Calibration of radial lens distortion using a Gaussian model

Artur Nowakowski; Władysław Skarbek

doi:10.1117/1.OE.56.10.103103

Abstract

We present a lens distortion model based on the Gaussian function. The model is a potential source function of the popular Brown distortion model and requires the practical estimation of one Boolean and one real parameter. We also present a general technique for lens distortion identification, which consists of three steps: image data acquisition, localization and indexation/matching of image features, and the estimation of distortion parameters. The method uses a structured light technique, in which bar patterns are indexed by a Gray code. Acquired images are automatically processed using a multistep approach that localizes and indexes calibration points. An iterative analysis of differences between localizations of distorted points and their undistorted counterparts is proposed to estimate distortion model parameters. To compute undistorted localizations, the method estimates a homography matrix that is based on both undistorted data and iterative processing of distorted coordinates, which improves compensation accuracy. Experiments with three cameras show that the indexing strategy significantly decreases compensation error (from 0.26 to 0.09 pixels). The newly introduced Gaussian model is shown to be slightly more accurate and considerably more stable than the popular Brown model.

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Artur Nowakowski, Władysław Skarbek, "Calibration of radial lens distortion using a Gaussian model," Optical Engineering 56(10), 103103 (12 October 2017). https://doi.org/10.1117/1.OE.56.10.103103 Submission: Received 10 May 2017; Accepted 6 September 2017

Submission: Received 10 May 2017; Accepted 6 September 2017

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