Abstract
This paper proposes a method of camera calibration that compares the appearance of two images. Unlike conventional methods that evaluate point-to-point correspondences, ours makes a dense evaluation of the correspondence between two images. This enables us to robustly and efficiently calibrate range finders that are camera based. We explain the main principles and algorithm underlying our method, and we also present the results obtained from simulations and experimentally obtained data.
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References
Datta, A., Kim, J.-S., Kanade, T.: Accurate camera calibration using iterative refinement of control points. In: Proceedings of 9th IEEE International Workshop on Visual Surveillance (2009)
Furukawa, R., Kawasaki, H.: Dense 3D reconstruction with an uncalibrated stereo system using coded structured light. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshop, p. 107 (2005)
Gray, F.: Pulse code communication, US Patent 2632058 (1953)
Heikkilä J.: Geometric camera calibration using circular control points. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1066–1077 (2000)
Inokuchi, S., Sato, K., Matsuda, F.: Range imaging system for 3-D object recognition. In: Proceedings of International Conference on Pattern Recognition, pp. 806–808 (1984)
Lanman D., Crispell D., Taubin G.: Surround structured lighting: 3D scanning with orthographic illumination. Comput. Vis. Image Underst. 113(11), 1107–1117 (2009)
Levenberg K.: A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 2, 164–168 (1944)
Matsuyama, T., Ohya, T., Habe, H.: Background subtraction for non-stationary scenes. In: Proceedings of Asian Conference on Computer Vision, pp. 662–667 (2000)
Sato, K., Inokuchi, S.: Range-imaging system utilizing nematic liquid crystal mask. In: Proceedings of International Conference on Computer Vision, pp. 657–661 (1987)
Sun W., Cooperstock J.R.: An empirical evaluation of factors influencing camera calibration accuracy using three publicly available techniques. Mach. Vis. Appl. 17(1), 51–67 (2006)
Tardif J.-P., Sturm P., Trudeau M., Roy S.: Calibration of cameras with radially symmetric distortion. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1552–1566 (2009)
Tsai, R.Y.: An efficient and accurate camera calibration technique for 3D machine vision. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 364–374 (1986)
Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Wiskott L., Fellous J.-M., Krüger N., von der Malsburg C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(07), 775–779 (1997)
Wu H.B., Chen Y., Wu M.Y., Guan C.R., Yu X.Y.: 3D measurement technology by structured light using stripe-edge-based gray code. J. Phys. Conf. Ser. 48, 537–541 (2006)
Zhang Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)
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Habe, H., Nakamura, Y. Appearance-based parameter optimization for accurate stereo camera calibration. Machine Vision and Applications 23, 313–325 (2012). https://doi.org/10.1007/s00138-011-0333-0
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DOI: https://doi.org/10.1007/s00138-011-0333-0