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A Calibration Method for the Generalized Imaging Model with Uncertain Calibration Target Coordinates

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Computer Vision – ACCV 2020 (ACCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12624))

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Abstract

The developments in optical metrology and computer vision require more and more advanced camera models. Their geometric calibration is of essential importance. Usually, low-dimensional models are used, which however often have insufficient accuracy for the respective applications. A more sophisticated approach uses the generalized camera model. Here, each pixel is described individually by its geometric ray properties. Our efforts in this article strive to improve this model. Hence, we propose a new approach for calibration. Moreover, we show how the immense number of parameters can be efficiently calculated and how the measurement uncertainties of reference features can be effectively utilized. We demonstrate the benefits of our method through an extensive evaluation of different cameras, namely a standard webcam and a microlens-based light field camera.

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Authors and Affiliations

  1. Institute of Industrial Information Technology, Karlsruhe Institute of Technology, Karlsruhe, Germany

    David Uhlig & Michael Heizmann

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  1. David Uhlig
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  2. Michael Heizmann
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Correspondence to David Uhlig .

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Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

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Uhlig, D., Heizmann, M. (2021). A Calibration Method for the Generalized Imaging Model with Uncertain Calibration Target Coordinates. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12624. Springer, Cham. https://doi.org/10.1007/978-3-030-69535-4_33

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  • DOI: https://doi.org/10.1007/978-3-030-69535-4_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69534-7

  • Online ISBN: 978-3-030-69535-4

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