<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>29849</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>172</pageFirst>
    <pageLast>180</pageLast>
    <pageNumber/>
    <edition/>
    <issue>CSRN 3201</issue>
    <volume/>
    <type>conferenceobject_ref</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-01-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Fast Shape Classification Using Kolmogorov-Smirnov Statistics</title>
    <abstract language="eng">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 construct 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 illustrate 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.</abstract>
    <parentTitle language="eng">WSCG'2022 - 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2022</parentTitle>
    <identifier type="doi">10.24132/CSRN.3201.22</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Alexander</firstName>
      <lastName>Köhler</lastName>
    </author>
    <submitter>
      <firstName>Annette</firstName>
      <lastName>Kallweit</lastName>
    </submitter>
    <author>
      <firstName>Ashkan</firstName>
      <lastName>Rigi</lastName>
    </author>
    <author>
      <firstName>Michael</firstName>
      <lastName>Breuß</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Statistical Shape Analysis; Shape Classification; Shape Similarity; Kolmogorov-Smirnov; Hypothesis Testing; Sampling Methods</value>
    </subject>
    <collection role="institutes" number="1303">FG Angewandte Mathematik</collection>
  </doc>
</export-example>
