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Stereo Matching

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Part of the Undergraduate Topics in Computer Science book series (UTICS)

Abstract

This chapter discusses the search for corresponding pixels in a pair of stereo images. We consider at first correspondence search as a labelling problem, defined by data and smoothness error functions, but also by the applied control structure. We describe belief-propagation stereo and semi-global matching. Finally we also discuss how to evaluate the accuracy of stereo-matching results on real-world input data, particularly on stereo video data.

Keywords

  • Stereo Match
  • Stereo Pair
  • Epipolar Line
  • Smoothness Constraint
  • Smoothness Term

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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  • DOI: 10.1007/978-1-4471-6320-6_8
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Notes

  1. 1.

    This notation is short for \(W_{p}^{2l+1,2k+1}(I)\), the notation used in Sect. 1.1.1.

  2. 2.

    See [H.S. Warren. Hacker’s Delight, pp. 65–72, Addison-Wesley Longman, New York, 2002].

  3. 3.

    Published in [S. Hermann and R. Klette. Iterative semi-global matching for robust driver assistance systems. In Proc. Asian Conf. Computer Vision 2012, LNCS 7726, pp. 465–478, 2012]; this stereo matcher was awarded the Robust Vision Challenge at the European Conference on Computer Vision in 2012.

  4. 4.

    See [S. Morales and R. Klette. A third eye for performance evaluation in stereo sequence analysis. In Proc. Computer Analysis Images Patterns, LNCS 5702, pp. 1078–1086, 2009].

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Klette, R. (2014). Stereo Matching. In: Concise Computer Vision. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-6320-6_8

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  • DOI: https://doi.org/10.1007/978-1-4471-6320-6_8

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6319-0

  • Online ISBN: 978-1-4471-6320-6

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