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Local appropriate scale in morphological scale-space

  • Conference paper
  • First Online: 01 January 2005
  • pp 219–228
  • Cite this conference paper
Computer Vision — ECCV '96 (ECCV 1996)
Local appropriate scale in morphological scale-space
  • Ullrich Köthe1 

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1064))

Included in the following conference series:

  • European Conference on Computer Vision
  • 334 Accesses

  • 11 Citations

Abstract

This paper discusses the problem of selecting appropriate scales for region detection prior to feature localization. We argue that an approach in morphological opening-closing scale-space is better than one in Gaussian scale-space. The proposed operator is based on a new shape decomposition method called morphological band-pass filter that decomposes an image into structures of different size and different curvature polarity. Local appropriate scale is then defined as the scale that maximizes the response of the band-pass filter at each point. This operator gives constant scale values in a region of constant width, and its zero-crossings coincide with local maxima of the gradient magnitudes. Its usefulness is demonstrated by some examples.

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Author information

Authors and Affiliations

  1. Fraunhofer Institute for Computer Graphics Rostock, Joachim-Jungius-Str. 9, 18059, Rostock, Germany

    Ullrich Köthe

Authors
  1. Ullrich Köthe
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Editor information

Bernard Buxton Roberto Cipolla

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© 1996 Springer-Verlag Berlin Heidelberg

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Cite this paper

Köthe, U. (1996). Local appropriate scale in morphological scale-space. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015538

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  • DOI: https://doi.org/10.1007/BFb0015538

  • Published: 09 June 2005

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61122-6

  • Online ISBN: 978-3-540-49949-7

  • eBook Packages: Springer Book Archive

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Keywords

  • Local Extremum
  • Constant Width
  • Gradient Magnitude
  • Fraunhofer Institute
  • Morphological Decomposition

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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