Abstract
An iterative camera calibration approach is presented in this paper. This approach allows computing the optimal camera parameters for a given set of data. If non linear estimation process is done, a risk of reaching a local minimum exists. With this method this risk is reduced and a best estimation is achieved. By one hand, an iterative improving of the estimated camera parameters is done maximizing a posteriori probability density function (PDF) for a given set of data. To resolve it, a Kalman filter is used based on the Bayesian standpoint. Each update is carried out starting with a new set of data, its covariance matrix and a previous estimation of the parameters. In this case, a different management of the input data is done to extract all its information. By the other hand, apart from the calibration algorithm, a method to compute an interval which contains camera parameters is presented. It is based on computing the covariance matrix of the estimated camera parameters.
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References
Faugeras, O., Toscani, G.: The calibration problem for stereo. In: IEEE Conference on computer vision and pattern recognition, Miami beach (June 1986)
Faugeras, O.: Three dimensional computer vision. The MIT Press, Massachusetts (1993)
Haralick, R.M.: Propagating covariance in computer vision. In: Workshop on Performance Characteristics of Vision Algorithms, Cambridge (April 1996)
Kanatani, K.: Statistical Optimization for geometric computation. Springer, Heidelberg (June 1995)
Luong, Q.T., Faugeras, O.: Self-calibration of moving cameras from point’s correspondences and fundamental matrices. The international journal on of computer vision (August 1997)
Salvi, J., Armagué, X., Batlle, J.: A comparative review of camera calibrating methods with accuracy evaluation. Pattern recognition (2002)
Sturm, P., Maybank, S.J.: On plane-based camera calibration: a general algorithm, singularities, applications. IEEE Transactions on Pattern Analysis and Machine Intelligence (1999)
Tsai, R.Y.: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the shelf TV cameras and lenses. IEEE Journal on robotics and automation (August 1987)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence (2000)
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© 2008 Springer-Verlag Berlin Heidelberg
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Ricolfe-Viala, C., Sánchez-Salmerón, AJ. (2008). An Iterative Kalman Filter Approach to Camera Calibration. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_13
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DOI: https://doi.org/10.1007/978-3-540-88458-3_13
Publisher Name: Springer, Berlin, Heidelberg
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