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 parameters 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. We show in this thesis that traditional modeling approaches cannot capture the complex relationships between control parameters and imaging processes. Furthermore, we demonstrate that the assumption of idealized behavior in traditional models can lead to significant performance problems in color imaging and focus ranging. By using more complex models and control strategies we were able to reduce or eliminate these performance problems. The principal contribution of our research is a methodology for empirically producing accurate camera models for systems with variable-parameter lenses. We also developed a comprehensive taxonomy for the property of "image center. To demonstrate the effectiveness of our methodology we applied it to produce an "adjustable," perspective-projection camera model based on Tsai's fixed camera model. We calibrated and tested our model on two different automated camera systems. In both cases the calibrated model operated across continuous ranges of focus and zoom with an average error of less than 0.14 pixels between the predicted and the measured positions of features in the image plane. We also calibrated and tested our model on one automated camera system across a continuous range of aperture and achieved similar results.