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A New Technique for Local Symmetry Estimation

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Scale Space and PDE Methods in Computer Vision (Scale-Space 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3459))

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Abstract

This paper introduces a new approach to symmetry estimation directly from image grey values. The method was inspired by the local phase based method, proposed by Kovesi. This method is examined in the light of a strict definition of local symmetry, and found to be wanting in two respects: that it is invariant to some apparently non-trivial symmetries and that its scale is ill defined. To avoid these difficulties we propose a non-linear analog of the local phase. This leads to a family of local symmetry measures, able to provide a rich, local characterisation of shape, invariant to rotations and affine intensity transformations, and robust to affine coordinate transformations.

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Author information

Authors and Affiliations

  1. Medical Vision Laboratory, University of Oxford, UK

    Matthew Mellor & Michael Brady

Authors
  1. Matthew Mellor
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  2. Michael Brady
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Technion – Israel Institute of Technology, 32000, Haifa, Israel

    Ron Kimmel

  2. School of Mathematical Sciences, Tel Aviv University, 69978, Tel Aviv, Israel

    Nir A. Sochen

  3. Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Saarland University, Building E1.1, 66041, Saarbrücken, Germany

    Joachim Weickert

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© 2005 Springer-Verlag Berlin Heidelberg

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Mellor, M., Brady, M. (2005). A New Technique for Local Symmetry Estimation. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds) Scale Space and PDE Methods in Computer Vision. Scale-Space 2005. Lecture Notes in Computer Science, vol 3459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408031_4

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  • DOI: https://doi.org/10.1007/11408031_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25547-5

  • Online ISBN: 978-3-540-32012-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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