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On the Second Order Statistics of Essential Matrix Elements

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Pattern Recognition (GCPR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8753))

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Abstract

In this paper, we investigate the second order statistics of essential matrix elements. Using the Taylor expansion for a rotation matrix up to second order terms and considering relatively high uncertainties for the rotation angles and translation parameters, a covariance matrix is obtained which includes the second order statistics of essential matrix elements. The covariance matrix is utilized along with the coplanarity equations and acts as a regularization term. Using the regularization term brings considerable improvements in the recovery of camera motion which will be proven based on simulation and different real image sequences.

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Authors and Affiliations

  1. GET Lab, University of Paderborn, Pohlwegstr 47-49, 33098, Paderborn, Germany

    M. Hossein Mirabdollah & Bärbel Mertsching

Authors
  1. M. Hossein Mirabdollah
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  2. Bärbel Mertsching
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Corresponding author

Correspondence to M. Hossein Mirabdollah .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Münster, Germany

    Xiaoyi Jiang

  2. Computer Science Department 5, University of Erlangen-Nürnberg, Erlangen, Germany

    Joachim Hornegger

  3. Department of Computer Science, University of Kiel, Kiel, Germany

    Reinhard Koch

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Mirabdollah, M.H., Mertsching, B. (2014). On the Second Order Statistics of Essential Matrix Elements. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_45

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  • DOI: https://doi.org/10.1007/978-3-319-11752-2_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11751-5

  • Online ISBN: 978-3-319-11752-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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