KEYWORDS: Thermography, Infrared imaging, 3D image processing, Infrared radiation, Nondestructive evaluation, 3D modeling, Inspection, Image fusion, 3D metrology, 3D vision
Three-dimensional (3D) vision scanning for metrology and inspection applications is an area that knows an increasing interest in the industry. This interest is driven by the recent advances in 3D technologies, permitting to attain high precision measurements at an affordable cost. 3D vision allows for the modelling and inspection of the visible surface of objects. When it is necessary to detect subsurface defects, active infrared (IR) thermography is one of the most used tools today for non-destructive testing (NDT) of materials. Fusion of these two modalities allows the simultaneous detection of surface and subsurface defects and to visualize these defects overlaid on a 3D model of the scanned and modelled parts or their 3D computer-aided design (CAD) models. In this work, we present a framework for automatically fusing 3D data (scanned or CAD) with the infrared thermal images for an NDT process in 3D space.
Tracking pedestrians is an area of computer vision that has attracted a lot of interest in recent years. Many of these work was conducted in the visible spectrum. Some work was also conducted in thermal infrared spectrum. The majority of the research work used one spectrum at a time. In this work, we present a fusion framework using thermal infrared and visible spectrums in order to robustly track the detected moving objects. The detected objects are then processed using HOG features in order to classify them as a pedestrian or a non-pedestrian using SVM. The tests were conducted in outdoor scenarios. The obtained results are promising and show the efficiency of the proposed framework.
The present study is focused on two topics. The first one is a mathematical model, useful to understand the deformation
of paintings, which uses straining devices, adjustable and micrometrically controlled via a pin supported in a hollow
cylinder. Strains were analyzed by holographic interferometry (HI) technique using an appropriate frame.
The second one concerns the need to improve the conservator’s knowledge about the defect’s detection and defect’s
propagation in acrylic painting characterized of underdrawings and pentimenti. To fulfill this task, a sample was
manufactured to clarify the several uncertainties inherent the influence of external factors on their conservation.
Subsurface anomalies were also retrieved by near-infrared reflectography (NIRR) and transmittography (NIRT)
techniques, using LED lamps and several narrow-band filters mounted on a CMOS camera, working at different
wavelengths each other and in combination with UV imaging. In addition, a sponge glued on the rear side of the canvas
was impregnated with a precise amount of water by means of a syringe to verify the “stretcher effect” by the digital
speckle photography (DSP) technique (using MatPIV). The same effect also affects the sharp transition of the canvas at
the stretcher’s edge. In this case, a possible mechanism is a direct mechanical contact between stretcher and canvas that
was investigated by HI technique. Finally, advanced algorithms applied to the square heating thermography (SHT) data
were very useful to detect three Mylar® inserts simulating different type of defects. These fabricated defects were also
identified by optical techniques, while the visual inspection was the only one capable of detecting a biological damage.
Face recognition is an area of computer vision that has attracted a lot of interest from the research community. A growing demand for robust face recognition in security applications has driven interesting advancements in this field. In this work, we introduce a new multistep approach for face recognition in the infrared spectrum. The proposed approach works in texture space using binary and ternary pattern descriptors. The approach operates in two steps. In the first step, dimensionality reduction techniques are used to classify the preprocessed infrared face image. This operation permits the selection of the highest score candidates. In the second step, a small set of these candidates are then classified using a correlation based approach. This last step permits the selection of the best matching candidate. The obtained results show a high increase in the face recognition performance when a multistep approach is used compared to dimensionality reduction face recognition techniques alone.
This work introduces a new framework for active and passive infrared image fusion for face recognition applications. Two multispectral face recognition databases were used in our experiments: Equinox Database (Visible, SWIR, MWIR, LWIR) and m-Faces Database (Visible, NIR, MWIR, LWIR). The proposed framework uses a fusion scheme in texture space in order to increase the performance of face recognition. The proposed texture space is based on the use of binary and ternary patterns. A new adaptive ternary pattern is also introduced. Active (SWIR and NIR) and passive (MWIR, LWIR) infrared modalities are used in this fusion scheme. An intraspectral and inter-spectral fusion approaches are introduced. The obtained results are promising and show an increase in the recognition performance when texture channels are fused in a multi-scale fusion scheme.
Natural fibers constitute an interesting alternative to synthetic fibers, e.g. glass and carbon, for the production of composites due to their environmental and economic advantages. The strength of natural fiber composites is on average lower compared to their synthetic counterparts. Nevertheless, natural fibers such as flax, among other bast fibers (jute, kenaf, ramie and hemp), are serious candidates for seismic retrofitting applications given that their mechanical properties are more suitable for dynamic loads. Strengthening of structures is performed by impregnating flax fiber reinforced polymers (FFRP) fabrics with epoxy resin and applying them to the component of interest, increasing in this way the load and deformation capacities of the building, while preserving its stiffness and dynamic properties. The reinforced areas are however prompt to debonding if the fabrics are not mounted properly. Nondestructive testing is therefore required to verify that the fabric is uniformly installed and that there are no air gaps or foreign materials that could instigate debonding. In this work, the use of active infrared thermography was investigated for the assessment of (1) a laboratory specimen reinforced with FFRP and containing several artificial defects; and (2) an actual FFRP retrofitted masonry wall in the Faculty of Engineering of the University of L’Aquila (Italy) that was seriously affected by the 2009 earthquake. Thermographic data was processed by advanced signal processing techniques, and post-processed by computing the watershed lines to locate suspected areas. Results coming from the academic specimen were compared to digital speckle photography and holographic interferometry images.
The Infrared Images and Other Data Acquisition Station enables a user, who is located inside a laboratory, to acquire visible and infrared images and distances in an outdoor environment with the help of an Internet connection. This station can acquire data using an infrared camera, a visible camera, and a rangefinder. The system can be used through a web page or through Python functions.
The increasing deterioration of panel paintings can be due to physical processes that take place during exhibition or
transit, or as a result of temperature and humidity fluctuations within a building, church or museum. In response to
environmental alterations, a panel painting can expand or contract and a new equilibrium state is eventually reached.
These adjustments though, are usually accompanied by a change in shape in order to accommodate to the new
conditions. In this work, a holographic method for detecting detached regions and micro-cracks is described. Some of
these defects are confirmed by Thermographic Signal Reconstruction (TSR) technique. In addition, Pulsed Phase
Thermography (PPT) and Principal Component Thermography (PCT) allow to identify with greater contrast two
artificial defects in Mylar which are crucial to understand the topic of interest: the discrimination between defect
materials. Finally, traditional contact ultrasounds applications, are widely applied for the evaluation of the wood quality
in several characterization procedures. Inspecting the specimen from the front side, the natural and artificial defects of
the specimen are confirmed. Experimental results derived by the application of the integrated methods on an Italian panel
painting reproduction, called The Angel specimen, are presented. The main advantages that these techniques can offer to
the conservation and restoration of artworks are emphasized.
The question of how to map the 3D indoor temperature by infrared thermography is solved by a hybrid method
which is a combination of infrared thermography and the well known heat diffusion equation. The idea is to use
infrared thermography to get the surface temperature of each frontier of the 3D domain of interest. A suitable
procedure is devoted to this, allowing an automatic scanning of the whole frontier, the registration of data and
computation. These surface temperatures constitute the boundary conditions of the heat equation solved in the
domain of interest. The solution of the heat equation allows analyzing and controlling the temperature of every point
belonging to the considered domain. This temperature distribution is controlled over the time with a period of the
same order than the necessary time to obtain the frontier temperatures and at the end to contribute to the analysis of
the thermal comfort.
The study is done for the steady-state conditions under various weather situations. In this case the temperature
depends only on space coordinates. With such procedure, we can have an idea about the time necessary to reach
thermal equilibrium; time which has a great impact on the thermal comfort sensation. The results yielded by this
method are compared with those given by others techniques used for temperature measurement. Finally, the method
is used to access 3D temperature distribution for various geometric shapes.
Active thermography has been extensively investigated in the past few years for the nondestructive evaluation of
different types of materials. Composites in particular have received considerable attention given that active
thermography has shown to be well suited for the detection and characterization of most kinds of defects typically found
in these materials such as impact damage, delaminations, disbonds and inclusions. Signal processing is a necessary step
of the inspection process, especially if defect characterization is required. A wide variety of techniques have been
developed from the classical thermal-based techniques to signal transformation algorithms (adapted from the area of
machine vision) on which temporal data is transformed to a different domain (frequency, Hough, principal components,
Laplace, high-order moments, etc.) with the purpose of simplifying data analysis. In this paper, a review of some of
these processing techniques is presented and exemplified using a Kevlar® panel and a GLARE specimen.
Advanced ceramic materials are increasingly employed in varied and new applications where improved electrical,
mechanical and/or thermal properties are sought. For instance, in a manner similar to carbon or glass fiber reinforced
plastics, ceramic matrix composites (CMCs) are designed to improve the naturally brittle characteristics of monolithic
ceramics thanks to the inclusion of fibers. Among the main interests for advanced ceramics are the increase in the
operation temperature of components, the elimination of the use of cooling fluids, and weight savings. In this paper, the
capabilities of infrared thermography and holographic interferometry are investigated and compared for the
nondestructive assessment of advanced ceramic materials using three experimental specimens: (1) a monolithic green
ceramic tile with fabricated defects, (2) a CMC specimen (from production reject) with a porous alumina matrix
reinforced with glass fibers, and (3) a sandwich structure consisting on a carbon fiber honeycomb core with a ceramic
plate bonded in one side.
Face recognition is an area of computer vision that has attracted a lot of interest from the research community. A
growing demand for robust face recognition software in security applications has driven the development of interesting
approaches in this field. A large quantity of research in face recognition deals with visible face images. In the visible
spectrum the illumination and face expressions changes represent a significant challenge for the recognition system. To
avoid these problems, researchers proposed recently the use of 3D and infrared imaging for face recognition.
In this work, we introduce a new framework for infrared face recognition using texture descriptors. This framework
exploits linear and non linear dimensionality reduction techniques for face learning and recognition in the texture space.
Active and passive infrared imaging modalities are used and comparison with visible face recognition is performed. Two
multispectral face recognition databases were used in our experiments: Equinox Database (Visible, SWIR, MWIR,
LWIR) and Laval University Multispectral Database (Visible, NIR, MWIR, LWIR).
The obtained results show high increase in recognition performance when texture descriptors like LBP (Local Binary
Pattern) and LTP (Local Ternary Pattern) are used. The best result was obtained in the short wave infrared spectrum
(SWIR) using non linear dimensionality reduction techniques.
In this work, different mosaics covered with various plasters (of thickness and compositions) were evaluated in lab by
means of active long wave and mid wave thermography approaches, with the intention of detecting the tesserae beneath
the plastered surface. Thermal images as well as thermal contrast curves between plastered surfaces and plastered
mosaics were recorded. Special considerations concerning the applicability and accuracy of the used approaches for this
specific application are presented. Results from the assessment are presented and discussed, indicating that images
seeing through the mortar-plaster on plastered mosaic surfaces can be obtained using active thermography approaches.
Face recognition in the infrared spectrum has attracted a lot of interest in recent years. Many of the techniques used in
infrared are based on their visible counterpart, especially linear techniques like PCA (Principal Component Analysis)
and LDA (Linear Discriminant Analysis).
In this work, we introduce non linear dimensionality reduction approaches for multispectral face recognition. For this
purpose, the following techniques were developed: global non linear techniques (Kernel-PCA, Kernel-LDA) and local
non linear techniques (Local Linear Embedding, Locality Preserving Projection). The performances of these techniques
were compared to classical linear techniques for face recognition like PCA and LDA.
Two multispectral face recognition databases were used in our experiments: Equinox Face Recognition Database and
Laval University Database. Equinox database contains images in the Visible, Short, Mid and Long waves infrared
spectrums. Laval database contains images in the Visible, Near, Mid and Long waves infrared spectrums with variations
in time and metabolic activity of the subjects.
The obtained results are interesting and show the increase in recognition performance using local non linear
dimensionality reduction techniques for infrared face recognition, particularly in near and short wave infrared spectrums.
The article aims first to present a new study on the thermal regulatory response of the human skin surface while
exposed to a cold environment. Our work has shown that when a cold stress is applied to the left hand, thermal
infrared imaging (MWIR spectral band: 3-5 μm) allows a clear observation of a temperature rise on the right
hand. Moreover, a frequency analysis was also carried out upon selected vein pixels of the images monitored
during the same cold stress experiment. The objective was to identify the specific frequencies that could be
linked to some physiological mechanisms of the human body. This kind of study could be very useful for the
characterization of possible thermo-physiological pathologies. Besides thermoregulation, we also present in this
article some results on the extraction of the hand vein pattern. Firstly, we show some vein extraction results
obtained after image processing of the thermal images recorded in the thermal band (MWIR), then we compare
this vein pattern to the signature obtained with a camera operating in the NIR spectral band (0.85-1.7 μm).
This method could be used as a complementary means for finger print signatures in biometrics.
The modified DAC version with thermal quadrupoles can be considered an interesting alternative to thermal
contrast computations since it provides an automated tool for depth retrieval and eliminates the need of selecting
a non-defective area. In practice it is important to have heat stimulus with complex shapes and long durations
(several seconds) in order to cover larger inspection areas, enhance thermal contrast between defective and sound
areas and increase the depth of inspection inside the material. In this work we present a heat stimulus correction
by using the thermal quadrupoles theory and its validation with several heat stimulus shapes and durations.
An imaging technique of the hand vein tree is presented in this paper. Using the natural human circulatory system and a controlled armband pressure around the arm, a lock-in thermography technique with an internal excitation is carried out. Since the stimulation frequency is inversely proportional to the inspection depth, the subcutaneous layer requires the use of a very slow frequency. Thus, a sawtooth waveform is preferred to minimize the duration of the pressure applied to the armband during the experiment. A frequency of approximately 0.03 Hz and a pressure range between 100 and 140 mmHg, according to the diastolic and systolic blood pressure, are used as stimulation. Then, dorsal hand amplitude and phase images are obtained with IR_view (Klein, 1999), a tool specifically designed to analyze infrared images.
The hand vein structure is thermally mapped by an infrared camera operating in the middle wavelength infrared range (MWIR) at room temperature. Parasitic frequencies are avoided by keeping the hand fixed. The resulting images show a gradient of temperature between surrounding tissues and the back-of-hand veins. The vascular signature segmentation is extracted from the amplitude and phase images by using a Fast Fourier Transform image processing technique. This work could be used for vein localization for perfusion or for the early diagnosis of vein diseases such as primitive varicose and deep vein thrombosis (DVT). A hand vein signature database for identification purposes is also possible.
IR-View, is a free and open source Matlab software that was released in 1998 at the Computer Vision and Systems Laboratory (CVSL) at Université Laval, Canada, as an answer to many common and recurrent needs in Infrared thermography. IR-View has proven to be a useful tool at CVSL for the past 10 years. The software by itself and/or its concept and functions may be of interest for other laboratories and companies working in research in the IR NDT field. This article describes the functions and processing techniques integrated to IR-View, freely downloadable under the GNU license at http://mivim.gel.ulaval.ca. Demonstration of IR-View functionalities will also be done during the DSS08 SPIE Defense and Security Symposium.
In non-destructive testing by Infrared Thermography it is usually needed to locate defects and region of interests suspected to contain defects. The defects cannot always be observed directly from one single IR image taken at a single given time t. Thus, in the case of pulsed thermography, direct course techniques as the Fourier transform process the information of many images recorded for a given duration into one resulting image. Another way to compile the temporal information of a sequence of images into a single one is to compute a correlation image. This paper details an approach to use a statistical correlation operator to help improving defect detection in pulsed infrared thermography.
The prerequisite for more competent and cost effective aircraft has led to the evolution of innovative testing and
evaluation procedures. Non-destructive testing and evaluation (NDT & E) techniques for assessing the integrity of an
aircraft structure are essential to both reduce manufacturing costs and out of service time of aircraft due to maintenance.
Nowadays, active - transient thermal NDT & E (i.e. thermography) is commonly used for assessing aircraft composites.
This research work evaluates the potential of pulsed thermography (PT) and/or pulsed phase thermography (PPT) for
assessing defects (i.e. impact damage and inclusions for delaminations) on GLARE and GLARE type composites.
Finally, in the case of the detection of inserts - delaminations C-Scan ultrasonic testing was also used with the intention
of providing supplementary results.
One of our industrial partners, Assek Technologie, is interested in developing a technique that would improve the
drying process of wood floor in basements after flooding. In order to optimize the procedure, the floor structure and the
damaged (wet) area extent must first be determined with minimum intrusion (minimum or no dismantling). The present
study presents the use of infrared thermography to reveal the structure of (flooded) wood floors. The procedure involves
opening holes in the floor. Injecting some hot air through those holes reveals the framing structure even if the floor is
covered by vinyl or ceramic tiles. This study indicates that thermal imaging can also be used as a tool to validate the
decontamination process after drying. Thermal images were obtained on small-scale models and in a demonstration
room.
Inspection of aerospace components has always been a challenge. Infrared thermography has demonstrated to be a useful tool for this matter. In this paper, we offer a comparative study involving three active techniques: pulsed thermography, lock-in thermography and vibrothermography. Some of these techniques have proven to be more effective than others for a specific type of system. We compare the experimental results from these three techniques as applied to two typical aerospace parts: honeycomb structures and Glare. The later is perhaps the most challenging of all as will be pointed out. Some insights are provided regarding the most suitable technique for a number of typical situations.
The Infrared Nondestructive Testing (IRNT) methods based on thermal contrast are strongly affected by non-uniform heating at the surface. Hence, the results obtained from these methods considerably depend on the chosen reference point. One of these methods is Artificial Neural Networks (ANN) that uses thermal contrast curves as input data for training and test in order to detect and estimate defect depth.
The Differential Absolute Contrast (DAC) has been successfully used as an alternative thermal contrast to eliminate the need of a reference point by defining the thermal contrast with respect to an ideal sound area. The DAC technique has been proven effective to inspect materials at early times since it is based on the 1D solution of the Fourier equation. A modified DAC version using thermal quadrupoles explicitly includes the sample thickness in the solution, extending in this way the range of validity when the heat front approaches the sample rear face.
We propose to use ANN to detect and quantify defects in composite materials using data extracted from the modified DAC with thermal quadrupoles in order to decrease the non-uniform heating and plate shape impact on the inspection.
While other non-destructive testing methods hardly reveal microscopic open cracks, ultrasound excited vibrothermography provides very promising results by converting mechanical waves into local heat by friction. This phenomenon enhances thermal gradients in temperature maps as compared to conventional techniques. To detect temperature gradients caused by hidden cracks, high temperature and spatial resolution infrared cameras are usually used.
Recently, it has been shown that the HVOF (High Velocity Oxy Fuel)-spraying of tungsten carbide or cobalt coatings onto steel substrates, seems to be a suitable alternative to the non-environmentally friendly chromium coating material. However one major issue with these thermal-sprayed coatings is the possibility of the appearance of microscopic cracks when they are submitted to excessive bending loads. If the open cracks spread through the whole coating thickness (typically 100 to 200 ?m), they might also propagate at the coating-substrate interface causing the coating to delaminate in between adjacent open cracks. The latter disbonding phenomenon is therefore strongly dependent on the distance between adjacent open cracks. Therefore, a non destructive technique enabling the detection of cracks and the evolution of their density is critical to preserve the components integrity.
The aim of this work is to investigate the ability of ultrasound excited vibrothermography to detect such cracks. To do so, we investigated tungsten carbide coatings where cracks were artificially generated using a controlled bending test. Results on different samples are presented and discussed.
In this paper, we review some of the discrete signal transforms that are in use in the field of thermography for defect detection and/or characterization. Signal transformation is used with the purpose of finding an alternative domain where data analysis is more straightforward. For instance, it is possible to pass from the time domain to the frequency spectra through the one-dimensional discrete Fourier transform (DFT). The DFT constitutes the basis of pulsed phase thermography (PPT), but other transformations are possible such as the discrete wavelet transform (DWT) with the advantage that, in this case, time information is preserved after the transformation. It is also possible to rearrange data into domains others than frequency. For instance, the Hough transform (HT) allows the detection of regular forms (e.g. lines, curves, etc.) in a parameter space. The HT has been used in two different ways in thermography: for the detection of lines in thermal profiles, with the goal of discriminating between defective and non-defective regions; or it can be used to locate the inflection points in phase profiles obtained by PPT to extract the blind frequencies. The Laplace transform can also be used in the time domain to improve flaws detection and their characterization in the transformed space. Eigenvector-based transforms, such as singular value decomposition (SVD), have also been implemented. Principal component thermography (PCT) uses SVD to decompose thermographic data into a set of orthogonal modes. We discuss all these transforms and provide some comparative results.
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