ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration

  • Simon Placht
  • Peter Fürsattel
  • Etienne Assoumou Mengue
  • Hannes Hofmann
  • Christian Schaller
  • Michael Balda
  • Elli Angelopoulou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8692)

Abstract

We present a new checkerboard detection algorithm which is able to detect checkerboards at extreme poses, or checkerboards which are highly distorted due to lens distortion even on low-resolution images. On the detected pattern we apply a surface fitting based subpixel refinement specifically tailored for checkerboard X-junctions. Finally, we investigate how the accuracy of a checkerboard detector affects the overall calibration result in multi-camera setups. The proposed method is evaluated on real images captured with different camera models to show its wide applicability. Quantitative comparisons to OpenCV’s checkerboard detector show that the proposed method detects up to 80% more checkerboards and detects corner points more accurately, even under strong perspective distortion as often present in wide baseline stereo setups.

Keywords

Checkerboard Detection Saddle-Based Subpixel Refinement Multi Camera Calibration Low Resolution Sensors Lens Distortion 

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References

  1. 1.
    Chen, D., Zhang, G.: A new sub-pixel detector for x-corners in camera calibration targets. WSCG (Short Papers) 5, 97–100 (2005)Google Scholar
  2. 2.
    Dao, V.N., Sugimoto, M.: A robust recognition technique for dense checkerboard patterns. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 3081–3084. IEEE (2010)Google Scholar
  3. 3.
    De 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.
    Fiala, M., Shu, C.: Self-identifying patterns for plane-based camera calibration. Machine Vision and Applications 19(4), 209–216 (2008)CrossRefGoogle Scholar
  5. 5.
    Heikkila, J.: Geometric camera calibration using circular control points. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1066–1077 (2000)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Lucchese, L., Mitra, S.K.: Using saddle points for subpixel feature detection in camera calibration targets. In: Asia-Pacific Conference on Circuits and Systems, vol. 2, pp. 191–195. IEEE (2002)Google Scholar
  7. 7.
    Mallon, J., Whelan, P.F.: Which pattern? biasing aspects of planar calibration patterns and detection methods. Pattern Recognition Letters 28(8), 921–930 (2007)CrossRefGoogle Scholar
  8. 8.
    Niblack, C.W., Gibbons, P.B., Capson, D.W.: Generating skeletons and centerlines from the distance transform. CVGIP: Graph. Models Image Process 54(5), 420–437 (1992)Google Scholar
  9. 9.
    Rufli, M., Scaramuzza, D., Siegwart, R.: Automatic detection of checkerboards on blurred and distorted images. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 3121–3126. IEEE (2008)Google Scholar
  10. 10.
    Scaramuzza, D.: Omnidirectional Vision: from Calibration to Root Motion Estimation. Ph.D. thesis, Swiss Federal Institute of Technology Zurich (ETHZ) (February 2008)Google Scholar
  11. 11.
    Sun, W., Cooperstock, J.R.: An empirical evaluation of factors influencing camera calibration accuracy using three publicly available techniques. Machine Vision and Applications 17(1), 51–67 (2006)CrossRefGoogle Scholar
  12. 12.
    Tsai, R.Y.: A versatile camera calibration technique for high-accuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses. IEEE Journal of Robotics and Automation 3(4), 323–344 (1987)CrossRefGoogle Scholar
  13. 13.
    Wang, Z., Wu, W., Xu, X., Xue, D.: Recognition and location of the internal corners of planar checkerboard calibration pattern image. Applied Mathematics and Computation 185(2), 894–906 (2007)CrossRefMATHGoogle Scholar
  14. 14.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Simon Placht
    • 1
    • 2
  • Peter Fürsattel
    • 1
    • 2
  • Etienne Assoumou Mengue
    • 2
  • Hannes Hofmann
    • 1
  • Christian Schaller
    • 1
  • Michael Balda
    • 1
  • Elli Angelopoulou
    • 2
  1. 1.Metrilus GmbHErlangenGermany
  2. 2.Pattern Recognition LabUniversity of ErlangenNurembergGermany

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