Search Results (92)

Objectives: To evaluate the feasibility and efficacy of a brief, scaffolded virtual reality (VR)-based perceptual-learning protocol to improve stereovision in adults without measurable baseline global stereopsis. Methods: Prospective interventional pilot study with a normal-vision reference group. Fourteen adults were enrolled; nine with nil global stereoacuity formed the intervention group and five with normal binocular vision served as controls. Intervention participants completed 8–10 VR sessions (~30 min each over two weeks) using a disparity-driven depth-matching task with individualized thresholds, virtual prism compensation, and interocular contrast balancing. Primary outcomes were changes in clinical stereoacuity (local and global) and Binocular Function (BF). Secondary outcomes included positive fusional vergence (PFV), binocular imbalance (inter-ocular contrast ratio), and in-game depth discrimination error (arcseconds). Results: Global stereoacuity improved with four of nine participants who initially showed no measurable global stereopsis achieving measurable thresholds after training (post: 861.11 ± 537.08 arcsec, IQR = 800.00; p = 0.06). Local stereoacuity improved near (pre: 616.67 ± 532.68 arcsec, IQR = 1100.00; post: 361.67 ± 537.11, IQR = 200.00; p = 0.03) and at a distance (pre: 715.11 ± 573.54 arcsec, IQR = 1114.00; post: 437.11 ± 510.41, IQR = 405.00; p = 0.03). BF score improved from 4.33 ± 0.50 (IQR = 1.00) to 3.51 ± 1.14 (IQR = 1.30) (p = 0.03). PFV break at distance increased (pre: 17.83 ± 17.53Δ, IQR = 23.50; post: 24.50 ± 17.12Δ, IQR = 28.50; p = 0.04), and the binocular imbalance showed a nonsignificant trend towards improvement (p = 0.09). In-game depth error decreased from 752.11 ± 384.50 arcsec (IQR = 553.00) to 221.78 ± 79.12 (IQR = 105.00) (p < 0.01). Control participants achieved a mean depth error of 43.00 ± 10.30 arcsec after three sessions. Conclusions: A short-dose, individualized VR protocol yielded gains in stereoacuity and binocular function in adults with severe stereodeficiency, with transfer from in-game learning to standard clinical measures. While the sample size was limited, the approach proved feasible and was well tolerated, showing encouraging efficiency versus prior high-dose regimens. Full article

Robotization of production processes and the use of 3D vision systems are currently becoming more and more popular. It allows for more flexibility in the robotic process as well as expands the possibilities of process control, depending on changes in the parameters of the object, its pose, and changes in the process itself. Unfortunately, the use of standard solutions is limited to a relatively small space in which the robot’s vision system operates. The use of the latest solutions in the field of Artificial Intelligence (AI) and external vision systems, in combination with the closed structures of industrial robot control systems, provides advantages by enhancing the digital awareness of the environment of robotic systems. This article presents an example of solving the problem of low digital awareness of the environment of robotic systems resulting from the limited field of view of vision systems used in industrial robots, while maintaining high precision of the systems consisting of the combination of a 3D vision system using a stereovision camera and software with AI elements with the control system of an industrial robot from FANUC and an integrated Robot Vision (iRVision) system to maintain the positioning accuracy of the robot tool. Full article

Some laser scanners utilize stepper motor-driven optomechanical assemblies to position the laser beam precisely during triangulation. In laser scanners such as the presented Technical Vision System (TVS), to enhance motion resolution, gear transmissions are implemented between the motor and the optical assembly. However, due to the customized nature of the mechanical design, errors in manufacturing or insufficient mechanical characterization can introduce deviations in the computed 3D coordinates. In this work, we present a novel method for estimating the degrees-per-step ratio at the output of the laser positioner’s transmission mechanism using a stereovision system. Experimental results demonstrate the effectiveness of the proposed method, which reduces the need for manual metrological instruments and simplifies the calibration procedure through vision-assisted measurements. The method yielded estimated angular resolutions of approximately 0.06° and 0.07° per motor step in the horizontal and vertical axes, respectively, key parameters that define the minimal resolvable displacement of the projected beam in dynamic triangulation. Full article

The use of 3D scanning technologies for surface scanning of objects is limited by environmental conditions and technology requirements based on their characteristics. Among the emerging fields is technical diagnostics in areas of hard-to-reach places with varying surface characteristics of objects of different materials, where the use of commercially available 3D scanning technologies is limited by space. Furthermore, in these areas it is not convenient to use external reference elements or to move the equipment during the digitization process. This paper presents a novel markerless 3D scanning system capable of digitizing objects in confined spaces without requiring external reference elements or repositioning the device relative to the object and aims to address this challenge by designing a 3D scanning technology using the Active Shape from Stereo technique utilizing laser vertical line projection. For this purpose, a testing and prototype design and a software solution using a unique method of calculating 3D surface coordinates have been proposed. In addition to hard-to-reach places, this solution can be used as a desktop 3D scanner and for other 3D digitizing applications for objects of different materials and surface characteristics. Furthermore, the device is well suited to inspecting 3D printed objects, enabling quick, markerless checks of surface geometry and dimensions during the process of 3D printing to ensure printing accuracy and quality. Full article

This research addresses the challenge of digitizing the surface of objects in hard-to-reach areas and focuses on the integration of reverse engineering techniques with innovative digitization approaches. Conventional non-destructive testing techniques, such as industrial videoscope inspection, lack the ability to capture accurate geometric and surface information without the need for disassembly of the components. To overcome these limitations, this research proposes a 3D digitizing prototype that integrates structured light, laser scanning, and active stereo techniques. The device utilizes ESP32-CAM modules and compact mechanical components designed for portability and usability in confined spaces. Experimental validation involved scanning complex and reflective surfaces, including printer components and the engine compartment of an automobile, demonstrating the device’s ability to produce detailed point clouds in challenging environments. Key innovations include a unique approach for utilizing 3D scanning techniques of active stereovision using a folding mechanism. The findings highlight the device’s potential for applications in technical diagnostics, industrial inspection, and environments where traditional digitizing technologies could not be utilized. Full article

Background/Objectives: The aim of this paper was to evaluate the safety of surgical intervention using anesthesia and ophthalmological parameters in pediatric strabismus patients. The design involved retrospective case series. Methods: The setting was the Department of Ophthalmology, Medical University Vienna, Austria. Participants: In total, 208 children aged 0–18 years who underwent strabismus surgery due to exotropia or esotropia between 2013 and 2020 were included. Main outcomes and measures: Information regarding the duration of surgery, intra- and postoperative complications, the postoperative angle of deviation (AoD), and functional outcomes (visual acuity, stereovision) were analyzed. Results: The mean age at the time of surgery was 6.0 ± 4.1 years (range 0.6–18.0). The mean anesthesia time among all patients was 75.9 ± 19.3 min. The mean surgery and anesthesia time did not differ between the age groups. Longer anesthesia durations and surgery durations did not have a significant effect on the occurrence of intraoperation complications (p = 0.610 and p = 0.190, respectively). Intraoperative complications were recorded in 53% (most frequent triggering of oculocardiac reflex (OCR)) of the patients, and postoperative complications in 22% (the most frequent were postoperative nausea and vomiting and pain). An OCR was triggered more often in children older than 6 years than in younger children (p = 0.016). The mean angle of deviation was significantly reduced from preoperative to postoperative measurements. Preoperative stereovision tests were positive in 35% of the patients and increased to over 80% postoperatively. Conclusions: Strabismus surgery performed under general anesthesia in children aged 0 to 18 years is safe with regard to both surgical and anesthetic complications. A significant decrease in the angle of deviation and high rate of stereovision could be achieved with a low rate of re-treatments. However, the retrospective design, absence of standardized documentation, and limited sample size may affect the consistency and comparability of this study’s findings. Full article

Point cloud segmentation is necessary for obtaining highly precise morphological traits in plant phenotyping. Although a huge development has occurred in point cloud segmentation, the segmentation of point clouds from complex plant leaves still remains challenging. Rapeseed leaves are critical in cultivation and breeding, yet traditional two-dimensional imaging is susceptible to reduced segmentation accuracy due to occlusions between plants. The current study proposes the use of binocular stereo-vision technology to obtain three-dimensional (3D) point clouds of rapeseed leaves at the seedling and bolting stages. The point clouds were colorized based on elevation values in order to better process the 3D point cloud data and extract rapeseed phenotypic parameters. Denoising methods were selected based on the source and classification of point cloud noise. However, for ground point clouds, we combined plane fitting with pass-through filtering for denoising, while statistical filtering was used for denoising outliers generated during scanning. We found that, during the seedling stage of rapeseed, a region-growing segmentation method was helpful in finding suitable parameter thresholds for leaf segmentation, and the Locally Convex Connected Patches (LCCP) clustering method was used for leaf segmentation at the bolting stage. Furthermore, the study results show that combining plane fitting with pass-through filtering effectively removes the ground point cloud noise, while statistical filtering successfully denoises outlier noise points generated during scanning. Finally, using the region-growing algorithm during the seedling stage with a normal angle threshold set at 5.0/180.0* M_PI and a curvature threshold set at 1.5 helps to avoid the under-segmentation and over-segmentation issues, achieving complete segmentation of rapeseed seedling leaves, while the LCCP clustering method fully segments rapeseed leaves at the bolting stage. The proposed method provides insights to improve the accuracy of subsequent point cloud phenotypic parameter extraction, such as rapeseed leaf area, and is beneficial for the 3D reconstruction of rapeseed. Full article

In modern autonomous systems, measurement repeatability and precision are crucial for robust decision-making algorithms. Stereovision, which is widely used in safety applications, provides information about an object’s shape, orientation, and 3D localisation. The camera’s lens distortion is a common source of systematic measurement errors, which can be estimated and then eliminated or at least reduced using a suitable correction/calibration method. In this study, a set of cameras equipped with Basler lenses (C125-0618-5M F1.8 f6mm) and Sony IMX477R matrices are calibrated using a state-of-the-art Zhang–Duda–Frese method. The resulting distortion coefficients are used to correct the images. The calibrations are evaluated with the aid of two novel methods for lens distortion measurement. The first one is based on linear regression with images of a vertical and horizontal line pattern. Based on the evaluation tests, outlying cameras are eliminated from the test set by applying the 2$\sigma$ criterion. For the remaining cameras, the MSE was reduced up to 75.4 times, to 1.8 px−6.9 px. The second method is designed to evaluate the impact of lens distortion on stereovision applied to bird tracking around wind farms. A bird’s flight trajectory is synthetically generated to estimate changes in disparity and distance before and after calibration. The method shows that at the margins of the image, lens distortion might introduce errors into the object’s distance measurement of +17%−+20% for cameras with the same distortion and from −41% up to $+\infty$ for camera pairs with different lens distortions. These results highlight the importance of having well-calibrated cameras in systems that require precision, such as stereovision bird tracking in bird–turbine collision risk assessment systems. Full article

To validate active human body models for investigating occupant safety in autonomous cars, it is crucial to comprehend the responses of vehicle occupants during evasive maneuvers. This study sought to quantify the behavior of midsize male and small female passenger seat occupants in both upright and reclined postures during three types of vehicle maneuvers. Volunteer tests were conducted using a minivan, where vehicle kinematics were measured with a DGPS sensor and occupant kinematics were captured with a stereo-vision motion capture system. Seatbelt loads, belt pull-out, and footrest reaction forces were also documented. The interior of the vehicle was 3D-scanned for modeling purposes. Results indicated that seatback angles significantly affected occupant kinematics, with small female volunteers displaying reduced head and torso movements, except during emergency braking with a upright posture seatback. Lane-change maneuvers revealed that maximum lateral head excursions varied depending on the maneuver’s direction. The study concluded that seatback angles were crucial in determining the extent of occupant movement, with notable variations in head and torso excursions observed. The collected data assist in understanding occupant behavior during evasive maneuvers and contribute to the validation of human body models, offering essential insights for enhancing safety systems in autonomous vehicles. Full article

This paper addresses image enhancement and 3D reconstruction techniques for dim scenes inside the vacuum chamber of a nuclear fusion reactor. First, an improved multi-scale Retinex low-light image enhancement algorithm with adaptive weights is designed. It can recover image detail information that is not visible in low-light environments, maintaining image clarity and contrast for easy observation. Second, according to the actual needs of target plate defect detection and 3D reconstruction inside the vacuum chamber, a defect reconstruction algorithm based on photometric stereo vision is proposed. To optimize the position of the light source, a light source illumination profile simulation system is designed in this paper to provide an optimized light array for crack detection inside vacuum chambers without the need for extensive experimental testing. Finally, a robotic platform mounted with a binocular stereo-vision camera is constructed and image enhancement and defect reconstruction experiments are performed separately. The results show that the above method can broaden the gray level of low-illumination images and improve the brightness value and contrast. The maximum depth error is less than 24.0% and the maximum width error is less than 15.3%, which achieves the goal of detecting and reconstructing the defects inside the vacuum chamber. Full article

There has been substantial research on multi-medium visual measurement in fields such as underwater three-dimensional reconstruction and underwater structure monitoring. Addressing the issue where traditional air-based visual-measurement models fail due to refraction when light passes through different media, numerous studies have established refraction-imaging models based on the actual geometry of light refraction to compensate for the effects of refraction on cross-media imaging. However, the calibration of refraction parameters inevitably contains errors, leading to deviations in these parameters. To analyze the impact of refraction-parameter deviations on measurements in underwater structure visual navigation, this paper develops a dual-media stereo-vision measurement simulation model and conducts comprehensive simulation experiments. The results indicate that to achieve high-precision underwater-measurement outcomes, the calibration method for refraction parameters, the distribution of the targets in the field of view, and the distance of the target from the camera must all be meticulously designed. These findings provide guidance for the construction of underwater stereo-vision measurement systems, the calibration of refraction parameters, underwater experiments, and practical applications. Full article

This paper describes a multistage framework for face image analysis in computer-aided speech diagnosis and therapy. Multimodal data processing frameworks have become a significant factor in supporting speech disorders’ treatment. Synchronous and asynchronous remote speech therapy approaches can use audio and video analysis of articulation to deliver robust indicators of disordered speech. Accurate segmentation of articulators in video frames is a vital step in this agenda. We use a dedicated data acquisition system to capture the stereovision stream during speech therapy examination in children. Our goal is to detect and accurately segment four objects in the mouth area (lips, teeth, tongue, and whole mouth) during relaxed speech and speech therapy exercises. Our database contains 17,913 frames from 76 preschool children. We apply a sequence of procedures employing artificial intelligence. For detection, we train the YOLOv6 (you only look once) model to catch each of the three objects under consideration. Then, we prepare the DeepLab v3+ segmentation model in a semi-supervised training mode. As preparation of reliable expert annotations is exhausting in video labeling, we first train the network using weak labels produced by initial segmentation based on the distance-regularized level set evolution over fuzzified images. Next, we fine-tune the model using a portion of manual ground-truth delineations. Each stage is thoroughly assessed using the independent test subset. The lips are detected almost perfectly (average precision and F1 score of 0.999), whereas the segmentation Dice index exceeds 0.83 in each articulator, with a top result of 0.95 in the whole mouth. Full article

Accurate information about the amount of standing biomass is important in pasture management for monitoring forage growth patterns, minimizing the risk of overgrazing, and ensuring the necessary feed requirements of livestock. The morphological features of plants, like crop height and density, have been proven to be prominent predictors of crop yield. The objective of this study was to evaluate the effectiveness of stereovision-based crop height and vegetation coverage measurements in predicting the aboveground biomass yield of bermudagrass (Cynodon dactylon) in a pasture. Data were collected from 136 experimental plots within a 0.81 ha bermudagrass pasture using an RGB-depth camera mounted on a ground rover. The crop height was determined based on the disparity between images captured by two stereo cameras of the depth camera. The vegetation coverage was extracted from the RGB images using a machine learning algorithm by segmenting vegetative and non-vegetative pixels. After camera measurements, the plots were harvested and sub-sampled to measure the wet and dry biomass yields for each plot. The wet biomass yield prediction function based on crop height and vegetation coverage was generated using a linear regression analysis. The results indicated that the combination of crop height and vegetation coverage showed a promising correlation with aboveground wet biomass yield. However, the prediction function based only on the crop height showed less residuals at the extremes compared to the combined prediction function (crop height and vegetation coverage) and was thus declared the recommended approach (R² = 0.91; SeY= 1824 kg-wet/ha). The crop height-based prediction function was used to estimate the dry biomass yield using the mean dry matter fraction. Full article

This literature review assesses the efficacy of image-processing techniques and machine-learning models in computer vision for wood log grading and scaling. Four searches were conducted in four scientific databases, yielding a total of 1288 results, which were narrowed down to 33 relevant studies. The studies were categorized according to their goals, including log end grading, log side grading, individual log scaling, log pile scaling, and log segmentation. The studies were compared based on the input used, choice of model, model performance, and level of autonomy. This review found a preference for images over point cloud representations for logs and an increase in camera use over laser scanners. It identified three primary model types: classical image-processing algorithms, deep learning models, and other machine learning models. However, comparing performance across studies proved challenging due to varying goals and metrics. Deep learning models showed better performance in the log pile scaling and log segmentation goal categories. Cameras were found to have become more popular over time compared to laser scanners, possibly due to stereovision cameras taking over for laser scanners for sampling point cloud datasets. Classical image-processing algorithms were consistently used, deep learning models gained prominence in 2018, and other machine learning models were used in studies published between 2010 and 2018. Full article

The appearance of construction defects in buildings can arise from a variety of factors, ranging from issues during the design and construction phases to problems that develop over time with the lifecycle of a building. These defects require repairs, often in the context of a significant shortage of skilled labor. In addition, such work is often physically demanding and carried out in hazardous environments. Consequently, adopting autonomous robotic systems in the construction industry becomes essential, as they can relieve labor shortages, promote safety, and enhance the quality and efficiency of repair and maintenance tasks. Hereupon, the present study introduces an end-to-end framework towards the automation of shotcreting tasks in cases where construction or repair actions are required. The proposed system can scan a construction scene using a stereo-vision camera mounted on a robotic platform, identify regions of defects, and reconstruct a 3D model of these areas. Furthermore, it automatically calculates the required 3D volumes to be constructed to treat a detected defect. To achieve all of the above-mentioned technological tools, the developed software framework employs semantic segmentation and 3D reconstruction modules based on YOLOv8m-seg, SiamMask, InfiniTAM, and RTAB-Map, respectively. In addition, the segmented 3D regions are processed by the volumetric modeling component, which determines the amount of concrete needed to fill the defects. It generates the exact 3D model that can repair the investigated defect. Finally, the precision and effectiveness of the proposed pipeline are evaluated in actual construction site scenarios, featuring reinforcement bars as defective areas. Full article