Calibrating a Three-Viewpoints Thermal Camera with Few Correspondences

Abstract

This paper proposes the approach to calibration of a thermal camera that has three viewpoints. This work estimates the intrinsic parameters of the camera by establishing geometric relationship between real-world scene points and image points. This work estimates the extrinsic parameters, i.e., rotation and translation of the camera using vanishing points. The present work is different from the existed work on the calibration from three perspectives. The first one is that this work calibrates thermal camera with three viewpoints whose relative rotation is greater than 180-degree. The second one is that the viewpoints shares few overlapped areas due to the structure of the camera, so the proposed work is more challenging and difficult than the previous work on the calibration. The third one is that projected image of a scene requires quality enhancement for the calibration. Since few overlapped images are given a priori, feature matching cannot be used for the calibration. Instead of using correspondence matching, vanishing points are exploited to estimate relative rotations of cameras. The intrinsic parameters are estimated by capturing thermal-checkerboard followed by image processing. Experimental results show the accuracy and stability of the proposed approach for estimation of the intrinsic and the extrinsic parameters.

Application of machine learning in polymer additive manufacturing: A review

“…Data augmentation techniques are particularly prevalent for image processing tasks such as defect detection and in‐situ monitoring 229,260,261 . Some techniques for enhancing image data include rotating images by a certain angle, 262 adjusting the brightness or contrast of the image, 263 translating the image in any direction, 264 and injecting noise 265 . Synthetic minority over‐sampling techniques, 266 and feature jittering 267 are examples of augmentation techniques for tabular data.…”