavební pláncs
dc.subjectzpracování obrazucs
dc.subjectstrojové učenícs
dc.titleLine Clustering and Contour Extraction in the Context of 2D Building Plansen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedFor the purpose of analyzing a building according to its accessibility or structural resilience, printed 2D floorplans are not sufficient because of the missing link to semantic information. This paper tackles this issue andintroduces a concept for clustering classified lines of a floor plan and for creating semantically enriched contourelements based on different image processing, computer vision and machine learning algorithms. Based on ageneral line clustering approach, we introduce type specific methods forwalls,windows,doorsandstairs. Theresulting clusters are in turn used for a contour creation, which uses minimal rotated rectangles. Those rectanglesare transformed to polygons that are refined using post processing steps. The approach is evaluated via positivetesting using a pixel-based comparison of the process’s result. For this, automatically generated as well as realworld building plans are used. The final evaluation shows, that the concept reaches a confidence of >90% for door,stair and windows and only around 10% for stairs with the run-time linearly scaling with the size of the input.en
dc.subject.translatedclusteringen
dc.subject.translatedcontour extractionen
dc.subject.translatedbuilding planen
dc.subject.translatedimage processingen
dc.subject.translatedmachine learningen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.2
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2021: Full Papers Proceedings

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