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AbstractPurposeMethodsResultsConclusionsCentral retinal artery occlusion, also known as an eye stroke, results in visual impairment and functional challenges. Our study objectives were to identify meaningful measures and factors that indicate or enable successful recovery after eye stroke and to determine optimal processes to support research, including exploring barriers and facilitators to successful research participation.We used qualitative methods including the 5Ts Framework (target population identification, team composition, time considerations, tips to accommodate older adults, tools for inclusive enrollment of older adults) to provide a guide to the development of the semi-structured interviews and to help facilitate the research process such as the set-up of interviews. We enrolled three groups: individuals living with the sequelae of eye stroke, care partners, and health care providers. We conducted a descriptive content analysis with an inductive approach.Twenty-five people participated. This group included 10 eye stroke survivors (median age, 62.5; range, 56–84 years; 20% were women), 4 care partners (median age, 57 years; range, 56–59 years; 100% were women), and 11 health care providers (median age, 33 years; 25–60 years; 54.5% were women). Four themes emerged from the data: (1) returning to full independence, (2) family support, (3) strategies for participation, and (4) lack of accessibility.The themes identified by participant groups indicated that strategies such as providing sufficient lighting and incorporating family support are key for research participation by individuals with vision impairment related to eye stroke. Furthermore, it is essential that researchers determine ways to address inequities, including lack of accessibility to research sites. This work has implications for how research teams develop research processes and implement research findings into clinical settings for eye stroke survivors. [ABSTRACT FROM AUTHOR]

The automated detection of yarn margins is crucial for ensuring the continuity and quality of production in textile workshops. Traditional methods rely on workers visually inspecting the yarn margin to determine the timing of replacement; these methods fail to provide real-time data and cannot meet the precise scheduling requirements of modern production. The complex environmental conditions in textile workshops, combined with the cylindrical shape and repetitive textural features of yarn bobbins, limit the application of traditional visual solutions. Therefore, we propose a visual measurement method based on the geometric characteristics of binocular imaging: First, all contours in the image are extracted, and the distance sequence between the contours and the centroid is extracted. This sequence is then matched with a predefined template to identify the contour information of the yarn bobbin. Additionally, four equations for the tangent line from the camera optical center to the edge points of the yarn bobbin contour are established, and the angle bisectors of each pair of tangents are found. By solving the system of equations for these two angle bisectors, their intersection point is determined, giving the radius of the yarn bobbin. This method overcomes the limitations of monocular vision systems, which lack depth information and suffer from size measurement errors due to the insufficient repeat positioning accuracy when patrolling back and forth. Next, to address the self-occlusion issues and matching difficulties during binocular system measurements caused by the yarn bobbin surface's repetitive texture, an imaging model is established based on the yarn bobbin's cylindrical characteristics. This avoids pixel-by-pixel matching in binocular vision and enables the accurate measurement of the remaining yarn margin. The experimental data show that the measurement method exhibits high precision within the recommended working distance range, with an average error of only 0.68 mm. [ABSTRACT FROM AUTHOR]

Background/Objectives: Acute retinal ischemia, including central retinal artery occlusion (CRAO), is recognized as a stroke equivalent by the American Heart Association/American Stroke Association (AHA/ASA), necessitating immediate multidisciplinary evaluation and management. However, referral patterns among ophthalmologists remain inconsistent, and evidence-based therapeutic interventions to improve visual outcomes are currently lacking. CRAO is associated with a significantly elevated risk of subsequent acute ischemic stroke (AIS), particularly within the first week following diagnosis, yet the role of intravenous thrombolysis (IVT) in this setting remains controversial. This case report presents a unique case of CRAO with concurrent internal carotid artery (ICA) occlusion, followed by an AIS affecting the middle cerebral artery (MCA). Case presentation: An 83-year-old woman presented with acute, painless monocular vision loss to the emergency department. IVT was administered within 4.5 h of admission for suspected CRAO associated with ICA occlusion (ICAO) identified on CT-angiography (CTA). One hour post-thrombolysis, CT-perfusion (CTP) confirmed MCA occlusion (MCAO), necessitating mechanical thrombectomy (MT). Successful recanalization was achieved without complications, and the patient demonstrated no functional impairments at discharge. Conclusions: This case underscores the importance of maintaining a vigilant approach to stroke management in CRAO patients. It highlights the diagnostic challenges encountered in clinical practice and advocates for further research into the role of IVT in CRAO cases with ICAO, emphasizing the need for consensus in treatment. [ABSTRACT FROM AUTHOR]

To assess a population-level perceived health status of common reconstructive urologic conditions using health utilities. Health utilities are generic quality-of-life measures that can help describe overall health status and can quantitatively compare different disease states and the perceived benefits of various interventions. An a priori determined, representative sample of adult men were recruited by Qualtrics to review standardized scenarios describing typical patients with reconstructive urologic conditions, surgeries to treat conditions, and control conditions (eg, blindness, osteoarthritis). Condition-specific health status was measured using four tools: visual analog scale, standard gamble, time-trade off and willingness-to-pay and reported in Quality-adjusted Life Years (QALY; range 0.0 (death) to 1.0 (perfect health)). We analyzed 562 completed surveys from condition-naive participants. Condition-specific health status ranged from a mean of 0.52 QALY to 0.66 QALY, with all reconstructive conditions perceived to be worse than osteoarthritis and monocular blindness (QALY 0.68). Health status was lowest for adult-acquired buried penis (0.52 QALY) and pelvic radiation disease (0.53 QALY), comparable to binocular blindness (QALY 0.52). Treatment of erectile dysfunction with inflatable penile prosthesis (∆QALY +0.02; P <.001) and stress urinary incontinence with artificial urinary sphincter (∆QALY +0.04; P <.001) offered only nominal perceived gain in health status. Health utilities may improve the ability to discriminate between heterogeneous reconstructive urologic disease states. For nononcologic disease states in which mortality is not measured, health utilities could improve the ability to measure societal impact and justification for clinical and research investment. [ABSTRACT FROM AUTHOR]

Purpose: To report the long-term anatomical and functional results of lens-sparing vitrectomy (LSV) for stage 4 retinopathy of prematurity (ROP). Design: Retrospective study. Methods: This is a study of 23 eyes of 16 patients with stage 4 ROP who underwent lens-sparing vitrectomy at a tertiary care center and had a minimum of 2 years of follow-up. The main outcome measures are the retinal status and visual outcome at the final follow-up. Results: After a mean follow-up of 7.36 years, the lenses remained clear in 69.57%; anatomical success in terms of the attached retina was achieved in 19/23 eyes (82.61%) and vision of ≥6/24 was achieved in 8/23 eyes (34.78%), whereas navigational vision (≥1/60) was achieved in 16/23 (69.56%). The mean spherical equivalent was −8.50 + 6.39 diopters. High myopia (≥7 dsph) was noted in 43.47% of patients. The severity of myopia was not related to previous lasers and was less severe in eyes with posterior pole residual folds compared to normalized fundus or eyes with macular drag but no fold. At the final follow-up, 3/16 (18.75%) patients had bilateral blindness due to total retinal detachments and 11/16 (68.75%) had recorded functional vision in one or both eyes, whereas two others could potentially be having vision but could not be evaluated. Conclusions: The long-term results of lens-sparing vitrectomy for stage 4 ROP are favorable with a majority retaining clear lenses and attached retina and two-thirds having functional vision. [ABSTRACT FROM AUTHOR]

Introduction: Amblyopia, the leading cause of monocular childhood vision loss, affects millions and is projected to increase. Early detection and treatment are crucial for preventing vision impairment. Areas Covered: This commentary reviews the current state and opportunities for improvement in amblyopia screening strategy and technology focused primarily within the United States. A review of current guidelines from organizations and a literature review of innovations between 1999 and 2024 informed this commentary. Organizations recommend routine vision screening for children, starting in infancy and continuing through adolescence. However, the variations in screening devices, components, timelines, and requirements leave room for improving outcomes. An exploration of instrument-based screening which has emerged as an alternative to traditional optotype testing will be investigated, and the risks of these instruments with high sensitivity and varying specificity will be acknowledged. Finally, new approaches to directly identify amblyopia and search for innovative biomarkers that indicate amblyopia risk or amblyopia itself will be discussed. Expert Opinion: Improving the treatment of amblyopia necessitates the reimagination of current practices in connecting patients to care. Technologies that incorporate novel diagnostic approaches, like the consideration of specific biomarkers or the use of artificial intelligence, hold promise to identify cases of amblyopia rather than its risk factors. [ABSTRACT FROM AUTHOR]

Regular inspections of pipelines are of great significance to ensure their long-term safe and stable operation, and the rapid 3D reconstruction of constant-diameter straight pipelines (CDSP) based on monocular images plays a crucial role in tasks such as positioning and navigation for pipeline inspection drones, as well as defect detection on the pipeline surface. Most of the traditional 3D reconstruction methods for pipelines rely on marked poses or circular contours of end faces, which are complex and difficult to apply, while some existing 3D reconstruction methods based on contour features for pipelines have the disadvantage of slow reconstruction speed. To address the above issues, this paper proposes a rapid 3D reconstruction method for CDSP. This method solves for the spatial pose of the pipeline axis based on the geometric constraints between the projected contour lines and the axis, provided that the radius is known. These constraints are derived from the perspective projection imaging model of the single-view CDSP. Compared with traditional methods, the proposed method improves the reconstruction speed by 99.907% while maintaining similar accuracy. [ABSTRACT FROM AUTHOR]

Autonomous technologies have revolutionized transportation, military operations, and space exploration, necessitating precise localization in environments where traditional GPS-based systems are unreliable or unavailable. While widespread for outdoor localization, GPS systems face limitations in obstructed environments such as dense urban areas, forests, and indoor spaces. Moreover, GPS reliance introduces vulnerabilities to signal disruptions, which can lead to significant operational failures. Hence, developing alternative localization techniques that do not depend on external signals is essential, showing a critical need for robust, GPS-independent localization solutions adaptable to different applications, ranging from Earth-based autonomous vehicles to robotic missions on Mars. This paper addresses these challenges using Visual odometry (VO) to estimate a camera's pose by analyzing captured image sequences in GPS-denied areas tailored for autonomous vehicles (AVs), where safety and real-time decision-making are paramount. Extensive research has been dedicated to pose estimation using LiDAR or stereo cameras, which, despite their accuracy, are constrained by weight, cost, and complexity. In contrast, monocular vision is practical and cost-effective, making it a popular choice for drones, cars, and autonomous vehicles. However, robust and reliable monocular pose estimation models remain underexplored. This research aims to fill this gap by developing a novel adaptive framework for outdoor pose estimation and safe navigation using enhanced visual odometry systems with monocular cameras, especially for applications where deploying additional sensors is not feasible due to cost or physical constraints. This framework is designed to be adaptable across different vehicles and platforms, ensuring accurate and reliable pose estimation. We integrate advanced control theory to provide safety guarantees for motion control, ensuring that the AV can react safely to the imminent hazards and unknown trajectories of nearby traffic agents. The focus is on creating an AI-driven model(s) that meets the performance standards of multi-sensor systems while leveraging the inherent advantages of monocular vision. This research uses state-of-the-art machine learning techniques to advance visual odometry's technical capabilities and ensure its adaptability across different platforms, cameras, and environments. By merging cutting-edge visual odometry techniques with robust control theory, our approach enhances both the safety and performance of AVs in complex traffic situations, directly addressing the challenge of safe and adaptive navigation. Experimental results on the KITTI odometry dataset demonstrate a significant improvement in pose estimation accuracy, offering a cost-effective and robust solution for real-world applications. [ABSTRACT FROM AUTHOR]

Binocular vision may serve as a good model for research on awareness. Binocular summation (BS) can be defined as the superiority of binocular over monocular visual performance. Early studies of BS found an improvement of a factor of about 1.4 (empirically), leading to models suggesting a quadratic summation of the two monocular inputs (√2). Neural interaction modulates a target's visibility within the same eye or between eyes (facilitation or suppression). Recent results indicated that at a closely flanked stimulus, BS is characterized by instability; it relies on the specific order in which the stimulus condition is displayed. Otherwise, BS is stable. These results were revealed in experiments where the tested eye was open, whereas the other eye was occluded (mono-optic glasses, blocked presentation); thus, the participants were aware of the tested eye. Therefore, in this study, we repeated the same experiments but utilized stereoscopic glasses (intermixed at random presentation) to control the monocular and binocular vision, thus potentially eliminating awareness of the tested condition. The stimuli consisted of a central vertically oriented Gabor target and high-contrast Gabor flankers positioned in two configurations (orthogonal or collinear) with target–flanker separations of either two or three wavelengths (λ), presented at four different presentation times (40, 80, 120, and 200 ms). The results indicate that when utilizing stereoscopic glasses and mixing the testing conditions, the BS is normal, raising the possibility that awareness may be involved. [ABSTRACT FROM AUTHOR]

The technology of intelligent vehicle perception system based on vision sensor is becoming more and more mature, and many distance measurement methods based on monocular vision have been proposed. However, less attention has been paid to the experiment and application of the distance measurement of the vehicle in front of the campus intelligent patrol vehicle. In this paper, two monocular visual distance measurement methods based on the width of the target detection object and the distance between the target detection object and the ground contact point are proposed. Two distance measurement models are proposed according to the principle of camera imaging model and the principle of coordinate system transformation. Then, two kinds of distance measurement experiments are designed and compared. The experimental results show that the measurement method based on the contact point between the target and the ground is poor, but the absolute error is less than 0.67 m. The overall error of distance measurement method based on license plate width is the smallest, and the absolute error is less than 0.15 m. The distance measurement error of the detection box based on the width of the vehicle body is large, and the absolute error is kept within 0.31 m. The measurement accuracy of the two methods for the distance measurement of the vehicle in front of the campus patrol vehicle meets the detection requirements. This work is significant to the research on the distance measurement of the vehicle in front of campus patrol vehicles and it is of great significance to enhance the safety of the campus patrol vehicle. [ABSTRACT FROM AUTHOR]

Vision-based stair modeling can help autonomous mobile robots deal with the challenge of climbing stairs, especially in unfamiliar environments. To address the problem that current monocular methods are difficult to model stairs accurately without depth information in scenes with fuzzy visual cues, this paper proposes a depth-aware stair modeling method for monocular vision. Specifically, we take the prediction of depth images and the extraction of stair geometric features as joint tasks in a convolutional neural network, with the designed information propagation architecture, we can achieve effective supervision for stair geometric feature learning by depth features. In addition, to complete the stair modeling, we take the convex lines, concave lines, tread surfaces and riser surfaces as stair geometric features and apply Gaussian kernels to enable StairNetV3 to predict contextual information within the stair lines. Combined with the depth information obtained by depth sensors, we propose a point cloud reconstruction method that can quickly segment point clouds of stair step surfaces. The experiments show that the proposed method has a significant improvement over the previous best monocular vision method, with an intersection over union increase of 3.4 % , and the lightweight version has a fast detection speed and can meet the requirements of most real-time applications. [ABSTRACT FROM AUTHOR]

Thelaziasis is generally a zoonotic disease that affects the eyes of domestic and wild animals. It is transmitted by flies belonging to the Drosophilidae family. While rare in humans, there have been occasional reported cases in low-socioeconomic families living in rural areas. An 83-year-old male farmer with a history of trauma and previous loss of vision in one eye presented with complaints of itching in the affected eye. Upon examination, worm-like parasites were observed in the inferior fornix of the affected eye, leading to a referral to our center. Two worms were mechanically extracted from the right eye. The diagnosis was confirmed as Thelazia spp. through parasitological laboratory testing. This case holds significance as it represents Türkiye's first reported human case of ocular thelaziasis. [ABSTRACT FROM AUTHOR]

The present study focuses on the simultaneous localization and mapping (SLAM) system based on point and line features. Aiming to address the prevalent issue of repeated detection during line feature extraction in low-texture environments, a novel method for merging redundant line features is proposed. This method effectively mitigates the problem of increased initial pose estimation error that arises when the same line is erroneously detected as multiple lines in adjacent frames. Furthermore, recognizing the potential for the introduction of line features to prolong the marginalization process of the information matrix, optimization strategies are employed to accelerate this process. Additionally, to tackle the issue of insufficient point feature accuracy, subpixel technology is introduced to enhance the precision of point features, thereby further reducing errors. Experimental results on the European Robotics Challenge (EUROC) public dataset demonstrate that the proposed LR-SLAM system exhibits significant advantages over mainstream SLAM systems such as ORB-SLAM3, VINS-Mono, and PL-VIO in terms of accuracy, efficiency, and robustness. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a method for fast and accurate vehicle speed measurement based on a monocular camera. Firstly, by establishing a new camera imaging model, the calibration method for variable focal lengths is optimized, simplifying the transformation process between the four coordinate systems in traditional camera imaging models, and the method does not need to restore the pixel coordinates to dedistortion. Secondly, based on the camera imaging model, a two-dimensional positioning algorithm is proposed. By leveraging the characteristics of the speed measurement problem, the complex three-dimensional positioning problem is simplified into a two-dimensional model, reducing the overall computational complexity of the positioning problem. Finally, the algorithm is combined with You Only Look Once version 7 (YOLOv7) and Deep Simple Online and Realtime Tracking (DeepSORT) algorithms, integrating multiple model structures to optimize the network, achieving precise multi-target speed measurement. Experiments show that under frame-by-frame measurement conditions, the minimum and average accuracies of this method reach 95.1% and 97.6%, respectively. Compared with other methods, it has significant advantages in speed measurement accuracy and computational efficiency. Therefore, this research outcome is expected to play an important role in intelligent transportation systems and road safety management.

Purpose: To describe diverse ocular manifestations in a patient with Myelin oligodendrocyte glycoprotein-associated disease (MOGAD). Methods: A 15-year-old Indian male had severe loss of vision in one eye, followed by a recurrent attack of optic neuritis in the fellow eye a few weeks later. He had a history of vision loss, speech disturbances, altered sensorium and was a confirmed case of Myelin oligodendrocyte glycoprotein-associated disease (MOGAD). Apart from optic neuritis, other rare ophthalmic associations, namely, macular neuroretinopathy, retinal haemorrhages, severe optic nerve head edema, peri neuritis, and orbital enhancement on magnetic resonance imaging (MRI) were noted. Results: He responded dramatically to treatment with intravenous pulse steroids and relapses were controlled with long-term immunomodulation therapy. Conclusion: This case report reiterates the need for early treatment with pulse steroids in MOGAD and depicts the heterogeneous involvement of various ocular structures in the disease. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate depth of focus (DOF) and visual acuities (VAs) by manifest refractive spherical equivalent (MRSE) and degree of preoperative corneal astigmatism with the IC-8 small aperture intraocular lens (SA IOL) (Apthera). Setting: 21 investigational sites in the United States. Design: Prospective, multicenter, open-label, parallel-group, nonrandomized, examiner-masked, 1-year clinical study. Methods: Included patients had cataract and ≤1.5 diopters (D) preoperative corneal astigmatism. Patients received either the SA IOL in 1 eye targeted to -0.75 D and a monofocal or monofocal toric IOL in the other targeted to plano (SA IOL group) or bilateral monofocal/monofocal toric IOLs targeted to plano (control group). Monocular and binocular assessments included defocus curves and uncorrected VAs (distance, intermediate, and near) by postoperative MRSE; monocular VAs were assessed by degree of preoperative corneal astigmatism. Results: The SA IOL group (n = 343) achieved 0.82 D additional binocular DOF vs the control group (n = 110), and SA IOL eyes achieved 0.91 D additional monocular DOF over fellow eyes. Across all MRSEs, the SA IOL group achieved monocular uncorrected VAs of 20/40 or better and binocular uncorrected VAs of 20/32 or better across all distances. In addition, SA IOL eyes with higher (1.0-1.5 D) vs lower (<1.0 D) preoperative corneal astigmatism achieved equivalent monocular uncorrected VAs. Conclusions: The SA IOL provides increased DOF vs monofocal/monofocal toric IOLs and consistent monocular and binocular vision across several postoperative MRSEs and up to 1.5 D of preoperative corneal astigmatism, giving patients with cataract and mild astigmatism the potential for an extended range of vision and reliable visual outcomes. [ABSTRACT FROM AUTHOR]

Human language is influenced by sensory-motor experiences. Sensory experiences gathered in a spatiotemporal world are used as raw material to create more abstract concepts. In language, one way to encode spatial relationships is through spatial prepositions. Spatial prepositions that specify the proximity of objects in space, like far and near or their variants, are found in most languages. The mechanism for determining the proximity of another entity to itself is a useful evolutionary trait. From the taxic behavior in unicellular organisms like bacteria to the tropism in the plant kingdom, this behavior can be found in almost all organisms. In humans, vision plays a critical role in spatial localization and navigation. This computational study analyzes the relationship between vision and spatial prepositions using an artificial neural network. For this study, a synthetic image dataset was created, with each image featuring a 2D projection of an object placed in 3D space. The objects can be of various shapes, sizes, and colors. A convolutional neural network is trained to classify the object in the images as far or near based on a set threshold. The study mainly explores two visual scenarios: objects confined to a plane (grounded) and objects not confined to a plane (ungrounded), while also analyzing the influence of camera placement. The classification performance is high for the grounded case, demonstrating that the problem of far/near classification is well-defined for grounded objects, given that the camera is at a sufficient height. The network performance showed that depth can be determined in grounded cases only from monocular cues with high accuracy, given the camera is at an adequate height. The difference in the network's performance between grounded and ungrounded cases can be explained using the physical properties of the retinal imaging system. The task of determining the distance of an object from individual images in the dataset is challenging as they lack any background cues. Still, the network performance shows the influence of spatial constraints placed on the image generation process in determining depth. The results show that monocular cues significantly contribute to depth perception when all the objects are confined to a single plane. A set of sensory inputs (images) and a specific task (far/near classification) allowed us to obtain the aforementioned results. The visual task, along with reaching and motion, may enable humans to carve the space into various spatial prepositional categories like far and near. The network's performance and how it learns to classify between far and near provided insights into certain visual illusions that involve size constancy. [ABSTRACT FROM AUTHOR]

The low positioning accuracy of industrial robots limits their application in industry. Vision-based kinematic calibration, known for its rapid processing and economic efficiency, is an effective solution to enhance this accuracy. However, most of these methods are constrained by the camera's field of view, limiting their effectiveness in large workspaces. This paper proposes a novel calibration framework composed of monocular vision and computer vision techniques using ArUco markers. Firstly, a robot positioning error model was established by considering the kinematic error based on the Modified Denavit-Hartenberg model. Subsequently, a calibrated camera was used to create an ArUco map as an alternative to traditional single calibration targets. The map was constructed by stitching images of ArUco markers with unique identifiers, and its accuracy was enhanced through closed-loop detection and global optimization that minimizes reprojection errors. Then, initial hand-eye parameters were determined, followed by acquiring the robot's end-effector pose through the ArUco map. The Levenberg-Marquardt algorithm was employed for calibration, involving iterative refinement of hand-eye and kinematic parameters. Finally, experimental validation was conducted on the KUKA kr500 industrial robot, with laser tracker measurements as the reference standard. Compared to the traditional checkerboard method, this new approach not only expands the calibration space but also significantly reduces the robot's absolute positioning error, from 1.359 mm to 0.472 mm. • A new calibration framework based on an ArUco map and vision system expands the workspace and improves positioning accuracy. • High-precision ArUco map created via local and global optimization, serving as an effective alternative to single targets. • The robot's kinematic parameters are identified by the monocular camera, with a fully autonomous and robust process. [ABSTRACT FROM AUTHOR]

The robust pose estimation of uncooperative space targets is a significant and challenging technology for on-orbit service. This work proposes a pose-recovering method for uncooperative targets by fusing one-dimensional (1D) laser and monocular camera units. Specifically, we utilize the laser projection on the camera’s pixel plane and the feature points extracted from images to develop a weight-based fusion method and estimate the accurate scale. To improve the accuracy, the camera and laser rangefinder are tightly coupled to optimize the estimated sequential poses. This method can overcome the known deficiency of the absolute scale of monocular cameras and maintain accurate pose tracking without the baseline constraint of RGB-D cameras. The performance of our proposed method is validated with synthetic images and real-world experiments, our proposed method shows more robust and accurate performance compared with other fusion methods. [ABSTRACT FROM AUTHOR]

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Background: This study aims to evaluate the clinical outcomes and efficacy of a modified tectonic corneoscleral graft (TCG) in patients suffering from devastating corneoscleral infections. Methods: Thirty-eight eyes from 38 patients who underwent the modified TCG were included in this study. The outcomes measured were recurrence rates, best-corrected visual acuity (BCVA), ocular surface stability, postoperative complications, and graft survival. Results: Among the 38 patients, 23 had fungal infections, 9 had bacterial infections and 6 had Pythium insidiosum infections. At the final follow-up, with an average duration of 25.1 ± 8.6 months, the rate of monocular blindness decreased from 100 to 58%. Significant improvements in LogMAR BCVA were observed from preoperative to postoperative measurements (P < 0.001). Thirty-two eyes (84.2%) maintained a stable ocular surface. The survival rate of ocular surface stability was 84.2%±5.9% at one year and 57.7%±9.7% at three years post-surgery. Twenty eyes (52.6%) retained a clear graft, with a survival rate for graft clarity was 81.6%±6.3% at one year and 36.0%±10.8% at three years post-surgery. The incidence of immune rejection was 36.8%. Corneal epithelial defects were observed in ten patients, and choroidal detachment occurred in four patients. No cases of elevated intraocular pressure were detected. Conclusions: The modified TCG is effective in eradicating infections, preserving the eyeball, and maintaining useful vision in cases of devastating corneoscleral infections. Regular use of tacrolimus, timely administration of glucocorticoids, and good patient compliance can help mitigate postoperative challenges. [ABSTRACT FROM AUTHOR]

Govetto's staging system (stages 1–4) for epiretinal membrane (ERM) based on optical coherence tomography images is a useful predictor of monocular visual function; however, an association between Govetto's stage and binocular vision has not been reported. This study aimed to investigate the factors associated with Govetto's stage among the monocular and binocular parameters. This retrospective study included consecutive patients with treatment-naïve eyes with unilateral ERM without pseudo-hole. We investigated Govetto's stage, degrees of aniseikonia and metamorphopsia, foveal avascular zone area, central retinal and choroidal thickness, vertical ocular deviation, stereopsis, and binocular single vision (BSV). We compared the parameters between the BSV-present and BSV-absent groups and investigated correlations between Govetto's stage and the monocular and binocular parameters. Twenty-eight eyes of 28 patients were examined (age, 66.6 ± 10.2 years). In multivariate correlation analyses, Govetto's stage correlated with BSV (P = 0.04, β = − 0.36) and central retinal thickness (P < 0.001, β = 0.74). Of the eyes, 18 were assigned to the BSV-present group and 10 to the BSV-absent group. Govetto's stage was significantly more advanced in the BSV-absent group than in the BSV-present group (3.2 ± 0.8 vs 2.5 ± 0.7, P = 0.03). Of the 28 patients, 11 (39%) showed small-angle vertical deviations (1–12Δ). In conclusion, our findings showed that Govetto's stage correlated with binocular vision in patients with monocular ERM. Govetto's staging is a useful parameter for predicting not only monocular but also binocular vision. [ABSTRACT FROM AUTHOR]

Introduction: Visual function deficits have been reported in adolescents following concussion. We compared vergence and accommodation deficits in paediatric and adolescent patients at a tertiary medical centre in the sub‐acute (15 days to 12 weeks) and chronic (12 weeks to 1 year) phases of concussion recovery. Methods: The study included patients aged 7 to <18 years seen between 2014 and 2021, who had a binocular vision (BV) examination conducted within 15 days and 1 year of their concussion injury. Included patients had to have 0.10 logMAR monocular best‐corrected vision or better in both eyes and be wearing a habitual refractive correction. BV examinations at near included measurements of near point of convergence, convergence and divergence amplitudes, vergence facility, monocular accommodative amplitude and monocular accommodative facility. Vergence and accommodation deficits were diagnosed using established clinical criteria. Group differences were assessed using nonparametric statistics and ANCOVA modelling. Results: A total of 259 patients were included with 111 in the sub‐acute phase and 148 in the chronic phase of concussion recovery. There was no significant difference in the rates of vergence deficits between the two phases of concussion recovery (sub‐acute = 48.6%; chronic = 49.3%). There was also no significant difference in the rates of accommodation deficits between the two phases of concussion recovery (sub‐acute = 82.0%; chronic = 77.0%). Conclusion: Patients in both the sub‐acute and chronic phases of concussion recovery exhibited a high frequency of vergence and accommodation deficits, with no significant differences between groups. Results indicate that patients exhibiting vision deficits in the sub‐acute phase may not resolve without intervention, though a prospective, longitudinal study is required to test the hypothesis. [ABSTRACT FROM AUTHOR]

Accurate fire ignition spatial location methods can serve the automatic fire suppression based on video. Although current fire detection systems based on monocular surveillance videos can quickly detect fires, it is impossible to obtain the 3D position due to the polysemy of 2D images. To further promote the universal application of automatic fire suppression, we propose a 3D indoor fire ignition location method based on monocular vision. This is the first study on spatial orientation of fire based on monocular vision. First, the indoor scene is quickly reconstructed as the basis of the scene. Second, based on our previous research on fire detection, the refined position of the fire in 2D images and its topological relationship with the space object are analyzed. The hierarchical constraints from 2 to 3D are established for the spatial orientation of the indoor fire ignition point. The experimental results show that the average absolute error is only 4.82 cm and that the average relative error is 1.71%. In addition, our method can be embedded into the existing fire prevention and control system at a low cost, further promoting the development of intelligent fire prevention and control. [ABSTRACT FROM AUTHOR]

Amblyopia is the most frequent cause of monocular vision loss. Transcranial Magnetic Stimulation (TMS) has been used to improve several vision parameters of the amblyopic eye in adulthood. This study is relevant in order to evaluate TMS effects and to raise awareness of the need for further research. Transcranial Magnetic Stimulation (TMS) is a neuromodulation technique capable of changing cortical excitability. In the last decade, it has been used to improve visual parameters in amblyopic patients. The main goal of this systematic review is to evaluate the influence of TMS in the amblyopic eye, in the visual parameters of amblyopic patients. Searches were done in PubMed and Embase databases, and a combined search strategy was performed using the following Mesh, EMBASE, and keywords: 'Amblyopia', 'Transcranial Magnetic Stimulation', and 'theta burst stimulation'. This review included randomised controlled studies, descriptive cases, and clinical case studies with adult amblyopes. All articles that had any of the following characteristics were excluded: children or animal studies, reviews, pathologies other than amblyopia, and other techniques rather than repetitive TMS (rTMS), or Theta Burst Stimulation (TBS). A total of 42 articles were found, of which only four studies (46 amblyopes) meet the criteria above. Three of the articles found significant improvement after one session of continuous TBS (cTBS) in visual parameters like visual acuity, contrast sensitivity, suppressive imbalance, and stereoacuity. One study found a significant visual improvement with 10 Hz rTMS. Only one stimulation-related dropout was reported. The few existing studies found in this review seem to show that through the usage of high-frequency rTMS and cTBS, it is possible to re-balance the eyes of an adult amblyope. However, despite the promising results, further research with larger randomised double-blind studies is needed for a better understanding of this process. [ABSTRACT FROM AUTHOR]

Due to the influence of illumination, noise, distortion and other factors on monocular vision images, the image quality is reduced, the difficulty of image information extraction is high, and there are often errors and uncertainties in background segmentation, which affect the effect of monocular vision image background virtualization. Therefore, a new depth information extraction monocular vision image automatic hierarchical background virtualization method is studied to improve the effect of background virtualization. The depth information map is extracted by anisotropic thermal diffusion equation. The morphology is used to fill the tiny holes in the depth information map, and its smoothing process is used to determine the image depth range, automatically layer the depth information map, and obtain the foreground layer and background layer. The background layer is virtualized by Gaussian blur operation. Pyramid image fusion method is used to fuse the foreground layer and the blurred background layer to complete the background virtualization of monocular vision image. Experimental results have shown that this method can effectively improve the clarity of depth information map edges, preserve a large amount of image edge information, and have high structural similarity, with an average value of 0.96. The efficiency is high, and the background virtualization time is only 15 ms. [ABSTRACT FROM AUTHOR]

Copyright of China Journal of Oral & Maxillofacial Surgery is the property of Shanghai Jiao Tong University, College of Stomatology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: Surgical robots effectively improve the accuracy and safety of surgical procedures. Current optical-navigated oral surgical robots are typically developed based on binocular vision positioning systems, which are susceptible to factors including obscured visibility, limited workplace, and ambient light interference. Hence, the purpose of this study was to develop a lightweight robotic platform based on monocular vision for oral surgery that enhances the precision and efficiency of surgical procedures. Methods: A monocular optical positioning system (MOPS) was applied to oral surgical robots, and a semi-autonomous robotic platform was developed utilizing monocular vision. A series of vitro experiments were designed to simulate dental implant procedures to evaluate the performance of optical positioning systems and assess the robotic system accuracy. The singular configuration detection and avoidance test, the collision detection and processing test, and the drilling test under slight movement were conducted to validate the safety of the robotic system. Results: The position error and rotation error of MOPS were 0.0906 ± 0.0762 mm and 0.0158 ± 0.0069 degrees, respectively. The attitude angle of robotic arms calculated by the forward and inverse solutions was accurate. Additionally, the robot's surgical calibration point exhibited an average error of 0.42 mm, with a maximum error of 0.57 mm. Meanwhile, the robot system was capable of effectively avoiding singularities and demonstrating robust safety measures in the presence of minor patient movements and collisions during vitro experiment procedures. Conclusion: The results of this in vitro study demonstrate that the accuracy of MOPS meets clinical requirements, making it a promising alternative in the field of oral surgical robots. Further studies will be planned to make the monocular vision oral robot suitable for clinical application. [ABSTRACT FROM AUTHOR]

The diameter at breast height (DBH) and crown base height (CBH) are important indicators in forest surveys. To enhance the accuracy and convenience of DBH and CBH extraction for standing trees, a method based on understory small motion clips (a series of images captured with slight viewpoint changes) has been proposed. Histogram equalization and quadtree uniformization algorithms are employed to extract image features, improving the consistency of feature extraction. Additionally, the accuracy of depth map construction and point cloud reconstruction is improved by minimizing the variance cost function. Six 20 m × 20 m square sample plots were selected to verify the effectiveness of the method. Depth maps and point clouds of the sample plots were reconstructed from small motion clips, and the DBH and CBH of standing trees were extracted using a pinhole imaging model. The results indicated that the root mean square error (RMSE) for DBH extraction ranged from 0.60 cm to 1.18 cm, with relative errors ranging from 1.81% to 5.42%. Similarly, the RMSE for CBH extraction ranged from 0.08 m to 0.21 m, with relative errors ranging from 1.97% to 5.58%. These results meet the accuracy standards required for forest surveys. The proposed method enhances the efficiency of extracting tree structural parameters in close-range photogrammetry (CRP) for forestry. A rapid and accurate method for DBH and CBH extraction is provided by this method, laying the foundation for subsequent forest resource management and monitoring. [ABSTRACT FROM AUTHOR]

Regular inspections of pipelines are of great significance to ensure their long-term safe and stable operation, and the rapid 3D reconstruction of constant-diameter straight pipelines (CDSP) based on monocular images plays a crucial role in tasks such as positioning and navigation for pipeline inspection drones, as well as defect detection on the pipeline surface. Most of the traditional 3D reconstruction methods for pipelines rely on marked poses or circular contours of end faces, which are complex and difficult to apply, while some existing 3D reconstruction methods based on contour features for pipelines have the disadvantage of slow reconstruction speed. To address the above issues, this paper proposes a rapid 3D reconstruction method for CDSP. This method solves for the spatial pose of the pipeline axis based on the geometric constraints between the projected contour lines and the axis, provided that the radius is known. These constraints are derived from the perspective projection imaging model of the single-view CDSP. Compared with traditional methods, the proposed method improves the reconstruction speed by 99.907% while maintaining similar accuracy.

The precise positioning of anchoring-hole centers on the steel belts used for anchor support in coal mines is essential for improving the automation and efficiency of roadway support. To address the issues of poor positioning accuracy and the low support efficiency caused by the manual determination of anchoring-hole-center positions, this paper proposes a monocular-vision-based method for locating anchoring-hole centers. Firstly, a laser pointer and an industrial camera are used to build an anchoring-hole positioning device, and its visual positioning model is constructed to achieve the automatic and precise localization of the anchoring-hole center. Secondly, to overcome the difficulty of obtaining high-precision spot centers using edge and grayscale information-based spot extraction methods, a spot center extraction method based on two-dimensional arctangent function fitting is proposed, achieving high precision and the stable acquisition of spot pixel coordinates. The experimental results show that the average measurement errors of the anchoring-hole centers in the camera's coordinate system along the X-axis, Y-axis, and Z-axis are 3.36 mm, 3.30 mm, and 5.75 mm, respectively, with maximum errors of 4.23 mm, 4.39 mm, and 6.63 mm. The average measurement errors of the steel belt's pitch, yaw, and roll angles in the camera's coordinate system are 0.16°, 0.16°, and 0.08°, respectively, with maximum errors of 0.21°, 0.27°, and 0.13°. The proposed method can achieve the precise localization of anchoring holes, improve the efficiency of roadway support, and provide new insights for the automation and intelligentization of roadway anchor support. [ABSTRACT FROM AUTHOR]

End pose detection of the excavator manipulator is one of the key components in the manufacture of automatic excavators. The vision-based pose estimation scheme has been identified as a potential low-cost alternative to the mechanical automation system and is gradually being applied to excavators. This paper presents an end pose estimation method for an excavator manipulator based on monocular vision, and the method network consists of two stages. In the first stage, the monocular RGB image is used as the input, and the advanced DeepLabv3 + network is utilized to segment the target, obtaining the excavator manipulator image without the background. Pose estimation is considered a regression problem in the second stage, taking segmentation results as inputs. End pose estimation is performed using the proposed pose regression network, P-ResNet, to ensure its independence from background influence. During the evaluation of pose estimation experiments, we collected a new dataset containing 2000 images based on a KOMATSU excavator. The results demonstrate that this approach exhibits strong robustness and accuracy. Its position error is less than 15 mm, and its attitude error is less than 3 degrees. [ABSTRACT FROM AUTHOR]

Recent years have witnessed great progress in creating vivid audio-driven portraits from monocular videos. However, how to seamlessly adapt the created video avatars to other scenarios with different backgrounds and lighting conditions remains unsolved. On the other hand, existing relighting studies mostly rely on dynamically lighted or multi-view data, which are too expensive for creating video portraits. To bridge this gap, we propose ReliTalk, a novel framework for relightable audio-driven talking portrait generation from monocular videos. Our key insight is to decompose the portrait's reflectance from implicitly learned audio-driven facial normals and images. Specifically, we involve 3D facial priors derived from audio features to predict delicate normal maps through implicit functions. These initially predicted normals then take a crucial part in reflectance decomposition by dynamically estimating the lighting condition of the given video. Moreover, the stereoscopic face representation is refined using the identity-consistent loss under simulated multiple lighting conditions, addressing the ill-posed problem caused by limited views available from a single monocular video. Extensive experiments validate the superiority of our proposed framework on both real and synthetic datasets. Our code is released in (https://github.com/arthur-qiu/ReliTalk). [ABSTRACT FROM AUTHOR]

Plant height is an important parameter of plant phenotype as one indicator of plant growth. In view of the complexity and scale limitation in current measurement systems, a scaleless method is proposed for the automatic measurement of plant height based on monocular computer vision. In this study, four peppers planted side by side were used as the measurement objects. Two color images of the measurement object were obtained by using a monocular camera at different shooting heights. Binary images were obtained as the images were processed by super-green grayscale and the Otsu method. The binarized images were transformed into horizontal one-dimensional data by the statistical number of vertical pixels, and the boundary points of multiple plants in the image were found and segmented into single-plant binarized images by filtering and searching for valleys. The pixel height was extracted from the segmented single plant image and the pixel displacement of the height was calculated, which was substituted into the calculation together with the reference height displacement to obtain the realistic height of the plant and complete the height measurements of multiple plants. Within the range of 2–3 m, under the light condition of 279 lx and 324 lx, this method can realize the rapid detection of multi-plant phenotypic parameters with a high precision and obtain more accurate plant height measurement results. The absolute error of plant height measurement is not more than ±10 mm, and the absolute proportion error is not more than ±4%. [ABSTRACT FROM AUTHOR]