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Journal of Real-Time Image Processing

, Volume 11, Issue 1, pp 193–200 | Cite as

Real-time precise detection of regular grids and matrix codes

  • Markéta Dubská
  • Adam HeroutEmail author
  • Jiří Havel
Original Research Paper

Abstract

The traditional approach in detecting sets of concurrent and/or parallel lines is to first detect lines in the image and then find such groups of them which meet the concurrence condition. The Hough Transform can be used for detecting the lines and variants of HT such as the Cascaded Hough Transform can be used to detect the vanishing points. However, these approaches disregard much of the information actually accumulated to the Hough space. This article proposes using the Hough space as a 2D signal instead of just detecting the local maxima and processing them. On the example of QRcode detection, it is shown that this approach is computationally cheap, robust, and accurate. The proposed algorithm can be used for efficient and accurate detection and localization of matrix codes (QRcode, Aztec, DataMatrix, etc.) and chessboard-like calibration patterns.

Keywords

Line detection Regular grids Hough transform Real-time detection PClines 

Notes

Acknowledgments

This research was supported by the CEZMSMT project IT4I—CZ 1.05/1.1.00/02.0070, by the TACR grant V3C TE01010415 and by MV CR grant VG20102015006.

References

  1. 1.
    Alfthan, J.: Robust detection of two-dimensional barcodes in blurry images. Master’s thesis, KTH Computer Science and Communication, Stockholm (2008)Google Scholar
  2. 2.
    Bhattacharya, P., Rosenfeld, A., Weiss, I.: Point-to-line mappings as Hough transforms. Pattern Recognit. Lett. 23(14), 1705–1710 (2002)zbMATHCrossRefGoogle Scholar
  3. 3.
    la Escalera, A., Armingol, J.M.: Automatic chessboard detection for intrinsic and extrinsic camera parameter calibration. Sensors 10(3), 2027–2044 (2010)CrossRefGoogle Scholar
  4. 4.
    Dubská, M., Herout, A., Havel, J.: PClines—line detection using parallel coordinates. In: Proceedings of CVPR (2011)Google Scholar
  5. 5.
    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)zbMATHCrossRefGoogle 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. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, second edition. Cambridge University Press, Cambridge (2004). ISBN:0521540518Google Scholar
  8. 8.
    Adam H., Markéta D., Jiří H.: Real-Time Detection of Lines and Grids By PClines and Other Approaches. Springer Briefs in Computer Science. Springer, Berlin (2012). ISBN:978-1-4471-4413-7Google Scholar
  9. 9.
    Inselberg, A.: Parallel Coordinates; Visual Multidimensional Geometry and Its Applications. Springer, Berlin (2009)Google Scholar
  10. 10.
    El Mejdani, S., Egli, R., Dubeau, F.: Old and new straight-line detectors: Description and comparison. Pattern Recognit. 41, 1845–1866 (2008)zbMATHCrossRefGoogle Scholar
  11. 11.
    Muniz, R., Junco, L., Otero, A.: A robust software barcode reader using the hough transform. In: International Conference on Information Intelligence and Systems, 1999. Proceedings, pp 313–319 (1999)Google Scholar
  12. 12.
    Schaffalitzky, F., Zisserman, A: Planar grouping for automatic detection of vanishing lines and points. Image Vis. Comput. 18, 647–658 (2000)CrossRefGoogle Scholar
  13. 13.
    Tuytelaars, T., Van Gool, L., Proesmans, M., Moons, T.: The cascaded hough transform as an aid in aerial image interpretation. In: Sixth International Conference on Computer Vision. pp 67–72 (1998)Google Scholar
  14. 14.
    Tinne, T., Marc, P., Luc Van, G., Esat, M.: The cascaded hough transform. In: Proceedings of ICIP (1998)Google Scholar
  15. 15.
    Zhongshi, W., Zhongge, W., Yingbin, W.: Recognition of corners of planar checkboard calibration pattern image. In: Control and Decision Conference (CCDC), Chinese, pp 3224–3228 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Brno University of TechnologyBrnoCzech Republic

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