Abstract
Camera calibration is an essential process in visual measurement. 1D target based camera calibration can great facilitate the operating procedure especially when multiple vision sensors should be calibrated. However, the current one-dimensional calibration algorithm is still imprecision in practice. In this work, the PSO algorithm is employed to improve the precision of one-dimensional camera calibration. Since the swarm intelligence algorithm is initial value sensitive, in this work, a data cluster algorithm is proposed to get a better initial value. To overcome the over optimizing problem accounted in swarm intelligence algorithm, prior knowledge, such as the picture’s size, is employed to make sure the parameters will converge toward the true values.
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Acknowledgements
The authors would like to acknowledge the supports by the National Natural Science Foundation of China (Grant No. 61601127, 51508105, and 61574038), the Fujian Provincial Department of Science and Technology of China (Grant No. 2016H6012, and 2018J0106), the Fujian Provincial Economic and Information Technology Commission of China (Grant No. 830020, 83016006), and the Science Foundation of Fujian Education Department of China (Grant No. JAT160073).
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Zhang, Y., Wu, L., Chen, Z., Cheng, S., Lin, P. (2018). One-Dimensional Camera Calibration Based on PSO Algorithm. In: Kaenampornpan, M., Malaka, R., Nguyen, D., Schwind, N. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2018. Lecture Notes in Computer Science(), vol 11248. Springer, Cham. https://doi.org/10.1007/978-3-030-03014-8_18
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DOI: https://doi.org/10.1007/978-3-030-03014-8_18
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