Circular coded target system for industrial applications


Coded targets are used as reference targets with a known location during camera calibration, for robust searching of corresponding features between images during various applications of machine vision like object tracking, robot navigation or 3D measurement. In this paper, a target system for industrial photogrammetric applications is outlined. The methods which have been chosen emphasize maximum robustness, along with accuracy at the expense of computational efficiency, since photogrammetric measurements are mainly evaluated offline. The outlined system combines widely used photogrammetric circular coded target design with an automatic library generator. It also utilizes robust methods of target detection and recognition with error correction (in case of 60 15-bit targets, up to 1 bit confused or 2 bits occluded) along with preserving the low false-positive or confusion rate. Any error correction method for this type of targets was not introduced before. The solution also allows for the creation of versatile target libraries or to work with the existing target libraries of a number of commercial photogrammetric systems. The properties of the target system were tested under challenging conditions (including heavy noise, blur, occlusion and geometrical image transformations) and compared to state-of-the-art systems, e.g. TRITOP (GOM), or ArUco, which it outperforms. The target system is already used for the camera calibration of a specialized photogrammetric system utilized in the heavy industry environment.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Availability of data and material

Data are available on request from the authors.


  1. 1.

    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up robust features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008).

    Article  Google Scholar 

  2. 2.

    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000).

    Article  Google Scholar 

  3. 3.

    Tsai, R.: 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)

    Article  Google Scholar 

  4. 4.

    Forbes, K., Voigt, A., Bodika, N.: An inexpensive, automatic and accurate camera calibration method. In: Nicolls, F. (ed.) Thirteenth Annual Symposium of the Pattern Recognition Association of South Africa. University of Cape Town, Dept. of Electrical Engineering (2002)

  5. 5.

    Schneider, D., Schwalbe, E., Maas, H.: Validation of geometric models for fisheye lenses. ISPRS J. Photogramm. Remote Sens. 64, 259–266 (2009).

    Article  Google Scholar 

  6. 6.

    Luhmann, T., Robson, S., Kyle, S., Harley, I.: Close Range Photogrammetry. Whittles Publishing, Scotland (2011)

    Google Scholar 

  7. 7.

    Luhmann, T.: Close range photogrammetry for industrial applications. Isprs J. Photogramm. Remote Sens. 65, 558–569 (2010).

    Article  Google Scholar 

  8. 8.

    Zhang, D., Liang, J., Guo, C., Liu, J., Zhang, X., Chen, Z.: Exploitation of photogrammetry measurement system. Opt. Eng. 49, 11 (2010).

    Article  Google Scholar 

  9. 9.

    Hu, H., Liang, J., Xiao, Z., Tang, Z., Asundi, A., Wang, Y.: A four-camera videogrammetric system for 3-D motion measurement of deformable object. Opt. Lasers Eng. 50, 800–811 (2012).

    Article  Google Scholar 

  10. 10.

    Bergamasco, F., Albarelli, A., Torsello, A.: Pi-Tag: a fast image-space marker design based on projective invariants. Mach. Vis. Appl. 24, 1295–1310 (2013).

    Article  Google Scholar 

  11. 11.

    Bergamasco, F., Albarelli, A., Cosmo, L., Torsello, A.: An accurate and robust artificial marker based on cyclic codes. IEEE Trans. Pattern Anal. Mach. Intell. 38, 2359–2373 (2016)

    Article  Google Scholar 

  12. 12.

    Ahn, S., Rauh, W.: Circular coded target for automation of optical 3D- measurement and camera calibration. Int. J. Pattern Recognit. Artif. Intell. 15, 905–919 (2001)

    Article  Google Scholar 

  13. 13.

    Calvet, L., Gurdjos, P., Griwodz, C., Gasparini, S.: Detection and accurate localization of circular fiducials under highly challenging conditions. In: 2016 IEEE Conf. Comput. Vis. Pattern Recognit. 562–570 (2016).

  14. 14.

    Li, W., Liu, G., Zhu, L., Li, X., Zhang, Y., Shan, S.: Efficient detection and recognition algorithm of reference points in photogrammetry. In: Conference on Optics, Photonics and Digital Technologies for Imaging Applications IV (2016)

  15. 15.

    Guo, C., Cheng, X., Cui, H., Dai, N., Weng, J.: A new technique of recognition for coded targets in optical 3D measurement. In: Optical Metrology and Inspection for Industrial Applications III (2014)

  16. 16.

    Xia, R.B., Zhao, J.B., Liu, W.J., Wu, J.H., Fu, S.P., Jiang, J., Li, J.: A robust recognition algorithm for encoded targets in close-range photogrammetry. J. Inf. Sci. Eng. 28, 407–418 (2012).

    MathSciNet  Article  Google Scholar 

  17. 17.

    Li, Z., Liu, M.: Research on decoding method of coded targets in close-range photogrammetry. J. Comput. Inf. Syst. 6, 2699–2705 (2010)

    Google Scholar 

  18. 18.

    Dosil, R., Pardo, X., Fdez-Vidal, X.: A new radial symmetry measure applied to photogrammetry A new radial symmetry measure applied to photogrammetry. Pattern Anal. Appl. 16, 637–646 (2012).

    Article  Google Scholar 

  19. 19.

    Chen, R., Zhong, K., Li, Z., Liu, M., Zhan, G.: An accurate and reliable circular coded target detection algorithm for vision measurement. In: Han, S, Yoshizawa, T, Zhang, S. (ed.) Optical Metrology and Inspection for Industrial Applications IV (2016).

  20. 20.

    Fiala, M.: ARTag, a fiducial marker system using digital techniques. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, Proceedings (2005)

  21. 21.

    Romero-Ramirez, F., Muñoz-Salinas, R., Medina-Carnicer, R.: Speeded up detection of squared fiducial markers. Image Vis. Comput. 76, 38–47 (2018).

    Article  Google Scholar 

  22. 22.

    Garrido-Jurado, S., Munoz-Salinas, R., Madrid-Cuevas, F.J., Marin-Jimenez, M.J.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognit. 47, 2280–2292 (2014).

    Article  Google Scholar 

  23. 23.

    Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F., Medina-Carnicer, R.: Generation of fiducial marker dictionaries using mixed integer linear programming. Pattern Recognit. 51, 481–491 (2016).

    Article  Google Scholar 

  24. 24.

    Mondéjar-Guerra, V., Garrido-Jurado, S., Muñoz-Salinas, R., Marín-Jiménez, M., Medina-Carnicer, R.: Robust identification of fiducial markers in challenging conditions. Expert Syst. Appl. 93, 336–345 (2018).

    Article  Google Scholar 

  25. 25.

    Canny, J.: A computational approach to edge-detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986).

    Article  Google Scholar 

  26. 26.

    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. ManCybern. 9, 62–66 (1979)

    Article  Google Scholar 

  27. 27.

    Zatocilova, A., Poliscuk, R., Palousek, D., Brandejs, J.: Photogrammetry based system for the measurement of cylindrical forgings axis straightness. In: Conference on Optical Measurement Systems for Industrial Inspection VIII. Spie-Int Soc Optical Engineering, Bellingham (2013).

  28. 28.

    Zatocilova, A., Palousek, D., Brandejs, J.: Development of a photogrammetry system for the measurement of rotationally symmetric forgings. In: Conference on Optical Measurement Systems for Industrial Inspection IX. Spie-Int Soc Optical Engineering, Bellingham (2015).

  29. 29.

    Zatocilova, A., Palousek, D., Brandejs, J.: Image-based measurement of the dimensions and of the axis straightness of hot forgings. Measurement 94, 254–264 (2016).

    Article  Google Scholar 

  30. 30.

    Hurník, J., Zatočilová, A., Paloušek, D.: Camera calibration method of optical system for large field measurement of hot forgings in heavy industry. In: Optical Measurement Systems for Industrial Inspection XI (2019).

  31. 31.

    Pebody, L.: The reconstructibility of finite abelian groups. Comb. Probab. Comput. 13, 867–892 (2004).

    MathSciNet  Article  MATH  Google Scholar 

  32. 32.

    Pebody, L.: Reconstructing odd necklaces. Comb. Probab. Comput. 13, 503–514 (2007).

    MathSciNet  Article  MATH  Google Scholar 

Download references


This research has received funding from Technology Agency of Czech Republic, project TREND TA1320S03000 and Faculty of Mechanical Engineering, Brno University of Technology internal specific project FSI-S-20-6296.

Author information



Corresponding author

Correspondence to Jakub Hurník.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Code availability

Code is available on request from the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hurník, J., Zatočilová, A. & Paloušek, D. Circular coded target system for industrial applications. Machine Vision and Applications 32, 39 (2021).

Download citation


  • Circular coded target
  • CCT
  • Coded target
  • Fiducial marker
  • Camera calibration
  • Photogrammetry