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Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy

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Abstract

In this paper, we present a simple and robust method for self-correction of camera distortion using single images of scenes which contain straight lines. Since the most common distortion can be modelled as radial distortion, we illustrate the method using the Harris radial distortion model, but the method is applicable to any distortion model. The method is based on transforming the edgels of the distorted image to a 1-D angular Hough space, and optimizing the distortion correction parameters which minimize the entropy of the corresponding normalized histogram. Properly corrected imagery will have fewer curved lines, and therefore less spread in Hough space. Since the method does not rely on any image structure beyond the existence of edgels sharing some common orientations and does not use edge fitting, it is applicable to a wide variety of image types. For instance, it can be applied equally well to images of texture with weak but dominant orientations, or images with strong vanishing points. Finally, the method is performed on both synthetic and real data revealing that it is particularly robust to noise.

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References

  1. Barreto, J.P., Daniilidis, K.: Fundamental matrix for cameras with radial distortion. In: 10th IEEE International Conference on Computer Vision, vol. 1, pp. 625–632. Springer, Beijing (2005)

  2. Beyer, H.: Accurate calibration of ccd-cameras. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. pp. 96–101 (1992)

  3. Bräuer-Burchardt, C., Voss, K.: Automatic correction of weak radial lens distortion in single views of urban scenes using vanishing points. In: 9th International Conference on Image Processing, vol. 3, pp. 865–868. IEEE Computer Society (2002)

  4. Brown D.C.: Close-range camera calibration. Photogramm. Eng. 37, 855–866 (1971)

    Google Scholar 

  5. Claus. D., Fitzgibbon, A.: A rational function lens distortion model for general cameras. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 213–219 (2005)

  6. Claus, D., Fitzgibbon, A.W.: A plumbline constraint for the rational function lens distortion model. In: Proceedings of the British Machine Vision Conference, pp. 99–108. British Machine Vision Assosciation, Oxford (2005)

  7. Devernay, F., Faugeras, O.: Straight lines have to be straight. Mach. Vis. Appl. 13(1) (2001)

  8. Drummond T., Cipolla R.: Real-time visual tracking of complex structures. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 932–946 (2002)

    Article  Google Scholar 

  9. Duda R.O., Hart P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)

    Article  Google Scholar 

  10. Eade, E., et al.: Camera calibration in [20]: progs/calib.cxx (2008, CVS tag SNAPSHOT_20080904)

  11. Faird H., Popescu A.C.: Blind removal of lens distortion. J. Opt. Soc. Am. A 18(9), 2072–2078 (2001)

    Article  Google Scholar 

  12. Harris, C., Stennett, C.: RAPID, a video rate object tracker. In: 1st British Machine Vision Conference, pp. 73–77. British Machine Vision Assosciation, Oxford (1990)

  13. Heikkila J.: Geometric camera calibration using circular control points. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1066–1077 (2000)

    Article  MathSciNet  Google Scholar 

  14. Heikkilä, J., Silvén, O.: A four-step camera calibration procedure with implicit image correction. In: 11th IEEE Conference on Computer Vision and Pattern Recognition, pp. 1106–1112. Springer, San Juan, Puerto Rico (1997)

  15. Lee M., Medioni G.: Grouping \({\cdot, -, \rightarrow, \bigcirc\hspace{-1ex}-}\) , into regions, curves and junctions. Int. J. Comput. Vis. Image Understand. 76(1), 54–69 (1999)

    Article  Google Scholar 

  16. Lenz, R., Tsai, R.: Techniques for calibration of the scale factor and image center for high accuracy 3-D machine vision metrology. IEEE Trans. Pattern Anal. Mach. Intell. 10(5), 713–720

  17. Nelder, J., Mead, R.: Computer Journal 7, 308–313 (1965)

    Google Scholar 

  18. Press W.H., Teukolsky S.A., Vetterling W.H., Flannery B.P.: Numerical Recipes in C. Cambridge University Press, Cambridge (1999)

    Google Scholar 

  19. Rosten, E., Cox, S.: Accurate extraction of reciprocal space information from transmission electron microscopy images. In: Advances in Visual Computing. LNCS 4292, vol. 1, pp. 373–382 (2006)

  20. Rosten, E., Drummond, T., Eade, E., Reitmayr, G., Smith, P., Kemp, C., Georg, K., et al.: libCVD: Cambridge vision dynamics library (2008). http://savannah.nongnu.org/projects/libcvd

  21. Sawhney H.S., Kumar R.: True multi-image alignment and its application to mosaicing and lens distortion correction. IEEE Trans. Pattern Anal. Mach. Intell. 21(3), 235–243 (1999)

    Article  Google Scholar 

  22. Shannon, C.M.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948)

    Google Scholar 

  23. Stein, G.: Accurate internal camera calibration using rotation, with analysis of sources of error. In: 5th IEEE International Conference on Computer Vision, p. 230. Springer, Boston (1995)

  24. Stein, G.: Lens distortion calibration using point correspondences. In: 10th IEEE Conference on Computer Vision and Pattern Recognition, p. 602. Springer, San Fransisco (1996)

  25. Strand, R., Hayman, E.: Correcting radial distortion by circle fitting. In: 16th British Machine Vision Conference. British Machine Vision Assosciation, Oxford (2005)

  26. Swaminathan R., Nayar S.: Nonmetric calibration of wide-angle lenses and polycameras. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1172–1178 (2000)

    Article  Google Scholar 

  27. Tordoff B., Murray D.W.: The impact of radial distortion on the self-calibration of rotating cameras. Comput. Vis. Image Understand. 96(1), 17–34 (2004)

    Article  Google Scholar 

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Authors and Affiliations

  1. Los Alamos National Laboratory, Los Alamos, NM, USA

    Edward Rosten & Rohan Loveland

  2. Department of Engineering, University of Cambridge, Cambridge, UK

    Edward Rosten

  3. Department of Engineering Science, University of Oxford, Oxford, UK

    Rohan Loveland

Authors
  1. Edward Rosten
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  2. Rohan Loveland
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Correspondence to Edward Rosten.

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Rosten, E., Loveland, R. Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy. Machine Vision and Applications 22, 77–85 (2011). https://doi.org/10.1007/s00138-009-0196-9

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  • DOI: https://doi.org/10.1007/s00138-009-0196-9

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