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A Camera Calibration Method in High-Precision Vision Measuring System

  • Wenchuan An
  • Zhongwen Gao
  • Xingang Wang
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 128)

Abstract

This paper presents a novel camera calibration method used in high-precision machine-vision-based measuring platform. This method depends on the high precision of the motion platform. Firstly we simplified the camera model to get the relation between the real-world coordinates and the pixel coordinates; secondly we move the motion platform to change the outer parameters of the camera precisely; then we detect the corresponding motion in pixel plane and get the calibration data using the simplified camera model. According to the experiment, the method’s accuracy is acceptable and has high utility value.

Keywords

Camera Calibration Camera Model RANdom Sample Consensus Microscope Lens Pixel Coordinate 
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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References

  1. 1.
    Hemayed, E.E.: A survey of camera self-calibration. In: 2003 IEEE International Conference on Advanced Video and Signal Based Surveillance, p. 351 (2003)Google Scholar
  2. 2.
    Clarke, T.A., Fryer, J.G.: The development of camera calibration methods and models. The Photogrammetric Record 16(91), 51–66 (1998)CrossRefGoogle Scholar
  3. 3.
    Forsyth, D.A., Ponce, J.: Computer Vision, A Modern Approach. Prentice-Hall (2003)Google Scholar
  4. 4.
    Brown, M., Lowe, D.G.: Automatic Panoramic Image Stitching using Invariant Features. International Journal of Computer Vision 74, 59–73 (2007)CrossRefGoogle Scholar
  5. 5.
    Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-Up Robust Features. Computer Vision and Image Understanding 110(3), 346–359 (2008)CrossRefGoogle Scholar
  6. 6.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of AutomationHarbin University of Science and TechnologyHarbingChina
  2. 2.High-tech Innovation Center, Institute of AutomationChinese Academy of SciencesBeijingChina

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