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Dr. Clemente Ibarra-Castanedo
Université Laval, Québec, Canada

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Research Keywords & Expertise

0 Nondestructive Testing (NDT)
0 Signal Processing
0 defect detection
0 infrared thermography
0 Image and Signal Processing

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infrared thermography
Nondestructive Testing (NDT)
defect detection
Signal Processing

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Short Biography

Clemente Ibarra-Castanedo is a professional researcher in the Computer Vision and Systems Laboratory of Laval University in Quebec City, Canada. As a member of the multipolar infrared vision Canada Chair (MIVIM), he has contributed to several publications in the field of infrared vision. His research interests are in signal processing and image analysis for the nondestructive characterization of materials by active thermography, as well as near and short-wave infrared reflectography/transmittography imaging. He is currently the Coordinator of the CREATE-oN DuTy! Program.

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Journal article
Published: 28 May 2021 in Remote Sensing
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Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

ACS Style

Bardia Yousefi; Clemente Ibarra-Castanedo; Martin Chamberland; Xavier Maldague; Georges Beaudoin. Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery. Remote Sensing 2021, 13, 2125 .

AMA Style

Bardia Yousefi, Clemente Ibarra-Castanedo, Martin Chamberland, Xavier Maldague, Georges Beaudoin. Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery. Remote Sensing. 2021; 13 (11):2125.

Chicago/Turabian Style

Bardia Yousefi; Clemente Ibarra-Castanedo; Martin Chamberland; Xavier Maldague; Georges Beaudoin. 2021. "Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery." Remote Sensing 13, no. 11: 2125.

Review
Published: 12 May 2021 in Applied Sciences
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Dimensional reduction methods have significantly improved the simplification of Pulsed Thermography (PT) data while improving the accuracy of the results. Such approaches reduce the quantity of data to analyze and improve the contrast of the main defects in the samples contributed to their popularity. Many works have been proposed in the literature mainly based on improving the Principal Component Thermography (PCT). Recently the Independent Component Analysis (ICA) has been a topic of attention. Many different approaches have been proposed in the literature to solve the ICA. In this paper, we investigated several recent ICA methods and evaluated their influence on PT data compared with the state-of-the-art methods. We conducted our evaluation on reference CFRP samples with known defects. We found that ICA outperform PCT for small and deep defects. For other defects ICA results are often not far from the results obtained by PCT. However, the frequency of acquisition and the ICA methods have a great influence on the results.

ACS Style

Julien Fleuret; Samira Ebrahimi; Clemente Ibarra-Castanedo; Xavier Maldague. Independent Component Analysis Applied on Pulsed Thermographic Data for Carbon Fiber Reinforced Plastic Inspection: A Comparative Study. Applied Sciences 2021, 11, 4377 .

AMA Style

Julien Fleuret, Samira Ebrahimi, Clemente Ibarra-Castanedo, Xavier Maldague. Independent Component Analysis Applied on Pulsed Thermographic Data for Carbon Fiber Reinforced Plastic Inspection: A Comparative Study. Applied Sciences. 2021; 11 (10):4377.

Chicago/Turabian Style

Julien Fleuret; Samira Ebrahimi; Clemente Ibarra-Castanedo; Xavier Maldague. 2021. "Independent Component Analysis Applied on Pulsed Thermographic Data for Carbon Fiber Reinforced Plastic Inspection: A Comparative Study." Applied Sciences 11, no. 10: 4377.

Journal article
Published: 22 April 2021 in Applied Sciences
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It was recently demonstrated that a coplanar capacitive sensor could be applied to the evaluation of materials without the disadvantages associated with the other techniques. This technique effectively detects changes in the dielectric properties of the materials due to, for instance, imperfections or variations in the internal structure, by moving a set of simple electrodes on the surface of the specimen. An AC voltage is applied to one or more electrodes and signals are detected by others. This is a promising inspection method for imaging the interior structure of the numerous materials, without the necessity to be in contact with the surface of the sample. In this paper, finite element (FE) modeling was employed to simulate the electric field distribution from a coplanar capacitive sensor and the way it interacts with a nonconducting sample. Physical experiments with a prototype capacitive sensor were also performed on a Plexiglas sample with subsurface defects, to assess the imaging performance of the sensor. A good qualitative agreement was observed between the numerical simulation and experimental result.

ACS Style

Farima Abdollahi-Mamoudan; Sebastien Savard; Tobin Filleter; Clemente Ibarra-Castanedo; Xavier P. V. Maldague. Numerical Simulation and Experimental Study of Capacitive Imaging Technique as a Nondestructive Testing Method. Applied Sciences 2021, 11, 3804 .

AMA Style

Farima Abdollahi-Mamoudan, Sebastien Savard, Tobin Filleter, Clemente Ibarra-Castanedo, Xavier P. V. Maldague. Numerical Simulation and Experimental Study of Capacitive Imaging Technique as a Nondestructive Testing Method. Applied Sciences. 2021; 11 (9):3804.

Chicago/Turabian Style

Farima Abdollahi-Mamoudan; Sebastien Savard; Tobin Filleter; Clemente Ibarra-Castanedo; Xavier P. V. Maldague. 2021. "Numerical Simulation and Experimental Study of Capacitive Imaging Technique as a Nondestructive Testing Method." Applied Sciences 11, no. 9: 3804.

Communication
Published: 16 April 2021 in Sensors
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Infrared thermography has been widely adopted in many applications for material structure inspection, where data analysis methods are often implemented to elaborate raw thermal data and to characterize material structural properties. Herein, a multiscale thermographic data analysis framework is proposed and applied to building structure inspection. In detail, thermograms are first collected by conducting solar loading thermography, which are then decomposed into several intrinsic mode functions under different spatial scales by multidimensional ensemble empirical mode decomposition. At each scale, principal component analysis (PCA) is implemented for feature extraction. By visualizing the loading vectors of PCA, the important building structures are highlighted. Compared with principal component thermography that applies PCA directly to raw thermal data, the proposed multiscale analysis method is able to zoom in on different types of structural features.

ACS Style

Katherine Tu; Clemente Ibarra-Castanedo; Stefano Sfarra; Yuan Yao; Xavier Maldague. Multiscale Analysis of Solar Loading Thermographic Signals for Wall Structure Inspection. Sensors 2021, 21, 2806 .

AMA Style

Katherine Tu, Clemente Ibarra-Castanedo, Stefano Sfarra, Yuan Yao, Xavier Maldague. Multiscale Analysis of Solar Loading Thermographic Signals for Wall Structure Inspection. Sensors. 2021; 21 (8):2806.

Chicago/Turabian Style

Katherine Tu; Clemente Ibarra-Castanedo; Stefano Sfarra; Yuan Yao; Xavier Maldague. 2021. "Multiscale Analysis of Solar Loading Thermographic Signals for Wall Structure Inspection." Sensors 21, no. 8: 2806.

Journal article
Published: 10 April 2021 in Sensors
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Pulsed Thermography (PT) data are usually affected by noise and as such most of the research effort in the last few years has been directed towards the development of advanced signal processing methods to improve defect detection. Among the numerous techniques that have been proposed, principal component thermography (PCT)—based on principal component analysis (PCA)—is one of the most effective in terms of defect contrast enhancement and data compression. However, it is well-known that PCA can be significantly affected in the presence of corrupted data (e.g., noise and outliers). Robust PCA (RPCA) has been recently proposed as an alternative statistical method that handles noisy data more properly by decomposing the input data into a low-rank matrix and a sparse matrix. We propose to process PT data by RPCA instead of PCA in order to improve defect detectability. The performance of the resulting approach, Robust Principal Component Thermography (RPCT)—based on RPCA, was evaluated with respect to PCT—based on PCA, using a CFRP sample containing artificially produced defects. We compared results quantitatively based on two metrics, Contrast-to-Noise Ratio (CNR), for defect detection capabilities, and the Jaccard similarity coefficient, for defect segmentation potential. CNR results were on average 40% higher for RPCT than for PCT, and the Jaccard index was slightly higher for RPCT (0.7395) than for PCT (0.7010). In terms of computational time, however, PCT was 11.5 times faster than RPCT. Further investigations are needed to assess RPCT performance on a wider range of materials and to optimize computational time.

ACS Style

Samira Ebrahimi; Julien Fleuret; Matthieu Klein; Louis-Daniel Théroux; Marc Georges; Clemente Ibarra-Castanedo; Xavier Maldague. Robust Principal Component Thermography for Defect Detection in Composites. Sensors 2021, 21, 2682 .

AMA Style

Samira Ebrahimi, Julien Fleuret, Matthieu Klein, Louis-Daniel Théroux, Marc Georges, Clemente Ibarra-Castanedo, Xavier Maldague. Robust Principal Component Thermography for Defect Detection in Composites. Sensors. 2021; 21 (8):2682.

Chicago/Turabian Style

Samira Ebrahimi; Julien Fleuret; Matthieu Klein; Louis-Daniel Théroux; Marc Georges; Clemente Ibarra-Castanedo; Xavier Maldague. 2021. "Robust Principal Component Thermography for Defect Detection in Composites." Sensors 21, no. 8: 2682.

Journal article
Published: 05 April 2021 in Applied Sciences
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Early diagnosis of breast cancer unequivocally improves the survival rate of patients and is crucial for disease treatment. With the current developments in infrared imaging, breast screening using dynamic thermography seems to be a great complementary method for clinical breast examination (CBE) prior to mammography. In this study, we propose a sparse deep convolutional autoencoder model named SPAER to extract low-dimensional deep thermomics to aid breast cancer diagnosis. The model receives multichannel, low-rank, approximated thermal bases as input images. SPAER provides a solution for high-dimensional deep learning features and selects the predominant basis matrix using matrix factorization techniques. The model has been evaluated using five state-of-the-art matrix factorization methods and 208 thermal breast cancer screening cases. The best accuracy was for non-negative matrix factorization (NMF)-SPAER + Clinical and NMF-SPAER for maintaining thermal heterogeneity, leading to finding symptomatic cases with accuracies of 78.2% (74.3–82.5%) and 77.7% (70.9–82.1%), respectively. SPAER showed significant robustness when tested for additive Gaussian noise cases (3–20% noise), evaluated by the signal-to-noise ratio (SNR). The results suggest high performance of SPAER for preserveing thermal heterogeneity, and it can be used as a noninvasive in vivo tool aiding CBE in the early detection of breast cancer.

ACS Style

Bardia Yousefi; Hamed Akbari; Michelle Hershman; Satoru Kawakita; Henrique Fernandes; Clemente Ibarra-Castanedo; Samad Ahadian; Xavier Maldague. SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography. Applied Sciences 2021, 11, 3248 .

AMA Style

Bardia Yousefi, Hamed Akbari, Michelle Hershman, Satoru Kawakita, Henrique Fernandes, Clemente Ibarra-Castanedo, Samad Ahadian, Xavier Maldague. SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography. Applied Sciences. 2021; 11 (7):3248.

Chicago/Turabian Style

Bardia Yousefi; Hamed Akbari; Michelle Hershman; Satoru Kawakita; Henrique Fernandes; Clemente Ibarra-Castanedo; Samad Ahadian; Xavier Maldague. 2021. "SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography." Applied Sciences 11, no. 7: 3248.

Preprint
Published: 15 March 2021
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It was recently demonstrated that a co-planar capacitive sensor could be applied to the evaluation of materials without the disadvantages associated with the other techniques. This technique effectively detects changes in the dielectric properties of the materials due to, for instance, imperfections or variations in the internal structure, by moving a set of simple electrodes on the surface of the specimen. An AC voltage is applied to one or more electrodes and signals are detected by others. This is a promising inspection method for imaging the interior structure of the numerous materials, without the necessity to be in contact with the surface of the sample. In this paper, Finite Element (FE) modelling was employed to simulate the electric field distribution from a co-planar capacitive sensor and the way it interacts with a non-conducting sample. Physical experiments with a prototype capacitive sensor were also performed on a Plexiglas sample with sub-surface defects, to assess the imaging performance of the sensor. A good qualitative agreement was observed between the numerical simulation and experimental result.

ACS Style

Farima Abdollahi Mamoudan; Sebastien Savard; Tobin Filleter; Clemente Ibarra-Castanedo; Xavier Maldague. Numerical Simulation and Experimental Study of Capacitive Imaging Technique as a Non-Destructive Testing Method. 2021, 1 .

AMA Style

Farima Abdollahi Mamoudan, Sebastien Savard, Tobin Filleter, Clemente Ibarra-Castanedo, Xavier Maldague. Numerical Simulation and Experimental Study of Capacitive Imaging Technique as a Non-Destructive Testing Method. . 2021; ():1.

Chicago/Turabian Style

Farima Abdollahi Mamoudan; Sebastien Savard; Tobin Filleter; Clemente Ibarra-Castanedo; Xavier Maldague. 2021. "Numerical Simulation and Experimental Study of Capacitive Imaging Technique as a Non-Destructive Testing Method." , no. : 1.

Journal article
Published: 26 February 2021 in Big Data and Cognitive Computing
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In quality evaluation (QE) of the industrial production field, infrared thermography (IRT) is one of the most crucial techniques used for evaluating composite materials due to the properties of low cost, fast inspection of large surfaces, and safety. The application of deep neural networks tends to be a prominent direction in IRT Non-Destructive Testing (NDT). During the training of the neural network, the Achilles heel is the necessity of a large database. The collection of huge amounts of training data is the high expense task. In NDT with deep learning, synthetic data contributing to training in infrared thermography remains relatively unexplored. In this paper, synthetic data from the standard Finite Element Models are combined with experimental data to build repositories with Mask Region based Convolutional Neural Networks (Mask-RCNN) to strengthen the neural network, learning the essential features of objects of interest and achieving defect segmentation automatically. These results indicate the possibility of adapting inexpensive synthetic data merging with a certain amount of the experimental database for training the neural networks in order to achieve the compelling performance from a limited collection of the annotated experimental data of a real-world practical thermography experiment.

ACS Style

Qiang Fang; Clemente Ibarra-Castanedo; Xavier Maldague. Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data. Big Data and Cognitive Computing 2021, 5, 9 .

AMA Style

Qiang Fang, Clemente Ibarra-Castanedo, Xavier Maldague. Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data. Big Data and Cognitive Computing. 2021; 5 (1):9.

Chicago/Turabian Style

Qiang Fang; Clemente Ibarra-Castanedo; Xavier Maldague. 2021. "Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data." Big Data and Cognitive Computing 5, no. 1: 9.

Journal article
Published: 25 February 2021 in Sensors
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Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.

ACS Style

Shashank Pant; Parham Nooralishahi; Nicolas Avdelidis; Clemente Ibarra-Castanedo; Marc Genest; Shakeb Deane; Julio Valdes; Argyrios Zolotas; Xavier Maldague. Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application. Sensors 2021, 21, 1604 .

AMA Style

Shashank Pant, Parham Nooralishahi, Nicolas Avdelidis, Clemente Ibarra-Castanedo, Marc Genest, Shakeb Deane, Julio Valdes, Argyrios Zolotas, Xavier Maldague. Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application. Sensors. 2021; 21 (5):1604.

Chicago/Turabian Style

Shashank Pant; Parham Nooralishahi; Nicolas Avdelidis; Clemente Ibarra-Castanedo; Marc Genest; Shakeb Deane; Julio Valdes; Argyrios Zolotas; Xavier Maldague. 2021. "Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application." Sensors 21, no. 5: 1604.

Journal article
Published: 22 January 2021 in Sensors
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The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.

ACS Style

Iván Garrido; Jorge Erazo-Aux; Susana Lagüela; Stefano Sfarra; Clemente Ibarra-Castanedo; Elena Pivarčiová; Gianfranco Gargiulo; Xavier Maldague; Pedro Arias. Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms. Sensors 2021, 21, 750 .

AMA Style

Iván Garrido, Jorge Erazo-Aux, Susana Lagüela, Stefano Sfarra, Clemente Ibarra-Castanedo, Elena Pivarčiová, Gianfranco Gargiulo, Xavier Maldague, Pedro Arias. Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms. Sensors. 2021; 21 (3):750.

Chicago/Turabian Style

Iván Garrido; Jorge Erazo-Aux; Susana Lagüela; Stefano Sfarra; Clemente Ibarra-Castanedo; Elena Pivarčiová; Gianfranco Gargiulo; Xavier Maldague; Pedro Arias. 2021. "Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms." Sensors 21, no. 3: 750.

Journal article
Published: 01 January 2021 in IEEE Transactions on Instrumentation and Measurement
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ACS Style

Bardia Yousefi; Clemente Ibarra Castanedo; Xavier P. V. Maldague. Measuring Heterogeneous Thermal Patterns in Infrared-Based Diagnostic Systems Using Sparse Low-Rank Matrix Approximation: Comparative Study. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -9.

AMA Style

Bardia Yousefi, Clemente Ibarra Castanedo, Xavier P. V. Maldague. Measuring Heterogeneous Thermal Patterns in Infrared-Based Diagnostic Systems Using Sparse Low-Rank Matrix Approximation: Comparative Study. IEEE Transactions on Instrumentation and Measurement. 2021; 70 ():1-9.

Chicago/Turabian Style

Bardia Yousefi; Clemente Ibarra Castanedo; Xavier P. V. Maldague. 2021. "Measuring Heterogeneous Thermal Patterns in Infrared-Based Diagnostic Systems Using Sparse Low-Rank Matrix Approximation: Comparative Study." IEEE Transactions on Instrumentation and Measurement 70, no. : 1-9.

Journal article
Published: 14 December 2020 in Sensors
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Nowadays, infrared thermography, as a widely used non-destructive testing method, is increasingly studied for impact evaluation of composite structures. Sparse pattern extraction is attracting increasing attention as an advanced post-processing method. In this paper, an enhanced sparse pattern extraction framework is presented for thermographic sequence processing and defect detection. This framework adapts cropping operator and typical component extraction as a preprocessing step to reduce the dimensions of raw data and applies sparse pattern extraction algorithms to enhance the contrast on the defect area. Different cases are studied involving several defects in four basalt-carbon hybrid fiber-reinforced polymer composite laminates. Finally, comparative analysis with intensity distribution is carried out to verify the effectiveness of contrast enhancement using this framework.

ACS Style

Jue Hu; Hai Zhang; Stefano Sfarra; Claudia Sergi; Stefano Perilli; Clemente Ibarra-Castanedo; Guiyun Tian; Xavier Maldague. Enhanced Infrared Sparse Pattern Extraction and Usage for Impact Evaluation of Basalt-Carbon Hybrid Composites by Pulsed Thermography. Sensors 2020, 20, 7159 .

AMA Style

Jue Hu, Hai Zhang, Stefano Sfarra, Claudia Sergi, Stefano Perilli, Clemente Ibarra-Castanedo, Guiyun Tian, Xavier Maldague. Enhanced Infrared Sparse Pattern Extraction and Usage for Impact Evaluation of Basalt-Carbon Hybrid Composites by Pulsed Thermography. Sensors. 2020; 20 (24):7159.

Chicago/Turabian Style

Jue Hu; Hai Zhang; Stefano Sfarra; Claudia Sergi; Stefano Perilli; Clemente Ibarra-Castanedo; Guiyun Tian; Xavier Maldague. 2020. "Enhanced Infrared Sparse Pattern Extraction and Usage for Impact Evaluation of Basalt-Carbon Hybrid Composites by Pulsed Thermography." Sensors 20, no. 24: 7159.

Data article
Published: 14 September 2020 in Data in Brief
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This paper presents a thermal imaging dataset from composite material samples (carbon and glass fiber reinforced plastic) that were inspected by pulsed thermography with the goal of detecting and characterizing subsurface defective zones (Teflon inserts representing delaminations between plies). The pulsed thermography experiment was applied to 6 academic plates (inspected from both sides) all having the dimensions of 300 mm x 300 mm x 2 mm and same distribution of defects but made of different materials: three plates on carbon fiber-reinforced plastic (CFRP) and three plates made on glass fiber reinforced plastic (GFRP) specimens with three different geometries: planar, curved and trapezoidal. Each plate contains 25 inserts having length/depth ratios between 1.7 and 75. Two FX60 BALCAR photographic flashes (6.2 kJ per flash) were used to generate the heat pulse (2 ms duration), an X6900 FLIR infrared camera using ResearchIR software to record the thermal images and a custom-built software/control unit to synchronize data recording with pulse generation. Finally, the dataset proposed consists of 12 sequences of approximately 2000 images of 512 × 512 pixels each.

ACS Style

Jorge Erazo-Aux; Humberto Loaiza-Correa; Andres David Restrepo-Giron; Clemente Ibarra-Castanedo; Xavier Maldague. Thermal imaging dataset from composite material academic samples inspected by pulsed thermography. Data in Brief 2020, 32, 106313 .

AMA Style

Jorge Erazo-Aux, Humberto Loaiza-Correa, Andres David Restrepo-Giron, Clemente Ibarra-Castanedo, Xavier Maldague. Thermal imaging dataset from composite material academic samples inspected by pulsed thermography. Data in Brief. 2020; 32 ():106313.

Chicago/Turabian Style

Jorge Erazo-Aux; Humberto Loaiza-Correa; Andres David Restrepo-Giron; Clemente Ibarra-Castanedo; Xavier Maldague. 2020. "Thermal imaging dataset from composite material academic samples inspected by pulsed thermography." Data in Brief 32, no. : 106313.

Project report
Published: 15 June 2020 in Sensors
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This work aims to address the effectiveness and challenges of non-destructive testing (NDT) by active infrared thermography (IRT) for the inspection of aerospace-grade composite samples and seeks to compare uncooled and cooled thermal cameras using the signal-to-noise ratio (SNR) as a performance parameter. It focuses on locating impact damages and optimising the results using several signal processing techniques. The work successfully compares both types of cameras using seven different SNR definitions, to understand if a lower-resolution uncooled IR camera can achieve an acceptable NDT standard. Due to most uncooled cameras being small, lightweight, and cheap, they are more accessible to use on an unmanned aerial vehicle (UAV). The concept of using a UAV for NDT on a composite wing is explored, and the UAV is also tracked using a localisation system to observe the exact movement in millimetres and how it affects the thermal data. It was observed that an NDT UAV can access difficult areas and, therefore, can be suggested for significant reduction of time and cost.

ACS Style

Shakeb Deane; Nicolas P. Avdelidis; Clemente Ibarra-Castanedo; Hai Zhang; Hamed Yazdani Nezhad; Alex A. Williamson; Tim Mackley; Xavier Maldague; Antonios Tsourdos; Parham Nooralishahi. Comparison of Cooled and Uncooled IR Sensors by Means of Signal-to-Noise Ratio for NDT Diagnostics of Aerospace Grade Composites. Sensors 2020, 20, 3381 .

AMA Style

Shakeb Deane, Nicolas P. Avdelidis, Clemente Ibarra-Castanedo, Hai Zhang, Hamed Yazdani Nezhad, Alex A. Williamson, Tim Mackley, Xavier Maldague, Antonios Tsourdos, Parham Nooralishahi. Comparison of Cooled and Uncooled IR Sensors by Means of Signal-to-Noise Ratio for NDT Diagnostics of Aerospace Grade Composites. Sensors. 2020; 20 (12):3381.

Chicago/Turabian Style

Shakeb Deane; Nicolas P. Avdelidis; Clemente Ibarra-Castanedo; Hai Zhang; Hamed Yazdani Nezhad; Alex A. Williamson; Tim Mackley; Xavier Maldague; Antonios Tsourdos; Parham Nooralishahi. 2020. "Comparison of Cooled and Uncooled IR Sensors by Means of Signal-to-Noise Ratio for NDT Diagnostics of Aerospace Grade Composites." Sensors 20, no. 12: 3381.

Journal article
Published: 01 June 2020 in Minerals Engineering
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Application of hyperspectral infrared imagery for mineral grain identification suffers from a lack of prediction on the irregular grain’s surface along with the mineral aggregates. Here, we present an investigation to determine the reliability of automatic mineral identification in the longwave Infrared (LWIR, 7.7–11.8 μm) with an LWIR-macro lens having a spatial resolution of 100 μm. We attempt to identify eleven different mineral grains (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). A machine learning-based algorithm (implemented by software) compares all of the pixel-spectra to the ASTER spectral library of JPL/NASA using spectral angle mapper (SAM) and normalized cross-correlation (NCC) to create false-color maps. Then a hue-saturation-value (HSV) principle component analysis (PCA) based K-means clustering approach groups the mineral regions in different categories. The results were compared to two different ground truths (GT) (i.e. rigid-GT and observed-GT) for quantitative calculation and as an integrated step for validating our approach. Observed-GT increased the accuracy up to 1.5 times higher than rigid-GT, from 45.67% to 69.39%. The samples were also examined by micro X-ray fluorescence (μXRF) and scanning electron microscope (SEM) in order to retrieve information on the mineral aggregates and the grain’s surface. The results of μXRF imagery (aggregate map) were compared to the results of automatic mineral identification techniques, using ArcGIS software, and the results represent a promising performance for automatic identification.

ACS Style

Bardia Yousefi; Clemente Ibarra Castanedo; Xavier P.V. Maldague; Georges Beaudoin. Assessing the reliability of an automated system for mineral identification using LWIR Hyperspectral Infrared imagery. Minerals Engineering 2020, 155, 106409 .

AMA Style

Bardia Yousefi, Clemente Ibarra Castanedo, Xavier P.V. Maldague, Georges Beaudoin. Assessing the reliability of an automated system for mineral identification using LWIR Hyperspectral Infrared imagery. Minerals Engineering. 2020; 155 ():106409.

Chicago/Turabian Style

Bardia Yousefi; Clemente Ibarra Castanedo; Xavier P.V. Maldague; Georges Beaudoin. 2020. "Assessing the reliability of an automated system for mineral identification using LWIR Hyperspectral Infrared imagery." Minerals Engineering 155, no. : 106409.

Conference paper
Published: 01 January 2020 in Proceedings of the 2020 International Conference on Quantitative InfraRed Thermography
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Serge-Olivier Gnessougou; Marc-Antoine Langevin; Clemente Ibarra Castenado; Alain DeChamplain; Xavier Maldague. Temperature Calculation of a Steel Plate under Kerosene Flame Attack using TwoColour Pyrometry. Proceedings of the 2020 International Conference on Quantitative InfraRed Thermography 2020, 1 .

AMA Style

Serge-Olivier Gnessougou, Marc-Antoine Langevin, Clemente Ibarra Castenado, Alain DeChamplain, Xavier Maldague. Temperature Calculation of a Steel Plate under Kerosene Flame Attack using TwoColour Pyrometry. Proceedings of the 2020 International Conference on Quantitative InfraRed Thermography. 2020; ():1.

Chicago/Turabian Style

Serge-Olivier Gnessougou; Marc-Antoine Langevin; Clemente Ibarra Castenado; Alain DeChamplain; Xavier Maldague. 2020. "Temperature Calculation of a Steel Plate under Kerosene Flame Attack using TwoColour Pyrometry." Proceedings of the 2020 International Conference on Quantitative InfraRed Thermography , no. : 1.

Journal article
Published: 07 November 2019 in Electronics
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Thermal imagery for monitoring of body temperature provides a powerful tool to decrease health risks (e.g., burning) for patients during medical imaging (e.g., magnetic resonance imaging). The presented approach discusses an experiment to simulate radiology conditions with infrared imaging along with an automatic thermal monitoring/tracking system. The thermal tracking system uses an incremental low-rank noise reduction applying incremental singular value decomposition (SVD) and applies color based clustering for initialization of the region of interest (ROI) boundary. Then a particle filter tracks the ROI(s) from the entire thermal stream (video sequence). The thermal database contains 15 subjects in two positions (i.e., sitting, and lying) in front of thermal camera. This dataset is created to verify the robustness of our method with respect to motion-artifacts and in presence of additive noise (2–20%—salt and pepper noise). The proposed approach was tested for the infrared images in the dataset and was able to successfully measure and track the ROI continuously (100% detecting and tracking the temperature of participants), and provided considerable robustness against noise (unchanged accuracy even in 20% additive noise), which shows promising performance.

ACS Style

Bardia Yousefi; Hossein Memarzadeh Sharifipour; Mana Eskandari; Clemente Ibarra-Castanedo; Denis Laurendeau; Raymond Watts; Matthieu Klein; Xavier P. V. Maldague. Incremental Low Rank Noise Reduction for Robust Infrared Tracking of Body Temperature during Medical Imaging. Electronics 2019, 8, 1301 .

AMA Style

Bardia Yousefi, Hossein Memarzadeh Sharifipour, Mana Eskandari, Clemente Ibarra-Castanedo, Denis Laurendeau, Raymond Watts, Matthieu Klein, Xavier P. V. Maldague. Incremental Low Rank Noise Reduction for Robust Infrared Tracking of Body Temperature during Medical Imaging. Electronics. 2019; 8 (11):1301.

Chicago/Turabian Style

Bardia Yousefi; Hossein Memarzadeh Sharifipour; Mana Eskandari; Clemente Ibarra-Castanedo; Denis Laurendeau; Raymond Watts; Matthieu Klein; Xavier P. V. Maldague. 2019. "Incremental Low Rank Noise Reduction for Robust Infrared Tracking of Body Temperature during Medical Imaging." Electronics 8, no. 11: 1301.

Journal article
Published: 01 October 2019 in NDT & E International
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Yuxia Duan; Shicai Liu; Caiqi Hu; Junqi Hu; Hai Zhang; Yiqian Yan; Ning Tao; Cunlin Zhang; Xavier Maldague; Qiang Fang; Clemente Ibarra-Castanedo; Dapeng Chen; Xiaoli Li; Jianqiao Meng. Automated defect classification in infrared thermography based on a neural network. NDT & E International 2019, 107, 1 .

AMA Style

Yuxia Duan, Shicai Liu, Caiqi Hu, Junqi Hu, Hai Zhang, Yiqian Yan, Ning Tao, Cunlin Zhang, Xavier Maldague, Qiang Fang, Clemente Ibarra-Castanedo, Dapeng Chen, Xiaoli Li, Jianqiao Meng. Automated defect classification in infrared thermography based on a neural network. NDT & E International. 2019; 107 ():1.

Chicago/Turabian Style

Yuxia Duan; Shicai Liu; Caiqi Hu; Junqi Hu; Hai Zhang; Yiqian Yan; Ning Tao; Cunlin Zhang; Xavier Maldague; Qiang Fang; Clemente Ibarra-Castanedo; Dapeng Chen; Xiaoli Li; Jianqiao Meng. 2019. "Automated defect classification in infrared thermography based on a neural network." NDT & E International 107, no. : 1.

Journal article
Published: 01 October 2019 in Journal of Materials in Civil Engineering
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Clemente Ibarra-Castanedo; Matthieu Klein; Martin Lavoie; Denis Proteau; Jean Dumoulin. Evaluation of Impact of Hot-Mix Asphalt Density Differentials on Thermal Streak Phenomenon by Passive Infrared Thermography. Journal of Materials in Civil Engineering 2019, 31, 04019215 .

AMA Style

Clemente Ibarra-Castanedo, Matthieu Klein, Martin Lavoie, Denis Proteau, Jean Dumoulin. Evaluation of Impact of Hot-Mix Asphalt Density Differentials on Thermal Streak Phenomenon by Passive Infrared Thermography. Journal of Materials in Civil Engineering. 2019; 31 (10):04019215.

Chicago/Turabian Style

Clemente Ibarra-Castanedo; Matthieu Klein; Martin Lavoie; Denis Proteau; Jean Dumoulin. 2019. "Evaluation of Impact of Hot-Mix Asphalt Density Differentials on Thermal Streak Phenomenon by Passive Infrared Thermography." Journal of Materials in Civil Engineering 31, no. 10: 04019215.

Journal article
Published: 01 July 2019 in Energies
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Ceramic-coated materials used in different engineering sectors are the focus of world-wide interest and have generated a need for inspection techniques that detect very small structural anomalies. Non-destructive testing is increasingly being used to evaluate coating thickness and to test for coating flaws. The main pros of non-destructive testing is that the tested object remains intact and available for continued use afterward. This paper reports on an integrated, non-destructive testing approach that combines infrared thermography and acousto-ultrasonics to evaluate advanced aerospace sandwich structure materials with the aim of exploring any potential for detecting defects of more than one type. Combined, these two techniques successfully detected fabrication defects, including inclusions and material loss.

ACS Style

Yuxia Duan; Hai Zhang; Stefano Sfarra; Nicolas P. Avdelidis; Theodoros H. Loutas; George Sotiriadis; Vassilis Kostopoulos; Henrique Fernandes; Florian Ion Petrescu; Clemente Ibarra-Castanedo; Xavier P.V. Maldague. On the Use of Infrared Thermography and Acousto—Ultrasonics NDT Techniques for Ceramic-Coated Sandwich Structures. Energies 2019, 12, 2537 .

AMA Style

Yuxia Duan, Hai Zhang, Stefano Sfarra, Nicolas P. Avdelidis, Theodoros H. Loutas, George Sotiriadis, Vassilis Kostopoulos, Henrique Fernandes, Florian Ion Petrescu, Clemente Ibarra-Castanedo, Xavier P.V. Maldague. On the Use of Infrared Thermography and Acousto—Ultrasonics NDT Techniques for Ceramic-Coated Sandwich Structures. Energies. 2019; 12 (13):2537.

Chicago/Turabian Style

Yuxia Duan; Hai Zhang; Stefano Sfarra; Nicolas P. Avdelidis; Theodoros H. Loutas; George Sotiriadis; Vassilis Kostopoulos; Henrique Fernandes; Florian Ion Petrescu; Clemente Ibarra-Castanedo; Xavier P.V. Maldague. 2019. "On the Use of Infrared Thermography and Acousto—Ultrasonics NDT Techniques for Ceramic-Coated Sandwich Structures." Energies 12, no. 13: 2537.