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dc.contributor.authorGosciewska, Katarzyna
dc.contributor.authorFrejlichowski, Dariusz
dc.contributor.editorSkala, Václav
dc.date.accessioned2020-07-27T08:14:39Z
dc.date.available2020-07-27T08:14:39Z
dc.date.issued2020
dc.identifier.citationWSCG 2020: full papers proceedings: 28th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 11-18.en
dc.identifier.isbn978-80-86943-35-0
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD-ROM)
dc.identifier.urihttp://wscg.zcu.cz/WSCG2020/2020-CSRN-3001.pdf
dc.identifier.urihttp://hdl.handle.net/11025/38446
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2020: full papers proceedingsen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectoddělení zubůcs
dc.subjecthrubá klasifikacecs
dc.subjectdeskriptory tvarucs
dc.subjectredukce datcs
dc.subjectrentgenové snímkycs
dc.titleCoarse Classification of Teeth using Shape Descriptorsen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper presents the problem of coarse classification in an application to teeth shapes. Coarse classification allows to separate a set of objects into several general classes and can precede more detailed identification or narrow the search space. Features of an object are mainly determined by its geometrical aspects, therefore we investigate the use of shape description algorithms, namely the Two-Dimensional Fourier Descriptor, UNL-Fourier Descriptor, Generic Fourier Descriptor, Curvature Scale Space, Zernike Moments and Point Distance Histogram. During the experiments we examine the accuracy of classification into two classes: single-rooted teeth and multi-rooted teeth—each class has five representatives. We also employ an additional step of data reduction. Reduced representations are obtained in three ways: by taking a part of an original representation, by predefining a shape description algorithm parameter or by applying an additional step of data reduction technique, i.e. the Principal Component Analysis or Linear Discriminant Analysis. Euclidean distance is used to match final feature vectors with class representatives in order to indicate the most similar one. The experimental results proved the effectiveness of the proposed approach.en
dc.subject.translatedteeth separationen
dc.subject.translatedcoarse classificationen
dc.subject.translatedshape descriptorsen
dc.subject.translateddata reductionen
dc.subject.translateddental radiographsen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2020.3001.2
dc.type.statusPeer-revieweden
Appears in Collections:WSCG 2020: Full Papers Proceedings

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