Search

Virtualization plays a critical role in enriching the user experience in Virtual Reality (VR) by offering heightened realism, increased immersion, safer navigation, and newly achievable levels of interaction and personalization, specifically in indoor environments. Traditionally, the creation of virtual content has fallen under one of two broad categories: manual methods crafted by graphic designers, which are labor-intensive and sometimes lack precision; traditional Computer Vision (CV) and Deep Learning (DL) frameworks that frequently result in semi-automatic and complex solutions, lacking a unified framework for both 3D reconstruction and scene understanding, often missing a fully interactive representation of the objects and neglecting their appearance. To address these diverse challenges and limitations, we introduce the Virtual Experience Toolkit (VET), an automated and user-friendly framework that utilizes DL and advanced CV techniques to efficiently and accurately virtualize real-world indoor scenarios. The key features of VET are the use of ScanNotate, a retrieval and alignment tool that enhances the precision and efficiency of its precursor, supported by upgrades such as a preprocessing step to make it fully automatic and a preselection of a reduced list of CAD to speed up the process, and the implementation in a user-friendly and fully automatic Unity3D application that guides the users through the whole pipeline and concludes in a fully interactive and customizable 3D scene. The efficacy of VET is demonstrated using a diversified dataset of virtualized 3D indoor scenarios, supplementing the ScanNet dataset. [ABSTRACT FROM AUTHOR]

Featured Application: MANTRA system has been validated in laboratory condition with an electronic board maintenance task, but it has been developed to work for almost any industrial maintenance task of electromechanical devices such as electric motors, power control panels or special machinery and custom automation systems. In recent years, the benefits of both Augmented Reality (AR) technology and infrared thermography (IRT) have been demonstrated in the industrial maintenance sector, allowing maintenance operations to be carried out in a safer, faster, and more efficient manner. However, there still exists no solution that optimally combines both technologies. In this work, we propose a new AR system—MANTRA—with specific application to industrial maintenance. The system can automatically align virtual information and temperature on any 3D object, in real time. This is achieved through the joint use of an RGB-D sensor and an IRT camera, leading to high accuracy and robustness. To achieve this objective, a pose estimation method that combines a deep-learning-based object detection method, YOLOV4, together with the template-based LINEMOD pose estimation method, as well as a model-based 6DOF pose tracking technique, was developed. The MANTRA system is validated both quantitatively and qualitatively through a real use-case, demonstrating the effectiveness of the system compared to traditional methods and those using only AR. [ABSTRACT FROM AUTHOR]

Artificial vision has wide-ranging applications in the food sector; it is easy to use, relatively low cost and allows to conduct rapid non-destructive analyses. The aim of this study was to use artificial vision techniques to control and model the coffee roasting process. Samples of Castillo variety coffee were used to construct the roasting curve, with captured images at different times. Physico-chemical determinations, such as colour, titratable acidity, pH, humidity and chlorogenic acids, and caffeine content, were investigated on the coffee beans. Data were processed by (i) Principal component analysis (PCA) to observe the aggrupation depending on the roasting time, and (ii) partial least squares (PLS) regression to correlate the values of the analytical determinations with the image information. The results allowed to construct robust regression models, where the colour coordinates (L*, a*), pH and titratable acidity presented excellent values in prediction (R2 Pred 0.95, 0.91, 0.94 and 0.92). The proposed algorithms were capable to correlate the chemical composition of the beans at each roasting time with changes in the images, showing promising results in the modelling of the coffee roasting process. [ABSTRACT FROM AUTHOR]

A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0-6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead's pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R² of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness. [ABSTRACT FROM AUTHOR]

The capability of the VIS/SW-NIR (visible/short wave near infrared) hyperspectral imaging system to characterize the heat treatment process of cake wheat flour was studied. Combinations of heat treatments of flour were run at different temperatures (80, 100 and 130 °C) and for various times (10, 20 and 30 min). The resulting treated flours were analyzed by the imaging technique. The hyperspectral results, studied by multivariate statistical methods, showed a pattern evolution of the flours treated by different heat treatments. The wavelengths that contributed the most, and implied in the differentiations, were detected. The selection of wavelengths allowed us to optimize the analysis, which reduced from 54 to 6 wavelengths. To ensure that the VIS/SW-NIR information depended on the heat treatment influence on flours, cakes were produced and characterized according to height, mass loss during the baking process, crumb structure and textural properties. The VIS/SW-NIR imaging analysis was capable of following the changes that occurred during the different heat treatments of flours. VIS/SW-NIR was applied to determine and adjust the heat treatment process variables to improve the features of flours during the cake production process. [ABSTRACT FROM AUTHOR]

Characterisation of wheat flour destined for the bread-making process was studied by using hyperspectral image analyses based on Short-wave NIR imaging (SW-NIR). The first step was to characterise wheat flour batches by considering physicochemical, rheological and fermentation kinetic parameters. The second step was to capture the hyperspectral images of each batch. The results showed some significant differences in fermentation capacity in relation to gluten and its protein sub fractions. However, no significant rheological differences were observed. The acquired images were firstly analysed by principal component analysis, where a clustering tendency was observed. Then linear correlation maps were done to study the relationship between the SW-NIR spectra response and the physicochemical, rheological and fermentation kinetic parameters. The results showed an important correlation between the SW-NIR spectra (760–900 nm) and the fermentation capacity parameters of the batches, as well as gluten and its sub fraction proteins. The SW-NIR imaging analysis enabled to obtain useful information about wheat flour behaviour related to the fermentation process. Thus the studied technique may represent a useful, rapid non-destructive method to help characterise wheat flours with non-different rheological parameters by considering their behaviour during the fermentation process. [ABSTRACT FROM AUTHOR]

A non-destructive Vis-NIR spectroscopy (400-1000 nm) method was developed to evaluate the loss of freshness of sliced and commercially packaged cooked ham and turkey ham without any sample manipulation. The spectra were recorded at 0, 30, 40, and 60 days using a camera, spectral filter (400-1000 nm) and a halogen floodlighting system which had been were developed and calibrated for the purpose. Physico-chemical, biochemical, and microbiological properties such as pH, total volatile basic nitrogen (TVB-N), ATP breakdown compounds, and colony-forming units were determined to predict the degradation of freshness. The image spectra obtained from visible and SW-NIR spectroscopy were related to the storage time of the samples. A PLS-DA model was developed independently for packaged or unpackaged samples using the second derivative of the spectra. Mean R²prediction obtained for cooked ham was 0.915 and 0.949 for Turkey ham. The technique developed could be applied to monitoring the freshness of commercial packed cooked ham and turkey ham as a non-destructive technique. Further studies will be needed to check the spectra obtained from samples of different commercial brands in order to evaluate more precisely the efficiency of the method. [ABSTRACT FROM AUTHOR]

The maintenance of industrial equipment extends its useful life, improves its efficiency, reduces the number of failures, and increases the safety of its use. This study proposes a methodology to develop a predictive maintenance tool based on infrared thermographic measures capable of anticipating failures in industrial equipment. The thermal response of selected equipment in normal operation and in controlled induced anomalous operation was analyzed. The characterization of these situations enabled the development of a machine learning system capable of predicting malfunctions. Different options within the available conventional machine learning techniques were analyzed, assessed, and finally selected for electronic equipment maintenance activities. This study provides advances towards the robust application of machine learning combined with infrared thermography and augmented reality for maintenance applications of industrial equipment. The predictive maintenance system finally selected enables automatic quick hand-held thermal inspections using 3D object detection and a pose estimation algorithm, making predictions with an accuracy of 94% at an inference time of 0.006 s. [ABSTRACT FROM AUTHOR]

Assistive technologies help all persons with disabilities to improve their accessibility in all aspects of their life. The AIDE European project contributes to the improvement of current assistive technologies by developing and testing a modular and adaptive multimodal interface customizable to the individual needs of people with disabilities. This paper describes the computer vision algorithms part of the multimodal interface developed inside the AIDE European project. The main contribution of this computer vision part is the integration with the robotic system and with the other sensory systems (electrooculography (EOG) and electroencephalography (EEG)). The technical achievements solved herein are the algorithm for the selection of objects using the gaze, and especially the state-of-the-art algorithm for the efficient detection and pose estimation of textureless objects. These algorithms were tested in real conditions, and were thoroughly evaluated both qualitatively and quantitatively. The experimental results of the object selection algorithm were excellent (object selection over 90%) in less than 12 s. The detection and pose estimation algorithms evaluated using the LINEMOD database were similar to the state-of-the-art method, and were the most computationally efficient. [ABSTRACT FROM AUTHOR]

Some chia seed flour effects relating to different bread-making process phases and variables were studied by distinct image analysis and physicochemical techniques. Wheat flours with three different degrees of substitution (5%, 10% and 15%) were tested. In technological terms, the aim was to study the influence and properties of chia flour on several relevant parameters, such as pasting properties, growth kinetics and internal crumb structure during dough fermentation; and baking process, mass loss, water activity and texture profile of the end product during its storage. Some changes in pasting properties were observed. The effects obtained by image analysis techniques proved that addition of chia improved gas retention in dough and cut the time required to reach maximum dough development. A delay in hardness and water loss during storage of breads was also observed. Bread presented reduced water activity, and contained the same amount of moisture compared with the control. The mucilage provided by chia has properties that can explain these observed effects given the influence on water-holding capacity and its interactions with gluten proteins throughout the gluten matrix-forming process. [ABSTRACT FROM AUTHOR]