Infrared image processing and data analysis

Ibarra-Castanedo, Clemente; González, Daniel A.; Klein, M.; Pilla, M.; Vallerand, Steve; Maldague, Xavier

doi:10.1016/j.infrared.2004.03.011

Cited by 191 publications

(96 citation statements)

References 9 publications

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“…The following chapter shall focus on the application of load that enables the technique to be taken out of the laboratory and into the field. 125 …”

Section: Discussionmentioning

confidence: 99%

“…It was found that whilst both image types produced good defect size measurements, the maximum contrast phase image was slightly more accurate. In Ibarra-Castanedo et al [125] it is suggested that defect sizing should take place either at the peak contrast slope, where the definition of edges is clearest, the maximum contrast where the contrast between defective and nondefective areas is greatest (as suggested in [87]), or as early as possible once the anomaly is detected in the thermal data. It is likely that the size of the defect will be over predicted as lateral thermal diffusion, i.e.…”

Section: Defect Size Predictionmentioning

confidence: 99%

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Development of Infrared Techniques for Practical Defect Identification in Bonded Joints

Waugh

2016

Springer Theses

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“…The following chapter shall focus on the application of load that enables the technique to be taken out of the laboratory and into the field. 125 …”

Section: Discussionmentioning

confidence: 99%

Section: Defect Size Predictionmentioning

confidence: 99%

Development of Infrared Techniques for Practical Defect Identification in Bonded Joints

Waugh

2016

Springer Theses

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“…It uses the assumption that a sound area can reasonably be modeled by a semi infinite body [2][3][9] [10]. This assumption is quite good for most cases of specimens inspected including composite and anisotropic materials such as CFRP.…”

Section: Principle Of Existing Extrapolated Contrast (Ec) Methodsmentioning

confidence: 99%

“…The second issue that arises from contrast computations is the non-uniform initial heating which produces sound areas with different shifted temperature evolution. In 2001, the Differentiated Absolute Contrast or DAC solved both issues which propelled the limits of thermal contrasts in terms of quality and accuracy [2][3]. The DAC belongs to a type of extrapolated contrast (EC) that is based on the extrapolation of the temperature measured at an early time through a transient heat transfer model.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Infrared Images Contrast for Pulsed Thermography

Klein¹,

Bendada²,

Pilla³

et al. 2008

Proceedings of the 2008 International Conference on Quantitative InfraRed Thermography

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In pulsed thermography the sample to test is briefly heated with a Dirac δ heat impulse and then observed through an infrared (IR) camera recording thermal images as the sample is cooling down. The difference of temperature on the IR images between the defective areas and the sound areas is referred to as a thermal contrast [1]. The Signal-to-Noise Ratio (SNR) of such a contrast is strongly affected by the non uniformity of the initial heating impulse over the specimen. This paper describes a way to enhance this SNR by comparing the temperature evolution of each pixel of the image against the temperature evolution of a heat transfer model. IntroductionIn non destructive testing (NDT) by infrared thermography, a thermal contrast designates the difference of heat observed on the IR image between a defective area and a sound or non defective area of the specimen under inspection. A smaller contrast indicates a smaller defect or a defect that is buried deeper inside the specimen. From this definition, a simple raw IR image or thermogram can be seen as the simplest form of thermal contrast affected by a given offset. Although, when a quantitative or a better qualitative analysis is needed it is often required to further improve the thermal contrast. The absolute contrast, the running contrast, the normalized contrast and standard contrast are typical methods of computing a thermal contrast in IR thermography [1]. The drawback is that computing any of these contrasts requires knowing at least one point belonging to a sound area. It is not always possible to precisely locate such sound areas from the raw IR images in advance. This means that only reasonable assumptions can be made about the location of sound areas from the raw IR images to calculate any of the previous thermal contrast. The second issue that arises from contrast computations is the non-uniform initial heating which produces sound areas with different shifted temperature evolution. In 2001, the Differentiated Absolute Contrast or DAC solved both issues which propelled the limits of thermal contrasts in terms of quality and accuracy [2][3]. The DAC belongs to a type of extrapolated contrast (EC) that is based on the extrapolation of the temperature measured at an early time through a transient heat transfer model. This paper recalls the fundamentals of the two existing EC methods that are in turn used to introduce a third EC model that slightly improves the limits of the EC. Principle of existing extrapolated contrast (EC) methodsIn EC methods, the temperature evolutions of the sound areas are not measured. Instead, they are modeled. This is achieved by computing the temperature evolution through a thermal model. The difference between the various EC's mostly lies in the thermal models used to simulate a sound area. The sound area temperature evolution of a given point is computed starting from its initial known temperature just right after the heat impulse. In this sense, the sound area temperature evolution is an extrapolation of temperature. T...

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“…In this case the basic function are not known a priori as for PPT but are built on the base of the criterion of the maximization of the variance in the temperature signal, with the constraint to represent an orthogonal system of functions as for PPT. A summary of these algorithms is given in [4]. More recently the TWSR (Thermal Waves Signal Reconstruction) [5] has been proposed to enhance the defects contrast in lock-in thermography experiments.…”

Section: Introductionmentioning

confidence: 99%

Thermographic Signal Reconstruction with periodic temperature variation applied to moisture classification

Bison

Cadelano

Grinzato

2011

Quantitative InfraRed Thermography Journal

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A method to analyse thermographic data with periodic temperature variation is proposed. A periodic heat flux is generated on the surface of a specimen with a non-zero average value. Functions describing the periodic temperature response plus the transient effect are considered. A possible linear trend is also taken into account aiming at considering slow environmental variations. Based on the superposition of the effects, thanks to the linearity of the heat conduction equation, the analysis of the thermographic signal is proposed that applies to the classification of the evaporative effects in moistened porous building materials.

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Infrared image processing and data analysis

Cited by 191 publications

References 9 publications

Development of Infrared Techniques for Practical Defect Identification in Bonded Joints

Development of Infrared Techniques for Practical Defect Identification in Bonded Joints

Enhancing Infrared Images Contrast for Pulsed Thermography

Thermographic Signal Reconstruction with periodic temperature variation applied to moisture classification

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