<title>Modeling and calibration of automated zoom lenses</title>

TLDR

Camera systems with automated zoom lenses are more useful than fixed‑parameter ones, enabling better images and measurements by matching sensing characteristics to scene conditions, yet they are rarely used in machine vision because modeling them across continuous lens settings is difficult. The study presents a methodology for producing accurate camera models for systems with automated, variable‑parameter lenses. To demonstrate the methodology’s effectiveness, the authors applied it to create an adjustable perspective‑projection camera model based on Tsai’s fixed camera model. The calibrated model was tested on an automated zoom lens across continuous ranges of focus and zoom, achieving an average error of less than 0.11 pixels between predicted and measured feature positions.

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 range 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