Abstract
For some applications it can be preferable to use images of spheres in order to calibrate a 2D camera. All published sphere-based algorithms need the complete knowledge of the elliptic sphere image, i.e. 5 geometric parameters, in particular the ellipse orientation. Because sphere images tend to be close to circular shapes, this orientation is often very noise-sensitive. For example, it is common to compute the principal point as the intersection of the lines through the major axes of the elliptic images, but this procedure is quite unstable. We present a new method for computing the principal point by means of three sphere images, without making use of the ellipse orientation. By mean of simulations and real experiments we demonstrate that the proposed method is more accurate and stable in finding the principal point as compared to sphere-based calibration algorithms that use the complete ellipse geometry.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, M., Davis, L.S.: Complete camera calibration using spheres: dual-space approach. IEEE 206, 782–789 (2003)
Agrawal, M., Davis, L.S.: Camera calibration using spheres: a semi-definite programming approach. In: IEEE International Conference on Computer Vision, pp. 782–789 (2003)
Beardsley, P., Murray, D., Zisserman, A.: Camera calibration using multiple images. In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588, pp. 312–320. Springer, Heidelberg (1992). https://doi.org/10.1007/3-540-55426-2_36
Bouguet, J.Y.: Camera calibration toolbox for Matlab. http://www.vision.caltech.edu/bouguetj/calib_doc/
Dandelin, G.P.: Mémoire sur quelques propriétés remarquables de la focale parabolique. Nouveaux mémoires de l’Académie Royale des Sciences et Belles-Lettres de Bruxelles T. II, 171–202 (1822)
Daucher, D., Dhome, M., Lapreste, J.: Camera calibration from spheres images. In: Proceedings European Conference Computer Vision, pp. 449–454 (1994)
Fitzgibbon, A., Pilu, M., Fisher, R.: Direct least-square fitting of ellipses. In: Proceedings International Conference on Pattern Recognition, pp. 253–257 (1996)
Guan, J., et al.: Extrinsic calibration of camera networks using a sphere. Sensors 15(8), 18985–19005 (2015). https://doi.org/10.3390/s150818985, http://www.mdpi.com/1424-8220/15/8/18985
Ho, C., Chen, L.: A fast ellipse/circle detector using geometric symmetry. Pattern Recognit. 28(1), 117–124 (1995)
Huang, H., Zhang, H., Cheung, Y.: Camera calibration based on the common self-polar triangle of sphere images. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 19–29. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16808-1_2
Lu, Y., Payandeh, S.: On the sensitivity analysis of camera calibration from images of spheres. Comput. Vis. Image Underst. 114(1), 8–20 (2010)
Penna, M.: Camera calibration: a quick and easy way to determine the scale factor. IEEE Trans. Pattern Anal. Mach. Intell. 13(12), 1240–1245 (1991)
Penne, R., Ribbens, B., Mertens, L., Levrie, P.: What does one image of one ball tell us about the focal length? In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2015. LNCS, vol. 9386, pp. 501–509. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25903-1_43
Sun, J., Chen, X., Gong, Z., Liu, Z., Zhao, Y.: Accurate camera calibration with distortion models using sphere images. Opt. Laser Technol. 65, 83–87 (2015)
Teramoto, H., Xu, G.: Camera calibration by a single image of balls: from conics to the absolute conic. In: Asian Conference on Computer Vision, pp. 499–506 (2002)
Xie, Y., Ji, Q.: A new efficient ellipse detection method. In: International Conference on Pattern Recognition, pp. 957–960 (2002)
Yin, P., Chen, L.H.: A new method for ellipse detection using symmetry. J. Electron. Imaging 3, 20–29 (1994)
Zhang, H., Wong, K., Zhang, G.: Camera calibration from images of spheres. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 499–503 (2007)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Penne, R., Ribbens, B., Puttemans, S. (2019). A New Method for Computing the Principal Point of an Optical Sensor by Means of Sphere Images. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11361. Springer, Cham. https://doi.org/10.1007/978-3-030-20887-5_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-20887-5_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20886-8
Online ISBN: 978-3-030-20887-5
eBook Packages: Computer ScienceComputer Science (R0)