Search Results (83)

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans. Full article

This article describes a machine vision-based method for measuring the angular velocity of a rotating disk to characterize the viscous resistance of a ball bearing. A bright marker was attached to a disk connected to a shaft supported by two ball bearings. Rotation of the marker was recorded with a digital camera. A simple algorithm was developed to track the trajectory of the marker and calculate angular displacement of the disk. For accurate detection of the rotating marker, the algorithm employed Multi-Otsu thresholding and the Least Squares Method (LSM). Verification of the proposed method was carried out through a direct comparison between the predicted rotational speeds and measured ones by a commercial tachometer. It was demonstrated that the percentage error of the proposed method was less than 1.75 percent. The evolution of angular velocity after motor power-off was measured and found to follow an exponential decay law. The exponent was found to remain consistent regardless of the induced rotational speed. This proposed measurement method will offer a simple and accurate non-contact solution for monitoring angular velocity and characterizing the resistance of a bearing. Full article

This paper will examine a methodology to enable the usage of Human Digital Twins (HDTs) for Quality Assurance in the aerospace manufacturing domain. Common-place hardware and infrastructure, including cloud-based facility security cameras, cloud-based commercial virtual environments, a virtual reality (VR) headset, and artificial intelligence (AI) detection algorithms, have been connected via application programming interfaces (API) to enable a 24-h surveillance and feedback capability for a representative aerospace manufacturing cell. Human operators who perform defined manufacturing assembly operations in real life in the cell can utilize this methodology to digitize their performance and provide objective evidence of conformity and safety messaging for their human-centric manufacturing operation in real time. The digitization of real human-centric performance using this methodology creates the foundation for a HDT. This paper will present the application of HDTs in a manner that can easily be scaled across manufacturing operations while utilizing technologies that are already commonly inserted into existing manufacturing operations, which facilitates the exploration of HDT concepts without the need for expensive capital purchases and emerging technologies. Full article

Rapid post-earthquake assessments of residential buildings are essential for preventing secondary disasters but typically require substantial human resources, with challenges related to accuracy and inspector safety. In wooden residential buildings, residual deformation can cause significant internal damage despite minor external indications. Thus, accurate evaluation of secondary components such as exterior walls and window frames is crucial. Although recent studies on digital assessment technologies focus mainly on reinforced concrete structures, limited research addresses wooden structures, especially considering residual deformation. This study proposes a rapid emergency risk assessment method utilizing 3D point cloud measurements obtained by a 3D scanning camera for densely built wooden residential areas. Its practicality was verified through three aspects. First, a comparison with conventional methods showed that the measurement accuracy of the proposed method is sufficient for practical use, with errors significantly lower than the inclination thresholds used in emergency risk assessments (e.g., 1/60 rad ≈ 1°). Second, in detection experiments using a deformed window frame model, the average error between the applied inclination and the measured values was less than 3%, demonstrating that deformation, dislodgement, and inclination of secondary components can be reliably detected from point cloud data. Third, field validation conducted in a commercial district confirmed that multiple buildings can be simultaneously measured and that individual buildings and their secondary components can be efficiently extracted and identified. Thus, this method demonstrates practical applicability and significantly improves the speed and efficiency of emergency assessments in densely built wooden residential areas. Full article

With the rise of digital video technologies and the proliferation of processing methods and storage systems, video-surveillance systems have received increasing attention over the last decade. However, the spread of cameras installed in public and private spaces makes it more difficult for human operators to perform real-time analysis of the large amounts of data produced by surveillance systems. Due to the advancement of artificial intelligence methods, many automatic video analysis tasks like violence detection have been studied from a research perspective, and are even beginning to be commercialized in industrial solutions. Nevertheless, most of these solutions adopt centralized architectures with costly servers utilized to process streaming videos sent from different cameras. Centralized architectures do not present the ideal solution due to the high cost, processing time issues, and network bandwidth overhead. In this paper, we propose a lightweight autonomous system for the detection and geolocation of violent acts. Our proposed system, named LAVID, is based on a depthwise separable convolution model (DSCNN) combined with a bidirectional long-short-term memory network (BiLSTM) and implemented on a lightweight smart camera. We provide in this study a lightweight video-surveillance system consisting of low-cost autonomous smart cameras that are capable of detecting and identifying harmful behavior and geolocate violent acts that occur over a covered area in real-time. Our proposed system, implemented using Raspberry Pi boards, represents a cost-effective solution with interoperability features making it an ideal IoT solution to be integrated with other smart city infrastructure. Furthermore, our approach, implemented using optimized deep learning models and evaluated on several public datasets, has shown good results in term of accuracy compared to state of the art methods while optimizing reducing power and computational requirements. Full article

We report a workflow and a software description for digital image colorimetry aimed at obtaining a quantitative dose–response curve and the minimal inhibitory concentration in the Resazurin Microtiter Assay (REMA) test of the activity of antimycobacterial drugs. The principle of this analysis is based on the newly established correspondence between the intensity of the ${\mathrm{a}}^{*}$ channel of the CIE L*a*b* colour space and the concentration of resorufin produced in the course of this test. The whole procedure can be carried out using free software. It has sufficiently mild requirements for the quality of colour images, which can be taken by a typical smartphone camera. Thus, the approach does not impose additional costs on the medical examination points and is widely accessible. Its efficiency is verified by applying it to the case of two representatives of substituted 2-(quinolin-4-yl) imidazolines. The direct comparison with the data on the indicator’s fluorescence obtained using a commercial microplate reader argues that the proposed approach provides results of the same range of accuracy on the quantitative level. As a result, it would be possible to apply the strategy not only for new low-cost studies but also for expanding databases on drug candidates by quantitatively reprocessing existing data, which were earlier documented by images of microplates but analysed only qualitatively. Full article

The digitization of pathology departments in hospitals around the world is now a reality. The current commercial solutions applied to digitize histopathological samples consist of a robotic microscope with an RGB-type camera attached to it. This technology is very limited in terms of information captured, as it only works with three spectral bands of the visible electromagnetic spectrum. Therefore, we present an automated system that combines RGB and hyperspectral technology. Throughout this work, the hardware of the system and its components are described along with the developed software and a working methodology to ensure the correct capture of histopathological samples. The software is integrated by the controller of the microscope, which features an autofocus functionality, whole slide scanning with a stitching algorithm, and hyperspectral scanning functionality. As a reference, the time to capture and process a complete sample with 20 regions of high biological interest using the proposed method is estimated at a maximum of 79 min, reducing the time required by a manual operator by at least three times. Both hardware and software can be easily adapted to other systems that might benefit from the advantages of hyperspectral technology. Full article

This paper proposes an advancement in the application of a Technical Vision System (TVS), which integrates a laser scanning mechanism with a single light sensor to measure 3D spatial coordinates. In this application, the system is used to scan and digitalize objects using a rotating table to explore the potential of the system for 3D scanning at reduced resolutions. The experiments undertaken searched for optimal scanning windows and used statistical data filtering techniques and regression models to find a method to generate a 3D scan that was still recognizable with the least amount of 3D points, balancing the number of points scanned and time, while at the same time reducing effects caused by the particularities of the TVS, such as noise and entropy in the form of natural distortion in the resulting scans. The evaluation of the experimentation results uses 3D point registration methods, joining multiple faces from the original volume scanned by the TVS and aligning it to the ground truth model point clouds, which are based on a commercial 3D camera to verify that the reconstructed 3D model retains substantial detail from the original object. This research finds it is possible to reconstruct sufficiently detailed 3D models obtained from the TVS, which contain coarsely scanned data or scans that initially lack high definition or are too noisy. Full article

Critical facilities are generally located in areas with restricted or controlled access, making it difficult for experts to monitor the structural health of enclosed infrastructures. Hence, a case study was conducted in South Korea to evaluate the applicability of digital image photogrammetry using commercial imaging devices in order to quickly measure the structural deformations of infrastructures in such areas. The applicability evaluation involved measuring the displacement of mechanically stabilized earth (MSE) walls. In the experiment, the displacement of MSE walls was first measured using the traditional monitoring method and the results were compared with those obtained via digital image photogrammetry using commercial imaging devices such as digital cameras and cellphones. The measurement results obtained with the cellphone camera had a maximum error of approximately 20 mm when compared with the results of the traditional monitoring method. Because this is a significant error, even when considering the mechanical error in the traditional monitoring method’s result, it was determined that monitoring using a cellphone camera is infeasible. However, the experimental results of digital image photogrammetry using a digital camera showed a maximum error of approximately 9 mm. Although this is a sizable error, the method was assessed to be technically feasible. Full article

The onset and early stages of dynamic wetting on different hydrophobic surfaces is investigated experimentally for aqueous solutions of two commercial trisiloxane surfacants of similar chemical structure, one of which exhibits superspreading behaviour, in order to investigate the spreading dynamics independently of the surface activity. Superspreading, or the ability of a surfactant solution to spread on a surface beyond the state determined by thermodynamic equilibrium, has been investigated for more than 30 years however its physical mechanism remains poorly understood to date despite its important applications in the formulation of agrochemicals. Surfactant solutions were prepared by dissolving S233 and S240 surfactants (Evonik Industries AG, Essen, Germany) into de-ionised water at a weight concentration of 0.1%. Drops of surfactant solutions and pure water were deposited on three horizontal substrates with different wettability (equilibrium contact angle of water ranging between ${55}^{\circ }$ and ${100}^{\circ }$), and observed from below with a high-frame rate camera to visualise the advancing contact line. The spreading ratio of drops as a function of time was extracted from high-speed videos by digital image processing. Results reveal that the superspreading solution exhibits an intermittent spreading rate, as well as peculiar features of the contact line, which are not observed for the non-superspreading solution, and confirm the superspreading effect becomes less significant when the surface energy of the substrate is decreased. Full article

Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel^® RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm’s skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges. Full article

Recent years have seen a rapid rise in the generation of high-resolution topographic data using custom-built or commercial-grade Unmanned Aerial Vehicles (UAVs). Though several studies have demonstrated the application potential of UAV data, significant knowledge gaps still exist in terms of proper documentation of protocols for data acquisition, post-flight data processing, error assessments, and their mitigation. This work documents and provides guidelines for UAV data acquisition and processing from several years of field experience in diverse geomorphic settings across India, including undulating topography (~17 km²), alluvial plains (~142 km²), lowland-river basin (~66 km²), and a highly urbanized area (~5 km²). A total of 37,065 images with 16 and 20 Megapixels and 604 ground control points (GCPs) were captured with multiple UAV systems and processed to generate point clouds for a total area of ~230 km². The Root Mean Square Error (RMSE) for each GCP for all sites ranged from 6.41 cm to 36.54 cm. This manuscript documents a comprehensive guideline for (a) pre-field flight planning and data acquisition, (b) generation and removal of noise and errors of the point cloud, and (c) generation of orthoimages and digital elevation models. We demonstrate that a well-distributed and not necessarily uniformly distributed GCP placement can significantly reduce doming error and other artifacts. We emphasize the need for using separate camera calibration parameters for each flight and demonstrate that errors in camera calibration can significantly impact the accuracy of the point cloud. Accordingly, we have evaluated the stability of lens calibration parameters between consumer-grade and professional cameras and have suggested measures for noise removal in the point cloud data. We have also identified and analyzed various errors during point cloud processing. These include systematic doming errors, errors during orthoimage and DEM generation, and errors related to water bodies. Mitigation strategies for various errors have also been discussed. Finally, we have assessed the accuracy of our point cloud data for different geomorphic settings. We concluded that the accuracy is influenced by Ground Sampling Distance (GSD), topographic features, and the placement, density, and distribution of GCPs. This guideline presented in this paper can be extremely beneficial to both experienced long-term users and newcomers for planning the UAV-based topographic survey and processing the data acquired. Full article

Despite the benefits of learning an instrument, many students drop out early because it can be frustrating for the student, expensive for the caregiver, and loud for the household. Virtual Reality (VR) and Extended Reality (XR) offer the potential to address these challenges by simulating multiple instruments in an engaging and motivating environment through headphones. To assess the potential for commercial VR to augment musical experiences, we used standard VR implementation processes to design four virtual trumpet interfaces: camera-tracking with tracked register selection (two ways), camera-tracking with voice activation, and a controller plus a force-feedback haptic glove. To evaluate these implementations, we created a virtual music classroom that produces audio, notes, and finger pattern guides loaded from a selected Musical Instrument Digital Interface (MIDI) file. We analytically compared these implementations against physical trumpets (both acoustic and MIDI), considering features of ease of use, familiarity, playability, noise, and versatility. The physical trumpets produced the most reliable and familiar experience, and some XR benefits were considered. The camera-based methods were easy to use but lacked tactile feedback. The haptic glove provided improved tracking accuracy and haptic feedback over camera-based methods. Each method was also considered as a proof-of-concept for other instruments, real or imaginary. Full article

Apricot stones have high commercial value and can be used for manufacturing functional foods, cosmetic products, active carbon, and biodiesel. The optimal processing of the stones is dependent on the cultivar and there is a need for methods to sort among different cultivars (which are often mixed in processing facilities). This study investigates the effectiveness of two low-cost colour imaging systems coupled with supervised learning to develop classification models to determine the cultivar of different stones. Apricot stones of the cultivars ‘Bella’, ‘Early Orange’, ‘Harcot’, ‘Skierniewicka Słodka’, and ‘Taja’ were used. The RGB images were acquired using a flatbed scanner or a digital camera; and 2172 image texture features were extracted within the R, G, B; L, a, b; X, Y, Z; U, and V colour coordinates. The most influential features were determined and resulted in 103 and 89 selected features for the digital camera and the flatbed scanner, respectively. Linear and nonlinear classifiers were applied including Linear Discriminant Analysis (LDA), Decision Trees (DT), k-Nearest Neighbour (kNN), Support Vector Machines (SVM), and Naive Bayes (NB). The models resulting from the flatbed scanner and using selected features achieved an accuracy of 100% via either quadratic diagonal LDA or kNN classifiers. The models developed using images from the digital camera and all or selected features had an accuracy of up to 96.77% using the SVM classifier. This study presents novel and simple-to-implement at-line (flatbed scanner) and online (digital camera) methodologies for apricot stone sorting. The developed procedure combining colour imaging and machine learning may be used for the authentication of apricot stone cultivars and quality evaluation of apricot from sustainable production. Full article

The investigation of linguistic landscapes (LL) among the Arab community in downtown Brooklyn, New York City, is an underserved public space in the literature. This research focused on social and commercial or ‘bottom-up signs’ in LL to understand their purpose, origin and target audience. Drawing upon discourse analysis, the study was conceptualized according to the principles of border theory and geosemiotics. The latter was used to analyze the data, which consisted of random photographs of shopfronts in Brooklyn taken with a digital camera during the summer of 2016. The three semiotic aggregates used for analysis consisted of interaction order, visual and place semiotics. The data analysis showed the multi-layered nature of LL in this urban community and the subjectiveness of spatial borders through a combination of text and symbolic imagery. The paper highlights the importance of commercial signs in the LL among ethnic minority communities. Full article