Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 1

Search results for: Monte Carlo Method

1 Robust Camera Calibration using Discrete Optimization

Authors: Stephan Rupp, Matthias Elter, Michael Breitung, Walter Zink, Christian K├╝blbeck

Abstract:

Camera calibration is an indispensable step for augmented reality or image guided applications where quantitative information should be derived from the images. Usually, a camera calibration is obtained by taking images of a special calibration object and extracting the image coordinates of projected calibration marks enabling the calculation of the projection from the 3d world coordinates to the 2d image coordinates. Thus such a procedure exhibits typical steps, including feature point localization in the acquired images, camera model fitting, correction of distortion introduced by the optics and finally an optimization of the model-s parameters. In this paper we propose to extend this list by further step concerning the identification of the optimal subset of images yielding the smallest overall calibration error. For this, we present a Monte Carlo based algorithm along with a deterministic extension that automatically determines the images yielding an optimal calibration. Finally, we present results proving that the calibration can be significantly improved by automated image selection.

Keywords: Discrete Optimization, Camera Calibration, Monte Carlo Method

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