OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Franco Gori
  • Vol. 29, Iss. 10 — Oct. 1, 2012
  • pp: 2134–2143

High-precision camera distortion measurements with a “calibration harp”

Zhongwei Tang, Rafael Grompone von Gioi, Pascal Monasse, and Jean-Michel Morel  »View Author Affiliations


JOSA A, Vol. 29, Issue 10, pp. 2134-2143 (2012)
http://dx.doi.org/10.1364/JOSAA.29.002134


View Full Text Article

Enhanced HTML    Acrobat PDF (971 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

This paper addresses the high-precision measurement of the distortion of a digital camera from photographs. Traditionally, this distortion is measured from photographs of a flat pattern that contains aligned elements. Nevertheless, it is nearly impossible to fabricate a very flat pattern and to validate its flatness. This fact limits the attainable measurable precisions. In contrast, it is much easier to obtain physically very precise straight lines by tightly stretching good quality strings on a frame. Taking literally “plumb-line methods,” we built a “calibration harp” instead of the classic flat patterns to obtain a high-precision measurement tool, demonstrably reaching 2 / 100 pixel precisions. The harp is complemented with the algorithms computing automatically from harp photographs two different and complementary lens distortion measurements. The precision of the method is evaluated on images corrected by state-of-the-art distortion correction algorithms, and by popular software. Three applications are shown: first an objective and reliable measurement of the result of any distortion correction. Second, the harp permits us to control state-of-the art global camera calibration algorithms: it permits us to select the right distortion model, thus avoiding internal compensation errors inherent to these methods. Third, the method replaces manual procedures in other distortion correction methods, makes them fully automatic, and increases their reliability and precision.

© 2012 Optical Society of America

OCIS Codes
(150.1488) Machine vision : Calibration
(100.3008) Image processing : Image recognition, algorithms and filters

ToC Category:
Machine Vision

History
Original Manuscript: April 5, 2012
Revised Manuscript: June 26, 2012
Manuscript Accepted: August 6, 2012
Published: September 18, 2012

Citation
Zhongwei Tang, Rafael Grompone von Gioi, Pascal Monasse, and Jean-Michel Morel, "High-precision camera distortion measurements with a “calibration harp”," J. Opt. Soc. Am. A 29, 2134-2143 (2012)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-29-10-2134


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. C. Slama, Manual of Photogrammetry, 4th ed. (American Society of Photogrammetry, 1980).
  2. R. Tsai, “A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses,” IEEE J. Robot. Autom. 3, 323–344 (1987). [CrossRef]
  3. Z. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000). [CrossRef]
  4. J. Lavest, M. Viala, and M. Dhome, “Do we really need accurate calibration pattern to achieve a reliable camera calibration?” in Computer Vision—ECCV’98, Vol. 1408 of Lecture Notes in Computer Science (Springer, 1998), pp. 158–174.
  5. M. H. J. Weng and P. Cohen, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992). [CrossRef]
  6. Z. Tang, “Calibration de caméra à haute précision,” Ph.D. dissertation (Ecole Normale Supérieure de Cachan, 2011).
  7. R. Grompone von Gioi, P. Monasse, J.-M. Morel, and Z. Tang, “Towards high-precision lens distortion correction,” in Proceedings of 17th IEEE International Conference on Image Processing (IEEE, 2010), pp. 4237–4240.
  8. G. P. Stein, “Lens distortion calibration using point correspondences,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1997), pp. 602–608.
  9. Z. Zhang, “On the epipolar geometry between two images with lens distortion,” in Proceedings of 13th International Conference on Pattern Recognition (IEEE, 1996), pp. 407–411.
  10. A. Fitzgibbon, “Simultaneous linear estimation of multiple view geometry and lens distortion,” in Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2001), pp. 125–132.
  11. B. Micusik and T. Pajdla, “Estimation of omnidirectional camera model from epipolar geometry,” in Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 485–490.
  12. H. Li and R. Hartley, “A non-iterative method for correcting lens distortion from nine-point correspondences,” in Proceedings OmniVision ’05, ICCV Workshop (2005).
  13. S. Thirthala and M. Pollefeys, “The radial trifocal tensor: a tool for calibrating the radial distortion of wide-angle cameras,” in Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 321–328.
  14. D. Claus and A. Fitzgibbon, “A rational function lens distortion model for general cameras,” in Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 213–219.
  15. J. Barreto and K. Daniilidis, “Fundamental matrix for cameras with radial distortion,” in Proceedings of Tenth IEEE International Conference on Computer Vision (IEEE, 2005), pp. 625–632.
  16. Z. Kukelova and T. Pajdla, “Two minimal problems for cameras with radial distortion,” in Proceedings of 11th IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1–8.
  17. Z. Kukelova, M. Bujnak, and T. Pajdla, “Automatic generator of minimal problem solvers,” in Computer Vision—ECCV 2008, Vol. 5304 of Lecture Notes in Computer Science (Springer, 2008), pp. 302–315.
  18. M. Byrod, Z. Kukelova, K. Josephson, T. Pajdla, and K. Astrom, “Fast and robust numerical solutions to minimal problems for cameras with radial distortion,” in Computer Vision—ECCV 2008, Vol. 5304 of Lecture Notes in Computer Science (Springer, 2008), pp. 1–8.
  19. Z. Kukelova and T. Pajdla, “A minimal solution to the autocalibration of radial distortion,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–7.
  20. K. Josephson and M. Byrod, “Pose estimation with radial distortion and unknown focal length,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), pp. 2419–2426.
  21. B. Triggs, P. Mclauchlan, R. Hartley, and A. Fitzgibbon, “Bundle adjustment—a modern synthesis,” Vision Algorithms: Theory and Practice, Vol. 1883 of Lecture Notes in Computer Science (Springer, 2000), pp. 298–372.
  22. D. Brown, “Close-range camera calibration,” Photogramm. Eng. 37, 855–866 (1971).
  23. L. Alvarez, L. Gomez, and J. Rafael Sendra, “An algebraic approach to lens distortion by line rectification,” J. Math. Imaging Vision 35, 36–50 (2009). [CrossRef]
  24. B. Prescott and G. McLean, “Line-based correction of radial lens distortion,” Graph. Mod. Image Process. 59, 39–47(1997). [CrossRef]
  25. T. Pajdla, T. Werner, and V. Hlavac, “Correcting radial lens distortion without knowledge of 3-D structure,” Research Report (Czech Technical University, 1997).
  26. F. Devernay and O. Faugeras, “Straight lines have to be straight,” Mach. Vision Appl. 13, 14–24 (2001). [CrossRef]
  27. D. Claus and A. Fitzgibbon, “A plumbline constraint for the rational function lens distortion model,” in Proceedings of British Machine Vision Conference (2005), pp. 99–108.
  28. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge University, 2004).
  29. E. Rosten and R. Loveland, “Camera distortion self-calibration using the plumb-line constraint and minimal hough entropy,” Mach. Vision Appl. 22, 77–85 (2011).
  30. F. Devernay, “A non-maxima suppression method for edge detection with sub-pixel accuracy,” Tech. Rep. 2724, (INRIA rapport de recherche, 1995).
  31. J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 679–698(1986). [CrossRef]
  32. R. Deriche, “Using Canny’s criteria to derive a recursively implemented optimal edge detector,” Int. J. Comput. Vis. 1, 167–187 (1987). [CrossRef]
  33. R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a fast line segment detector with a false detection control,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 722–732(2010). [CrossRef]
  34. R. Grompone von Gioi, J. Jakubowicz, J. Morel, and G. Randall, “LSD: a line segment detector,” IPOP (2012), doi: http://dx.doi.org/10.5201/ipol.2012.gjmr-lsd .
  35. J. Morel and G. Yu, “Is SIFT scale invariant?” Inverse Problems Imaging 5, 115–136 (2011). [CrossRef]
  36. L. G. Luis Alvarez and J. R. Sendra, “Algebraic lens distortion model estimation,” IPOP (2010), http://dx.doi.org/10.5201/ipol.2010.ags-alde .

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited