Periodic Patterns Recovery for Multicamera Calibration

Lorenzo Sorgi, Andrey Bushnevskiy

2015

Abstract

Camera calibration is an essential step for most computer vision applications. This task usually requires the consistent detection of a 2D periodic pattern across multiple views and in practice one of the main difficulties is a correct localization of the pattern origin and its orientation in case of partial occlusion. To overcome this problem many calibration tools require a full visibility of the calibration pattern, which is not always possible, especially when a multicamera systems are used. This paper addresses the specific problem of consistent recovery of the calibration pattern, captured by a multicamera systems under the condition of partial occlusion of the calibration object in several (even all) calibration images. The proposed algorithm is structured in two sequential steps aimed at the removal of the rotational and the translational components of the pattern offset transformation, which is essential for a correct calibration. The paper focuses on two common calibration patterns, the checkerboard grid and the bundle of parallel lines; however, the technique can be easily rearranged in order to cope with other classes of periodic patterns. The algorithm effectiveness has been successfully proven on the simulated data and two real calibration datasets, captured using a fisheye stereo rig.

References

  1. Agisoft Lens (2014). Agisoft lens automatic lens calibration software. http://www.agisoft.ru/products/lens/. [21 August 2014].
  2. Atcheson, B., Heide, F., and Heidrich, W. (2010). Caltag: High precision fiducial markers for camera calibration. In Proc. VMV, pages 41-48.
  3. Bouguet, J. (2013). Camera calibration toolbox for matlab. http://www.vision.caltech.edu/bouguetj/calib doc/. [21 August 2014].
  4. CAMcal (2014). Camcal camera calibration program. http://people.scs.carleton.ca/cshu/Research/Projects/ CAMcal/. [8 September 2014].
  5. Datta, A., Kim, J., and Kanade, T. (2009). Accurate camera calibration using iterative renement of control points. In ICCV Workshop on Visual Surveillance (VS).
  6. Douskos, V., Grammatikopoulos, L., Kalisperakis, I., Karras, G., and Petsa, E. (2009). Fauccal: An open source toolbox for fully automatic camera calibration. In XXII CIPA Symposium.
  7. Faugeras, O. (1993). Three-Dimensional Computer Vision: a Geometric Viewpoint. MIT Press, ISBN: 9780262061582.
  8. Fiala, M. and Shu., C. (2008). Self-identifying patterns for plane-based camera calibration. 19(4):209-216.
  9. Hartley, R. I. and Zisserman, A. (2000). Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521623049.
  10. Heikkila, J. (2000). Geometric camera calibration using circular control points. 22(10):1066-1077.
  11. Kanatani, K. (1994). Analysis of 3-d rotation fitting. IEEE T-PAMI, 16(5):543-549.
  12. Kanatani, K. (2009). Calibration of ultrawide fisheye lens cameras by eigenvalue minimization. IEEE T-PAMI, 35(4):813-822.
  13. Kassir, A. and Peynot, T. (2010). Reliable automatic camera-laser calibration. In Proc. of ACRA.
  14. Lepetit, V., F.Moreno-Noguer, and P.Fua (2009). Epnp: An accurate o(n) solution to the pnp problem. IJCV, 81(2).
  15. Mei, C. and Rives, P. (2007). Single view point omnidirectional camera calibration from planar grids. In Proc. IEEE ICRA, pages 3945-3950.
  16. OpenCV (2014). Open source computer vision library. http://opencv.org/. [8 September 2014].
  17. Oyamada, Y., Fallavollita, P., and Navab, N. (2012). Single camera calibration using partially visible calibration objects based on random dots marker tracking algorithm.
  18. Shu, C., Brunton, A., and Fiala, M. (2003). Automatic grid finding in calibration patterns using delaunay triangulation. Tech. Rep.
  19. Tsai, Y. R. (1986). An efficient and accurate camera calibration technique for 3D machine vision. In Proc. CVPR.
  20. Vaish, V. (2006). The stanford calibration grid detector. http://graphics.stanford.edu/software/findgrid. [21 August 2014].
  21. Vo, M., Wang, Z., luu, L., and Ma., J. (2011). Advanced geometric camera calibration for machine vision. Optical Engineering, 50(11).
  22. Wedekind, J., Penders, J., Howarth, M., Lockwood, A. J., and Sasada, K. (2013). Using generic image processing operations to detect a calibration grid. Tech. Rep.
  23. Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330-1334.
  24. Zhang, Z. (2002). Camera calibration with one-dimensional objects. volume 4, pages 161-174.
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Paper Citation


in Harvard Style

Sorgi L. and Bushnevskiy A. (2015). Periodic Patterns Recovery for Multicamera Calibration . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 62-67. DOI: 10.5220/0005281300620067


in Bibtex Style

@conference{visapp15,
author={Lorenzo Sorgi and Andrey Bushnevskiy},
title={Periodic Patterns Recovery for Multicamera Calibration},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={62-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005281300620067},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Periodic Patterns Recovery for Multicamera Calibration
SN - 978-989-758-089-5
AU - Sorgi L.
AU - Bushnevskiy A.
PY - 2015
SP - 62
EP - 67
DO - 10.5220/0005281300620067