A novel iterative calibration approach for thermal infrared cameras

Ellmauthaler, Andreas; Silva, Eduardo A. B. da; Pagliari, Carla L.; Gois, Jonathan N.; Neves, S.R.

doi:10.1109/icip.2013.6738450

Cited by 26 publications

(24 citation statements)

References 17 publications

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“…An index commonly used for evaluating the quality of a camera calibration is the mean reprojection error (MRE) of the corner points': in this work, an MRE = 0.13 pixels has been achieved after processing 12 checkerboard images. Such MRE value is quite in line with published data on thermal cameras calibration employing standard checkerboard patterns [28], even though lower MRE values can be achieved with dedicated calibration devices [29]. The estimated intrinsic camera parameters are reported in Table 3.…”

Section: Experimental Testbedsupporting

confidence: 87%

Attitude Sensor from Ellipsoid Observations: A Numerical and Experimental Validation

Modenini

Locarini

Zannoni

2020

Sensors

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The preliminary design and validation of a novel, high accuracy horizon-sensor for small satellites is presented, which is based on the theory of attitude determination from ellipsoid observations. The concept consists of a multi-head infrared sensor capturing images of the Earth limb. By fitting an ellipse to the imaged limb arcs, and exploiting some analytical results available from projective geometry, a closed form solution for computing the attitude matrix is provided. The algorithm is developed in a dimensionless framework, requiring the knowledge of the shape of the imaged target, but not of its size. As a result, the solution is less sensitive to the limb shift caused by the atmospheric own radiance. To evaluate the performance of the proposed method, a numerical simulator is developed, which generates images captured in low Earth orbit, including also the presence of the atmosphere. In addition, experimental validation is provided due to a dedicated testbed, making use of a miniature infrared camera. Results show that our sensor concept returns rms errors of few hundredths of a degree or less in determining the local nadir direction.

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Section: Experimental Testbedsupporting

confidence: 87%

Attitude Sensor from Ellipsoid Observations: A Numerical and Experimental Validation

Modenini

Locarini

Zannoni

2020

Sensors

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“…However, since the checkerboard pattern is not visible in the spectrum of thermal infrared, it is not possible to utilize regular checkerboard patterns for thermal sensors. Geometric calibration of thermal sensors is therefore often done by utilizing a pattern consisting of lamps (Lagüela et al, 2011a, Weinmann et al, 2014, Ellmauthaler et al, 2013. By creating a regular grid of lamps, the possibility of using standard calibration procedures for sensors is enabled.…”

Section: Calibrationmentioning

confidence: 99%

Concept for Classifying Facade Elements Based on Material, Geometry and Thermal Radiation Using Multimodal Uav Remote Sensing

Ilehag

Schenk

Hinz

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents a concept for classification of facade elements, based on the material and the geometry of the elements in addition to the thermal radiation of the facade with the usage of a multimodal Unmanned Aerial Vehicle (UAV) system. Once the concept is finalized and functional, the workflow can be used for energy demand estimations for buildings by exploiting existing methods for estimation of heat transfer coefficient and the transmitted heat loss. The multimodal system consists of a thermal, a hyperspectral and an optical sensor, which can be operational with a UAV. While dealing with sensors that operate in different spectra and have different technical specifications, such as the radiometric and the geometric resolution, the challenges that are faced are presented. Addressed are the different approaches of data fusion, such as image registration, generation of 3D models by performing image matching and the means for classification based on either the geometry of the object or the pixel values. As a first step towards realizing the concept, the result from a geometric calibration with a designed multimodal calibration pattern is presented.

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“…Thus, key point extraction for heated chessboard pattern using IR camera is less precise compared to calibration process for optical cameras. Review of the literature in this area indicated that using a heated chessboard pattern alone, and the same calibration algorithm as used for optical cameras will result in projection error greater than 1 [55]. In this study re-projection error for FLIR PathFindIR infra-red camera using a heated chessboard was found to be between 1.2-1.5 after multiple repetitions.…”

Section: Infrared Camera Calibrationmentioning

confidence: 66%

“…Due to the physical characteristics of the IR acquisition process, accurate localization of the calibration features is generally not as easy as the optical process, subsequently causing unsatisfying calibration results. Review of the literature in this area indicates that using a heated chessboard pattern alone, and the same calibration algorithm as used for optical cameras will result in projection error greater than 1 [55].…”

Section: Ir Camera Calibrationmentioning

confidence: 99%

“…To improve the calibration quality for IR cameras, innovative methods were found in the literature including using additional sensors such as a laser range finder [56], or using an ellipse algorithm [55] which claim to be able to reduce the re-projection error IR cameras close to 0.5 or less which are typically recommended for calibration with optical cameras.…”

Section: Ir Camera Calibrationmentioning

confidence: 99%

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