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European Conference on Computer Vision

ECCV 1996: Computer Vision — ECCV '96 pp 1–16Cite as

Self-calibration from image triplets

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1064)

Abstract

We describe a method for determining affine and metric calibration of a camera with unchanging internal parameters undergoing planar motion. It is shown that affine calibration is recovered uniquely, and metric calibration up to a two fold ambiguity.

The novel aspects of this work are: first, relating the distinguished objects of 3D Euclidean geometry to fixed entities in the image; second, showing that these fixed entities can be computed uniquely via the trifocal tensor between image triplets; third, a robust and automatic implementation of the method.

Results are included of affine and metric calibration and structure recovery using images of real scenes.

Keywords

  • Screw Axis
  • Camera Centre
  • Absolute Conic
  • Affine Structure
  • Horizon Line

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Robotics Research Group, Department of Engineering Science, Oxford University, England

    Martin Armstrong & Andrew Zisserman

  2. The General Electric Corporate Research and Development Laboratory, Schenectady, NY, USA

    Richard Hartley

Authors
  1. Martin Armstrong
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  2. Andrew Zisserman
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  3. Richard Hartley
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    © 1996 Springer-Verlag Berlin Heidelberg

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    Armstrong, M., Zisserman, A., Hartley, R. (1996). Self-calibration from image triplets. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015519

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    • DOI: https://doi.org/10.1007/BFb0015519

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    • Publisher Name: Springer, Berlin, Heidelberg

    • Print ISBN: 978-3-540-61122-6

    • Online ISBN: 978-3-540-49949-7

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