计算热像仪的本征？
我正在一个项目中使用热感相机，但我对如何考虑计算其内在函数感到有点困惑。通常的相机会确定棋盘或类似物体上的不同点，但热感相机不会真的能够区分这些点。有谁对热像仪的内部结构有任何了解吗？
编辑
-除了我目前提出的很好的建议之外，我还考虑在上面使用铝箔。让我知道你对这个想法的看法。
I"m using a Thermal camera for a project and I'm a little stumped so as to how to think about calculating intrinsics for it. The usual camera's would determine different points on a chessboard or something similar, but the thermal camera won't really be able to differentiate between those points. Does anyone have any insight on what the intrinsics for thermal cameras would really look like?
Cheers!
EDIT - In addition to the great suggestions I currently have, I'm also considering using aluminum foil on the whites to create a thermal difference. Let me know what you think of this idea as well.
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这可能有效，也可能无效，具体取决于您需要的准确性：
This might or might not work, depending on the accuracy you need:
这个问题在基于掩模的几何校准方法中得到解决热红外相机，基本上提倡在计算机显示器等辐射源前放置一个切有棋盘格方块的不透明掩模。
相关代码可以在 mm-calibrator 中找到。
This problem is addressed in A Mask-Based Approach for the Geometric Calibration of Thermal Infrared Cameras, which basically advocates placing an opaque mask with checkerboard squares cut out of it in front of a radiating source such as a computer monitor.
Related code can be found in mm-calibrator.
如果您的相机对光谱的可见光端也敏感（即大多数红外相机 - 毕竟这是大多数热成像技术的基础），那么只需获取一个红外截止滤光片并将其安装在红外相机的前面即可。相机镜头（您可以获得一些基于 C 接口的优质镜头）。正常校准固定光学器件，然后拆下滤光片。本质应该是相同的 - 因为光学特性是相同的（对于大多数用途）。
If you have a camera that is also sensitive to the visible light end of the spectrum (i.e. most IR cameras - which is what most Thermography is based on after all) then simply get a IR cut-off filter and fit this to front of the cameras lens (you can get some good c-mount based ones). Calibrate as normal to the fixed optics then remove the filter. Intrinsics should be the same - since optical properties are the same (for most purposes).
引用5图像融合部分
在GADE，Rikke； MOESLUND，Thomas B. 热感相机和应用：一项调查。 机器视觉和应用，2014 年，25.1：245-262。（2014 年 6 月免费下载）：
[30] 程信子 Y.；帕克，桑戈； TRIVEDI，Mohan M。用于 3D 身体跟踪和驾驶员活动分析的多视角热红外和视频阵列。见：计算机视觉和模式识别研讨会，2005 年。CVPR 研讨会。 IEEE 计算机学会会议。 IEEE，2005 年。 3-3.
[146] PRAKASH、Surya 等人。强大的热像仪校准和物体表面温度的 3D 映射。见：国防与安全研讨会。国际光学与光子学学会，2006 年。 62050J-62050J-8。
[180] VIDAS，Stephen 等人。一种基于掩模的热红外相机几何校准方法。 仪器与测量，IEEE Transactions，2012 年，61.6：1625-1635。
[68] HILSENSTEIN, V. 使用热成像立体成像对水波进行表面重建。地点：新西兰图像与视觉计算。 2005 年。 102-107。
[195] NG，Harry 等人。获取车身3D表面温度分布。见：信息采集，2005 年 IEEE 国际会议。 IEEE，2005 年。 5 页。
Quoting from the section 5 Image fusion
in GADE, Rikke; MOESLUND, Thomas B. Thermal cameras and applications: a survey. Machine Vision and Applications, 2014, 25.1: 245-262. (freely downloadable in June 2014):
[30] CHENG, Shinko Y.; PARK, Sangho; TRIVEDI, Mohan M. Multiperspective thermal ir and video arrays for 3d body tracking and driver activity analysis. In: Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on. IEEE, 2005. p. 3-3.
[146] PRAKASH, Surya, et al. Robust thermal camera calibration and 3D mapping of object surface temperatures. In: Defense and Security Symposium. International Society for Optics and Photonics, 2006. p. 62050J-62050J-8.
[180] VIDAS, Stephen, et al. A mask-based approach for the geometric calibration of thermal-infrared cameras. Instrumentation and Measurement, IEEE Transactions on, 2012, 61.6: 1625-1635.
[68] HILSENSTEIN, V. Surface reconstruction of water waves using thermographic stereo imaging. In: Image and Vision Computing New Zealand. 2005. p. 102-107.
[195] NG, Harry, et al. Acquisition of 3D surface temperature distribution of a car body. In: Information Acquisition, 2005 IEEE International Conference on. IEEE, 2005. p. 5 pp.
您可能需要考虑在图案的线上运行热电阻线（您还需要电源）。
You may want to consider running a thermal resistor wire on the lines of the pattern (you also need a power source).
您可以在金属板上钻孔，然后加热板，希望孔会比板冷，并且在图像中显示为圆圈。
然后你可以使用OpenCV（>2.0）找到圆心 http ://docs.opencv.org/doc/tutorials/calib3d/camera_calibration/camera_calibration.html#cameracalibrationopencv
参见还有函数
findCirclesGrid。You can drill holes in a metal plate and then heat the plate, hopefully the holes will be colder than the plate and will appear as circles in the image.
Then you can use OpenCV (>2.0) to find circle centers http://docs.opencv.org/doc/tutorials/calib3d/camera_calibration/camera_calibration.html#cameracalibrationopencv
See also the function
findCirclesGrid.