Zoom-lens Camera Calibration from Noisy Data with Outliers
Moumen Ahmed and Aly. A. Farag,
The 11th British Machine Vision Conference (BMVC'00), Bristol, UK,
Sept., 2000.
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Abstract
Camera systems with zoom lenses are inherently more useful
than those with passive lenses due to their flexibility and controllability.
However, their calibration raises several challenges. In this paper, we
present a neural framework for zoom-lens camera calibration that can capture
complex variations in the camera model parameters across continuous ranges
in the lens control space, while minimizing the calibration error over
all the calibration data. To automate the tedious process of collecting
calibration data, the calibration approach should be prepared to handle
possible outliers in the data. We demonstrate how the calibration approach
can be robust and less sensitive to outliers. The validity and performance
of our approach are tested using both synthetic data with outliers,
and with real experiments to calibrate two Hitachi CCD cameras with H10x11E
Fujinon active lenses.