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In recent years, the conservation and protection of ancient cultural heritage have received increasing attention, and non-destructive testing (NDT), which can minimize the damage done to the test subject, plays an integral role therein. For instance, NDT through active infrared thermal imaging can be applied to ancient polyptychs, which can realize accurate detection of damage and defects existing on the surface and interior of the polyptychs. In this study, infrared thermography is used for non-invasive investigation and evaluation of two polyptych samples with different pigments and artificial defects, but both reproduced based on a painting by Pietro Lorenzetti (1280/85–1348) using the typical tempera technique of the century. It is noted that, to avoid as far as possible secondary damages done to the ancient cultural heritages, repeated damage-detection experiments are rarely carried out on the test subjects. To that end, numerical simulation is used to reveal the heat transfer properties and temperature distributions, as to perform procedural verification and reduce the number of experiments that need to be conducted on actual samples. Technique-wise, to improve the observability of the experimental results, a total variation regularized low-rank tensor decomposition algorithm is implemented to reduce the background noise and improve the contrast of the images. Furthermore, the efficacy of image processing is quantified through the structural-similarity evaluation. [ABSTRACT FROM AUTHOR]

With increasing attention paid to the protection of cultural relics, non-destructive testing (NDT) technologies are thought to be profoundly rewarding, resulting in a widespread uptake of feature-extraction algorithms and defect-detection techniques. Among various alternatives, infrared thermography (IRT) and Terahertz time-domain spectroscopy (THz-TDS) are non-invasive in nature and thus are appropriate for applications involving ancient buildings and artworks. The present study is motivated by the fact that online/offline background segmentation algorithms based on the Gaussian mixture model and widely used in video processing to distinguish between foreground and background, can be successfully integrated as a feature extraction tool with NDT. Since IRT and THz-TDS image sequences resemble a video, the image length and width can be taken as the first two dimensions, and time can serve as the third one. Such sequences can be processed effectively by the background segmentation algorithms, thus can help detect defects of different types at varying depths. The experimental section of the paper considers a tempera painting (a replica of Botticelli's "The Birth of Venus") with artificiallyintroduced defects. For benchmarking purposes, the background segmentation algorithms (based on a mixture of Gaussian models) are compared with the fast Fourier transform and principal component analysis to demonstrate the superior performance of the proposed novel algorithms. [ABSTRACT FROM AUTHOR]

The non-uniformity of non-planar object inspection data makes their analysis challenging. This paper reports a study of the use of recurrent neural network and artificial feed-forward neural network in pulsed thermography during the automated inspection of non-planar carbon fiber reinforced plastic samples. The time series, including the raw temperature–time series and sequenced signals obtained from the first derivative after thermographic signal reconstruction was used to train and test the models respectively. Quantitative comparisons of testing results showed that the long short-term memory recurrent neural network model was more accurate in handling time dependent information compared to the artificial feed-forward neural network model. [ABSTRACT FROM AUTHOR]

With the recent developments in artificial intelligence, deep learning (convolutional neural networks) has been investigated for the quality management of materials by infrared thermography (IRT). In this work, we present an approach to efficiently process thermal data in order to improve the defect detection performance of IRT. A defect-enhanced algorithm based on Generative Adversarial Networks (GANs) has been designed and implemented qualitatively analysis and improve defects visibility based on data augmentation from a deep learning approach. We implemented a data dimension reduction method based on Partial Least Square Thermography (PLST) merged with GAN Networks (PLS-GANs) to achieve interpretable feature extraction and visualization, and compare result with thermal data of Pulsed Thermography in order to evaluate the efficacy of the proposed algorithm. By applying PLS-GANs, a small dataset of thermal data can be able to enlarge the diversity of data in order to improve the performance of the detection model. The experimental results were empirically illustrated over the benchmark specimens: Carbon Fiber Reinforced Polymers (CFRPs). Consequently, the experimental detection results on the CFRPs demonstrated its feasibility of the PLST-GANs method. [ABSTRACT FROM AUTHOR]

The application of the thermal and infrared technology in different areas of research is considerably increasing. These applications involve nondestructive testing, medical analysis (computer aid diagnosis/detection—CAD), and arts and archeology, among many others. In the arts and archeology field, infrared technology provides significant contributions in terms of finding defects of possible impaired regions. This has been done through a wide range of different thermographic experiments and infrared methods. The proposed approach here focuses on application of some known factor analysis methods such as standard nonnegative matrix factorization (NMF) optimized by gradient-descent-based multiplicative rules (SNMF1) and standard NMF optimized by nonnegative least squares active-set algorithm (SNMF2) and eigen-decomposition approaches such as principal component analysis (PCA) in thermography, and candid covariance-free incremental principal component analysis in thermography to obtain the thermal features. On the one hand, these methods are usually applied as preprocessing before clustering for the purpose of segmentation of possible defects. On the other hand, a wavelet-based data fusion combines the data of each method with PCA to increase the accuracy of the algorithm. The quantitative assessment of these approaches indicates considerable segmentation along with the reasonable computational complexity. It shows the promising performance and demonstrated a confirmation for the outlined properties. In particular, a polychromatic wooden statue, a fresco, a painting on canvas, and a building were analyzed using the above-mentioned methods, and the accuracy of defect (or targeted) region segmentation up to 71.98%, 57.10%, 49.27%, and 68.53% was obtained, respectively. [ABSTRACT FROM AUTHOR]

In this work, the potentialities of the infrared vision to explore sub-superficial defects in polychromatic statues were investigated. In particular, it was possible to understand how the reflector effect of the exterior golden layers could be minimized, applying advanced statistical algorithms to thermal images. Since this noble metal is present as external coating in both artworks, an in-depth discussion concerning its physicochemical properties is also added. In this context, the principal component thermography technique and, the more recent, partial least squares thermography technique were used on three different datasets recorded, providing long thermal stimuli. The main images were compared both to phasegrams and to the thermographic signal reconstruction results in order to have a clear outline of the situation to be debated. The effects of view factors on the radiation transfer linked to the specular reflections from the surface did not falsely highlight certain features inadvertently. Indeed, the raw thermograms were analyzed one by one. Reflectograms were used to pinpoint emissivity variations due to, e. g., possible repainting. The paper concludes that, as it is possible to understand from a physical point of view, the near-infrared reflectography technique is able to examine the state of conservation of the upper layers in cultural heritage objects, while the infrared thermography technique explores them more in-depth. The thesis statement is based on the thermal and nonthermal parts of the infrared region, therefore, indicating what can be detected by heating the surface and what can be visualized by illuminating the surface, bearing in mind the nature of the external coating. [ABSTRACT FROM AUTHOR]

With a view to map the health status of mosaics, non-destructive testing methods ought to be used for data collection. Among these, the infrared thermography is highly recommended since it is non-contact, non-intrusive, non-invasive and able to convert the invisible thermal energy into a video signal, in which the energy level is usually correlated with a colour or a greyscale. The need to provide the position of sub-superficial defects in the clear way is of paramount importance when the diagnostician is not the final client. In the cultural heritage field, raw thermograms, sometimes, do not provide interesting results for the restorer, since they are affected by an undesirable content of noise that limits the detection of what is present beneath the surface. In this work, the multi-dimensional ensemble empirical mode decomposition technique was used-to the best of our knowledge for the first time-as regards the thermographic diagnosis of mosaics. It seems to overcome the thermal barrier of the tessellatum layer, composed by aggregates of different natures, as typical in the Roman era. The results obtained after the inspection via a very long pulse are encouraging, above all when compared with the results coming from recent and non-recent algorithms also applied herein. The use of intelligent sensors placed inside and outside the mosaic sample, which measured the temperature evolution along the heating-up and cooling-down phases, helped in the understanding the optimal heat flux to be provided. [ABSTRACT FROM AUTHOR]

' Restoration is the methodological moment in which the artwork is appreciated in its material form and in its historical and aesthetic duality, with a view to transmitting it to the future' (C. Brandi). This work is inspired by this memorable definition. It is based both on the study of an ancient fresco and a fresco's reproduction, assessing the principal defects afflicting this type of structures and aiming at reconstructing the restoration phases through the definition of a combined thermographic and reflectographic procedure that has the purpose of mapping the quality of the restoration itself and thinking of the future. According to Brandi, the timeline for an artwork can be divided into three parts. The first corresponding to the duration of the creative process conducted by the artist and culminating with the completion of the work. The second corresponds to an interval, which is the historical time elapsing from the conclusion of the work to the present. Last but not least, the moment, which is acknowledged in the consciousness of the observer who takes the responsibility of transmitting it to the future. The observer might also be the restorer. Indeed, the restoration takes place at the latter stage and it aims at re-establishing the potential unity of the artwork, to the highest level possible, without producing an artistic or historical forgery and without erasing any trace of the natural passage of time on the artwork. The infrared vision might help to reveal whether the restoration is done properly or not. [ABSTRACT FROM AUTHOR]

Infrared thermography (IT) is a safe non-destructive evaluation technique that has a fast inspection rate and is generally contactless. It is used for diagnostics and monitoring in several fields including composite materials. In this paper carbon fiber-reinforced polymer plates submitted to tensile testing are inspected using IT. More specifically, carbon/polyether ether ketone panels made of random-oriented strands by compression moulding are submitted to tensile testing and then inspected using three different IT active approaches. The first two approaches use optical sources however with different scanning modes. The first active approach tested is a static surface scanning inspection in reflection mode. The second one is a dynamic line scanning technique where the energy source and camera are in movement with regards to the test sample. The last active IT approach tested uses a mechanical source (ultrasound excitation) to transfer heat to the sample being tested. This last approach is commonly called vibrothermography. Results obtained were then compared to results obtained by micro computed tomography inspection and microscopy. Results revealed voids associated with resin-rich regions as well as cracks. [ABSTRACT FROM AUTHOR]

Wood has been routinely employed in decorative arts, as well as in sculptures and paintings (support) during the Middle Ages, because of its unique aesthetic virtues. It may safely be assumed that wood, as a material for monumental sculpture, was much more commonly employed in the mediaeval period than existing examples would seem to indicate (Bulletin of the metropolitan Museum of Art, ). Wood is easily obtainable; it could be carved and put in place with less difficulty than stone, it is chemically stable when dry, and its surface offers a compatible substrate for paint application. However, the use of wood is not without pitfalls, and requires an understanding of its anisotropic and hygroscopic nature. It is also dimensionally unstable and subject to deterioration by fungi and insects. Moisture-related dimensional changes are certainly among the most challenging problems in painting conservation. With the purpose of preventing important damages, the use of non-or microdestructive testing (NDT) techniques is undoubtedly of paramount interest for painted wooden statues of great value. This work has a threefold purpose: (1) to validate the effectiveness of an integrated approach using near-infrared (NIR) reflectography, square pulse thermography (SPT), and holographic interferometry (HI) techniques for discovering old repairs and/or inclusions of foreign materials in a wooden structure, (2) to confirm and approximately date the restoration carried out by x-ray fluorescence (XRF) spectroscopy and energy-dispersive x-ray spectroscopy (EDS) (that is assembled with a scanning electron microscopy-SEM) techniques, and (3) to combine into a multidisciplinary approach two quantitative NDT results coming from optical and thermographic methods. The subject of the present study was a statue named 'Virgin with her Child' (XIV century), whose origins are mysterious and not properly documented. [ABSTRACT FROM AUTHOR]

This paper presents a weed/crop classification method using computer vision and morphological analysis. Subsequent supervised and unsupervised learning methods are applied to extract dominant morphological characteristics of weeds present in corn and soybean fields. The novelty of the presented technique resides in the feature extraction process that is based on spatial localization of vegetation in fields. Features from the weed leaf area distribution are extracted from the cultivation inter-rows, then features from the crop are inferred from the mixture model equation. Those extracted features are then passed to a naive bayesian classifier and a gaussian mixture clustering algorithm to discriminate weed from crop plant. The presented technique correctly classifies an average of 94 % of corn and soybean plants and 85 % of the weed (multiple species) without any prior knowledge on the species present in the field. [ABSTRACT FROM AUTHOR]

In this study, a Probability of Detection (PoD) experimental study was carried out in the framework of a Belgian-Quebec/Canada collaborative research project called ThermoPoD. Experiments were implemented on a Carbon Fiber Reinforced Plastic (CFRP) specimen with embedded material simulating delamination. For active infrared thermography, different heating sources (optical or ultrasound), heating forms (pulsed or lock-in), and data processing methods, such as Fourier Transform, Thermal Signal Reconstruction, Wavelet Transform, Differential Absolute Contrast, and Principal Component Thermography are of interest. In the present study, the effects of various data processing methods on PoD curves are compared. [ABSTRACT FROM AUTHOR]

The interest toward lightweight composites in the aeronautical industry grows year by year. The challenge is the identification and characterization of defects by using an integration of different techniques. The use of the infrared thermography (IRT) method for the inspection of lightweight composites is poorly documented in the open literature, due to the low heat diffusion through the honeycomb cores. In this study, IRT was used to retrieve the unknown positions of internal inserted objects in three different lightweight composite structures, by using three new thermal image processing integration methods. The corresponding post-processed results were compared against the traditional principal component thermography and pulsed phase thermography image processing techniques. The comparison showed that the integrated signal smoothing processing enhanced the image quality compared to the established techniques. Then, a thermal-physical analysis corresponding to the experimental results was conducted, in order to explain the experimental results. Finally, the advantages and disadvantages of the different presented methods when applied on the different lightweight composite structures were summarized. [ABSTRACT FROM AUTHOR]

Recent advancements in dimensionality reduction techniques have significantly contributed to the field of active infrared thermography (AIRT) for defect detection, aiding in data processing and feature extraction. Among these techniques, principal component thermography (PCT) and deep autoencoder thermography (DAT) are particularly notable. PCT is based on conventional linear multivariate analysis, while DAT leverages deep learning paradigms to better handle nonlinearity. These methods consolidate defect information from multiple thermograms into a concise set of feature images, enhancing the visibility of subsurface material defects. However, these feature images often suffer from disturbances, particularly non-uniform backgrounds caused by uneven heating in AIRT experiments. Such interferences can obscure defect information, necessitating further post-processing. In our research, we explore the efficacy of Adaptive Iteratively Reweighted Penalized Least Squares (AIR-PELS) as a refinement technique for PCT and DAT, focusing on background suppression. The adaptive iterative weighting with PELS smoothing effectively reduces noise and removes background disturbances. Case studies involving carbon fiber-reinforced polymer samples with inherent defects demonstrate the effectiveness of this post-processing approach. [ABSTRACT FROM AUTHOR]