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Issue No.02 - February (2009 vol.31)
pp: 376-383
Damien Douxchamps , NAIST, Ikoma
Kunihiro Chihara , NAIST, Ikoma
Accurate measurement of the position of features in an image is subject to a fundamental compromise: The features must be both small, to limit the effect of nonlinear distortions, and large, to limit the effect of noise and discretization. This constrains both the accuracy and the robustness of image measurements, which play an important role in geometric camera calibration as well as in all subsequent measurements based on that calibration. In this paper, we present a new geometric camera calibration technique that exploits the complete camera model during the localization of control markers, thereby abolishing the marker size compromise. Large markers allow a dense pattern to be used instead of a simple disc, resulting in a significant increase in accuracy and robustness. When highly planar markers are used, geometric camera calibration based on synthetic images leads to true errors of 0.002 pixels, even in the presence of artifacts such as noise, illumination gradients, compression, blurring, and limited dynamic range. The camera parameters are also accurately recovered, even for complex camera models.
Camera calibration, imaging geometry, image measurement, high resolution, noise, ray tracing, subpixel.
Damien Douxchamps, Kunihiro Chihara, "High-Accuracy and Robust Localization of Large Control Markers for Geometric Camera Calibration", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 2, pp. 376-383, February 2009, doi:10.1109/TPAMI.2008.214
[1] R.Y. Tsai, “A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses,” IEEE Trans. Robotics and Automation, vol. 3, pp. 323-344, Aug. 1987.
[2] J. Salvi, X. Armangu, and J. Batlle, “A Comparative Review of Camera Calibrating Methods with Accuracy Evaluation,” Pattern Recognition, vol. 35, no. 7, pp. 1617-1635, 2002.
[3] Z. Zhang, “A Flexible New Technique for Camera Calibration,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330-1334, Nov. 2000.
[4] H. Zollner and R. Sablatnig, “Comparison of Methods for Geometric Camera Calibration Using Planar Calibration Targets,” Proc. 28th Workshop Austrian Assoc. of Pattern Recognition, pp. 234-244, 2004.
[5] J. Heikkilä, “Moment and Curvature Preserving Technique for Accurate Ellipse Boundary Detection,” Proc. 14th Int'l Conf. Pattern Recognition, vol. 1, pp. 734-737, Aug. 1998.
[6] J. Heikkilä and O. Silvén, “A Four-Step Camera Calibration Procedure with Implicit Image Correction,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 1106-1112, June 1997.
[7] J. Heikkilä, “Geometric Camera Calibration Using Circular Control Points,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp.1066-1077, Oct. 2000.
[8] A. Redert, E. Hendriks, and J. Biemond, “Accurate and Robust Marker Localization Algorithm for Camera Calibration,” Proc. First Int'l Symp. 3D Data Visualization and Transmission, pp. 522-525, June 2002.
[9] L. Robert, “Camera Calibration without Feature Extraction,” Computer Vision and Image Understanding, vol. 63, no. 2, pp. 314-325, Feb. 1996.
[10] Manual of Photogrammetry, C. McGlone, E. Mikhail, and J. Bethel, eds., fifth ed. Am. Soc. of Photogrammetry and Rem. Sens., 2004.
[11] R. Jain, R. Kasturi, and B.G. Schunck, Machine Vision. McGraw-Hill, 1995.
[12] O. Faugeras, Three-Dimensional Computer Vision: A Geometric Viewpoint. MIT Press, 1993.
[13] J. Weng, P. Cohen, and M. Herniou, “Camera Calibration with Distorsion Models and Accuracy Evaluation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 10, pp. 965-980, Oct. 1992.
[14] J.-M. Lavest, G. Rives, and J. Lapresté, “Underwater Camera Calibration,” Proc. European Conf. Computer Vision, vol. 2, pp. 654-668, June 2000.
[15] R. Klette, K. Schlüns, and A. Koschan, Computer Vision. Springer, May 1998.
[16] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision. Cambridge Univ. Press, 2003.
[17] O. Faugeras and Q.-T. Luong, The Geometry of Multiple Images. MIT Press, 2001.
[18] J.A. Nelder and R. Mead, “A Simplex Method for Function Minimization,” The Computer J., vol. 7, pp. 308-313, 1963.
[19] R.M. Haralick and L.G. Shapiro, Computer and Robot Vision, vol. 2. Addison-Wesley, 1993.
[20] J.-M. Lavest and M. Dhome, “Short Focal Length Camera Calibration,” Proc. Reconnaissance des Formes et Intelligence Artificielle, vol. 3, pp. 81-90, Feb. 2000.
[21] N.V. Reinfeld and W.R. Vogel, Mathematical Programming. Prentice Hall, 1958.
[22] D. DeMenthon and L.S. Davis, “Model-Based Object Pose in 25 Lines of Code,” Int'l J. Computer Vision, vol. 15, nos. 1/2, pp. 123-141, June 1995.
[23] M. Galassi, J. Davies, J. Theiler, G. Jungman, M. Booth, and F. Rossi, GNU Scientific Library Reference Manual, second ed., B. Gough, ed. Network Theory, Aug. 2006.
[24] J.-M. Lavest, M. Viala, and M. Dhome, “Do We Really Need an Accurate Calibration Pattern to Achieve a Reliable Camera Calibration,” Proc. Fifth European Conf. Computer Vision, vol. 1, pp. 158-174, June 1998.
[25] K. Hirakawa and T.W. Parks, “Adaptive Homogeneity-Directed Demosaicing Algorithm,” IEEE Trans. Image Processing, vol. 14, no. 3, pp. 360-369, Mar. 2005.
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