License: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license (CC BY-NC-ND 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.VLUDS.2010.36
URN: urn:nbn:de:0030-drops-30951
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2011/3095/
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Köhler, Johannes ; Pagani, Alain ; Stricker, Didier

Detection and Identification Techniques for Markers Used in Computer Vision

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Abstract

This paper summarizes and compares techniques for detecting and identifying markers in the context of computer vision. Existing approaches either use correlation, digital or topological methods for marker identification. The comparison points out, that all marker processing algorithms which employ sophisticated digital codes perform more robust and reliable. Existing bit representation schemes for these codes and marker designs are compared with each other. In the overall context it is illustrated, why the marker processing algorithm is the best performer regarding marker occlusion and minimal detectable pattern size.

BibTeX - Entry

@InProceedings{khler_et_al:OASIcs:2011:3095,
  author =	{Johannes K{\"o}hler and Alain Pagani and Didier Stricker},
  title =	{{Detection and Identification Techniques for Markers Used in Computer Vision}},
  booktitle =	{Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop)},
  pages =	{36--44},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-29-3},
  ISSN =	{2190-6807},
  year =	{2011},
  volume =	{19},
  editor =	{Ariane Middel and Inga Scheler and Hans Hagen},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2011/3095},
  URN =		{urn:nbn:de:0030-drops-30951},
  doi =		{10.4230/OASIcs.VLUDS.2010.36},
  annote =	{Keywords: Marker Identification, Computer Vision}
}

Keywords: Marker Identification, Computer Vision
Collection: Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop)
Issue Date: 2011
Date of publication: 13.04.2011

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