This paper proposes a novel framework for implicit multi-camera system calibration utilizing Gaussian Process (GP) regression. Conventional explicit calibration methods are constrained by rigid mathematical models and struggle with complex, non-linear distortions from unconventional optics, while existing neural network-based implicit approaches are typically data-hungry and lack inherent uncertainty quantification (UQ). Our GP-based model directly learns the complex, non-linear mapping from 2D image coordinates across all cameras to a 3D world coordinate, completely bypassing time-consuming estimation of explicit intrinsic and extrinsic parameters. Moreover, the inherent UQ is critical for transforming a simple 3D point prediction into a verifiable 3D measurement, complete with statistically-sound confidence bounds. To further enhance data efficiency and practical deployment, we integrate Active Learning (AL), which intelligently leverages the GP's predictive uncertainty to strategically guide the acquisition of new calibration data. This approach results in a robust, data-efficient, and reliable calibration solution, proving particularly effective in practical scenarios where collecting extensive calibration data is a dominant constraint. Our experiments show that the uncertainty for the 3D predictions is higher closer to the cameras. The data points in uv-coordinate space are more sparse in that region, even though they are not in 3D space. This work is relevant for anyone who is tasked with the calibration of complex multi-camera systems.

Cold atmospheric plasma (CAP) finds numerous applications across various sectors, including industry (e.g. surface modification) and medicine (e.g. tissue regeneration, wound healing, oncology, and dentistry). However, understanding the mechanical properties of materials undergoing CAP treatment is of great importance, particularly for applications involving changes in material properties. This study aims to utilize CAP as an excitation device for assessing the mechanical properties of a polymethyl methacrylate (PMMA) sample. CAP was employed to induce vibrations on a PMMA sample around its resonance frequency, and the resulting vibrations were measured by a scanning laser doppler vibrometer. The elastic modulus of the PMMA sample was then calculated based on the stress and strain profiles obtained from the measured vibrations. The obtained elastic modulus value of 4.87 GPa showed excellent agreement with the 4.83 GPa value obtained using other excitation devices, indicating the reliability of CAP in mechanical characterization. This study is the first step toward potential applications that can break new ground in the use of CAP in monitoring and characterization of mechanical properties during CAP treatment (e.g. surface treatment), paving the way for enhanced control and optimization of CAP-based processes in various applications.

This paper provides a comprehensive investigation into characterizing viscoelastic properties in soft materials, focusing on silicone. Conventional measurement techniques such as tensile testing and dynamic mechanical analysis prove inadequate for soft materials. The proposed method utilizes Rayleigh surface wave propagation, employing a laser Doppler vibrometer to measure phase speed and amplitude, induced by air jet pulses in a contactless manner. Usually, a contact probe is used for excitation, which results a higher signal to noise ratio. The study involves a regression analysis to obtain dispersion curves, filtering outcomes with a coefficient of determination threshold above 95% as a quality indicator for the measurements. Mechanical parameters, including complex dynamic modulus and shear modulus components, are determined from Rayleigh and shear complex wave-numbers. Repeatability assessments, including changes in measuring direction, underscore the stability of the experimental setup and sample uniformity. Noteworthy is the comparison of results from the Surface Wave Elastography (SWE) method through an experimental test, a novel aspect in this study. The evaluation method, based on a spring-damper model rooted in the exact solution of Newton's second law differential equation, serves as a reference. The comparison of mean storage modulus values between the SWE method and the free-loading mass method revealed errors of 6.36% and 10.23% for two tested samples.

The aim of this research was to investigate the application possibilities of thermal simulations in the textile industry, focusing on simulating the cooling of a woven textile. This simulation aimed to compare the inspection results with the simulated ones to find defects in textiles and examine where they critically differed. This approach would cut down the time between iterations and could be an option to detect faults in textiles in substitution of the currently used method of applying machine learning on images. The simulation was based on the finite element method. The thermograms were measured by an infrared camera setup. From these data, we extrapolated the height and texture of the weaving. In a later phase of research, the results of this simulation were benchmarked against real measurements.

When studying paintings with active infrared thermography (IRT), minimizing the temperature fluctuations and thermal shock during a measurement becomes important. Under these conditions, it might be beneficial to use lock-in thermography instead of the conventionally used pulse thermography (PT). This study compared the observations made with lock-in thermography (LIT) and pulse phase thermography (PPT) with halogen light excitation. Three distinctly different paintings were examined. The LIT measurements caused smaller temperature fluctuations and, overall, the phase images appeared to have a higher contrast and less noise. However, in the PPT phase images, the upper paint layer was less visible, an aspect which is of particular interest when trying to observe subsurface defects or the structure of the support. The influence of the spectral range of the cameras on the results was also investigated. All measurements were taken with a mid-wave infrared (MWIR) and long wave infrared (LWIR) camera. The results show that there is a significant number of direct reflection artifacts, caused by the use of the halogen light sources when using the MWIR camera. Adding a long-pass filter to the MWIR camera eliminated most of these artifacts. All results are presented in a side-by-side comparison.

In non-destructive inspections today, the size of the sample being examined is often limited to fit within the field of view of the camera being used. When examining larger specimens, multiple image sequences need to be stitched together into one image. Due to uneven illumination, the combined image may have artificial defects. This manuscript provides a solution for performing line-scan measurements from a sample and combining the images to avoid these artificial defects. The proposed algorithm calculates the pixel shift, either through checkerboard detection or by field of view (FOV) calculation, for each image to create the stitched image. This working principle eliminates the need for synchronisation between the motion speed of the object and the frame rate of the camera. The algorithm is tested with several cameras that operate in different wavelengths (ultraviolet (UV), visible near infrared (Vis-NIR) and long-wave infrared (LWIR)), each with the corresponding light sources. Results show that the algorithm is able to achieve subpixel stitching accuracy. The side effects of heterogeneous illumination can be solved using the proposed method.

Melanoma remains a deadly cancer despite significant advances in immune checkpoint blockade and targeted therapies. The incidence of melanoma is also growing worldwide, which highlights the need for novel treatment options and strategic combination of therapies. Here, we investigate non-thermal plasma (NTP), an ionized gas, as a promising, therapeutic option. In a melanoma mouse model, direct treatment of tumors with NTP results in reduced tumor burden and prolonged survival. Physical characterization of NTP treatment in situ reveals the deposited NTP energy and temperature associated with therapy response, and whole transcriptome analysis of the tumor identified several modulated pathways. NTP treatment also enhances the cancer-immunity cycle, as immune cells in both the tumor and tumor-draining lymph nodes appear more stimulated to perform their anti-cancer functions. Thus, our data suggest that local NTP therapy stimulates systemic, anti-cancer immunity. We discuss, in detail, how these fundamental insights will help direct the translation of NTP technology into the clinic and inform rational combination strategies to address the challenges in melanoma therapy.

Spot welds have a significant part in the creation of automotive vehicles. Since the integrity of, for example, a car, is dependent on the performance of multiple welds, it is important to ensure the quality of each spot weld. Several attempts have been made in order to determine the quality of spot welds, but most of them do not focus on the applicability in the manufacturing process. Spot weld inspections are often performed using back heating. However, during manufacturing, robotic inspections are desired, and since the bodywork of a car is a complex shape, the accessibility from the inside of the vehicle is minor. Therefore, inspections using front heating are more suitable. In this manuscript, multiple excitation methods are compared as well as different post-processing techniques. The used excitation techniques can be divided into light heating and inductive heating. Light heating is further divided in lock-in thermography and pulse thermography. The used post-processing techniques are principle component analysis and fast Fourier transform. Inductive heating turns out to be the most suitable measurement technique since it is fast and can be performed as front and back heating. Both investigated post-processing techniques deliver suitable information, such as relief images and information of the internal structure of the spot weld.

To automatically evaluate the ergonomics of workers, 3D skeletons are needed. Most ergonomic assessment methods, like REBA, are based on the different 3D joint angles. Thanks to the huge amount of training data, 2D skeleton detectors have become very accurate. In this work, we test three methods to calculate 3D skeletons from 2D detections: using the depth from a single RealSense range camera, triangulating the joints using multiple cameras, and combining the triangulation of multiple camera pairs. We tested the methods using recordings of a person doing different assembly tasks. We compared the resulting joint angles to the ground truth of a VICON marker-based tracking system. The resulting RMS angle error for the triangulation methods is between 12° and 16°, showing that they are accurate enough to calculate a useful ergonomic score from.

The aim of this research topic and paper is to investigate the application possibilities of vision technology in the textile industry. These include RGB, active thermography and hyperspectral imaging techniques. In the future, this approach will be supplemented by a machine learning algorithm (e.g., in Matlab or Python) to enable the detection of defects in textiles and to correctly categorize these defects. In the first place, the various options for building such a convolutional neural network are discussed. The focus was on the models used in the literature. Based on the effectiveness of these ML models and the feasibility to build them, choices can be made to determine the most suitable models. Sufficient samples are an important link to properly train a model. Because there is a shortage of open data, it is also discussed how samples obtained from the textile industry, were measured in the lab. At first, we will limit ourselves to the five most common defects. In a later phase of research, the results with this dataset and the open datasets are benchmarked against the results from the literature.

Nowadays, performing dynamic line scan thermography (DLST) is very challenging, and therefore an expert is needed in order to predict the optimal set-up parameters. The parameters are mostly dependent on the material properties of the object to be inspected, but there are also correlations between the parameters themselves. The interrelationship is not always evident even for someone skilled in the art. Therefore, optimisation using response surface can give more insights in the interconnections between parameters, but also between the material properties and the variables. Performing inspections using an optimised parameter set will result in high contrast thermograms showing the size and shape of the defect accurately. Using response surfaces to predict the optimal parameter set enables to perform fast measurements without the need of extensive testing to find adequate measurement parameters. Differing from the optimal parameters will result in contrast loss or detail loss of the size and shape of the detected defect.

Bayesian inference using Gaussian processes on large datasets have been studied extensively over the past few years. However, little attention has been given on how to apply these on a high resolution input space. By approximating the set of test points (where we want to make predictions, not the set of training points in the dataset) by a kd-tree, a multi-resolution data structure arises that allows for considerable gains in performance and memory usage without a significant loss of accuracy. In this paper, we study the feasibility and efficiency of constructing and using such a kd-tree in Gaussian process regression. We propose a cut-off rule that is easy to interpret and to tune. We show our findings on generated toy data in a 3D point cloud and a simulated 2D vibrometry example. This survey is beneficial for researchers that are working on a high resolution input space. The kd-tree approximation outperforms the naïve Gaussian process implementation in all experiments.

Using an optimised heating source in active thermography can facilitate the processing of measurement results. By designing a custom heat source for dynamic line scan thermography, we reduced the excitation power needed to heat the sample and decreased the unwanted side effects originating of a wide-range heating source. The design started from a regular halogen tube lamp and a reflector is composed to provide the desired heating power in a narrow band. The reflector shape is optimised using ray-tracing software to concentrate the electromagnetic radiation along with the heat in a slim line. A comparison between the optimised heat source and a commercially available line-heater is performed. The width of the heated region from the Mehler Engineering + Service-heater is larger than prescribed in the datasheet. The optimised line heater has several advantages over the comercially available heat source.

Quantitive infrared thermography like active thermography is a non-destructive testing technique that is used to inspect surfaces of components for defects. A problem with infrared-based defect detection is that misclassifications based on geometrically dependent measurement characteristics can occur. This problem becomes more problematic with the inspection of complex 3D shapes. In this paper, a new infrared quality control procedure using a quality map is proposed that models the geometrically dependent measurement characteristics based on available CAD data and CAD matching techniques. Misclassifications are reduced by using this quality map in combination with a modified version of principal component thermography post-processing. We applied our proposed methodology on a prototype bicycle part and a plaster cast angel figurine. In these experiments, the procedure using quality maps is able to prevent false defect detection.

In order to identify the exact location of the useful perforator for DIEP flap breast reconstruction, infrared images can be used.The thermal images will also help to determine which parts of the flap can be safely used for the DIEP flap reconstruction.By means of infrared thermography a blood vessel distribution of the abdominal wall will be visualized.The subjective interpretation of IR images is crucial as the results can generate additional information concerning the hemodynamic properties of the flap.One specific case will be used to explain the state-of-the-art post-processing methods.

For some applications it can be preferable to use images of spheres in order to calibrate a 2D camera. All published sphere-based algorithms need the complete knowledge of the elliptic sphere image, i.e. 5 geometric parameters, in particular the ellipse orientation. Because sphere images tend to be close to circular shapes, this orientation is often very noise-sensitive. For example, it is common to compute the principal point as the intersection of the lines through the major axes of the elliptic images, but this procedure is quite unstable. We present a new method for computing the principal point by means of three sphere images, without making use of the ellipse orientation. By mean of simulations and real experiments we demonstrate that the proposed method is more accurate and stable in finding the principal point as compared to sphere-based calibration algorithms that use the complete ellipse geometry.

The fatigue performance and healing ability of asphalt pavements are important for mixture design, rehabilitation, and maintenance of the roads. By analyzing these parameters in an asphalt mixture, it is possible to predict the crack formation and propagation in asphalt pavements and healing of these cracks during the rest periods. The healing effect in asphalt mixtures has been observed and verified by many researchers and different methods exist to evaluate this phenomenon. However, current methods are still inadequate to fully observe and quantify the healing phenomenon in asphalt mixtures. In this study, a digital image correlation (DIC) method is used to calculate the strain map on the surface of cylindrical asphalt specimens during the indirect tensile fatigue test. This strain map is used to detect the location of crack initiations and development of the microcracks during the experiments. Next, the specimens are unloaded and the temperature of the climate chamber is increased to 60 °C for 18 h to investigate the healing phenomenon on the surface of the specimens. It is observed that the strain reduces near the microcrack areas especially at the tip of the microcracks. Furthermore, using DIC it is possible to observe the healing rate and the minimum time required to heal the microcracks on the surface of the specimens.

In order to determine camera parameters, a calibration procedure involving the camera recordings of a checkerboard is usually performed. In this paper, we propose an alternative approach that uses Gray-code patterns displayed on an LCD screen. Gray-code patterns allow us to decode 3D location information of points of the LCD screen at every pixel in the camera image. This is in contrast to checkerboard patterns where the number of corresponding locations is limited to the number of checkerboard corners. We show that, for the case of a UEye CMOS camera, the precision of focal-length estimation is 1.5 times more precise than when using a standard calibration with a checkerboard pattern.

In order to identify the exact location of a useful perforator for DIEP flap breast reconstruction, CT images are made in the pre-operative phase. The aim of this research is to evaluate if dynamic infrared thermography is a helpful tool to check and visualize the blood flow in the flap during the pre- and peroperative phase. The results will be used in order to pinpoint the usefulness of IR thermography as an alternative method for perforator mapping and flapdesign. By means of infrared thermography the blood vessel distribution and its vascularisation of the abdominal wall will be visualized. The thermal images can help to detect the correct perforator and can help to decide which parts of the flap are best perfused and can be used for the DIEP flap reconstruction.