Articles | Volume 10, issue 2
https://doi.org/10.5194/jsss-10-207-2021
https://doi.org/10.5194/jsss-10-207-2021
Regular research article
 | 
11 Aug 2021
Regular research article |  | 11 Aug 2021

Iterative feature detection of a coded checkerboard target for the geometric calibration of infrared cameras

Sebastian Schramm, Jannik Ebert, Johannes Rangel, Robert Schmoll, and Andreas Kroll

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Cited articles

Aalerud, A., Dybedal, J., and Hovland, G.: Automatic calibration of an industrial RGB-D camera network using retroreflective fiducial markers, Sensors, 19, 1561, https://doi.org/10.3390/s19071561, 2019. a
Beauvisage, A. and Aouf, N.: Low cost and low power multispectral thermal-visible calibration, Proc. IEEE Sensors, 1–3, https://doi.org/10.1109/icsens.2017.8234358, 2017. a
Bradski, G.: The OpenCV library, Dr. Dobb's Journal of Software Tools, 120, 122–125, 2000. a
Brown, D. C.: Close-range camera calibration, Photogramm. Eng, 37, 855–866, 1971. a
Choinowski, A., Dahlke, D., Ernst, I., Pless, S., and Rettig, I.: AUTOMATIC CALIBRATION AND CO-REGISTRATION FOR A STEREO CAMERA SYSTEM AND A THERMAL IMAGING SENSOR USING A CHESSBOARD, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1631–1635, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1631-2019, 2019. a, b, c, d, e, f, g, h, i
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Short summary
During the geometric calibration of infrared cameras, the parameters that describe where object points are mapped onto thermal images are determined. The required reference data is obtained by capturing so-called calibration targets. In established approaches, it is difficult to estimate the lens distortions precisely. A newly developed target and its evaluation algorithm make it possible to increase the sensitivity of the calibration by finding reference points close to image borders.