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Multi-Image Analysis pp 105–114Cite as

Matching Multiple Views by the Least Square Correlation

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2032))

Abstract

We consider the potentialities of matching multiple views of a 3D scene by the least square correlation provided that relative projective geometric distortions of the images are affinely approximated. The affine transformation yielding the (sub)optimal match is obtained by combining an exhaustive and directed search in the parameter space. The directed search is performed by a proposed modification of the Hooke-Jeeves unconstrained optimization. Experiments with the RADIUS multipleview images of a model board show a feasibility of this approach.

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References

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Author information

Authors and Affiliations

  1. Centre for Image Technology and Robotics, and Department of Computer Science, University of Auckland, Tamaki Campus Private Bag 92019, Auckland 1, New Zealand

    Georgy Gimel’farb & Jian Zhong

Authors
  1. Georgy Gimel’farb
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  2. Jian Zhong
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Editor information

Editors and Affiliations

  1. Center for image Technology and Robotics (CITR Tamaki), Auckland, New Zealand

    Reinhard Klette  & Georgy Gimel’farb  & 

  2. 2039 Beckman Institute, University of Illinois, 405 N. Mathews, Urbana, IL, 61801, USA

    Thomas Huang

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© 2001 Springer-Verlag Berlin Heidelberg

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Gimel’farb, G., Zhong, J. (2001). Matching Multiple Views by the Least Square Correlation. In: Klette, R., Gimel’farb, G., Huang, T. (eds) Multi-Image Analysis. Lecture Notes in Computer Science, vol 2032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45134-X_8

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  • DOI: https://doi.org/10.1007/3-540-45134-X_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42122-1

  • Online ISBN: 978-3-540-45134-1

  • eBook Packages: Springer Book Archive

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