• Thomas Bräunl
  • Stefan Feyrer
  • Wolfgang Rapf
  • Michael Reinhardt


In many image processing applications the enormous amount of image data causes problems in processing and storage. To achieve simplified and faster processing as well as reduced memory requirements, it is often useful to convert the original image data to a more compact representation. This conversion should remove as much of the redundant information as possible, but must preserve the basic structure of the digitized image. In the case of images that are predominantly line-based, e.g. text or line drawings, a number of methods exist that will extract so-called skeletons from the original images. These are called skeletonizing operators or thinning-operators.


Iteration Step Image Object Contour Point Neighbourhood Type Naive Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Thomas Bräunl
    • 1
  • Stefan Feyrer
    • 2
  • Wolfgang Rapf
    • 3
  • Michael Reinhardt
    • 4
  1. 1.Department of Electrical and Electronic EngineeringThe University of Western AustraliaNedlands, PerthAustralia
  2. 2.University of Tübingen, WSITübingenGermany
  3. 3.GAO mbHMünchenGermany
  4. 4.ProSieben Information Service GmbHUnterföhringGermany

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