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

Abstract

Abstract. The geometric calibration of cameras becomes necessary when images should be undistorted, geometric image information is needed or data from more than one camera have to be fused. This process is often done using a target with a checkerboard or circular pattern and a given geometry. In this work, a coded checkerboard target for thermal imaging cameras and the corresponding image processing algorithm for iterative feature detection are presented. It is shown that, due in particular to the resulting better feature detectability at image borders, lower uncertainties in the estimation of the distortion parameters are achieved.

Integration and evaluation of the high-precision MotionCam-3D into a 3D thermography system

“…The results presented in the paper were obtained with the code available in the following git repository: https://doi.org/10.5281/zenodo.11355331 (Mendez, 2024). For the geometric calibration, the calibration toolbox presented in Schramm et al (2021b) and the coded target presented in Schramm et al (2021a) were utilized. The codes can be downloaded from the following git repositories: https://doi.org/10.5281/zenodo.11367429 (Schramm et al, 2024) and https://doi.org/10.5281/zenodo.4596313 (Schramm and Ebert, 2021).…”

“…The intrinsic and extrinsic calibrations were performed using the coded target presented and discussed in Schramm et al (2021a). This target improves the boundary detection, reduces the uncertainty of the geometric calibration, and still meets the requirement of being observable in the considered spectral range (LWIR, NWIR and VIS) with a sufficient contrast-to-noise ratio, as is explained in Schramm et al (2021b).…”