Systematic approach for thermal imaging camera calibration for machine vision applications

Swamidoss, Issac Niwas; Amro, Amani Bin; Sayadi, Slim

doi:10.1016/j.ijleo.2021.168039

Cited by 20 publications

(8 citation statements)

References 23 publications

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“…Similarly, a study involved the geometric calibration of thermal cameras, part of a target tracking project, and used a square checkerboard pattern made of a white highly emissive tape placed on an aluminum foil base [ 38 ]. Likewise, a recent study tested a square checkerboard pattern built from thick aluminum foil and thick thermocol (polystyrene) square pieces covered with white polyvinyl acetate (PVA) glue [ 19 ]. Geometric calibration was performed indoors, without external heating on long-wave infrared (LWIR) and mid-wave infrared (MWIR) cameras.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Intrinsic parameters are values of the interior orientation elements of a camera, such as the principal points in image coordinate systems, focal length, skewness and, most importantly the radial and tangential distortions caused by the lens [ 16 , 17 , 18 ]. Radial and tangential distortions caused by the camera’s lens disfigure the captured images by introducing barrel or pincushion effects [ 19 ], as displayed in Figure 1 . Extrinsic parameters, however, allow the transformation of a 3D geometrical scene to the camera’s 3D coordinate frame.…”

Section: Introductionmentioning

confidence: 99%

“…This permits the representation of the spatial location of the camera in a 3D scene, allowing for measurements in world coordinates [ 6 ]. Thus, geometric calibration is crucial in photogrammetry to determine the relationship between 3D world coordinate points and 2D image coordinate points on the detector level of the camera [ 19 ]. Visible camera calibration is usually performed using the widely known checkerboard pattern that is printed using inexpensive off-the-shelf printers [ 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Infrared Camera Geometric Calibration: A Review and a Precise Thermal Radiation Checkerboard Target

Elsheikh

Abu-Nabah

Hamdan

et al. 2023

Sensors

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Different infrared (IR) planar geometric calibration targets have been developed over the years that exploit a well-established and flexible optical camera geometric calibration procedure following the pinhole approximation. This geometric calibration is typically neglected in IR cameras, due to the relatively low resolution of thermal images and the complex IR targets needed for the geometric calibration in comparison to the optical targets. In this study, a thorough literature review of numerous IR camera geometric calibration targets, along with their respective outcomes, were summarized and leveraged to deliver a practical checkerboard target for less experienced end users, while offering the lowest reprojection errors. It was concluded that the fabrication of high emissivity contrast and precise square points of intersection within a checkerboard pattern extends the accuracy of capturing these control points in a thermal image for an optimized IR camera geometric calibration. Accordingly, two simple planar checkerboard targets were fabricated using laser engraving and ultraviolet (UV) printing technologies on a polished stainless steel (SS304) plate. The UV-printed checkerboard target on a polished metallic alloy delivered the lowest mean reprojection error (MRE) of 0.057 pixels and the lowest root mean square error (RMSE) of reprojection of 0.063 pixels, with a standard deviation lower than 0.003 pixels. The UV-printed design offers better accuracy than any other checkerboard calibration target, and comparable results to the best prominent circular pattern results reported in the literature.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Infrared Camera Geometric Calibration: A Review and a Precise Thermal Radiation Checkerboard Target

Elsheikh

Abu-Nabah

Hamdan

et al. 2023

Sensors

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show abstract

“…The difference in the position of the shrimp with the distance of the shrimp object with the camera varies, causing the shrimp captured by the camera in the form of a 2D image to have an inaccurate size [10]. A method is needed to determine the estimated distance of the position of shrimp underwater with a camera, so that from the estimated distance obtained, it can be used to increase the accuracy of monitoring shrimp size [11].…”

Section: Introductionmentioning

confidence: 99%

“…Edge detection is the stage for detecting objects after the thresholding process. In this operation, the edges of objects are detected to make the boundary between foreground and background clearer [11]. Canny algorithm is the most widely used method in edge detection [14].…”

Section: Introductionmentioning

confidence: 99%

Distance Estimation Between Camera and Shrimp Underwater Using Euclidian Distance and Triangles Similarity Algorithm

Setiawan¹,

Hadiyanto²,

Widodo³

2022

ISI

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Camera is the main tool for monitoring shrimp underwater with a noninvasive method. Distance of the shrimp underwater with the varying camera causes the monitoring of the estimated shrimp size to be less accurate. This study provides a new solution to detect the distance of shrimp underwater with a camera using the Euclidian distance and triangle similarity algorithm. The problem raised in this study is how to measure the distance of a shrimp underwater with a camera. The method used has several stages, including using the grayscale image method, image thresholding, edge detection, detection of Region of Interest (ROI), determining the position of shrimp coordinates, calculating the length of shrimp coordinates, camera calibration using the triangle similarity algorithm, calculating the estimated distance of shrimp with the camera. The study results obtained a focal length value of 1298.58, the value of the distance between the shrimp and the camera (D') for 5 positions of shrimp underwater from 50 cm – 19.86 cm, and the RE value from 0% - 0.13%. In conclusion, this method can be used to measure the estimated distance between of moving shrimp and a camera with a low error rate.

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Thermal Imaging

Mohd Ali,

Hashim,

Maringgal

et al. 2024

Non-Invasive and Non-Destructive Methods for Food Integrity

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Systematic approach for thermal imaging camera calibration for machine vision applications

Cited by 20 publications

References 23 publications

Infrared Camera Geometric Calibration: A Review and a Precise Thermal Radiation Checkerboard Target

Infrared Camera Geometric Calibration: A Review and a Precise Thermal Radiation Checkerboard Target

Distance Estimation Between Camera and Shrimp Underwater Using Euclidian Distance and Triangles Similarity Algorithm

Thermal Imaging

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