Visual Measurements Research Articles

Changes in peripapillary retinal nerve fibre layer and ocular parameters in acute anterior uveitis

ABSTRACT Clinical relevance Acute anterior uveitis (AAU) can lead to the thickening of the peripapillary retinal nerve fibre layer (pRNFL) and induce refractive changes during its active phase. Background AAU is a common form of uveitis characterised by inflammation in the anterior chamber. A notable prevalence of optical coherence tomography – defined pRNFL thickening was observed among patients with AAU. The alterations in pRNFL thickness and their associations with other relevant ocular parameters in patients with AAU were investigated. Methods A retrospective, consecutive case series was conducted at a specialised uveitis referral clinic in Taiwan. This study gathered data on various demographic characteristics and various ocular parameters, namely anterior chamber cell grading, refractive error, best-corrected visual acuity, intraocular pressure, and optical coherence tomography measurements. A comparative analysis of baseline and subsequent follow-up data was conducted. Additionally, this study examined the correlations between alterations in pRNFL thickness and various ocular parameters. Twenty-one patients with AAU (21 affected eyes/21 unaffected eyes) were examined. Results Initial measurements revealed pRNFL thickening in 20 patients. Treatment led to significant improvements in best-corrected visual acuity, intraocular pressure recovery, and pRNFL thickening (p < 0.01). The correlation between changes in pRNFL thickness and best-corrected visual acuity was weak (r = 0.20, p = 0.41). By contrast, a significant negative correlation was identified between changes in pRNFL thickness and refractive error alterations (r =  −0.71, p = 0.01). Conclusion This study demonstrated that AAU is associated with pRNFL thickening, which in turn is inversely correlated with changes in refractive error alterations throughout the disease course. Monitoring changes in pRNFL thickness can be effective in assessing ocular inflammation status.

Efficacy and safety of the implantation of a single-piece angulated foldable IOL in the sulcus.

To study the visual results and tolerance of a Zeiss CT Lucia 601P intraocular lens (IOL) implanted in the sulcus after complicated cataract surgery or during IOL exchange for clouded IOL. In total, 64 patients who underwent sulcus implantation were recalled to the hospital to undergo subjective and objective refraction, best corrected visual acuity measurement, tonometry, optical coherence tomography, laser flare photometry, biometry, and wavefront aberrometry. In spite of a large variation in preoperative refraction, the target refraction was obtained within 1.5 diopters in approximately 97% of patients and within 0.5 diopter in 53% of patients. Average BCVA was high (Snellen 0.86) and related to concomitant (mostly retinal) pathologies in eyes with poorer visual performance. Wavefront aberrometry showed no evidence of IOL tilting or decentration after long-term implantation in the sulcus. Tonometry was not different from the fellow eye of the patient (p > 0.5). In 53 patients with bilateral pseudophakia, the laser flare photometry was not significantly different from the fellow eye (p < 0.05). This study demonstrates that this single-piece angulated foldable acrylic IOL can be considered for implantation in the sulcus. The visual results are favorable, and the IOL can be well-positioned and tolerated in the sulcus. Moreover, there were no safety issues found since there was no evidence of elevated IOP or chronic uveitis.

Exploring machine vision measurements through innovative cyclic-lead-follower statistical technique: an experimental study

In metrology systems, machine vision systems are often utilized for non-contact inspection. The most important phase in ensuring measurement accuracy is camera calibration and estimation of pixel measurement errors, which establish the correspondence between image coordinates and object coordinates. Multiple calibration techniques improve the effectiveness of machine vision systems. However, a number of factors lead to variations in the camera calibration procedure, which must be optimized. This study explains a novel ‘Cyclic-Lead-Follower’ statistical methodology proposed for camera calibration and measurement to estimate the errors in pixel measurement, employing four slip gauges for measurement. Several multi-criteria decision-making techniques, including WSM, WPM, WASPAS, and TOPSIS, were used to optimize the results of the proposed Cyclic-Lead-Follower methods. The proposed Cyclic-Lead-Follower method improves the accuracy of the camera calibration and measurement system, which employs the exponential moving average statistical method when compared to the traditional calibration method. The proposed calibration method produces lower exponential moving average values than the traditional calibration method, with an average percentage error of approximately 46% in the exponential moving average. The use of an exponential moving average in a validation experiment of the Cyclic-Lead-Follower method decreased the measurement percentage errors, which were estimated to be 0.57%. The proposed method can be used in machine vision systems due to its robustness, accuracy, and cost-effectiveness.

Residual SwinV2 transformer coordinate attention network for image super resolution

Swin Transformers have been designed and used in various image super-resolution (SR) applications. One of the recent image restoration methods is RSTCANet, which combines Swin Transformer with Channel Attention. However, for some channels of images that may carry less useful information or noise, Channel Attention cannot automatically learn the insignificance of these channels. Instead, it tries to enhance their expression capability by adjusting the weights. It may lead to excessive focus on noise information while neglecting more essential features. In this paper, we propose a new image SR method, RSVTCANet, based on an extension of Swin2SR. Specifically, to effectively gather global information for the channel of images, we modify the Residual SwinV2 Transformer blocks in Swin2SR by introducing the coordinate attention for each two successive SwinV2 Transformer Layers (S2TL) and replacing Multi-head Self-Attention (MSA) with Efficient Multi-head Self-Attention version 2 (EMSAv2) to employ the resulting residual SwinV2 Transformer coordinate attention blocks (RSVTCABs) for feature extraction. Additionally, to improve the generalization of RSVTCANet during training, we apply an optimized RandAugment for data augmentation on the training dataset. Extensive experimental results show that RSVTCANet outperforms the recent image SR method regarding visual quality and measures such as PSNR and SSIM.

A method to partition boiling heat transfer mechanisms using synchronous through-substrate high-speed visual and infrared measurements

Heat transfer during boiling involves a variety of transport mechanisms. Available experimental techniques cannot yet fully delineate these mechanisms which has contributed to a long-standing, persistent challenge of constructing accurate mechanistic models for boiling. In this work, we develop a method to identify and distinguish between the individual heat transfer mechanisms that occur during boiling using synchronous, through-substrate, high-speed visual and infrared measurements. Local heat fluxes are deduced from temperature measurements and a synchronized set of binarized phase maps are obtained from processing high-speed visual measurements. Experimental pool boiling investigation of HFE-7100 fluid on an indium tin oxide surface revealed four distinct heat transfer signatures in the heat flux maps corresponding to liquid convection, contact line evaporation, vapor convection, and local microconvection due to rewetting during boiling. To classify these regions, pixel-wise binary and morphological operations are performed on the phase maps of the entire surface. In contrast with prior partitioning techniques which use standalone heat flux measurements, our synchronous high-resolution visualization enables the region classification around fine bubble footprints that otherwise would not be detected with heat flux maps alone. The heat fluxes and superheats are then partitioned into their underlying mechanisms using classified regions from the phase maps. Analysis of the nucleate boiling regime data shows that different mechanisms contribute in varying degrees to the overall heat transfer at different points along the boiling curve: for the surface-fluid combination studied, single-phase liquid heat transfer is the primary contributor at low heat fluxes while at high heat fluxes, contact line evaporation contributed the most. We further employ the experimental approach to partitioning dynamic processes resulting from step increases in heat input demonstrating its potential for investigating transient phenomena such as dryout. The experimental and post-processing method introduced in this study is the first to delineate partitioning between different heat transfer mechanism regions in the presence of multiple interacting bubbles over the entire surface throughout the boiling curve in both steady and transient operating conditions.

Calibration Method Based on Virtual Gear Artefact for Computer Vision Measuring Instrument of Fine Pitch Gear.

The verification of the correctness, adaptability, and robustness of software systems in modern precision measurement instruments is of great significance. Due to the difficulty in processing and calibrating high-precision fine-pitch gear artefacts, the function verification and accuracy calibration of vision measurement instruments for the fine-pitch gear have become a challenge. The calibration method of the gear vision measurement system based on the virtual gear artefact involves two steps, namely obtaining and applying the virtual artefact. The obtained virtual gear artefact has the same geometric features, error features, and image edge features as the real artefact. The calibration method based on the virtual artefact can complete the correctness verification of the gear vision measurement system, and is superior to the traditional methods in adaptability verification, robustness verification, and fault analysis. In a test, the characteristic error of the virtual gear artefact could be reproduced with the original shape in the evaluation results of the computer vision gear measurement (CVGM) system, while the reproduction error did not exceed 1.9 μm. This can meet the requirements of the verification of the gear vision measurement software. The application of the virtual gear artefact can significantly improve the accuracy and robustness of the computer vision measuring instrument of the fine-pitch gear.

Calibration and pose measurement of a combined vision sensor system for industrial robot grasping of brackets

Abstract Recently, visual sensing measurement and its application in industrial robot operations have been widely researched, promoting the development of instrumentation and automation. This study proposes a combined vision sensor system for robot grasping, focusing on combined sensor system calibration and bracket pose measurements. The system configuration and working strategy of the combined vision system are introduced. Thereafter, the calibration of the combined vision coordinate systems is presented, wherein a global vision system (GVS) acts as the external measuring equipment for accurately calibrating the local vision. Furthermore, a pose estimation method using a local vision system (LVS) is proposed, including morphology-based image enhancement and principal component analysis (PCA)-based corner recognition methods. Verification experiments, including combined calibration and bracket pose measurements, were performed to validate the effectiveness and accuracy of the proposed combined vision measurement strategy. The results demonstrated that the proposed system applies to industrial robot grasping of brackets. In addition, the proposed robot-sensor calibration method improves calibration accuracy. Finally, the proposed corner detection method is effective and accurate for different bracket detection applications. This study provides a system that improves robot grasping results by considering key factors, such as vision measurement accuracy, and calibration methods.

Calibration of visual measurement system for excavator manipulator pose

The automatic control of excavator operation trajectories is a pivotal technology for autonomous excavators, with the essential prerequisite being the real-time measurement of manipulator poses. Given the complexity of the operating environment, traditional sensor-based measurement methods face limitations, whereas visual measurement emerges as a promising technique. Accurately measuring excavator manipulator poses involves a crucial aspect: mapping the relationship between image information and poses. First, to address the significant errors in pose prediction encountered with machine learning techniques like artificial neural networks, this work introduces a mathematical model for mapping this relationship, referred to as the pose mapping mathematical model, which includes calibrating model parameters. Second, to address the sensitivity of initial values in the calibration process, we propose a residual-guided initialization algorithm. This algorithm aims to ensure that initial values closely approximate the ground truth values, thus preventing matrix singularity at the source and avoiding parameter estimation divergence. Third, to tackle challenges such as unstable lighting conditions and discrepancies between the dataset and the mathematical model, we introduce the random sample consensus-driven Levenberg–Marquardt parameter optimization algorithm to enhance parameter estimation accuracy. Experiments with static and dynamic online measurement demonstrate that our method reduces pose measurement errors compared to existing methods. This research lays a solid foundation for developing visual measurement techniques for excavators and automated manipulator control based on visual measurements, also serving as a valuable reference for research on mechanical arms.

Adaptive adjustment of brightness and blur of the camera for high precision internal parameter calibration

Camera calibration is a prerequisite for many computer vision tasks, such as visual measurement and visual localization. This paper proposes a new method for high-precision calibration of camera intrinsic parameters. By dynamically adjusting image brightness and blur to cope with changes in external lighting conditions and camera working distance, high-precision internal parameter measurement is achieved. This method solves the problems of the traditional camera intrinsic parameter calibration process being cumbersome and susceptible to changes in the placement of calibration boards and fluctuations in external lighting conditions. Firstly, an adaptive exposure adjustment algorithm is proposed to adjust the camera exposure value by statistically analyzing the average gray value of randomly selected rectangular regions on the calibration board, thereby solving the problem of the camera calibration accuracy being affected by overly bright or dark environments. Subsequently, an evaluation criterion and adaptive adjustment strategy for adjusting image blur are proposed. By extracting edge points of feature circles and obtaining the slope of the fitted curve, the relationship between edge sharpness and slope is utilized to adjust the image blur. Finally, the proposed method achieves high-precision semi-automatic calibration. Experimental results demonstrate that the proposed method has significant advantages in terms of correctness, robustness, and flexibility, with an average reprojection error of 0.0167.

Simultaneous Vision and Surface Electromyography Measurements to Evaluate Masticatory Robot TMJ Reaction Forces During Mastication

Simultaneous Vision and Surface Electromyography Measurements to Evaluate Masticatory Robot TMJ Reaction Forces During Mastication

Erratum: Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes.

This corrects the article 10.3791/66272.

Visual impact control of urban waterfront development on the background mountain view: Examining its justifiability through two types of immersive virtual reality experiments

Several cities built along the waterfront are also accompanied by mountainous backgrounds, and thus have characteristic panoramas contributing to their visual image and identity. However, urban waterfronts are also often subject to high-rise development, which may damage that identity by shielding the mountainous background. Building regulations are adopted as visual impact management policies to retain mountain visibility, but it is difficult to find either scientific evidence to support the effectiveness of such private property control or further empirical guidance for its elaboration. Through a case study of Seoul's Han River, this study thus analyzed the actual impact of relevant building characteristics on the visual preference of urban waterfront views with mountainous backgrounds. Immersive virtual reality experiments using both actual and simulated cityscape images were conducted. Hierarchical linear regression and quasi-rank-based conjoint analysis results confirm that controls on buildings to retain background mountain visibility would actually serve as reasonable visual impact management measures. Insights into what kinds of regulation could be desirable in which circumstances were further obtained—excessively high buildings are especially unrecommended when the height of the background mountain is low, and in all cases a combination of view corridors with convex skylines are advisable for mitigating the negative impacts of mid-to-high building heights.

Vision-based finite-time prescribed performance control for uncooperative UAV target-tracking subject to field of view constraints

This paper presents a vision-based finite-time prescribed performance controller for unmanned aerial vehicle (UAV) tracking of uncooperative aerial targets. The relative states between UAV and target are estimated by an onboard monocular camera. The inability of visual measurements to accurately determine the initial state of the target renders conventional prescribed performance controllers ineffective in such situations. As a result, it becomes essential to address the problem of prescribed performance control under conditions of uncertain initial values By utilizing an auxiliary transforming function, an Asymmetric Barrier Lyapunov Function (ABLF) and a finite-time prescribed performance function, a robust adaptive controller based on backstepping framework is proposed to deal with state constraints under unknown initial tracking conditions. It is proved that, the closed-loop relative position is capable of reaching the prescribed performance bound before the preset transforming time and converging to the prescribed steady-state error before a finite setting time. Simulation examples are provided to illustrated the effectiveness of the proposed tracking algorithm.

Dynamic measurement of a long‐distance moving object using multi‐binocular high‐speed videogrammetry with adaptive‐weighting bundle adjustment

AbstractThe dynamic measurement of position and attitude information of a long‐distance moving object is a common demand in ground testing of aerospace engineering. Due to the movement from far to near and the limitations of camera resolution, it is necessary to use multi‐binocular cameras for segmented observation at different distances. However, achieving accurate and continuous position and attitude estimation is a challenging task. Therefore, this paper proposes a dynamic monitoring technique for long‐distance movement based on a multi‐binocular videogrammetric system. Aiming to solve the problem that the scale in images changes constantly during the moving process, a scale‐adaptive tracking method of circular targets is presented. Bundle adjustment (BA) with joint segments using an adaptive‐weighting least‐squares strategy is developed to enhance the measurement accuracy. The feasibility and reliability of the proposed technique are validated by a ground testing of relative measurement for spacecraft rendezvous and docking. The experimental results indicate that the proposed technique can obtain the actual motion state of the moving object, with a positioning accuracy of 3.2 mm (root mean square error), which can provide a reliable third‐party verification for on‐orbit measurement systems in ground testing. Compared with the results of BA with individual segments and vision measurement software PhotoModeler, the accuracy is improved by 45% and 30%, respectively.

Trading-Off Safety with Agility Using Deep Pose Error Estimation and Reinforcement Learning for Perception-Driven UAV Motion Planning

Navigation and planning for unmanned aerial vehicles (UAVs) based on visual-inertial sensors has been a popular research area in recent years. However, most visual sensors are prone to high error rates when exposed to disturbances such as excessive brightness and blur, which can lead to catastrophic performance drops in perception and motion planning systems. This study proposes a novel framework to address the coupled perception-planning problem in high-risk environments. This achieved by developing algorithms that can automatically adjust the agility of the UAV maneuvers based on the predicted error rate of the pose estimation system. The fundamental idea behind our work is to demonstrate that highly agile maneuvers become infeasible to execute when visual measurements are noisy. Thus, agility should be traded-off with safety to enable efficient risk management. Our study focuses on navigating a quadcopter through a sequence of gates on an unknown map, and we rely on existing deep learning methods for visual gate-pose estimation. In addition, we develop an architecture for estimating the pose error under high disturbance visual inputs. We use the estimated pose errors to train a reinforcement learning agent to tune the parameters of the motion planning algorithm to safely navigate the environment while minimizing the track completion time. Simulation results demonstrate that our proposed approach yields significantly fewer crashes and higher track completion rates compared to approaches that do not utilize reinforcement learning.

Study on ammonia/methanol blends with ammonia cracking for low-carbon combustion and NO reduction

This work aims to study the ammonia/methanol blended fuel with ammonia cracking in lean-burn condition. Ammonia on-line cracking to produce hydrogen production has the potential to realize the high-efficiency combustion. A new ammonia/methanol/hydrogen mechanism was updated and simplified using several reduction methods to optimize the computation time in 3D simulation. Visualization measurements in constant volume combustion chamber (CVCC) for flame propagation and NO generation were conducted, and 3D computational fluid dynamics (CFD) modeling was also established with the simplified mechanism simultaneously to investigate the impact of methanol addition and ammonia cracking in the lean-burn mode. The findings showed that methanol addition and ammonia cracking can significantly improve the combustion rate, and 60%ammonia/40%methanol with ammonia cracking ratio of 90% (N60R90) at equivalence ratio (ER) of 0.7 yields the similar LBV as pure methanol at ER of 1.0 in laminar burning condition. Its ER can be extended to 0.4 to obtain the similar level of LBV as pure ammonia at 1.0. The experiments and 3D simulations revealed that N60R90 at 0.4 yields the equivalent level of combustion rate as N60R0 at 0.8, while obtaining much lower NO emission. In addition, N60R100 at 0.4 without fuel-NO formation during the combustion can get ultra-low NO emission. This study provides the insights on low-carbon combustion strategy for ammonia cracking with low NO.

A fast dynamic pose estimation method for vision-based trajectory tracking control of industrial robots

Vision-based robotic trajectory tracking control is considered a promising technology. However, the slow sampling rate and latency of the vision sensor enormously limit the tracking performance. To conquer the issues, this paper proposes a dual-space error-state Kalman filter (DS-ESKF). By combining the encoder measurement with the vision measurement, the end-effector’s pose between adjacent vision measurements is restored, and the pose estimation cycle is synchronized with the control cycle. The critical distinguishing of DS-ESKF is that the encoder-driven error-state kinematics for the industrial robot is reconstructed. The experimental results on the Staubli TX60 industrial robot show that compared with the existing dual-rate Kalman filters (DR-KF), DS-ESKF can reduce estimation errors by about 50 % and have robust estimation performance. By applying the trajectory tracking control scheme combining DS-ESKF with a simple PID controller to Staubli TX60, the tracking accuracy is significantly improved (±0.11 mm for position and ± 0.05°for orientation).

Examining the directionality of mobility patterns in activity spaces: Introducing the ‘mobility snowflake’ visual analytic and measurement framework

Activity spaces characterise an individual's mobility patterns and provide critical insights important to a broad suite of applications. Emergent forms of disaggregate space-time data offer new opportunities to exploit a more granular approach to measuring and visualising activity spaces. Drawing on individual GPS trajectories for 365 participants covering a seven-day period the current paper presents a new measurement and visualisation framework examining the directionality of mobility patterns. This framework moves beyond existing work that measures activity spaces via a simple geometric shape (traditionally a convex hull) to introduce a new metric and associated visual analytic capturing a new dimension of the concept. Results have important consequences for policy and planning, with the hope that the framework can be redeployed across various situational and cultural contexts, leading to the creation of an expanding collection of comparative studies.

Influence of dispersed phase flow-rate pulsations on the liquid–liquid parallel flow in a T-junction microchannel

Active flow control in microfluidic devices to broaden the range of desired conditions is a significant practical concern. In this study, we experimentally investigate liquid–liquid parallel flow under the dispersed phase flow-rate pulsations in a T-junction microchannel. Sinusoidal flow rate disturbances with variations in amplitude and period were applied to dispersed phases of different viscosities. The study involves flow visualization and velocity field measurements using the micro-PIV technique for both free and excited flow conditions. The analysis of flow pattern maps, velocity fields, velocity gradients, and phase-averaged velocity profiles in a T-junction region and far downstream provided insights into the evolution of disturbances, which is different in low- and high-viscosity liquids. In the less viscous liquid, transverse waves amplify longitudinal ones due to relaxation of the liquid–liquid interface, while in the high-viscosity dispersed phase, longitudinal waves are damped significantly, with the transverse wave amplitude remaining almost constant. As a result, we revealed two distinct mechanisms of disturbance wave propagation and the loss of parallel flow stability, which are both dependent on the Ohnesorge number of the dispersed phase. Destabilization of the parallel flow led to the formation of plugs with a narrow length distribution. Single-mode, double-mode, triple-mode, and multi-mode plug formation regimes were identified. The results of the study can potentially be used to extend the range of segmented flow regimes and generate plugs of a desired length.

Clinical and functional eye condition changes in the dry form of age macular degeneration depending on the AREDS stage

Purpose: to assess the functional state of the eyes in age-related macular degeneration depending on the AREDS classification stage.Material and methods. A clinical study of 60 patients (120 eyes) with the dry AMD form, aged 55.1 ± 4.2 years, involved 30 patients (60 eyes) aged 52.7 ± 3.9 years with AREDS 2 category (initial AMD stage, group1) and 30 patients (60 eyes) aged 57.9 ± 7.8 years with AREDS 3 category (intermediate AMD stage, group 2). The complex research involved visual acuity measurement, including the assessment of near vision in various illumination conditions according to the authors’ methodology, fluorescein angiography, refractometry, tonometry, optical coherence tomography (OCT), ultrasound duplex scanning of the eye; and an immunological study aimed at determining interleukins and interferons in blood serum.Results. Visual acuity was found to depend on test illumination in healthy subjects and to substantially drop at all illumination levels in AMD, especially in the 3rd stage. With the development of AMD, the activity of the immune response was recorded.The patients of groups 1 and 2 showed an increase in the inflammatory response and a depression of the anti-inflammatory response. In group 1, an increase was recorded in IL-I by 23.5% (p &lt; 0.05), in IL5 by 25.4% (p &lt; 0.05), and in IL8 by 19.9% (p &lt; 0.05). In group 2, the value of these parameters exceeded the norm by 32.1, 39.5 and 25.4% (p &lt; 0.05), respectively. The parameters of anti-inflammatory immunity (IL10 and TGF l) were lower than the reference level by 18.3 and 24.6% (p &lt; 0.05) of patients of group 1 and by 24.3 and 30.75 (p &lt; 0.05) of group 2. It was established that the degree of imbalance of the immune system is associated with the stage of AREDS. In group 2 patients with AREDS 3, the content of pro-inflammatory interleukins exceeded those of group 1: IL-I by 12.5% (p &lt; 0.05), IL5 by 11.9% (p &lt; 0.05), IL8 by 11, 2% (p &lt; 0.05). IL10 and TGF l in group 2 were lower than in group 1 by 10.2 and 11.8% (p &lt; 0.05).Conclusion. Near visual acuity measurement depending on test illumination may be used as a sensitive diagnostic test in AMD in patients. At an early stage of AMD development, a disorder in the immune defense is observed, taking the form of activation of the pro-inflammatory spectrum and depression of the anti-inflammatory component, as well as microcirculatory changes Homeostatic changes of the eye appear at an early stage of the disease and are progressing as the clinical picture is deteriorating. The changes are the greatest at patients with stage 3 of AREDS.