Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Self-Calibration of a Moving Camera from PointCorrespondences and Fundamental Matrices
International Journal of Computer Vision
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Propagating Covariance in Computer Vision
Proceedings of the Theoretical Foundations of Computer Vision, TFCV on Performance Characterization in Computer Vision
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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.