Abstract
Many computer vision algorithms rely on the assumption of the pinhole camera model, but lens distortion with off-the-shelf cameras is significant enough to violate this assumption. Many methods for radial distortion estimation have been proposed, but they all have limitations. Robust automatic radial distortion estimation from a single natural image would be extremely useful for some applications. We propose a new method for radial distortion estimation based on the plumb-line approach. The method works from a single image and does not require a special calibration pattern. It is based on Fitzgibbon’s division model, robust estimation of circular arcs, and robust estimation of distortion parameters. In a series of experiments on synthetic and real images, we demonstrate the method’s ability to accurately identify distortion parameters and remove radial distortion from images.
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
Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1330–1334 (2000)
Strand, R., Hayman, E.: Correcting radial distortion by circle fitting. In: British Machine Vision Conference, BMVC (2005)
Friel, M., Hughes, C., Denny, P., Jones, E., Glavin, M.: Automatic calibration of fish-eye cameras from automotive video sequences. Intelligent Transport Systems, IET 4, 136–148 (2010)
Hughes, C., Glavin, M., Jones, E., Denny, P.: Wide-angle camera technology for automotive applications: a review. Intelligent Transport Systems, IET 3, 19–31 (2009)
Wang, A., Qiu, T., Shao, L.: A simple method of radial distortion correction with centre of distortion estimation. Journal of Mathematical Imaging and Vision 35, 165–172 (2009)
Devernay, F., Faugeras, O.: Straight lines have to be straight: Automatic calibration and removal of distortion from scenes of structured enviroments. Machine Vision and Applications 13, 14–24 (2001)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)
Tsai, R.Y.: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. Radiometry, 221–244 (1992)
Braüer-Burchardt, C.: A simple new method for precise lens distortion correction of low cost camera systems. In: German Pattern Recognition Symposium, pp. 570–577 (2004)
Barreto, J.P., Daniilidis, K.: Fundamental matrix for cameras with radial distortion. In: International Conference on Computer Vision (ICCV), pp. 625–632 (2005)
Hartley, R., Kang, S.: Parameter-free radial distortion correction with center of distortion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1309–1321 (2007)
Fitzgibbon, A.W.: Simultaneous linear estimation of multiple view geometry and lens distortion. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 125–132 (2001)
Stein, G.P.: Lens distortion calibration using point correspondences. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 602–608 (1996)
Ramalingam, S., Sturm, P., Lodha, S.K.: Generic self-calibration of central cameras. Computer Vision and Image Understanding 114, 210–219 (2010)
Thormählen, T., Broszio, H., Wassermann, I.: Robust line-based calibration of lens distortion from a single view. In: Computer Vision / Computer Graphics Collaboration for Model-based Imaging Rendering, Image Analysis and Graphical Special Effects, pp. 105–112 (2003)
Brown, D.C.: Close-range camera calibration. Photogrammetric Engineering 37, 855–866 (1971)
Swaminathan, R., Nayar, S.: Non-Metric Calibration of Wide-Angle Lenses and Polycameras. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1172–1178 (2000)
Alvarez, L., Gómez, L., Sendra, J.R.: An algebraic approach to lens distortion by line rectification. Journal of Mathematical Imaging and Vision 35, 36–50 (2009)
Brauer-Burchardt, C., Voss, K.: A new algorithm to correct fish-eye- and strong wide-angle-lens-distortion from single images. In: IEEE International Conference on Image Processing, vol. 1, pp. 225–228 (2001)
Tavakoli, H.R., Pourreza, H.R.: Automated center of radial distortion estimation, using active targets. In: Asian Conference on Computer Vision, ACCV (2010)
Chernov, N.: Circular and Linear Regression: Fitting Circles and Lines by Least Squares. Chapman & Hall, Boca Raton (2010)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 381–395 (1981)
Al-Sharadqah, A., Chernov., N.: Error analysis for circle fitting algorithms. The Electronic Journal of Statistics 3, 886–911 (2009)
Taubin, G.: Estimation of planar curves, surfaces, and nonplanar space curves defined by implicit equations with applications to edge and range image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 1115–1138 (1991)
Tomasi, C.: Sample image for CPS 296.1 homework assignment (2007), http://www.cs.duke.edu/courses/spring06/cps296.1/homework/1/lab.gif
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bukhari, F., Dailey, M.N. (2010). Robust Radial Distortion from a Single Image. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17274-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-17274-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17273-1
Online ISBN: 978-3-642-17274-8
eBook Packages: Computer ScienceComputer Science (R0)