A neural optimization framework for zoom lens camera calibration

Abstract

Camera systems with zoom lenses are inherently more useful than those with passive lenses due to their flexibility and controllability. However, calibration techniques for active-cameras, still, lag behind those developed for calibration of passive-lens cameras. In this paper, we present a neural framework for zoom-lens camera calibration based on our proposed neurocalibration approach, which maps the classical problem of geometric camera calibration into a learning problem of a multi-layered feedforward neural network (MLFN). After discussing the features and advantages of the neurocalibration network, we present how this neural framework can capture the complex variations in the camera model parameters, both intrinsic and extrinsic, while minimizing the calibration error over all the calibration data across continuous ranges in the lens control space. The framework consists of a number of MLFNs learning concurrently, independently and cooperatively, the perspective projection transformation of the camera over its optical setting ranges. The calibration results of this technique applied to Hitachi CCD cameras with H10x11E Fujinon active lenses are reported.