Modeling and Calibration of Automated Zoom Lenses

Abstract

Camera systems with automated zoom lenses are inherently more useful than those with fixed-parameter lenses. Variable-parameter lenses enable us to produce better images by matching the camera's sensing characteristics to the conditions in a scene. They also allow us to make measurements by noting how the scene's image changes as the lens settings are varied. The reason variable-parameter lenses are not more commonly used in machine vision is that they are difficult to model for continuous ranges of lens settings. In this paper we present a methodology for producing accurate camera models for systems with automated, variable-parameter lenses. To demonstrate our methodology's effectiveness we applied it to produce an "adjustable," perspective-projection camera model based on Tsai's fixed camera model. Our model was calibrated and tested on an automated zoom lens where it operated across continuous ranges of focus and zoom with an average error of less than 0.11 pixels between the predicted and measured positions of features in the image plane.