Abstract
In recent years, researchers around the world have been researching and improving the technique of 1D calibration of cameras. The previous work has been primarily focused on reducing the motion constraints of one-dimensional calibration objects, however the accuracy of the existing methods still needs to be improved when random noise is introduces. In order to improve the accuracy of the one-dimensional calibration of the camera, in this paper, we propose a new calibration method by combining a weighted similar invariant linear algorithm and an improved nonlinear optimization algorithm. Specifically, we use the weighted similar invariant linear algorithm to obtain the camera parameters as the initial calibration parameters, and then optimize the parameters by using improved nonlinear algorithm. Finally, in the case of introducing random noise, the results of computer simulations and laboratory experiments show that when the noise level reaches 2 pixels, the parameter error of this method is mostly reduced to 0.2% compared with other methods, which verifies the feasibility of our proposed method.
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References
Tsai, R., et al.: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J. Rob. Autom. 3(4), 323–344 (2003)
Yuan, T., Zhang, F., Tao, X.P.: Flexible geometrical calibration for fringe-reflection optical three-dimensional shape measurement. Appl. Opt. 54(31), 9102–9107 (2015)
Yoo, J.S., et al.: Improved LiDAR-camera calibration using marker detection based on 3D plane extraction 13(6), 2530–2544 (2018)
Duan, F., Wu, F., et al.: Pose determination and plane measurement using a trapezium. Pattern Recogn. Lett. 29(3), 223–231 (2008)
Li, N., Hu, Z.Z., Zhao, B.: Flexible extrinsic calibration of a camera and a two-dimensional laser rangefinder with a folding pattern. Appl. Opt. 55(9), 2270–2280 (2016)
Zhang, Z.Y.: Flexible camera calibration by viewing a plane from unknown orientation. In: IEEE International Conference on Computer Vision (1999)
Peng, E., Li, L.: Camera calibration using one-dimensional information and its applications in both controlled and uncontrolled environments. Pattern Recogn. 43(1), 1188–1198 (2010)
Zhang, Z.Y.: Camera calibration with one-dimensional objects. IEEE Trans. Pattern Anal. Mach. Intell. 26(7), 892–899 (2004)
Wang, L., Wang, W.W., et al.: A convex relaxation optimization algorithm for multi-camera calibration with 1D objects. Neurocomputing 215, 82–89 (2016)
Lv, Y.W., Liu, W., et al.: Methods based on 1D homography for camera calibration with 1D objects. Appl. Opt. 57(9), 2155–2164 (2018)
Akkad, N.E., Merras, M., et al.: Camera self-calibration with varying intrinsic parameters by an unknown three-dimensional scene. Int. J. Comput. Graphics. 30(5), 519–530 (2014)
Sun, Q., Wang, X., et al.: Camera self-calibration with lens distortion. Optik Int. J. Light Electron Optics. 127(10), 4506–4513 (2016)
Finsterle, S., Kowalsky, M.B.: A truncated Levenberg-Marquardt algorithm for the calibration of highly parameterized nonlinear models 37(5), 731–738 (2011)
Li, J.H., Zheng, W.X., et al.: The Parameter estimation algorithms for Hammerstein output error systems using Levenberg-Marquardt optimization method with varying interval measurements. IFAC Papersonline 48(8), 457–462 (2015)
Franca, J.D., Stemmer, M.R., et al.: Revisiting Zhang’s 1D calibration algorithm. Pattern Recogn. 43(3), 1180–1187 (2010)
Kunfeng, S., Qiulei, D., Fuchao, W.: Weighted similarity-invariant linear algorithm for camera calibration with rotating 1D objects. IEEE Trans. Image Process. 21(8), 3806–3812 (2012)
Wang, L., Duan, F.: Zhang’s one-dimensional calibration revisited with the heteroscedastic error-in-variables model. In: 8th IEEE International Conference on Image Processing. IEEE (2011)
Acknowledgements
This work is financially supported in parts by the Fujian Provincial Department of Science and Technology of China (Grant No. 2019H0006 and 2018J01774), the National Natural Science Foundation of China (Grant No. 61601127), and the Foundation of Fujian Provincial Department of Industry and Information Technology of China (Grant No. 82318075).
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Lin, L., Wu, L., Lai, S., Chen, Z., Lin, P., Wu, Z. (2019). An Accurate 1D Camera Calibration Based on Weighted Similar-Invariant Linear Algorithm. In: Chamchong, R., Wong, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2019. Lecture Notes in Computer Science(), vol 11909. Springer, Cham. https://doi.org/10.1007/978-3-030-33709-4_6
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DOI: https://doi.org/10.1007/978-3-030-33709-4_6
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