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Image Features and the 1-D, 2nd Order Gaussian Derivative Jet

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

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Abstract

We review a previously presented proposal – Geometric Texton Theory (GTT) – that feature categories naturally arise through consideration of the maximum likelihood explanations for image measurements by gaussian derivative filters. We present results relevant to this proposal for the case of 1-D measurement by filters of 0th, 1st and 2nd order. The results are consistent with GTT.

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Author information

Authors and Affiliations

  1. Computer Science, University College London, UK

    Lewis D Griffin

  2. IT University of Copenhagen, Denmark

    Martin Lillholm

Authors
  1. Lewis D Griffin
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  2. Martin Lillholm
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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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Griffin, L.D., Lillholm, M. (2005). Image Features and the 1-D, 2nd Order Gaussian Derivative Jet. 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_3

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  • DOI: https://doi.org/10.1007/11408031_3

  • 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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