Title:	Fast Shape Classification Using Kolmogorov-Smirnov Statistics
Authors:	Köhler, Alexander Rigi, Ashkan Breuß, Michael
Citation:	WSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 172-180.
Issue Date:	2022
Publisher:	Václav Skala - UNION Agency
Document type:	conferenceObject
URI:	http://hdl.handle.net/11025/49592
ISBN:	978-80-86943-33-6
ISSN:	2464-4617
Keywords:	statistická analýza tvaru;klasifikace tvaru;tvarová podobnost;Kolmogorov-Smirnov;testování hypotéz
Keywords in different language:	statistical shape analysis;shape classification;shape similarity;Kolmogorov-Smirnov;hypothesis testing
Abstract in different language:	The 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.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2022: Full Papers Proceedings

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