Title Participants Abstract "A novel line-scan algorithm for unsynchronised dynamic measurements" "Simon Verspeek, Thomas De Kerf, Bart Ribbens, Xavier Maldague, Steve Vanlanduit, Gunther Steenackers" "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." "Into a rapid polymer characterization employing optical measurement systems and high-power ultrasonic excitation" "Navid Hasheminejad, Steve Vanlanduit, Mohammadtaher Ghalandari, Fabrice Pierron, Cedric Vuye" "This study presents a novel methodology for estimating the master curve of the complex modulus of viscoelastic materials using a combination of optical measurement systems and ultrasonic excitation. Traditional techniques for characterizing properties of viscoelastic materials are often time-consuming and encounter limitations that hinder their accuracy at high strain rates. To address this, a method was proposed that leverages two optical measurement systems to quickly assess material properties at multiple points on a sample. A high-power ultrasonic transducer was employed to excite the material at its first longitudinal natural frequency, creating non-uniform temperature variations and strain rates. A scanning laser Doppler vibrometer measured vibrations across the material, enabling computation of the complex modulus magnitude under varying conditions. These results were correlated with temperature readings obtained from an infrared camera. The constructed master curve using the proposed methodology closely resembled those established through quasi-static and dynamic uniaxial compression tests in the literature. Additionally, this method revealed a more substantial increase in complex modulus at high strain rates compared to traditional experiments, where this characteristic is less pronounced." "Performance assessment of discrete wavelet transform for de-noising of FBG sensors signals embedded in asphalt pavement" "Seyed Ali Golmohammadi Tavalaei, Navid Hasheminejad, David Hernando, Steve Vanlanduit, Wim Van den bergh" "In recent years, the Fiber Bragg Grating (FBG) sensor technology has been increasingly utilized as an optical measurement system in various engineering applications, particularly for structural health monitoring (SHM) purposes. This trend can be attributed to the inherent benefits of FBG sensors, such as their small size, immunity to electromagnetic interference, resistance to corrosion, and high accuracy and sensitivity. Various factors cause noise in the FBG sensor signal, which has a significant effect on measurement precision. As a result, de-noising plays an important role in the use of FBG sensor systems. In this study, strain data collected from FBG sensors embedded in a road section were used to evaluate the performance of discretized wavelet transform (DWT) for denoising FBG signals. The presence of noise poses a significant challenge in accurately measuring low-amplitude strains and light loads. To address this issue, various approaches have been investigated, including the selection of appropriate mother wavelets, levels of decomposition, thresholding functions, and thresholding selection approaches, with the aim of identifying the optimal parameters for effective denoising. The results show that FBG signals could be denoised successfully and low amplitude strains appeared completely without any loss of valuable data." "Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression" "Ivan De Boi, Elissa Embrechts, Quirine Schatteman, Rudi Penne, Steven Truijen, Wim Saeys" "Visuospatial neglect is a disorder characterised by impaired awareness for visual stimuli located in regions of space and frames of reference. It is often associated with stroke. Patients can struggle with all aspects of daily living and community participation. Assessment methods are limited and show several shortcomings, considering they are mainly performed on paper and do not implement the complexity of daily life. Similarly, treatment options are sparse and often show only small improvements. We present an artificial intelligence solution designed to accurately assess a patient’s visuospatial neglect in a three-dimensional setting. We implement an active learning method based on Gaussian process regression to reduce the effort it takes a patient to undergo an assessment. Furthermore, we describe how this model can be utilised in patient oriented treatment and how this opens the way to gamification, tele-rehabilitation and personalised healthcare, providing a promising avenue for improving patient engagement and rehabilitation outcomes. To validate our assessment module, we conducted clinical trials involving patients in a real-world setting. We compared the results obtained using our AI-based assessment with the widely used conventional visuospatial neglect tests currently employed in clinical practice. The validation process serves to establish the accuracy and reliability of our model, confirming its potential as a valuable tool for diagnosing and monitoring visuospatial neglect. Our VR application proves to be more sensitive, while intra-rater reliability remains high." "A phase correlation based peak detection method for accurate shape from focus measurements" "Jona Gladines, Seppe Sels, Ivan De Boi, Steve Vanlanduit" "Estimating a 3D shape from 2D images is a classic computer vision problem. Shape from focus is a commonly used method for this purpose. With shape from focus, 3D depth is estimated using a so-called focus measure operator. Pixel focus follows a Gaussian-like distribution in which the location of the peak is an indicator of the 3D depth. Locating the peak in this distribution is complicated due to noise coming from various sources. We investigate the accuracy of some existing algorithms and introduce a new algorithm based on phase correlation. Phase correlation is a powerful method for finding correlations between signals, especially in a noisy environment. The accuracy and robustness to noise of the proposed method are tested and proven by applying it to synthetic data as well as measurements of a calibration target. The proposed method is over 30% more accurate than comparable methods, yet requires more computational effort." "Seaweed growth monitoring with a low-cost vision-based system" "Jeroen Gerlo, Dennis G. Kooijman, Ivo W. Wieling, Ritchie Heirmans, Steve Vanlanduit" "In this paper, we introduce a method for automated seaweed growth monitoring by combining a low-cost RGB and stereo vision camera. While current vision-based seaweed growth monitoring techniques focus on laboratory measurements or above-ground seaweed, we investigate the feasibility of the underwater imaging of a vertical seaweed farm. We use deep learning-based image segmentation (DeeplabV3+) to determine the size of the seaweed in pixels from recorded RGB images. We convert this pixel size to meters squared by using the distance information from the stereo camera. We demonstrate the performance of our monitoring system using measurements in a seaweed farm in the River Scheldt estuary (in The Netherlands). Notwithstanding the poor visibility of the seaweed in the images, we are able to segment the seaweed with an intersection of the union (IoU) of 0.9, and we reach a repeatability of 6% and a precision of the seaweed size of 18%." "On the angular control of rotating lasers by means of line calculus on hyperboloids" "Ivan De Boi, Steve Vanlanduit" "We propose a new paradigm for modelling and calibrating laser scanners with rotation symmetry, as is the case for lidars or for galvanometric laser systems with one or two rotating mirrors. Instead of bothering about the intrinsic parameters of a physical model, we use the geometric properties of the device to model it as a specific configuration of lines, which can be recovered by a line-data-driven procedure. Compared to universal data-driven methods that train general line models, our algebraic-geometric approach only requires a few measurements. We elaborate the case of a galvanometric laser scanner with two mirrors, that we model as a grid of hyperboloids represented by a grid of 3x3 lines. This provides a new type of look-up table, containing not more than nine elements, lines rather than points, where we replace the approximating interpolation with exact affine combinations of lines. The proposed method is validated in a realistic virtual setting. As a collateral contribution, we present a robust algorithm for fitting ruled surfaces of revolution on noisy line measurements." "Design and metrological analysis of a backlit vision system for surface roughness measurements of turned parts" "Alessia Baleani, Nicola Paone, Jona Gladines, Steve Vanlanduit" "The focus of this study is to design a backlit vision instrument capable of measuring surface roughness and to discuss its metrological performance compared to traditional measurement instruments. The instrument is a non-contact high-magnification imaging system characterized by short inspection time which opens the perspective of in-line implementation. We combined the use of the modulation transfer function to evaluate the imaging conditions of an electrically tunable lens to obtain an optimally focused image. We prepared a set of turned steel samples with different roughness in the range R-a 2.4 mu m to 15.1 mu m. The layout of the instrument is presented, including a discussion on how optimal imaging conditions were obtained. The paper describes the comparison performed on measurements collected with the vision system designed in this work and state-of-the-art instruments. A comparison of the results of the backlit system depends on the values of surface roughness considered; while at larger values of roughness the offset increases, the results are compatible with the ones of the stylus at lower values of roughness. In fact, the error bands are superimposed by at least 58% based on the cases analyzed." "Enhanced checkerboard detection using Gaussian processes" "Michaël Hillen, Ivan De Boi, Thomas De Kerf, Seppe Sels, Edgar Cardenas, Jona Gladines, Gunther Steenackers, Rudi Penne, Steve Vanlanduit" "Accurate checkerboard detection is of vital importance for computer vision applications, and a variety of checkerboard detectors have been developed in the past decades. While some detectors are able to handle partially occluded checkerboards, they fail when a large occlusion completely divides the checkerboard. We propose a new checkerboard detection pipeline for occluded checkerboards that has a robust performance under varying levels of noise, blurring, and distortion, and for a variety of imaging modalities. This pipeline consists of a checkerboard detector and checkerboard enhancement with Gaussian processes (GP). By learning a mapping from local board coordinates to image pixel coordinates via a Gaussian process, we can fill in occluded corners, expand the board beyond the image borders, allocate detected corners that do not fit an initial grid, and remove noise on the detected corner locations. We show that our method can improve the performance of other publicly available state-of-the-art checkerboard detectors, both in terms of accuracy and the number of corners detected. Our code and datasets are made publicly available. The checkerboard detector pipeline is contained within our Python checkerboard detection library, called PyCBD. The pipeline itself is modular and easy to adapt to different use cases." "Wanneer het leven in 8 of 24 dimensies meer comfort biedt" "Paul Levrie, Rudi Penne, Stijn Dierckx" "3D mag dan wel onze vertrouwde wereld zijn, voor wiskundigen is het leven in 8D of 24D soms veel eenvoudiger."