An effective compression algorithm for pulsed thermography data

Lugin, S.; Netzelmann, U.

doi:10.1016/j.ndteint.2005.01.005

Cited by 14 publications

(15 citation statements)

References 2 publications

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“…The inspected sample response is recorded with an infrared camera in digital form for careful analysis to characterize the existing defects [5,6,7,8].There are several methods in the literature who studied the thermal responses samples for determining the geometric characteristics of the defects such as depth and size. Among these methods, there are recent methods in the nondestructive testing field, there are intelligent methods using neural networks…”

Section: Pulsed Infrared Thermographymentioning

confidence: 99%

An intelligent method using neural networks for Depth detection by standard thermal contrast in active thermography

Halloua¹,

Elhassnaoui²,

Saifi³

et al. 2016

Proceedings of the 2016 International Conference on Quantitative InfraRed Thermography

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Today the infrared thermography is among the nondestructive testing methods (NDT) most used for detection and characterization of internal defects in materials. It has become a reference method in industrial installations control. As the interpretation of thermal images provided by the infrared cameras is often difficult; therefore, it is necessary; to seek new methods fast and reliable for intelligent nondestructive evaluation. In our work we propose a fast method using artificial neural networks for internal defects depth evaluation from the thermal contrast. Experimental results have confirmed the method efficiency in predicting the defects depths.

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Section: Pulsed Infrared Thermographymentioning

confidence: 99%

An intelligent method using neural networks for Depth detection by standard thermal contrast in active thermography

Halloua¹,

Elhassnaoui²,

Saifi³

et al. 2016

Proceedings of the 2016 International Conference on Quantitative InfraRed Thermography

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“…In the proposed technique, we use synthetic PT data with low noise to significantly simplify their processing. Synthetic data may be obtained from experimental data by using one of the procedures previously reported [28][29][30][31][32][33]. Then, we build a nodal network of a defect-free sample with the same geometry, structure, and physical properties as the inspected object.…”

Section: Nondestructive Testing Through Thermographic Techniquesmentioning

confidence: 99%

Three-dimensional reconstruction of subsurface defects using finite-difference modeling on pulsed thermography

2012

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We develop a technique to analyze pulsed thermography videos in order to detect and reconstruct subsurface defects in homogeneous and layered objects. The technique is based on the analysis of the thermal response of an object to a heat pulse. This thermal response is compared to the predictions of a finite-difference model that is systematically and progressively adjusted to minimize a cost function. With this minimization process, we obtain a depth and a thickness function that allow us to determine the three-dimensional shape, size, depth, thickness, and location of internal defects. The detected defects are reliably reconstructed with graphics of easy interpretation.

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“…due to operational requirements. The space/time mapping (STM) method [8,9] transforms the input image sequence into a single image, which is then processed by a standard image compression algorithm like JPEG. Although originally designed for compressing the same type of data as those for TSR, STM does not necessarily require a diffusion process or a stationary object under test.…”

Section: Introductionmentioning

confidence: 99%

Real-time compression of high-bandwidth measurement data of thermographic cameras with high temporal and spatial resolution

Wang¹,

Najmabadi²,

Baroud³

et al. 2016

Proceedings of the 2016 International Conference on Quantitative InfraRed Thermography

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The high temporal and spatial resolution of industrial thermographic cameras creates a rapid growth in the bandwidth of measurement data, such that the bandwidth of widely used standard data transmission links is becoming a performance bottleneck for thermographic systems. This paper proposes the use of real-time compression for the measurement data in future high-performance thermographic cameras. Real-time compression of high-throughput input stream is achieved via a lightweight and low-latency algorithm based on predictive coding. To obtain a high compression ratio while ensuring the quality of compressed data, the compression core exploits image-dependent measurement information such as calibration range and controls the compression quality correspondingly. Experimental results show that the proposed camera integrated thermographic data compression reduces the average required bandwidth by over 85%.

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An effective compression algorithm for pulsed thermography data

Cited by 14 publications

References 2 publications

An intelligent method using neural networks for Depth detection by standard thermal contrast in active thermography

An intelligent method using neural networks for Depth detection by standard thermal contrast in active thermography

Three-dimensional reconstruction of subsurface defects using finite-difference modeling on pulsed thermography

Real-time compression of high-bandwidth measurement data of thermographic cameras with high temporal and spatial resolution

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