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DC poleHodnotaJazyk
dc.contributor.authorKöhler, Alexander
dc.contributor.authorRigi, Ashkan
dc.contributor.authorBreuß, Michael
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-09-01T10:59:36Z
dc.date.available2022-09-01T10:59:36Z
dc.date.issued2022
dc.identifier.citationWSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 172-180.en
dc.identifier.isbn978-80-86943-33-6
dc.identifier.issn2464-4617
dc.identifier.urihttp://hdl.handle.net/11025/49592
dc.format9 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectstatistická analýza tvarucs
dc.subjectklasifikace tvarucs
dc.subjecttvarová podobnostcs
dc.subjectKolmogorov-Smirnovcs
dc.subjecttestování hypotézcs
dc.titleFast Shape Classification Using Kolmogorov-Smirnov Statisticsen
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe fast classification of shapes is an important problem in shape analysis and of high relevance for many possible applications. In this paper, we consider the use of very fast and easy to compute statistical techniques for assessing shapes, which may for instance be useful for a first similarity search in a shape database. To this end, we con- struct shape signatures at hand of stochastic sampling of distances between points of interest in a given shape. By employing the Kolmogorov-Smirnov statistics we then propose to formulate the problem of shape classification as a statistical hypothesis test that enables to assess the similarity of the signature distributions. In order to illus- trate some important properties of our approach, we explore the use of simple sampling techniques. At hand of experiments conducted with a variety of shapes in two dimensions, we give a discussion of potentially interesting features of the method.en
dc.subject.translatedstatistical shape analysisen
dc.subject.translatedshape classificationen
dc.subject.translatedshape similarityen
dc.subject.translatedKolmogorov-Smirnoven
dc.subject.translatedhypothesis testingen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3201.22
dc.type.statusPeer-revieweden
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