Low-cost RGB-D cameras are increasingly being used in several research fields, including human–machine interaction, safety, robotics, biomedical engineering and even reverse engineering applications. Among the plethora of commercial devices, the Intel RealSense cameras have proven to be among the most suitable devices, providing a good compromise between cost, ease of use, compactness and precision. Released on the market in January 2018, the new Intel model RealSense D415 has a wide acquisition range (i.e., ~160–10,000 mm) and a narrow field of view to capture objects in rapid motion. Given the unexplored potential of this new device, especially when used as a 3D scanner, the present work aims to characterize and to provide metrological considerations for the RealSense D415. In particular, tests are carried out to assess the device performance in the near range (i.e., 100–1000 mm). Characterization is performed by integrating the guidelines of the existing standard (i.e., the German VDI/VDE 2634 Part 2) with a number of literature-based strategies. Performance analysis is finally compared against the latest close-range sensors, thus providing a useful guidance for researchers and practitioners aiming to use RGB-D cameras in reverse engineering applications.

RGB-D cameras are employed in several research fields and application scenarios. Choosing the most appropriate sensor has been made more difficult by the increasing offer of available products. Due to the novelty of RGB-D technologies, there was a lack of tools to measure and compare performances of this type of sensor from a metrological perspective. The recent ISO 10360-13:2021 represents the most advanced international standard regulating metrological characterization of coordinate measuring systems. Part 13, specifically, considers 3D optical sensors. This paper applies the methodology of ISO 10360-13 for the characterization and comparison of three RGB-D cameras produced by Intel^® RealSense™ (D415, D455, L515) in the close range (100–1500 mm). ISO 10360-13 procedures, which focus on metrological performances, are integrated with additional tests to evaluate systematic errors (acquisition of flat objects, 3D reconstruction of objects). The present paper proposes an off-the-shelf comparison which considers the performance of the sensors throughout their acquisition volume. Results have exposed the strengths and weaknesses of each device. The D415 device showed better reconstruction quality on tests strictly related to the short range. The L515 device performed better on systematic depth errors; finally, the D455 device achieved better results on tests related to the standard.

RGB-D cameras have been widely used in various research fields and applications in recent years. With the growing availability of commercial products, choosing the most suitable sensor for a specific application has become more complex. The lack of tools to evaluate and compare the metrological performance of these sensors was addressed by the recent ISO 10360-13:2021 standard, which defines the standards for the metrological characterization of 3D optical coordinate measuring systems. This paper applies the ISO 10360-13 methodology to characterize and compare four RGB-D cameras: three Intel® RealSense™ (D415, D435i, D455) and one Microsoft® Azure Kinect DK. The procedures described in the standard were integrated with three tests: the analysis of systematic errors measured acquiring a planar surface at increasing distances; a test to analyze the resolution performances with a specifically devised artifact; the evaluation of 3D reconstruction performances on two objects with different geometric characteristics. The results highlighted the strengths and weaknesses of each device. The D415 showed better performance in reconstructions from close-range acquisitions. The Azure Kinect DK had the best results for systematic error and resolution. The D455 generally achieved the best performance in the standard tests, followed by the D435i.

The development of eco-sustainable systems for the textile industry is a trump card for attracting expanding markets aware of the ecological challenges that society expects in the future. For companies willing to use regenerated wool as a raw material for creating plain, colored yarns and/or fabrics, building up a number of procedures and tools for classifying the conferred recycled materials based on their color is crucial. Despite the incredible boost in automated or semi-automated methods for color classification, this task is still carried out manually by expert operators, mainly due to the lack of systems taking into account human-related classification. Accordingly, the main aim of the present work was to devise a simple, yet effective, machine vision-based system combined with a probabilistic neural network for carrying out reliable color classification of plain, colored, regenerated wool fabrics. The devised classification system relies on the definition of a set of color classes against which to classify the recycled wool fabrics and an appositely devised acquisition system. Image-processing algorithms were used to extract helpful information about the image color after a set of images has been acquired. These data were then used to train the neural network-based algorithms, which categorized the fabric samples based on their color. When tested against a dataset of fabrics, the created system enabled automatic classification with a reliability index of approximately 83%, thus demonstrating its effectiveness in comparison to other color classification approaches devised for textile and industrial fields.

Emotion recognition through machine learning techniques is a widely investigated research field, however the recent obligation to wear a face mask, following the COVID19 health emergency, precludes the application of systems developed so far. Humans naturally communicate their emotions through the mouth; therefore, the intelligent systems developed to date for identifying emotions of a subject primarily rely on this area in addition to other anatomical features (eyes, forehead, etc..). However, if the subject is wearing a face mask this region is no longer visible. For this reason, the goal of this work is to develop a tool able to compensate for this shortfall. The proposed tool uses the AffectNet dataset which is composed of eight class of emotions. The iterative training strategy relies on well-known convolutional neural network architectures to identify five sub-classes of emotions: following a pre-processing phase the architecture is trained to perform the task on the eight-class dataset, which is then recategorized into five classes allowing to obtain 96.92% of accuracy on the testing set. This strategy is compared to the most frequently used learning strategies and finally integrated within a real time application that allows to detect faces within a frame, determine if the subjects are wearing a face mask and recognize for each one the current emotion.

Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.