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Joint Pattern Recognition Symposium

DAGM 2002: Pattern Recognition pp 379–386Cite as

Dense Parameter Fields from Total Least Squares

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2449))

Abstract

A method for the interpolation of parameter fields estimated by total least squares is presented. This is applied to the study of dynamic processes where the motion and further values such as divergence or brightness changes are parameterised in a partial differential equation. For the regularisation we introduce a constraint that restricts the solution only in the subspace determined by the total least squares procedure. The performance is illustrated on both synthetic and real test data.

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References

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Author information

Authors and Affiliations

  1. ICG-III: Phytosphere, Research Center Jülich, 52425, Jülich, Germany

    Hagen Spies

  2. Interdisciplinary Center for Scientific Computing, University of Heidelberg, INF 368, 69120, Heidelberg, Germany

    Christoph S. Garbe

Authors
  1. Hagen Spies
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  2. Christoph S. Garbe
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Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, ETH Zürich, Gloriastrasse 35, 8092, Zürich, Switzerland

    Luc Van Gool

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© 2002 Springer-Verlag Berlin Heidelberg

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Spies, H., Garbe, C.S. (2002). Dense Parameter Fields from Total Least Squares. In: Van Gool, L. (eds) Pattern Recognition. DAGM 2002. Lecture Notes in Computer Science, vol 2449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45783-6_46

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  • DOI: https://doi.org/10.1007/3-540-45783-6_46

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44209-7

  • Online ISBN: 978-3-540-45783-1

  • eBook Packages: Springer Book Archive

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