Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
SIAM Journal on Computing
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
Epipolar Geometry in Stereo, Motion, and Object Recognition: A Unified Approach
Epipolar Geometry in Stereo, Motion, and Object Recognition: A Unified Approach
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
3D Computer Vision: Efficient Methods and Applications
3D Computer Vision: Efficient Methods and Applications
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Camera calibration is a process of optimizing the camera parameters. This paper describes an evaluation of different stochastic and heuristic estimators for cost function minimization used in camera calibration. The optimization algorithm is a standard gradient walk on the epipolarconstraint. The results show estimators work similar on the given set of correspondence. Correspondences selected to a given distribution over the frame gives better calibration results, especially the results on the yaw angle estimation show more robust results. In this paper the distribution will uniformly distributed over the frame using binning [1, 2]. The data used in this paper shows binning does lower the error behavior in most calibrations. The L1-norm and L2-norm using binning does not reach an error with respect to the ground truth higher 4 pix. The calibrations rejecting binning shows an impulse on the 970 calibration. To avoid this impulse binning is used. Binning influences the calibration result more as the choice of the right m-estimator or the right norm.