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International Workshop on Combinatorial Image Analysis

IWCIA 2004: Combinatorial Image Analysis pp 534–547Cite as

Comparison of Nonparametric Transformations and Bit Vector Matching for Stereo Correlation

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3322))

Abstract

The paper describes and compares stereo matching methods based on nonparametric image transformations. The new nonparametric measures for local neighborhoods of pixels are proposed as well. These are extensions to the well known Census transformation, successively used in many computer vision tasks. The resulting bit-fields are matched with the binary vectors comparison measures: Hamming, Tanimoto and Dixon-Koehler. The presented algorithms require only integer arithmetic what makes them very useful for real-time applications and hardware implementations. Many experiments with the presented techniques, employed to the stereovision, showed their robustness and competing execution times.

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Author information

Authors and Affiliations

  1. AGH – University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Bogusław Cyganek

Authors
  1. Bogusław Cyganek
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Editor information

Editors and Affiliations

  1. Department of Computer Science, The University of Auckland, New Zealand

    Reinhard Klette

  2. Computer Science, Exeter University, Harrison Building, EX4 4QF, Exeter, U.K.

    Joviša Žunić

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© 2004 Springer-Verlag Berlin Heidelberg

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Cyganek, B. (2004). Comparison of Nonparametric Transformations and Bit Vector Matching for Stereo Correlation. In: Klette, R., Žunić, J. (eds) Combinatorial Image Analysis. IWCIA 2004. Lecture Notes in Computer Science, vol 3322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30503-3_39

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  • DOI: https://doi.org/10.1007/978-3-540-30503-3_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23942-0

  • Online ISBN: 978-3-540-30503-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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