A neural approach to zoom-lens camera calibration from data with outliers

摘要

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 Hitachi CCD cameras with H10×11E Fujinon active lenses.