Skip to main content
SpringerLink
Log in

A New Camera Calibration Algorithm Based on Adaptive Image Selection

  • Conference paper
  • First Online:
Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14497))

Included in the following conference series:

  • 175 Accesses

Abstract

Camera calibration plays an important role in the 3D reconstruction task. However, in the calibration process, users need to select some key images from a large number of calibration board images for further performing the maximum likelihood estimation of camera model parameters. Due to the subjectivity of this estimation, it is difficult to guarantee the consistency of the results obtained by different testers. In this paper, a new camera calibration image selection algorithm is proposed to obtain high accuracy intrinsic parameters. Users only need to acquire a series of checkerboard image sequences, randomly select one image from the image sequence each time, and determine whether the image can be used for camera calibration by calculating the angle error of single frame checkerboard corner. This method can adaptively select a small number of images from the image sequence for camera calibration. The experimental results show that this self-built calibration algorithm is not only simple in the operation process, but also has higher accuracy and consistency in calibration results when compared with traditional calibration method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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(4), 323–344 (1987)

    Article  Google Scholar 

  2. Geiger, A., Ziegler, J., Stiller, C.: Stereoscan: dense 3d reconstruction in real-time. In: Proceedings of the 2011 IEEE intelligent vehicles symposium (IV), pp. 963–968. IEEE (2011)

    Google Scholar 

  3. Sturm, P., Maybank, S.: On plane-based camera calibration: A general algorithm, singularities, applications. In: Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision & Pattern Recognition, pp. 432–437. IEEE (1999)

    Google Scholar 

  4. Zhang, Z.: Flexible camera calibration by viewing a plane from unknown orientations. In: Proceedings of the seventh IEEE international conference on computer vision, pp. 666–673. IEEE (1999)

    Google Scholar 

  5. Hammarstedt, P., Sturm, P., Heyden, A.: Degenerate cases and closed-form solutions for camera calibration with one-dimensional objects. In: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05), pp. 317–324. IEEE (2005)

    Google Scholar 

  6. Sturm, P.: A case against Kruppa’s equations for camera self-calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1199–1204 (2000)

    Article  Google Scholar 

  7. Buchanan, T.: The twisted cubic and camera calibration. Computer Vision, Graphics, and Image Processing 42(1), 130–132 (1988)

    Article  MathSciNet  Google Scholar 

  8. Ren, Y., Hu, F.: Camera calibration with pose guidance. In: Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2180–2184. IEEE (2021)

    Google Scholar 

  9. Triggs, B.: Autocalibration from planar scenes. In: Proceedings of the Computer Vision—ECCV'98: 5th European Conference on Computer Vision Freiburg, Germany, June, e pp. 89–105. Springer Berlin Heidelberg (1998)

    Google Scholar 

  10. Sun, W., Cooperstock, J.: Requirements for camera calibration: must accuracy come with a high price?. In: Proceedings of the Seventh IEEE Workshops on Applications of Computer Vision, pp. 356–361. IEEE (2005)

    Google Scholar 

  11. Richardson, A., Strom, J., Olson, E.: AprilCal: assisted and repeatable camera calibration. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1814–1821. IEEE (2013)

    Google Scholar 

  12. Tan, L., Wang, Y., Yu, H., Zhu, J.: Automatic camera calibration using active displays of a virtual pattern. Sensors 17(4), 685–698 (2017)

    Article  Google Scholar 

  13. Mason, S., Grün, A.: Automatic sensor placement for accurate dimensional inspection. Comput. Vis. Image Underst. 61(3), 454–467 (1995)

    Article  Google Scholar 

  14. Gonzalez-Barbosa, J., Garcia-Ramirez, T., Salas, J., Hurtado-Ramos, J.: Optimal camera placement for total coverage. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 844–848. IEEE (2009)

    Google Scholar 

  15. Ricolfe-Viala, C., Sanchez-Salmeron, A.: Camera calibration under optimal conditions. Opt. Express 19(11), 10769–10775 (2011)

    Article  Google Scholar 

  16. Quigley, M., et al.: ROS: an open-source Robot Operating System. In: Proceedings of the ICRA workshop on open source software (2009)

    Google Scholar 

  17. Rojtberg, P., Kuijper, A.: Efficient pose selection for interactive camera calibration. In: Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 31–36. IEEE (2018)

    Google Scholar 

  18. Peng, S., Sturm, P.: Calibration wizard: a guidance system for camera calibration based on modelling geometric and corner uncertainty. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1497–1505 (2019)

    Google Scholar 

  19. Forsyth, D.A., Ponce, J.: Computer vision: a modern approach. A modern approach 17, 21–48 (2003)

    Google Scholar 

  20. Bradski, G.: The openCV library. Dr. Dobb's Journal: Software Tools for the Professional Programmer 25(11), 120–123 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China

    Huang Jian & Zeng Shan

Authors
  1. Huang Jian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zeng Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zeng Shan .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneva, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jian, H., Shan, Z. (2024). A New Camera Calibration Algorithm Based on Adaptive Image Selection. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14497. Springer, Cham. https://doi.org/10.1007/978-3-031-50075-6_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50075-6_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50074-9

  • Online ISBN: 978-3-031-50075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation