Abstract
The generic camera model considered in this work can be regarded as a mapping between image pixels and viewing rays. These rays are independent of each other which prohibits a standard parametric approach for calibration and modeling of these cameras. Spline surfaces are used here to calibrate and model generic imaging devices. This allows the utilization of sparse planar calibration boards and facilitates general forward projection as well as subpixel back projection. In contrast to other works the complete image area is to be calibrated, not only a part of it. This is done by adding further views of calibration patterns after an initial calibration step, which expands the calibrated region of the camera image. Results with two different imaging devices prove the general applicability of the proposed method and the comparison to an established parametric calibration procedure shows its superiority.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tsai, R.: A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-shelf TV Cameras and Lenses. IEEE Journal of Robotics and Automation 3, 323–344 (1987)
Kannala, J., Brandt, S.: A generic camera calibration method for fish-eye lenses. In: Proc. of the Int. Conf. on Pattern Recognition (ICPR), pp. 10–13 (2004)
Scaramuzza, D., Martinelli, A., Siegwart, R.: A Flexible Technique for Accurate Omnidirectional Camera Calibration and Structure from Motion. In: Int. Conf. on Computer Vision Systems, p. 45 (2006)
Vincent, C.Y., Tjahjadi, T.: Multiview Camera-Calibration Framework for Nonparametric Distortion Removal. IEEE Transactions on Robotics 21, 1004–1009 (2005)
Hanning, T.: High Precision Camera Calibration. Vieweg + Teubner (2011)
Geyer, C., Daniilidis, K.: A Unifying Theory for Central Panoramic Systems and Practical Implications. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 445–461. Springer, Heidelberg (2000)
Puig, L., Bermúdez, J., Sturm, P., Guerrero, J.: Calibration of Omnidirectional Cameras in Practice. A Comparison of Methods. Computer Vision and Image Understanding 116, 120–137 (2011)
Grossberg, M., Nayar, S.: A general imaging model and a method for finding its parameters. In: Proc. of the Int. Conf. on Computer Vision, pp. 108–115 (2001)
Sturm, P., Ramalingam, S.: A Generic Concept for Camera Calibration. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 1–13. Springer, Heidelberg (2004)
Dunne, A.K., Mallon, J., Whelan, P.F.: Efficient generic calibration method for general cameras with single centre of projection. Computer Vision and Image Understanding 114, 220–233 (2010)
Rosebrock, D., Wahl, F.: Generic camera calibration and modeling using spline surfaces. In: IEEE Intelligent Vehicles Symposium, pp. 51–56 (2012)
Mallon, J., Whelan, P.: Which pattern? Biasing aspects of planar calibration patterns and detection methods. Pattern Recognition Letters 28 (2007)
Piegl, L., Tiller, W.: The NURBS Book, 2nd edn. Springer (1997)
Sturm, P., Maybank, S.: On plane-based camera calibration: A general algorithm, singularities, applications. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 432–437 (1999)
Hartley, R., Zisserman, A.: Multiple View Geometry, 2nd edn. Cambridge University Press (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rosebrock, D., Wahl, F.M. (2013). Complete Generic Camera Calibration and Modeling Using Spline Surfaces. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7725. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37444-9_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-37444-9_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37443-2
Online ISBN: 978-3-642-37444-9
eBook Packages: Computer ScienceComputer Science (R0)