Skip to content
BY-NC-ND 3.0 license Open Access Published by De Gruyter Open Access September 9, 2010

Improving the performance of double-plane stereo vision system calibration, using a virtual plane

  • Paweł Rotter , Witold Byrski , Michał Dajda and Grzegorz Łojek

Abstract

In the double-plane method for stereo vision system calibration, the correspondence between screen coordinates and location in 3D space is calculated based on four plane-to-plane transformations; there are two planes of the calibration pattern and two cameras. The method is intuitive, and easy to implement, but, the main disadvantage is ill-conditioning for some spatial locations. In this paper we propose a method which exploits the third plane which physically does not belong to the calibration pattern, but can be calculated from the set of reference points. Our algorithm uses a combination of three calibration planes, with weights which depend on screen coordinates of the point of interest; a pair of planes which could cause numerical errors receives small weights and have practically no influence on the final results. We analyse errors, and their distribution in 3D space, for the basic and the improved algorithm. Experiments demonstrate high accuracy and reliability of our method compared to the basic version; root mean square error and maximum error, are reduced by factors of 4 and 20 respectively.

References

[1] A. Bhatti (Ed.) Stereo Vision InTech 2008. Free access at: http://intechweb.org/book.php?id=87.10.5772/89Search in Google Scholar

[2] B. Cyganek, J.P. Siebert An Introduction to 3D Computer Vision Techniques and Algorithms. Wiley 2009.10.1002/9780470699720Search in Google Scholar

[3] V. Drenk, F. Hildebrand, M. Kindler & D. Kliche (1999). A 3D video technique for analysis of swimming in a flume. In R.H. Sanders, & B.J. Gibson (eds.), Scientific Proceedings of the XVII International Symposium on Biomechanics in Sports (pp. 361-364). Perth, Australia: Edith-Cowan University.Search in Google Scholar

[4] O. Faugeras Three-Dimensional Computer Vision: A Geometric Viewpoint, MIT Press, 1993.Search in Google Scholar

[5] S. Fuksa, W. Byrski Czteropunktowa metoda identyfikacji transformacji stereowizyjnej (Four-pointidentification method of stereovision transformation) Automatyka 2005, 9(3).Search in Google Scholar

[6] R. Hartley, A. Zisserman Multiple View Geometry in Computer Vision. Cambridge University Press, 2003.10.1017/CBO9780511811685Search in Google Scholar

[7] R. Klette, K. Schluns, A. Koschan Computer Vision: Three-Dimensional Data from Images. Springer-Verlag, 1998.Search in Google Scholar

[8] P.F. Sturm, S.J. Maybank On Plane-Based Camera Calibration: A General Algorithm, Singularities, Applications, CVPR, vol. 1, pp.1432, 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’99) - Volume 1, 1999.Search in Google Scholar

[9] A.V. Trucco Introductory Techniques for 3-D Computer Vision, Prentice Hall 1998.Search in Google Scholar

[10] Z. Zhang A flexible new technique for camera calibration. IEEE Transactions on PAMI, Volume 22, Issue 11, Nov 2000, pp. 1330–1334.10.1109/34.888718Search in Google Scholar

[11] Z.Y. Zhang Flexible Camera Calibration by Viewing a Plane from Unknown Orientations, ICCV99(666-673), 1999. IEEE DOI Link BibRef 9900.Search in Google Scholar

Received: 2010-5-17
Accepted: 2010-7-19
Published Online: 2010-9-9
Published in Print: 2010-6-1

© Paweł Rotter et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Downloaded on 4.1.2025 from https://www.degruyter.com/document/doi/10.2478/s13230-010-0013-1/html
Scroll to top button