Improving calibration of thermal stereo cameras using heated calibration board

Saponaro, Philip; Sorensen, Scott; Rhein, Stephen; Kambhamettu, Chandra

doi:10.1109/icip.2015.7351702

Cited by 39 publications

(22 citation statements)

References 13 publications

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“…After the bilateral filtration, edges of the visible image and edges extracted from Ours 9/10 7/10 10/10 10/10 10/10 10/10 8/10 10/10 10/10 10/10 10/10 10/10 10/10 10/10 10/10 SVM -thermal 6/10 6/10 7/10 8/10 8/10 7/10 5/10 10/10 9/10 7/10 8/10 9/10 9/10 10/10 8/10 SVM -visible 5/10 3/10 6/10 6/10 6/10 5/10 5/10 7/10 6/10 5/10 5/10 6/10 7/10 7/10 7/10 MTL-CASO [38] 7/10 6/10 8/10 9/10 9/10 9/10 6/10 10/10 9/10 9/10 9/10 9/10 10/10 10/10 8/10 MTL-Dirty [12] 6/10 6/10 7/10 9/10 8/10 7/10 7/10 9/10 9/10 7/10 8/10 7/10 9/10 10/10 9/10 MTL-FTC [37] 8/10 8/10 8/10 10/10 9/10 8/10 7/10 10/10 9/10 9/10 8/10 9/10 9/10 10/10 8/10 MTL-Robust [10] 8/10 7/10 8/10 9/10 9/10 9/10 7/10 10/10 9/10 9/10 9/10 9/10 10/10 9/10 8/10 thermal images following DoG are quite similar. As our infrared camera's field-of-view is much smaller than that of our visible image camera, we perform the thermal-visible camera calibration [21] for finding the corresponding part of the thermal image on the visible images.We divide the whole thermal image in 8 * 8 grids (Fig.8). From the individual grids, we extract a MPEG-7 edge-orientation histogram [16], each feature of which is a 5 dimensional histogram.…”

Section: Results For Mtl Thermal Images Localizationmentioning

confidence: 99%

Localize Me Anywhere, Anytime: A Multi-task Point-Retrieval Approach

Yan

Ren

et al. 2015

2015 IEEE International Conference on Computer Vision (ICCV)

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Image-based localization is an essential complement to GPS localization. Current image-based localization methods are based on either 2D-to-3D or 3D-to-2D to find the correspondences, which ignore the real scene geometric attributes. The main contribution of our paper is that we use a 3D model reconstructed by a short video as the query to realize 3D-to-3D localization under a multi-task point retrieval framework. Firstly, the use of a 3D model as the query enables us to efficiently select location candidates. Furthermore, the reconstruction of 3D model exploits the correlation among different images, based on the fact that images captured from different views for SfM share information through matching features. By exploring shared information (matching features) across multiple related tasks (images of the same scene captured from different views), the visual feature's view-invariance property can be improved in order to get to a higher point retrieval accuracy. More specifically, we use multi-task point retrieval framework to explore the relationship between descriptors and the 3D points, which extracts the discriminant points for more accurate 3D-to-3D correspondences retrieval. We further apply multi-task learning (MTL) retrieval approach on thermal images to prove that our MTL retrieval framework also provides superior performance for the thermal domain. This application is exceptionally helpful to cope with the localization problem in an environment with limited or even no light sources.

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Section: Results For Mtl Thermal Images Localizationmentioning

confidence: 99%

Localize Me Anywhere, Anytime: A Multi-task Point-Retrieval Approach

Yan

Ren

et al. 2015

2015 IEEE International Conference on Computer Vision (ICCV)

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“…For this reason, the calibration performed may be inaccurate after a few seconds [21]. In 2015, Saponaro et al [22] suggested placing a checkerboard, printed on sheet of paper, on a ceramic surface and heating the surface with a heating lamp. Their approach extended the time of calibration to several minutes, but it was tested only indoors under strictly controlled conditions.…”

Section: Calibration Of Multimodal Vis-irmentioning

confidence: 99%

Online Correction of the Mutual Miscalibration of Multimodal VIS–IR Sensors and 3D Data on a UAV Platform for Surveillance Applications

et al. 2019

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Unmanned aerial vehicles (UAVs) are widely used to protect critical infrastructure objects, and they are most often equipped with one or more RGB cameras and, sometimes, with a thermal imaging camera as well. To obtain as much information as possible from them, they should be combined or fused. This article presents a situation in which data from RGB (visible, VIS) and thermovision (infrared, IR) cameras and 3D data have been combined in a common coordinate system. A specially designed calibration target was developed to enable the geometric calibration of IR and VIS cameras in the same coordinate system. 3D data are compatible with the VIS coordinate system when the structure from motion (SfM) algorithm is used. The main focus of this article is to provide the spatial coherence between these data in the case of relative camera movement, which usually results in a miscalibration of the system. Therefore, a new algorithm for the detection of sensor system miscalibration, based on phase correlation with automatic calibration correction in real time, is introduced.

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“…Indeed, the difference of emissivity of the black and white regions of a regular chessboard is insufficient for the thermal camera to have stable corners to perform the calibration. A common method to increase the temperature difference between the two regions is to heat the pattern with a flood lamp [17,20]. Nevertheless, Vidas et al [23] pointed out the struggle to detect crisp corners for accurate calibration and the difficulty to execute it.…”

Section: Calibration Setupmentioning

confidence: 99%

“…A conductive rubber tape is placed on the black parts of the printed chessboard pattern coupled with a low heat capacity support to increase the thermal radiation. Then, instead of using a flood lamp that produces a non-uniform heating [20], we cool down the calibration board. The Figure 2 shows the calibration chessboard captured simultaneously by both cameras.…”

Section: Calibration Setupmentioning

confidence: 99%

Registration of RGB and Thermal Point Clouds Generated by Structure From Motion

Truong

Yamawaki

Mori

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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Thermal imaging has become a valuable tool in various fields for remote sensing and can provide relevant information to perform object recognition or classification. In this paper, we present an automated method to obtain a 3D model fusing data from a visible and a thermal camera. The RGB and thermal point clouds are generated independently by structure from motion. The registration process includes a normalization of the point cloud scale, a global registration based on calibration data and the output of the structure from motion, and a fine registration employing a variant of the Iterative Closest Point optimization. Experimental results demonstrate the accuracy and robustness of the overall process.

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Improving calibration of thermal stereo cameras using heated calibration board

Cited by 39 publications

References 13 publications

Localize Me Anywhere, Anytime: A Multi-task Point-Retrieval Approach

Localize Me Anywhere, Anytime: A Multi-task Point-Retrieval Approach

Online Correction of the Mutual Miscalibration of Multimodal VIS–IR Sensors and 3D Data on a UAV Platform for Surveillance Applications

Registration of RGB and Thermal Point Clouds Generated by Structure From Motion

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