Abstract
While camera calibration is a fundamental yet challenging problem for 3D measurement, it has attracted intensive attention from 3D vision community. In this paper, we propose a new model to characterise camera distortion in the process of the camera calibration. This model attempts to blindly characterise the overall camera distortion without taking the specific radial, decentring, or thin prism distortion into account. To estimate the parameters of interest, the well-known Levernburg-Marquardt algorithm is applied. To initialise the Levernburg-Marquardt algorithm, the results from the classical Tsai algorithm are estimated. After both the camera intrinsic and distortion parameters have been estimated, the distorted image points are corrected using again the Levernburg-Marquardt algorithm initialised by these distorted image points themselves. The performance of algorithms is measured as absolute and relative correction errors and collinear fitting errors. The experimental results based on both synthetic data and real images show that the proposed algorithm often successfully characterises the camera overall distortion, producing encouraging results for camera calibration and correction.
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Liu, Y., Al-Obaidi, A., Jakas, A., Liu, J. (2010). A Fraction Distortion Model for Accurate Camera Calibration and Correction. In: Liu, H., Gu, D., Howlett, R., Liu, Y. (eds) Robot Intelligence. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84996-329-9_8
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DOI: https://doi.org/10.1007/978-1-84996-329-9_8
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