Abstract
Camera calibration is the process of finding the true parameters of a camera. In this article, we study the application of improved particle swarm optimization (PSO) algorithm in camera calibration. Then proposes a new algorithm CPSO, combining PSO with chaos optimization algorithm, to optimize camera’s intrinsic parameters based on absolute conics. Finally we carry out experiments to test its performance. Re-projection errors show that the method used in this paper is flexible and feasible, and the re-projection error is small enough to meet the accuracy demand of welding guidance.
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References
Abdel-Aziz Y, Karara H (1971) Direct linear transformation into object space coordinates in close-range photogrammetry. In: The symposium on close-range photogrammetry. University of Illinois, Illinois, pp 1–18
Tsai RY (1986) An efficient and accurate camera calibration technique for 3D machine vision. In: Proceedings of IEEE conference on computer vision and pattern recognition
Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334
Faugeras OD, Luong Q-T, Maybank SJ (1992) Camera self-calibration: theory and experiments. In: Computer vision—ECCV’92[C], pp 321–334
Maybank SJ, Faugeras OD (1992) A theory of self-calibration of a moving camera. Int J Comput Vision 8(2):123–151
Hartley R (1997) Kruppa’s equations derived from the fundamental matrix. IEEE Trans Pattern Anal Mach Intell 19(2):133–135
Triggs B (1997) Autocalibration and the absolute quadric. In: Computer vision and pattern recognition, 1997. Proceedings, 1997 conference on IEEE computer society, pp 609–614
Zhan-Yi HU, Fu- Chao WU (2002) A review on some active vision based camera calibration techniques. Chin J Comput 25(11):1149–1156
Hua LI, Fu-Chao WU, Zhan-Yi HU (2000) A new linear camera self-calibration technique. Chin J Comput 23(11):1121–1129
Chang-Jiang YANG, Wei WANG, Zhan-Yi HU (1998) An active vision based camrea intrinsic parameters self-calibration technique. Chin J Comput 21(5):428–435
Zhang Z (2014) An adaptive particle swarm optimization algorithm for reservoir operation optimization. Appl Soft Comput 18:167–177
Wang S, Meng B (2007) Chaos particle swarm optimization for resource allocation problem. In: Proceedings of the IEEE international conference on automation and logistics. Jinan, China, 18–21 August 2007
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, 1995, pp 1942–1948
Zhu H, Yang C, Gui W, Li Y (2010) Particle swarm optimization with chaotic mutation. Comput Sci 37(3):215–217
Fan P, Zhang X, Yang T (2009) Particle swarm optimization for breaching local convergence. J Comput Appl 29:122–124
Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of congress on computational intelligence, Washington DC, USA, pp 1945–1950
Meng X-Q, Hu Z-Y (2003) Recent progress in camera self-calibration. Acta Automatica Sinica 29(1):110–123
Acknowledgments
This work is supported by the National Natural Science Foundation of China under the Grant No. 61374071, No. 51275301 and Shanghai Sciences & Technology Committee under Grant No. 11111100302.
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Ni, WF., Wei, SC., Lin, T., Chen, SB. (2015). A Self-calibration Algorithm with Chaos Particle Swarm Optimization for Autonomous Visual Guidance of Welding Robot. In: Tarn, TJ., Chen, SB., Chen, XQ. (eds) Robotic Welding, Intelligence and Automation. RWIA 2014. Advances in Intelligent Systems and Computing, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-319-18997-0_16
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DOI: https://doi.org/10.1007/978-3-319-18997-0_16
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