Search Results (1,638)

Motion capture experiments can be conducted more easily if marker-based motion (marker-based MoCap) can be captured using an asynchronous multicamera system (Async MCS). However, camera calibration is essential for marker-based MoCap, and a wand calibration method that utilizes timestamp functions has been proposed for Async MCS. However, in practice, many cameras do not provide accurate timestamp functions, limiting the applicability of existing methods in such environments. A wand calibration method for Async MCS that does not rely on timestamp functions is proposed to evaluate the accuracy of estimated camera parameters. In conventional methods, the time offset in image acquisition is obtained from timestamp information, and synchronous coordinates are estimated by interpolating time-series digitized coordinates of wand markers. In this study, the time offset is treated as an optimization variable, which enables camera parameter estimation without using timestamp functions. Consequently, the three-dimensional reconstruction errors of fixed points obtained using the proposed method are significantly smaller (1.445 ± 0.833 and 1.746 ± 0.908 mm) compared to estimations that ignore time offsets. These findings demonstrate that the proposed method enables more accurate camera calibration. Full article

Low-cost RGB intraoral cameras are accessible alternatives to intraoral scanners; however, generating panoramic images is challenging due to narrow fields of view, textureless surfaces, and specular highlights. This study proposes a robust stitching framework and identifies optimal acquisition parameters to overcome these limitations. All experiments were conducted exclusively on a mandibular dental phantom model. Geometric consistency was further validated using repeated physical measurements of mandibular arch dimensions as ground-truth references. We employed a deep learning-based approach using SuperPoint and SuperGlue to extract and match features in texture-poor environments, enhanced by a central-reference stitching strategy to minimize cumulative drift errors. To validate the feasibility in a controlled setting, we conducted experiments on dental phantoms varying working distances (1.5–3.0 cm) and overlap ratios. The proposed method detected approximately 19–20 times more valid inliers than SIFT, significantly improving matching stability. Experimental results indicated that a working distance of 2.5 cm offers the optimal balance between stitching success rate and image detail for handheld operation, while a 1/3 overlap ratio yielded superior geometric integrity. This system demonstrates that robust 2D dental mapping is achievable with consumer-grade sensors when combined with advanced deep feature matching and optimized acquisition protocols. Full article

To address the bottlenecks of traditional jaw crusher liner wear detection—high safety risks, insufficient precision, and limited full-range analysis—this paper proposes a non-contact, high-precision wear analysis method based on binocular vision and deep learning. At its core is the integration of the state-of-the-art FoundationStereo zero-shot stereo matching algorithm, following scenario-specific adaptations, into the 3D reconstruction of industrial liners for wear analysis. A novel wear quantification methodology and corresponding indicator system are also proposed. After calibrating the ZED2 binocular camera and fine-tuning the algorithm, FoundationStereo achieves an Endpoint Error (EPE) of 0.09, significantly outperforming traditional algorithms. To meet on-site efficiency requirements, a “single-view rapid acquisition + CUDA engineering acceleration” strategy is implemented, reducing point cloud generation latency from 165 ms to 120 ms by rewriting kernel functions and optimizing memory access patterns. Geometric accuracy verification shows a Mean Absolute Error (MAE) ≤ 0.128 mm, fully meeting industrial measurement standards. A complete process of “3D reconstruction–model registration–quantitative analysis” is constructed, utilizing three core indicators (maximum wear depth, average wear depth, and wear area ratio) to characterize liner wear. Statistical results—such as an average maximum wear depth of 55.05 mm—are highly consistent with manual inspection data, providing a safe, efficient, and precise digital solution for the predictive maintenance and intelligent operation and maintenance (O&M) of liners. Full article

Automated optical inspection (AOI) technologies are widely used in PCB and semiconductor manufacturing to improve accuracy and reduce human error during quality inspection. While existing AOI systems can perform defect detection, they often rely on pre-defined camera positions and lack flexibility for interactive inspection, especially when the operator needs to visually verify solder pad conditions or examine specific layout regions. This study focuses on the front-end optical positioning and inspection stage of the AOI workflow, providing an automated mechanism to link digitally generated layout reports from EDA layout tools with real PCB inspection tasks. The proposed system operates on component-placement reports exported by EDA layout environments and uses them to automatically guide the camera to the corresponding PCB coordinates. Since PCB design reports may vary in format and structure across EDA tools, this study proposes a vision-based extraction approach that employs Hough transform-based region detection and a CNN-based digit recognizer to recover component coordinates from visually rendered design data. A dual-axis sliding platform is driven through a hierarchical control architecture, where coarse positioning is performed via TB6600 stepper control and Bluetooth-based communication, while fine alignment is achieved through a non-contact, gesture-based interface designed for clean-room operation. A high-resolution autofocus camera subsequently displays the magnified solder pads on a large screen for operator verification. Experimental results show that the proposed platform provides accurate, repeatable, and intuitive optical positioning, improving inspection efficiency while maintaining operator ergonomics and system modularity. Rather than replacing defect-classification AOI systems, this work complements them by serving as a positioning-assisted inspection module for interactive and semi-automated PCB quality evaluation. Full article

While 3D Gaussian Splatting (3DGS) has significantly advanced large-scale 3D reconstruction and novel view synthesis, it still suffers from high memory consumption and slow training speed. To address these issues without compromising reconstruction quality, we propose a novel 3DGS-based framework tailored for large-scale scenes. Specifically, we introduce a visibility-aware camera selection strategy within a divide-and-conquer training approach to dynamically adjust the number of input views for each sub-region. During training, a spatially aware densification strategy is employed to improve the reconstruction of distant objects, complemented by depth regularization to refine geometric details. Moreover, we apply an enhanced Gaussian pruning method to re-evaluate the importance of each Gaussian, prune redundant Gaussians with low contributions, and improve efficiency while reducing memory usage. Experiments on multiple large-scale scene datasets demonstrate that our approach achieves superior performance in both quality and efficiency. With its robustness and scalability, our method shows great potential for real-world applications such as autonomous driving, digital twins, urban mapping, and virtual reality content creation. Full article

Early-age shrinkage in 3D-printed concrete constitutes a critical applied challenge due to the rapid development of deformations and the absence of conventional reinforcement systems. From a scientific standpoint, a clear knowledge gap exists in materials science concerning the reliable quantification of very small, rapidly evolving strains in fresh and early-age cementitious materials produced by additive manufacturing. This study investigates practical and low-cost alternatives to commercial optical systems for monitoring early-age shrinkage in 3D-printed concrete, a key challenge given the rapid deformation of printed elements and their typical lack of reinforcement. The work focuses on identifying both the most precise method for capturing minor, fast-developing strains and affordable tools suitable for laboratories without access to advanced equipment. Three mixtures with different aggregate types were examined to broaden the applicability of the findings and to evaluate how aggregate selection affects fresh properties, hardened performance, and shrinkage behavior. Shrinkage measurements were carried out using a commercial digital image correlation system, which served as the reference method, along with simplified optical setups based on a smartphone camera and a GoPro device. Additional measurements were performed with laser displacement sensors and Linear Variable Differential Transformer LVDT transducers mounted in a dedicated fixture. Results were compared with the standardized linear shrinkage test to assess precision, stability, and the influence of curing conditions. The findings show that early-age shrinkage must be monitored immediately after printing and under controlled environmental conditions. When the results obtained after 12 h of measurement were compared with the values recorded using the commercial reference system, differences of 19%, 13%, 16%, and 14% were observed for the smartphone-based method, the GoPro system, the laser sensors, and the LVDT transducers, respectively. Full article

The structural analysis of paper fibers is vital for the noninvasive classification and conservation of traditional handmade paper in cultural heritage. Although digital still cameras (DSCs) offer a low-cost and noninvasive imaging solution, their inferior image quality compared to white-light confocal microscopy (WCM) limits their effectiveness in fiber classification. To address this modality gap, we propose an unpaired image-to-image translation approach using cycle-consistent adversarial networks (CycleGANs). Our study targets a multifiber setting involving kozo, mitsumata, and gampi, using publicly available domain-specific datasets. Generated WCM-style images were quantitatively evaluated using peak signal-to-noise ratio, structural similarity index measure, mean absolute error, and Fréchet inception distance, achieving 8.24 dB, 0.28, 172.50, and 197.39, respectively. Classification performance was tested using EfficientNet-B0 and Inception-ResNet-v2, with F1-scores reaching 94.66% and 98.61%, respectively, approaching the performance of real WCM images (99.50% and 98.86%) and surpassing previous results obtained directly from DSC inputs (80.76% and 84.19%). Furthermore, Grad-CAM visualization confirmed that the translated images retained class-discriminative features aligned with those of the actual WCM inputs. Thus, the proposed CycleGAN-based image conversion effectively bridges the modality gap, enabling DSC images to approximate WCM characteristics and support high-accuracy paper fiber classification, which is a practical alternative for noninvasive material analysis. Full article

This study presents an integrated, low-cost system for measuring extremely small displacements in Ionic Polymer–Metal Composite (IPMC) actuators operating in aqueous environments. A custom optical setup was developed, combining a glass tank, a tubular microscope with a 10× achromatic objective, a digital USB camera and uniform LED backlighting, enabling side-view imaging of the actuator with high contrast. The microscopy system achieves a spatial sampling of 0.536 μm/pixel on the horizontal axis and 0.518 μm/pixel on the vertical axis, while lens distortion is limited to a maximum edge deviation of +0.015 μm/pixel (≈+2.8%), ensuring consistent geometric magnification across the field of view. On the image-processing side, a predictive grid-based tracking algorithm is introduced to localize the free tip of the IPMC. The method combines edge detection, Harris corners and a constant-length geometric constraint with an adaptive search over selected grid cells. On 1920 × 1080-pixel frames, the proposed algorithm achieves a mean processing time of about 10 ms per frame and a frame-level detection accuracy of approximately 99% (98.3–99.4% depending on the allowed search radius) for actuation frequencies below 2 Hz, enabling real-time monitoring at 30 fps. In parallel, dedicated electronic circuitry for supply and load monitoring provides overvoltage, undervoltage, open-circuit and short-circuit detection in 100 injected fault events, all faults were detected and no spurious triggers over 3 h of nominal operation. The proposed microscopy and tracking framework offer a compact, reproducible and high-resolution alternative to laser-based or Digital Image Correlation techniques for IPMC displacement characterization and can be extended to other micro-displacement sensing applications in submerged or challenging environments. Full article

Freshwater availability is one of the most pressing environmental concerns in arid ecosystems. The use of free-standing water by raptors has been little studied, and in the context of climate change has become increasingly important as extended droughts are expected to become more frequent. We analyzed digital images from camera traps captured in the freshwater springs of Sierra El Mechudo, during summer to early autumn of 2023 and 2024 in Baja California Sur, Mexico. We recorded 165 detections of four raptor species. The Turkey Vulture (Cathartes aura) was the most frequently detected (n = 55), followed by the Great Horned Owl (Bubo virginianus) (n = 50), the Red-tailed Hawk (Buteo jamaicensis) (n = 45), and the Cooper’s Hawk (Astur cooperii), which was observed only in early autumn 2024 (n = 15). The Great Horned Owl exhibited a distinct detection pattern (mainly crepuscular, with the highest peak at 6 a.m.), in contrast with the other three species, which were detected mainly at midday and in the afternoon, during the hottest hours of the day. All raptors were recorded drinking water; however, species differed in the proportion of behaviors they exhibited at the freshwater springs. The Turkey Vulture showed the highest drinking activity (76.3%), whereas both hawks exhibited the same lowest proportions (26.6%) among all species detected. The proportion of behaviors remained constant across years. The time spent at the freshwater springs did not differ across species or years. The Red-tailed Hawk, the Great Horned Owl, and the Turkey Vulture increased their detections at the springs in 2024, when a severe and prolonged drought affected the southern peninsula. The results showed that the importance of freshwater springs for raptors extends beyond their use for drinking only; the surrounding habitat as a refuge and availability of prey in the area are evidently essential for these birds of prey. Further studies should extend research into the diverse use of springs and home ranges of raptors in the southern Baja California peninsula. Full article

With new applications continuously emerging in the fields of manufacturing, quality control and inspection, the need to develop three-dimensional (3D) scanning solutions suitable for industrial environments increases. 3D scanning is the process of analyzing one or more objects in order to convert and store the object’s features in a digital format. Due to the increased costs of industrial 3D scanning solutions, this paper proposes an open-source, low-cost architecture for obtaining a 3D model that can be used in manufacturing, which involves a linear laser beam that is swept across the object via a rotating mirror, and a camera that grabs images, to further be used to extract the dimensions of the object through a technique inspired by laser triangulation. The 3D models for several objects are obtained, analyzed and compared to the dimensions of their respective real-world counterparts. For the tested objects, the proposed system yields a maximum mean height error of 2.56 mm, a maximum mean length error of 1.48 mm and a maximum mean width error of 1.30 mm on the raw point cloud and a scanning time of ∼4 s per laser line. Finally, a few observations and ways to improve the proposed solution are mentioned. Full article

The proper control of cathode spot motion is the key issue in the application of vacuum arc deposition (VAD). Improving the arc spot discharge requires considering the structure and properties of the deposited films/coatings as well as the deposition velocity, target utilization, etc. To achieve this goal, it is necessary to precisely observe cathode spots during discharge at both microscopic and macroscopic scales. However, how to capture the motion and splitting behavior of cathode spots using simple equipment remains an open question. In this work, we analyze the motion and the splitting behavior of cathode spots considering industry application on a Cr target for VAD under arc currents in the direct mode (70 A, 100 A, 150 A 200 A) and 400 A as a peak value in the pulse mode with two applied magnetic fields (20 Gs and 40 Gs) at the macroscopic scale using a high-speed digital camera, employing different exposure times under an extracted interval of 1 ms ranging from 0.01 ms to 20 ms. Furthermore, the images are also compared when the interval is extended to 2 ms and 4 ms. Considering all factors in the available data, 0.05 ms is suggested as the best exposure time, and 1 ms is suggested as the most suitable extracted interval for cathode spot observation under these conditions. Hopefully, this paper provides a suitable technique for observing the motion and splitting behavior of cathode spots at a macroscopic scale with the purpose of suppressing/eliminating the injection of macroparticles at the source for industrial VAD coating products of high quality. Full article

This study presents the design and optimization of a digital-imaging afocal telescope system that integrates an afocal telescope architecture with an imaging optical subsystem. The proposed system employs a combination of spherical and aspherical optical elements to enhance imaging flexibility, reduce aberrations, and ensure effective system coupling. Proper pupil matching is achieved by aligning the exit pupil of the afocal telescope with the entrance pupil of the imaging system, ensuring minimal vignetting and optimal energy transfer. Circular apertures and lens elements are used throughout the system to simplify alignment and minimize pupil-matching errors. The complete system comprises three imaging optical subsystems and a digital camera module, each independently optimized to ensure balanced optical performance. The design achieves an overall magnification of 16×, with near-diffraction-limited quality confirmed by an RMS wavefront error of 0.0474λ and a Strehl ratio of 0.915. The modulation transfer function (MTF) reaches 0.42 at 80 lp/mm, while the distortion remains below 4.87%. Chromatic performance is well controlled, with maximum lateral color deviations of 1.007 µm (short-to-long wavelength) and 1.52 µm (short-to-reference wavelength), evaluated at 656 nm, 587 nm, and 486 nm. The results demonstrate that the proposed digital-imaging afocal telescope system provides high-resolution, low-aberration imaging suitable for precision optical applications. Full article

To address the critical problem of 3D object detection in autonomous driving scenarios, we developed a novel digital twin architecture. This architecture combines AI models with geometric optics algorithms of camera systems for autonomous vehicles, characterized by low computational cost and high generalization capability. The architecture leverages monocular images to estimate the real-world heights and 3D positions of objects using vanishing lines and the pinhole camera model. The You Only Look Once (YOLOv11) object detection model is employed for accurate object category identification. These components are seamlessly integrated to construct a digital twin system capable of real-time reconstruction of the surrounding 3D environment. This enables the autonomous driving system to perform real-time monitoring and optimized decision-making. Compared with conventional deep-learning-based 3D object detection models, the architecture offers several notable advantages. Firstly, it mitigates the significant reliance on large-scale labeled datasets typically required by deep learning approaches. Secondly, its decision-making process inherently provides interpretability. Thirdly, it demonstrates robust generalization capabilities across diverse scenes and object types. Finally, its low computational complexity makes it particularly well-suited for resource-constrained in-vehicle edge devices. Preliminary experimental results validate the reliability of the proposed approach, showing a depth prediction error of less than 5% in driving scenarios. Furthermore, the proposed method achieves significantly faster runtime, corresponding to only 42, 27, and 22% of MonoAMNet, MonoSAID, and MonoDFNet, respectively. Full article

The increasing threat of Glacial Lake Outburst Floods (GLOFs), intensified by climate change, underscores the urgency for developing advanced early warning systems. The near-annual, cyclical outbursts of Lake Merzbacher in the Tien Shan mountains present a severe downstream threat, yet its remote location and lack of instrumentation pose a significant challenge to traditional monitoring. To bridge this gap, we develop and validate a dynamic risk assessment framework driven entirely by remote sensing data. Methodologically, the framework introduces an innovative Ice-Water Composite Index (IWCI) to resolve the challenge of lake area extraction under mixed ice-water conditions. This is coupled with a high-fidelity 5 m resolution Digital Elevation Model (DEM) of the lake basin, autonomously generated from GF-7 Dual-Line Camera (DLC) imagery, which enables accurate daily volume retrieval. Through systematic feature engineering, nine key hydro-thermal drivers are quantified from MODIS and other products to train a Random Forest (RF) machine learning model, establishing the non-linear relationship between catchment processes and lake volume. The model demonstrates robust predictive performance on an independent validation set (2023–2024) (R² = 0.80, RMSE = 5.15 × 10⁶ m³), accurately captures the complete lake-filling cycle from initiation to near-peak stage. Furthermore, feature importance analysis quantitatively confirms that Positive Accumulated Temperature (PAT) is the dominant physical mechanism governing the lake’s storage dynamics. This end-to-end framework offers a transferable paradigm for GLOF hazard management, enabling a critical shift from static, regional assessments to dynamic, site-specific early warning in data-scarce alpine regions. Full article

During drill-and-blast construction, complex and variable rock masses are frequently encountered. Owing to the transient nature of the explosion process and the randomness of crack propagation, the response of different rock masses to explosive loading is highly intricate. This study primarily investigates the dynamic response of rock masses with varying strengths under two different charge configurations. First, four cement mortar specimens of differing strengths were prepared then subjected to general blasting and slit charge blasting, respectively. High-speed cameras and digital image correlation techniques were employed to capture and analyse stress wave propagation and crack propagation during detonation. Fractal dimension analysis was subsequently employed to quantify and compare the extent of damage in the specimens. Findings indicate that rock strength influences stress wave attenuation patterns: lower-strength rocks exhibit higher peak strains but faster decay rates. Crack propagation velocity was calculated by deploying monitoring points along fracture paths and defining fracture initiation thresholds. Higher rock strength correlates with both peak and average crack propagation velocities. Slit charge blasting effectively optimizes damage distribution, concentrating it within the intended directions while reducing chaotic fracturing. These findings provide scientific justification for blasting operations in complex rock formations. Full article