Self-Calibration of Stationary Cameras
International Journal of Computer Vision
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trust-region methods
Self-Calibration of Rotating and Zooming Cameras
International Journal of Computer Vision
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Using Quaternions for Parametrizing 3-D Rotations in Unconstrained Nonlinear Optimization
VMV '01 Proceedings of the Vision Modeling and Visualization Conference 2001
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Real Time Pattern Matching Using Projection Kernels
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Active self-calibration of multi-camera systems
Proceedings of the 32nd DAGM conference on Pattern recognition
Intrinsic and extrinsic active self-calibration of multi-camera systems
Machine Vision and Applications
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Self-calibration methods allow estimating the intrinsic camera parameters without using a known calibration object. However, such methods are very sensitive to noise, even in the simple special case of a purely rotating camera. Suitable pan-tilt-units can be used to perform pure camera rotations. In this case, we can get partial knowledge of the rotations, e.g. by rotating twice about the same axis. We present extended self-calibration algorithms which use such knowledge. In systematic simulations, we show that our new algorithms are less sensitive to noise. Experiments on real data result in a systematic error caused by non-ideal hardware. However, our algorithms can reduce the systematic error. In the case of complete rotation knowledge, it can even be greatly reduced.