Camera Calibration from a Single Frame of Planar Pattern

  • Jianhua Wang
  • Fanhuai Shi
  • Jing Zhang
  • Yuncai Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)


A method to calibrate camera from a single frame of planar pattern is presented in this paper. For a camera model with four intrinsic parameters and visible lens distortion, the principal point and distortion coefficients are firstly determined through analysis of the distortion in an image. The distortion is then removed. Finally, the other intrinsic and extrinsic parameters of the camera are obtained through direct linear transform followed by bundle adjustment. Theoretically, the method makes it possible to analyze the calibration result at the level of a single frame. Practically, such a method provides a easy way to calibrate a camera used in industrial vision system on line and used in desktop vision system. Experimental results of both simulated data and real images validate the method.


Camera Calibration Intrinsic Parameter Translational Vector Calibration Result Single Frame 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brown, D.C.: Close-Range Camera Calibration. Photogrammetric Eng. 37(8) (1971)Google Scholar
  2. 2.
    Faig, W.: Calibration of Close-Range Photogrammetry Systems: Mathematical Formulation. Photogrammetric Eng. and Remote Sensing 41(12) (1975)Google Scholar
  3. 3.
    Tsai, R.Y.: A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses. IEEE J. Robotics and Automation 3(4), 323–344 (1987)CrossRefGoogle Scholar
  4. 4.
    Caprile, B., Torre, V.: Using Vanishing Points for Camera Calibration. Int. J. Computer Vision 4(2), 127–140 (1990)CrossRefGoogle Scholar
  5. 5.
    Weng, J., Cohen, P., Herniou, M.: Camera Calibration with Distortion Models and Accuracy Evaluation. IEEE Trans. Pattern Analysis and Machine Intelligence 14(10), 965–980 (1992)CrossRefGoogle Scholar
  6. 6.
    Faugeras, O., Luong, T., Maybank, S.: Camera Self-Calibration: Theory and Experiments. In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588, pp. 321–334. Springer, Heidelberg (1992)Google Scholar
  7. 7.
    Wei, G.Q., Ma, S.D.: A Complete Two-Plane Camera Calibration Method and Experimental Comparisons. In: Proc. Fourth Intl. Conf. Computer Vision, May 1993, pp. 439–446 (1993)Google Scholar
  8. 8.
    Willson, R.G., Shafer, S.A.: What is the center of the image? Technical Report CMU-CS-93-122, Carnegie Mellon University (1993)Google Scholar
  9. 9.
    Clarke, T.A., Fryer, J.G.: The Development of Camera Calibration Methods and Models. Photogrammetric Record 16(91), 51–66 (1998)CrossRefGoogle Scholar
  10. 10.
    Triggs, B.: Autocalibration from Planar Scenes. In: Proc. Fifth European Conf. Computer Vision, June 1998, pp. 89–105 (1998)Google Scholar
  11. 11.
    Liebowitz, D., Zisserman, A.: Metric Rectification for Perspective Images of Planes. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 1998, pp. 482–488 (1998)Google Scholar
  12. 12.
    Shimizu, I., Zhang, Z., Akamatsu, S., Deguchi, K.: Head pose determination from one image using ageneric model. In: Int’l. Conf. on Automatic Face and Gesture Recognition (1998)Google Scholar
  13. 13.
    Sturm, P., Maybank, S.: On Plane-Based Camera Calibration: A General Algorithm,Singularities, Applications. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 1999, pp. 432–437 (1999)Google Scholar
  14. 14.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, Cambridge (2000)Google Scholar
  15. 15.
    Zhang, Z.: A Flexible New Technique for Camera Calibration. IEEE Trans. Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)CrossRefGoogle Scholar
  16. 16.
    Devernay, F., Faugeras, O.: Straight lines have to be straight. Machine Vision and Applications 13, 14–24 (2001)CrossRefGoogle Scholar
  17. 17.
    Heyden, A., Pollefeys, M.: Multiple View Geometry, Emerging Topics in Computer Vision. In: Medioni, G., Kang, S.B. (eds.) Prentice Hall, Englewood Cliffs (2003)Google Scholar
  18. 18.
    McGlone, C., Mikhail, E., Bethel, J.: Manual of Photogrammetry, 5th edn. (2004)Google Scholar
  19. 19.
    Zhang, Z.: Camera calibration with onedimensional objects. IEEE Trans. Pattern Analysis and Machine Intelligence 26(7), 892–899 (2004)CrossRefGoogle Scholar
  20. 20.
    Hartley, R.I., Kang, S.B.: Parameter-free radial distortion correction with centre of distortion estimation. In: ICCV (2005)Google Scholar
  21. 21.
    Basu, A., Licardio, S.: Alternative models for fish-eye lenses. Pattern Recognition Letters 16, 433–441 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jianhua Wang
    • 1
  • Fanhuai Shi
    • 1
  • Jing Zhang
    • 1
  • Yuncai Liu
    • 1
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiP.R. China

Personalised recommendations