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Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks. [ABSTRACT FROM AUTHOR]

Headland boundary identification and ranging are the key supporting technologies for the automatic driving of intelligent agricultural machinery, and they are also the basis for controlling operational behaviors such as autonomous turning and machine lifting. The complex, unstructured environments of farmland headlands render traditional image feature extraction methods less accurate and adaptable. This study utilizes deep learning and binocular vision technologies to develop a headland boundary identification and ranging system built upon the existing automatic guided tractor test platform. A headland image annotation dataset was constructed, and the MobileNetV3 network, notable for its compact model structure, was employed to achieve binary classification recognition of farmland and headland images. An improved MV3-DeeplabV3+ image segmentation network model, leveraging an attention mechanism, was constructed, achieving a high mean intersection over union (MIoU) value of 92.08% and enabling fast and accurate detection of headland boundaries. Following the detection of headland boundaries, binocular stereo vision technology was employed to measure the boundary distances. Field experiment results indicate that the system's average relative errors of distance in ranging at distances of 25 m, 20 m, and 15 m are 6.72%, 4.80%, and 4.35%, respectively. This system is capable of meeting the real-time detection requirements for headland boundaries. [ABSTRACT FROM AUTHOR]

The accuracy of material extrusion-based 3D printers is greatly affected by the high temperature in the chamber due to the thermal-induced deformation of components. However, most existing measurement equipment cannot be applied to high-temperature environments, which hinders the corresponding error measurement. To address this issue, a geometric error detection system and identification algorithm based on binocular vision are proposed. Firstly, a corner detection algorithm and a ray-intersection binocular model are used to identify the three-dimensional displacement of the target. Secondly, an error separation and identification algorithm is proposed to identify 21 position-dependent geometric errors. Error measurement experiments are conducted on a 3D printer at room temperature and high temperature, respectively. The experimental results at room temperature are verified using a double-ball bar. Finally, an error compensation experiment is conducted to verify the effectiveness of error identification, which also shows the contribution of error motions of linear axes on the printing accuracy. [ABSTRACT FROM AUTHOR]

Naturally occurring collective motion is a fascinating phenomenon in which swarming individuals aggregate and coordinate their motion. Many theoretical models of swarming assume idealized, perfect perceptual capabilities, and ignore the underlying perception processes, particularly for agents relying on visual perception. Specifically, biological vision in many swarming animals, such as locusts, utilizes monocular non-stereoscopic vision, which prevents perfect acquisition of distances and velocities. Moreover, swarming peers can visually occlude each other, further introducing estimation errors. In this study, we explore necessary conditions for the emergence of ordered collective motion under restricted conditions, using non-stereoscopic, monocular vision. We present a model of vision-based collective motion for locust-like agents: elongated shape, omni-directional visual sensor parallel to the horizontal plane, and lacking stereoscopic depth perception. The model addresses (i) the non-stereoscopic estimation of distance and velocity, (ii) the presence of occlusions in the visual field. We consider and compare three strategies that an agent may use to interpret partially-occluded visual information at the cost of the computational complexity required for the visual perception processes. Computer-simulated experiments conducted in various geometrical environments (toroidal, corridor, and ring-shaped arenas) demonstrate that the models can result in an ordered or near-ordered state. At the same time, they differ in the rate at which order is achieved. Moreover, the results are sensitive to the elongation of the agents. Experiments in geometrically constrained environments reveal differences between the models and elucidate possible tradeoffs in using them to control swarming agents. These suggest avenues for further study in biology and robotics. Author summary: Swarm collective motion is a wide-ranging phenomenon in nature, with applications in multi-agent, multi-robot systems. In most natural swarming species, individuals rely on monocular, non-stereoscopic vision as the key sensory modality for their interactions. For example, the desert locust (Schistocerca gregaria) displays large swarms of individuals, moving in alignment and relying solely on non-stereoscopic visual perception. Inspired by these locust swarms, we have developed a monocular, non-stereoscopic vision-based model that achieves synchronized motion in a swarm of two-dimensional agents, even with inaccurate estimates of distances and velocities, particularly in the presence of occlusions. We explore three general strategies for handling occlusions, which differ in the requirements they place on the complexity of the visual perception process. We show that strategies may reach a highly ordered motion state but differ in their rate of convergence to this ordered state. [ABSTRACT FROM AUTHOR]

Introduction: Modeling work on binocular rivalry has described how ocular opponency neurons represent interocular conflict. These neurons have recently been considered to mediate an ocular dominance shift to the eye that has viewed a backward movie for long during which time the other eye is presented with a regular movie. Unlike typical short-term monocular deprivation, the visual inputs are comparable across eyes in that "dichoptic-backward-movie" paradigm. Therefore, it remains unclear whether the ocular opponency neurons are also responsible for the short-term monocular deprivation effect which is prevalently explained by the homeostatic compensation theory. We designed two experiments from distinct perspectives to investigate this question. Methods: In Experiment 1, we mitigated the imbalance in the activity of opponency neurons between the two eyes during monocular deprivation by presenting video stimuli alternately. In Experiment 2, we directly evaluated the response of opponency neurons before and after monocular deprivation using SSVEP techniques. Results: Consistent with each other, both experiments failed to provide reliable evidence supporting the involvement of ocular opponency neurons in the shortterm monocular deprivation effect. Discussion: Our results suggest that ocular opponency neurons may not play an essential role in the short-term monocular deprivation effect, potentially due to interference from the homeostatic plasticity mechanism. [ABSTRACT FROM AUTHOR]

With the continuous evolution of autonomous driving and unmanned driving systems, traditional limitations such as a limited field-of-view, poor ranging accuracy, and real-time display are becoming inadequate to satisfy the requirements of binocular stereo-perception systems. Firstly, we designed a binocular stereo-imaging-perception system with a wide-field-of-view and infrared- and visible light-dual-band fusion. Secondly we proposed a binocular stereo-perception optical imaging system with a wide field-of-view of 120.3°, which solves the small field-of-view of current binocular stereo-perception systems. Thirdly, For image aberration caused by the wide-field-of-view system design, we propose an ellipsoidal-image-aberration algorithm with a low consumption of memory resources and no loss of field-of-view. This algorithm simultaneously solves visible light and infrared images with an aberration rate of 45% and 47%, respectively. Fourthly, a multi-scale infrared- and visible light-image-fusion algorithm is used, which improves the situational-awareness capabilities of a binocular stereo-sensing system in a scene and enhances image details to improve ranging accuracy. Furthermore, this paper is based on the Taylor model-calibration binocular stereo-sensing system of internal and external parameters for limit correction; the implemented algorithms are integrated into an NVIDIA Jetson TX2 + FPGA hardware framework, enabling near-distance ranging experiments. The fusion-ranging accuracy within 20 m achieved an error of 0.02 m, outperforming both visible light- and infrared-ranging methods. It generates the fusion-ranging-image output with a minimal delay of only 22.31 ms at a frame rate of 50 Hz. [ABSTRACT FROM AUTHOR]

Thermal feedback plays an important role in tactile perception, greatly influencing fields such as autonomous robot systems and virtual reality. The further development of intelligent systems demands enhanced thermosensation, such as the measurement of thermal properties of objects to aid in more accurate system perception. However, this continues to present certain challenges in contact-based scenarios. For this reason, this study innovates by using the concept of semi-infinite equivalence to design a thermosensation system. A discrete transient heat transfer model was established. Subsequently, a data-driven method was introduced, integrating the developed model with a back propagation (BP) neural network containing dual hidden layers, to facilitate accurate calculation for contact materials. The network was trained using the thermophysical data of 67 types of materials generated by the heat transfer model. An experimental setup, employing flexible thin-film devices, was constructed to measure three solid materials under various heating conditions. Results indicated that measurement errors stayed within 10% for thermal conductivity and 20% for thermal diffusion. This approach not only enables quick, quantitative calculation and identification of contact materials but also simplifies the measurement process by eliminating the need for initial temperature adjustments, and minimizing errors due to model complexity. [ABSTRACT FROM AUTHOR]

Binocular vision requires the segregation of retinal ganglion cell (RGC) axons extending from the retina into the ipsilateral and contralateral optic tracts. RGC axon segregation occurs at the optic chiasm, which forms at the ventral diencephalon midline. Using expression analyses, retinal explants and genetically modified mice, we demonstrate that CXCL12 (SDF1) is required for axon segregation at the optic chiasm. CXCL12 is expressed by the meninges bordering the optic pathway, and CXCR4 by both ipsilaterally and contralaterally projecting RGCs. CXCL12 or ventral diencephalon meninges potently promoted axon outgrowth from both ipsilaterally and contralaterally projecting RGCs. Further, a higher proportion of axons projected ipsilaterally in mice lacking CXCL12 or its receptor CXCR4 compared with wild-type mice as a result of misrouting of presumptive contralaterally specified RGC axons. Although RGCs also expressed the alternative CXCL12 receptor ACKR3, the optic chiasm developed normally in mice lacking ACKR3. Our data support a model whereby meningeal-derived CXCL12 helps drive axon growth from CXCR4-expressing RGCs towards the diencephalon midline, enabling contralateral axon growth. These findings further our understanding of the molecular and cellular mechanisms controlling optic pathway development. [ABSTRACT FROM AUTHOR]

Aerial robots, or unmanned aerial vehicles (UAVs), are widely used in 3D reconstruction tasks employing a wide range of sensors. In this work, we explore the use of wide baseline and non-parallel stereo vision for fast and movement-efficient long-range 3D reconstruction with multiple aerial robots. Each viewpoint of the stereo vision system is distributed on separate aerial robots, facilitating the adjustment of various parameters, including baseline length, configuration axis, and inward yaw tilt angle. Additionally, multiple aerial robots with different sets of parameters can be used simultaneously, including the use of multiple baselines, which allows for 3D monitoring at various depth ranges simultaneously, and the combined use of horizontal and vertical stereo, which improves the quality and completeness of depth estimation. Depth estimation at a distance of up to 400 m with less than 10% error using only 10 m of active flight distance is demonstrated in the simulation. Additionally, estimation of a distance of up to 100 m with flight distance of up to 10 m on the vertical axis and horizontal axis is demonstrated in an outdoor mapping experiment using the developed prototype UAVs. [ABSTRACT FROM AUTHOR]

In the process of grading and dynamically optimizing the design and construction parameters of the surrounding rock mass of a rock tunnel face, efficiently and accurately acquiring the geometrical parameters of the rock discontinuities is an important basic task. To address the problems of time consuming, low accuracy, and high danger associated with traditional methods of obtaining the structural information of rock mass, this paper proposes a method for three-dimensional reconstruction and intelligent information extraction of tunnel face based on binocular stereo vision (BSV). First, the parallel binocular device with a single camera was improved, calibrated using the checkerboard calibration method. By integrating with the semi-global matching algorithm, the BSV based method for the three-dimensional reconstruction of the rock mass of the tunnel face was optimized. Furthermore, based on the results from on-site engineering applications, this study leveraged two parameters, point cloud density and algorithm runtime, to determine the optimal values for the disparity range and window size parameters within the semi-global stereo matching algorithm. This enhancement improved the performance of the 3D reconstruction method based on binocular stereo vision. Finally, efficient and refined intelligent methods for extracting structural parameters of the rock mass were proposed based on k-nearest neighbor search and kernel density estimation. The research results can provide reliable technical support for the intelligent and efficient acquisition of rock mass structural information in rock tunnel engineering faces. [ABSTRACT FROM AUTHOR]

To analyze the status of binocular visual functions, the relationship between binocular visual function and computer vision-related symptoms in the high-tech industry group. The study sample was comprised of 33 participants aged between 20 and 40 years of age. After completing basic information and the Computer Vision Symptom Scale (CVSS-17) questionnaire, the participants underwent a comprehensive examination of binocular visual function. All data were statistically analyzed with SPSS V26.0 software. The value of the binocular vision function of the Taiwan high-tech industry group was significantly different compared with the Scheiman and Morgan standard value. Study subjects were generally found to exhibit larger exophoric at distance, which in turn might lead to a lower ability to maintain binocular fusion to a single image, or recover from fusional disruption at distance. Subjects also experienced accommodation and convergence problems at near at the same time. Age, gender, and refractive errors had no significant impact on CVSS-17 scores, only the duration of computer usage showed a significant effect, particularly for internal symptom factor (ISF) dimensions. In addition, the interaction between the ISF and external symptom factor resulted in more severe visual symptoms. Long-term use of electronic devices may lead to an imbalance in binocular vision function, thereby increasing or exacerbating visual symptoms. If the use of electronic devices is an unchangeable trend, interventions in prescription, visual training or the visual design of electronic products become worthwhile topics for development. [ABSTRACT FROM AUTHOR]

The study of the large paraphyletic group of extinct 'palaeoniscoid' fishes has shed light on the diversity and evolutionary history of basal actinopterygians. However, only a little ontogenetic information about 'palaeoniscoids' is known because their records in the early stages of development are scarce. Here, we report on a growth series of 'palaeoniscoids' in the juvenile stage from the Upper Triassic Amisan Formation of South Korea. Fourteen specimens, including five counterpart specimens, represent a new taxon, Megalomatia minima gen. et sp. nov., exhibiting ontogeny and exceptional preservation with the eyes possibly containing the crystalline lens, the otoliths, and the lateral line canals without covering scales. This discovery allows us to discuss the adaptations and evolution of basal actinopterygians in more detail than before. The otoliths in situ of Megalomatia support the previous interpretation that basal actinopterygians have a sagitta as the largest otolith. The trunk lateral line canal, which runs under the scales instead of passing through them, represents a plesiomorphic gnathostome trait. Notably, the large protruded eyes suggest that Megalomatia probably has binocular vision, which would have played a significant role in targeting and catching prey with the primitive jaw structure. In addition, the firstly formed skeletal elements such as the jaws, pectoral girdle, and opercular series, and the posteroanterior pattern of squamation development are likely linked to the adaptation of young individuals to increase their viability for feeding, respiration, and swimming. [ABSTRACT FROM AUTHOR]

To further improve the disparity prediction accuracy of stereo matching algorithm in the ill-posed regions such as repeating textures, no texture, and edge, an improved dense multi-scale feature guided aggregation network (DGNet) based on PSMNet was proposed. Firstly, a dense multi-scale feature extraction module was designed based on the dense atrous spatial pyramid pooling structure. This module extracted region-level features of different scales by using atrous convolution of different expansion rates, and effectively fused image features of different scales through dense connection, so that the network can capture contextual information. Secondly, the initial cost volume was obtained by concatenating left feature maps with their corresponding right feature maps across each disparity level. Then, a dense multi-scale feature guided cost aggregation module was proposed, which adaptively fused the cost volume and dense multi-scale features while aggregating the cost volume, so that the subsequent decoding layers can decode more accurate and high-resolution geometry information with the guidance of multi-scale context information. Finally, the high-resolution cost volume with global optimization was input into the regression module to obtain the disparity map. Comprehensive experimental results demonstrated that the mismatching rate of the proposed algorithm on KITTI 2015 and KITTI 2012 datasets was respectively reduced to 1.76% and 1.24%, and the endpoint error on SceneFlow dataset was reduced to 0.56 px. Compared with existing stereo matching algorithms such as GWCNet and CPOP-Net, the proposed algorithm performs well in the ill-posed regions. [ABSTRACT FROM AUTHOR]

In recent years, multi-stream gaze estimation methods have become mainstream, which estimate gaze point by eye picture or combine with facial appearance, have achieved considerable accuracy. However, these methods based on a single camera fail to obtain accurate eye spatial position information. To address this issue, we propose a multi-stream gaze estimation model that incorporates spatial position information. We acquire eye spatial position information using a stereo camera and fuse eye image features with eye spatial position information using a ResNet network with a fused attention mechanism. Additionally, we perform calibration of eye image features using the computed eye spatial position information. Our model demonstrates superior performance on our experimental dataset. [ABSTRACT FROM AUTHOR]

Binocular visual acuity is an important index of functional performance. Optometrists need to know how binocular visual acuity is affected by aniseikonia, and whether reduced binocular visual acuity is a marker for aniseikonia. Aniseikonia, the perception of unequal image sizes between the eyes, can occur spontaneously or can be induced after different types of eye surgery, or trauma. It is known to affect binocular vision, but there are no prior studies about how it affects visual acuity. Visual acuity was measured for 10 healthy well-corrected participants aged 18–21 years of age. Aniseikonia of up to 20% was induced in one of two ways: (1) size lenses, which provided minification of field of view in one eye of each participant and (2) polaroid filters, which allowed vectographic viewing of optotypes on a 3D computer monitor. The best corrected acuity was measured on conventional logarithmic progression format vision charts and isolated optotypes, under both induced aniseikonia conditions. Induced aniseikonia caused binocular visual acuity thresholds to increase by small but statistically significant amounts, with the largest deficit being 0.06 logMAR for 20% size differences between the eyes. Binocular visual acuity was worse than monocular visual acuity for aniseikonia of 9% and greater. Acuity measured with the vectographic presentation gave slightly higher thresholds (by 0.01 logMAR) than for those viewed with size lenses. Acuity measured with charts gave slightly higher thresholds (by 0.02 logMAR) than with isolated letters. An acuity change of 0.06 logMAR is small and may be missed in a clinical examination. Therefore, visual acuity cannot be used as a marker of aniseikonia in clinical settings. Even with very marked induced aniseikonia, binocular visual acuity remained well within standards for licen*c*sing of drivers. [ABSTRACT FROM AUTHOR]

Real-time 3D scanning of a scene or object using multiple depth cameras is often required in many applications but is still a challenging task for the computer vision community, especially when the object or scene is partially occluded and dynamic. If active depth sensors are used in this case, their resulting depth map quality gets degraded due to interference between active radiations from each depth sensor. Passive 3D sensors like stereo cameras can avoid the issue of interference as they do not emit any active radiation, but they face correspondence problems. Since releasing the commodity depth sensor Microsoft Kinect, researchers are getting more interested in active depth-sensing. However, Kinect sensors have some easily noticeable limitations concerning 3D reconstruction such as: they can provide depth maps for a limited range, their field of view is restricted and holes are observed in the depth map due to occlusion. The above-mentioned limitations can be overcome if multiple Kinect sensors are used simultaneously instead of a single Kinect sensor. Still, the challenge here is to avoid interference between these sensors. We present a comprehensive review of possible solutions to avoid interference between multiple Kinect sensors. Furthermore, we introduce the Kinect technology in detail along with applications where multiple Kinect sensors are used in the literature. We expect that this paper will be helpful to the researchers who want to use multiple Kinect sensors in sharing the workplace in their research. [ABSTRACT FROM AUTHOR]

The Mars rotorcraft is an aerial exploration platform capable of repeated take-offs and landings to extend the range of rover explorations. NASA's Ingenuity helicopter is a full y functioning coaxial helicopter for technology demonstration. Increasing the rotor diameter or the number of blades can enhance the load capacity of the coaxial helicopter but would make it difficult to be folded. A more suitable configuration for Mars exploration is the multi-rotor aircraft, which has a stronger load capacity with the same rotor diameter. Although the aerodynamic performance and operational capabilities of the Mars multi-rotor aircraft have been partially considered, mechatronic system design and autonomous flight control have not been fully addressed. Harbin Institute of Technology and China Academy of Space Technology jointly proposed a deployable Mars quadcopter for air patrol and sampling with a sample mass of 100 g. This paper presents a concept for a Mars quadcopter and its mission operations, including operational procedures and flight modes, to provide a framework for future Mars flight sampling missions. We describe the structure and avionics architecture of the Mars quadcopter. Then, we present the hardware and software design of the guidance, navigation, and control (GNC) system to illustrate the autonomous flight control method. Finally, we introduce the deployment mechanism of the Mars quadcopter. • A foldable Mars quadcopter improves the efficiency and flexibility of Mars sample return tasks. • Five-module avionics system augments the Mars quadcopter for survival and operation. • A navigation method combining inertial and binocular vision enables autonomous flight on Mars. • The guidance system based on mode commands can independently plan exploration operations. [ABSTRACT FROM AUTHOR]

Obtaining 3D coordinates of fire by binocular vision is beneficial for intelligent fire extinguishing. However, binocular vision technology has a small field of view (FOV) for long-distance targets, which results in the fire not appearing in the FOV of binocular cameras. To solve this problem, an automatic fire alignment system with monocular and binocular cameras is constructed, and an alignment control method, including a one-time alignment method and visual servo is proposed. First, the one-time alignment method based on the monocular camera is designed to solve the problem of time. The alignment angle independent of depth information ensures that the binocular camera can reach the vicinity of the fire in a short time with only a single camera. Then, image-based visual servo (IBVS) methods based on a monobinocular vision system are designed to improve the alignment precision of the binocular camera, and a logic controller is designed for automatically switching the vision servo control method based on different cameras. Finally, experiments are designed to compare the proposed method with other methods. Results demonstrate that the method in this paper is better than other methods in terms of both time and accuracy and contributes to improving the efficiency of fire extinguishing. [ABSTRACT FROM AUTHOR]

Proprioception is the sense that lets us perceive the location, movement and action of the body parts. The proprioceptive apparatus includes specialized sense organs (proprioceptors) which are embedded in the skeletal muscles. The eyeballs are moved by six pairs of eye muscles and binocular vision depends on fine‐tuned coordination of the optical axes of both eyes. Although experimental studies indicate that the brain has access to eye position information, both classical proprioceptors (muscle spindles and Golgi tendon organ) are absent in the extraocular muscles of most mammalian species. This paradox of monitoring extraocular muscle activity in the absence of typical proprioceptors seemed to be resolved when a particular nerve specialization (the palisade ending) was detected in the extraocular muscles of mammals. In fact, for decades there was consensus that palisade endings were sensory structures that provide eye position information. The sensory function was called into question when recent studies revealed the molecular phenotype and the origin of palisade endings. Today we are faced with the fact that palisade endings exhibit sensory as well as motor features. This review aims to evaluate the literature on extraocular muscle proprioceptors and palisade endings and to reconsider current knowledge of their structure and function. What is the topic of this review?This review aims to evaluate the literature on proprioceptors and particular nerve specializations (palisade endings) in mammalian extraocular muscles (EOMs) and to reconsider current knowledge of their structure and function.What advances does it highlight?Classical proprioceptors (muscle spindles and Golgi tendon organs) are absent in the EOMs of most mammals. Instead, palisade endings are present in most mammalian EOMs. For many years, palisade endings were considered to be sensory but recent studies show that they combine sensory and motor features. The functional significance of palisade endings is still debated. [ABSTRACT FROM AUTHOR]

Purpose: To describe the clinical features and management of latent convergent strabismus among Sudanese patients. Study Design: Descriptive observational study. Place and Duration of Study: Makkah Eye Complex, Khartoum, Sudan, from December 2018 to August 2020. Methods: The study included 137 patients, diagnosed with latent convergent strabismus (esophoria). Demographic data and ocular examination findings including visual acuity, refractive error, and degree of esophoria was collected. Esophoria was assessed by prism cover test (PCT) at near and distance. Results: Esophoria was more common among females (n=107, 78.1%) and in age group 15 to 25 years. Headache was the most common complaint (n=64, 46.7%). Most of the patients (n=114, 83.2%) had 1 to 5 Δ of esophoria with a mean of 4.17±2.99Δ. Majority of the patients (n=132, 96.3%) had normal near point of convergence (NPC) 6.98±3.10 cm. Positive fusional vergence (PFV) was high at 24.06±9.01Δ, whereas negative fusional vergence (NFV) was low at 5.0±0.21ΔD, P=0.001. Majority of the patients (76.6%) responded well to the refractive correction, 11.7% patients to vision therapy and remaining 11.7% to both refractive correction and vision therapy. Correlation coefficients revealed that there was a positive correlation between, refraction, NPC and PFV (P > 0.05). Conclusion: Esophoria was more common among females and young adults. Most of the esophoric patients had uncorrected hyperopia, good PFV and relatively poor NFV amplitude. Early detection and management of young esophoric patients is essential to improve vision and binocular functions. [ABSTRACT FROM AUTHOR]

Head‐mounted displays (HMDs) for virtual and augmented reality applications could have severe nonuniformities due to complex optical components and the choice of light source technologies. In addition, module variations between the two eyes can result in different colors being seen with the left and right eyes. Binocular rivalry is expected for large color differences between the two eyes, which can severely affect product usability. Thus, it is important to characterize and predict the binocular rivalry in HMDs caused by interocular color differences. As a step toward this end, we propose the first binocular color difference metric, ΔE*bino, to predict the just noticeable binocular rivalry for a pair of solid colors. A psychophysical experiment was conducted using Meta Quest 2, in which we presented different colors to the left and right eye. The collected data were analyzed to derive the metric using ROC curves. We show that the ΔE*bino metric showed significant improvements over conventional color difference formulae. Both the ΔE*bino formula and the anonymized experiment results are available for download. [ABSTRACT FROM AUTHOR]

BACKGROUND: Amblyopia is a neurological deficit in binocular vision that affects 3% of the population and is the result of disruptions in early visual development. OBJECTIVE: In this study, we used a visual perceptual learning system for the short-term treatment of children with ametropic amblyopia and evaluated the clinical efficacy of this system in terms of visual plasticity. METHODS: We conducted a retrospective analysis of the clinical data of 114 children (228 eyes) with refractive amblyopia, who were aged 6.51 ± 1.51 years. Prior to the treatment, we evaluated all children with amblyopia using the visual information processing test. We determined the type of amblyopic defect according to the type of amblyopia, corrected visual acuity, and advanced visual function test results. Based on the type of defect, each child with amblyopia was given short-term visual perception training for 10 days. Finally, we compared the results of visual acuity and visual information processing tests before and after the treatment. RESULTS: The best-corrected visual acuity of patients was better after 10 days of visual training than that before training (P < 0.05). The perceptual eye position after training improved with statistically significant differences in horizontal and vertical perceptual eye position (both P < 0.05) compared to that before training. The number of amblyopic children without suppression in both eyes was 81 cases (71.1%) after training which was higher than that (65 cases, or 57.0%) before training, with a statistically significant difference (P < 0.05). Binocular fine stereopsis and dynamic stereopsis improved after training with a statistically significant difference (both P < 0.05). CONCLUSION: In this study, it was found that patients with amblyopia showed visual plasticity. Moreover, continuous visual perceptual learning improved the best-corrected visual acuity and recovered stereopsis in children with refractive amblyopia. [ABSTRACT FROM AUTHOR]

A space occupant's real-time exposure to light reaching his/her eyes has been a concern in lighting research and design practice. This study assumed a schematic eye to estimate the luminous flux received by the entrance pupil from the visible luminous environment. As the first part of this study, this paper focused on the fundamentals of luminous flux calculation that considers real-time human eye reactions. The research work includes: (a) developing a dynamic binocular/monocular visual field model that takes the eye movement into account, based on a conventional visual field model in orthomode, (b) retrieving the apparent area of the entrance pupil of the schematic eye, and (c) quantifying the light passing through the entrance pupil from the luminous environment fallen in the binocular/monocular visual field. The fundamental theory was tested in a theoretical uniform lighting scene to calculate the luminous flux emitted from the human visual field along multiple gaze directions at different light levels. A foundation was thus laid to estimate the real-time luminous flux passing through the entrance pupil with the aid of the camera as disclosed in Part 2 of the present study. [ABSTRACT FROM AUTHOR]

A space occupant's real-time exposure to light reaching his/her eyes has been a concern in lighting research and design practice. Following the parallel fundamental research in Part 1, Part 2 of this study explored a way to use a Canon 550D fitted with the Sigma 4.5 mm F2.8 circular fisheye lens to estimate the luminous flux arriving at the entrance pupil from the visible luminous environment. By taking two 180° HDR images at the same camera position but in different aiming directions and merging them into a single luminance map covering the observer's entire binocular visual field (214° horizontally and 125° vertically), the monocular and binocular luminance map can be generated with the aid of the dynamic visual field model proposed in Part 1. In a field verification experiment, the luminous flux passing through the entrance pupil in the preset scenes and gaze angles was estimated. Individual luminous contribution from an object or a region of interest within the occupant's dynamic binocular visual field or subfields was also calculated. This study expanded the capacity of HDR photography in lighting measurement to capture binocular luminous flux entering human eyes as input for optimizing human-light interaction in various luminous environments. [ABSTRACT FROM AUTHOR]

The purpose of the study is to develop an approach to planning the trajectory of a robot manipulator using an intelligent system based on neural networks. For this purpose, the work considered the processes of planning and deploying the movement of the robot. The analysis of existing methods of planning the movement of robot manipulators and the review of intelligent control systems provided a comprehensive picture of the current state of this issue. A system is proposed that can perceive the environment and controls the movement of the robot by generating correct control commands. For this purpose, 3 tasks were solved, namely, the analysis of the environment in order to determine its features, the determination of the trajectory in order to neutralize the collision, and the determination of controlled influences for the executive bodies in order to implement the movement. The functionality and structure of the neural network for solving each of the tasks is proposed. The proposed approach is compared with existing approaches on key parameters, such as the execution time of the planned movement and the time of calculating the movement trajectory. The results confirmed that the use of neural network to optimize the trajectory and dynamic prediction to avoid obstacles significantly increased the adaptability of the system to the changing conditions of the production environment, which opens up new opportunities for improving automated processes and providing optimal conditions for the functioning of manipulator robots in real-time. [ABSTRACT FROM AUTHOR]

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Head‐mounted displays (HMDs) for virtual and augmented reality applications could have severe nonuniformities due to complex optical components and the choice of light source technologies. In addition, module variations between the two eyes can result in different colors being seen with the left and right eyes. Binocular rivalry is expected for large color differences between the two eyes, which can severely affect product usability. Thus, it is important to characterize and predict the binocular rivalry in HMDs caused by interocular color differences. As a step toward this end, we propose the first binocular color difference metric, ΔE*bino, to predict the just noticeable binocular rivalry for a pair of solid colors. A psychophysical experiment was conducted using Meta Quest 2, in which we presented different colors to the left and right eye. The collected data were analyzed to derive the metric using ROC curves. We show that the ΔE*bino metric showed significant improvements over conventional color difference formulae. Both the ΔE*bino formula and the anonymized experiment results are available for download. [ABSTRACT FROM AUTHOR]

The three-dimensional reconstruction of forest ecological environment by low-altitude remote sensing photography from Unmanned Aerial Vehicles (UAVs) provides a powerful basis for the fine surveying of forest resources and forest management. A stereo vision system, D-SLAM, is proposed to realize simultaneous localization and dense mapping for UAVs in complex forest ecological environments. The system takes binocular images as input and 3D dense maps as target outputs, while the 3D sparse maps and the camera poses can be obtained. The tracking thread utilizes temporal clue to match sparse map points for zero-drift localization. The relative motion amount and data association between frames are used as constraints for new keyframes selection, and the binocular image spatial clue compensation strategy is proposed to increase the robustness of the algorithm tracking. The dense mapping thread uses Linear Attention Network (LANet) to predict reliable disparity maps in ill-posed regions, which are transformed to depth maps for constructing dense point cloud maps. Evaluations of three datasets, EuRoC, KITTI and Forest, show that the proposed system can run at 30 ordinary frames and 3 keyframes per second with Forest, with a high localization accuracy of several centimeters for Root Mean Squared Absolute Trajectory Error (RMS ATE) on EuRoC and a Relative Root Mean Squared Error (RMSE) with two average values of 0.64 and 0.2 for trel and Rrel with KITTI, outperforming most mainstream models in terms of tracking accuracy and robustness. Moreover, the advantage of dense mapping compensates for the shortcomings of sparse mapping in most Smultaneous Localization and Mapping (SLAM) systems and the proposed system meets the requirements of real-time localization and dense mapping in the complex ecological environment of forests. [ABSTRACT FROM AUTHOR]

Background: Posterior reversible encephalopathy syndrome (PRES), a neurological disorder with an unknown aetiology, is characterised by visual impairment, headache, vomiting, seizures, and transient alterations in consciousness. Case report: We present the case of a 49-year-old woman with advanced cervical carcinoma who received second-line therapy with oral anlotinib (12 mg, days 1-14, every 21 days) and injectable tislelizumab (200 mg, day 1, every 21 days). After 7 days of anlotinib administration, she began experiencing symptoms suggestive of PRES and was diagnosed on day 11. Interruption of anlotinib and supportive treatment led to recovery of her binocular vision. The Naranjo score (+5) graded the causality of this reaction as probable, suggesting the possibility that the event may have been an adverse reaction to anlotinib. Ethics: This case report was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine (Reference no. K-2023-068, 2023/06/09). Informed consent was obtained from the patient and her family. [ABSTRACT FROM AUTHOR]

With the rapid development of vision sensing, artificial intelligence, and robotics technology, one of the challenges we face is installing more advanced vision sensors on welding robots to achieve intelligent welding manufacturing and obtain high-quality welding components. Depth perception is one of the bottlenecks in the development of welding sensors. This review provides an assessment of active and passive sensing methods for depth perception and classifies and elaborates on the depth perception mechanisms based on monocular vision, binocular vision, and multi-view vision. It explores the principles and means of using deep learning for depth perception in robotic welding processes. Further, the application of welding robot visual perception in different industrial scenarios is summarized. Finally, the problems and countermeasures of welding robot visual perception technology are analyzed, and developments for the future are proposed. This review has analyzed a total of 2662 articles and cited 152 as references. The potential future research topics are suggested to include deep learning for object detection and recognition, transfer deep learning for welding robot adaptation, developing multi-modal sensor fusion, integrating models and hardware, and performing a comprehensive requirement analysis and system evaluation in collaboration with welding experts to design a multi-modal sensor fusion architecture. [ABSTRACT FROM AUTHOR]

Radar data can be presented in various forms, unlike visible data. In the field of radar target recognition, most current work involves point cloud data due to computing limitations, but this form of data lacks useful information. This paper proposes a semantic segmentation network to process high-dimensional data and enable automatic radar target recognition. Rather than relying on point cloud data, which is common in current radar automatic target recognition algorithms, the paper suggests using a radar heat map of high-dimensional data to increase the efficiency of radar data use. The radar heat map provides more complete information than point cloud data, leading to more accurate classification results. Additionally, this paper proposes a dimension collapse module based on a vision transformer for feature extraction between two modules with dimension differences during dimension changes in high-dimensional data. This module is easily extendable to other networks with high-dimensional data collapse requirements. The network's performance is verified using a real radar dataset, showing that the radar semantic segmentation network based on a vision transformer has better performance and fewer parameters compared to segmentation networks that use other dimensional collapse methods. [ABSTRACT FROM AUTHOR]

Unlike ballistic arm movements such as reaching, the contribution of depth information to the performance of manual tracking movements is unclear. Thus, to understand how the brain handles information, we investigated how a required movement along the depth axis would affect behavioral tracking performance, postulating that it would be affected by the amount of depth movement. We designed a visually guided planar tracking task that requires movement on three planes with different depths: a fronto-parallel plane called ROT (0), a sagittal plane called ROT (90), and a plane rotated by 45° with respect to the sagittal plane called ROT (45). Fifteen participants performed a circular manual tracking task under binocular and monocular visions in a three-dimensional (3D) virtual reality space. As a result, under binocular vision, ROT (90), which required the largest depth movement among the tasks, showed the greatest error in 3D. Similarly, the errors (deviation from the target path) on the depth axis revealed significant differences among the tasks. Under monocular vision, significant differences in errors were observed only on the lateral axis. Moreover, we observed that the errors in the lateral and depth axes were proportional to the required movement on these axes under binocular vision and confirmed that the required depth movement under binocular vision determined depth error independent of the other axes. This finding implies that the brain may independently process binocular vision information on each axis. Meanwhile, the required depth movement under monocular vision was independent of performance along the depth axis, indicating an intractable behavior. Our findings highlight the importance of handling depth movement, especially when a virtual reality situation, involving tracking tasks, is generated. [ABSTRACT FROM AUTHOR]

The adaptation to an aquatic lifestyle has occurred several times during the evolution of birds. The transition from a terrestrial to an aquatic lifestyle requires enormous changes in morphology, physiology, and behaviour. In addition to many physical parameters, aquatic foraging is also a limiting factor, despite the fact that aquatic habitats are often rich in food and prey. Despite many previous studies (foraging, physiology, anatomy, ecology, etc.) and a large amount of data regarding piscivore foot-propelled diving birds, our knowledge on the possible relationships between cranial morphology, feeding mechanism, visual abilities and binocularity is still very limited. In this study, we attempt to achieve a deeper understanding of the visual abilities and foraging related attributes of 5 recent and 1 extinct species of foot-propelled diving birds. We attempted to measure the horizontal visual fields of these species using 3D visualization techniques. According to our model, the narrowest horizontal binocular field was measured in Anhinga (Anhinga anhinga), and the widest was measured in the cases of Great Cormorant (Phalacrocorax carbo) and Goosander (Mergus merganser). Our results support the prediction that binocular field variation among aquatic birds is primarily associated with foraging methods and activities. [ABSTRACT FROM AUTHOR]

Purpose: To compare the motor and sensory outcomes of strabismus surgery and the factors affecting surgical success in acquired acute non-accomodative esotropia (ANAET) and partially accommodative refractive esotropia (pARET). Methods: A retrospective chart review of patients with ANAET and pARET who underwent unilateral or bilateral horizontal rectus muscle surgery between January 2020 and December 2021 was conducted. Patients with postoperative follow-up of at least six weeks were included. Patients with pattern deviation, lateral incomitance, and near-distance disparity were excluded. Motor success was defined as a postoperative deviation within eight prism diopters of orthophoria. Sensory success was defined as presence of binocular single vision (BSV) for both distance and near (Worth four dot test). The effect of factors like age at onset, age at surgery, amblyopia before surgery, duration of squint before treatment, presence or absence and magnitude of vertical deviation, preoperative angle of deviation, and spherical equivalent on the motor and sensory success in each group were analyzed and compared. Results: 38 patients with ANAET and 33 patients with pARET were included. The mean age of onset of esotropia was 8.55 ± 4.65 years and 4.39 ± 2.27 years (p <.001) and the mean age at surgery was 10.62 ± 4.99 years and 7.89 ± 2.84 years (p =.006) in the ANAET and the pARET group respectively. The mean duration of the final follow-up was 38.51 weeks in the ANAET and 48.68 weeks in the pARET group (p =.089). Patients were successfully aligned for both distance and near in 81.5% of patients in the ANAET and 78.9% of patients in the pARET group at the final follow-up (p. 0.775). A BSV for both distance and near at the final follow-up was seen in 81.2% vs 66.6% of patients in the ANAET and the pARET group respectively (p =.25). A good near stereoacuity (<120 arcsecs) was seen in 60.6% and 41.9% of the ANAET and the pARET groups respectively (p =.175). The percentage of patients in the ANAET group who had orthophoria, any esodeviation, and any exodeviation for distance at the final follow-up was 63.1%, 34.2% and, 2.6%. The percentage of patients in the pARET group in similar categories was 36.3%, 42.4% and, 21.2%. None of the demographic and preoperative factors were found to affect the surgical outcomes in the two groups. Conclusions: The motor and sensory outcomes were similar in the two groups. A higher proportion of ANAET patients remained orthophoric during the follow-up. The patients in the pARET group showed a tendency toward exodrift. [ABSTRACT FROM AUTHOR]

The deformation detection of large machinery is usually achieved using threedimensional displacement measurement. Binocular stereo vision measurement technology, as a commonly used digital image correlation method, has received widespread attention in the academic community. Binocular stereo vision achieves the goal of three-dimensional displacement measurement by simulating the working mode of the human eyes, but the measurement is easily affected by light refraction. Based on this, the study introduces particle swarm optimization algorithm for target displacement measurement on Canon imaging dataset, and introduces backpropagation neural network for mutation processing of particles in particle swarm algorithm to generate fusion algorithm. It combines the four coordinate systems of world, pixel, physics, and camera to establish connections. Taking into account environmental factors and lens errors, the camera parameters and deformation coefficients were revised by shooting a black and white checkerboard. Finally, the study first conducted error analysis on binocular stereo vision technology in three dimensions, and the relative error remained stable at 1 % within about 60 seconds. At the same time, three algorithms, including the spotted hyena algorithm, were introduced to conduct performance comparison experiments using particle swarm optimization and backpropagation network algorithms. The experiment shows that the three-dimensional error of the fusion algorithm gradually stabilizes within the range of [-0.5 %, 0.5 %] over time, while the two-dimensional error generally hovers around 0 value. Its performance is significantly superior to other algorithms, so the binocular stereo vision of this fusion algorithm can achieve good measurement results. [ABSTRACT FROM AUTHOR]

With the ongoing digitization in archives, an increasing number of historical data becomes available for research. This includes historical aerial images which provide detailed information about the depicted area. Among the applications enabled by these images are change detection of land use, land cover, glaciers, and coastal environments as well as the observation of land degradation, and natural hazards. Studying the depicted areas and occurring 3D deformations requires the generation of a digital surface model (DSM) which is usually obtained via photogrammetric Structure-from-Motion (SfM). However, conventional SfM workflows often fail in registering historical aerial images due to their radiometric characteristics introduced by digitization, original image quality, or vast temporal changes between epochs. We demonstrate that the feature matching step in the Structure from Motion (SfM) pipeline is particularly crucial. To address this issue, we apply the two synergetic neural network methods SuperGlue and DISK, improving feature matching for historical aerial images. This requires several modifications to enable rotational invariance and leveraging the high resolution of aerial images. In contrast to other studies our workflow does not require any prior information such as DSMs, flight height, focal lengths, or scan resolution which are often no more extent in archives. It is shown that our methods using adapted parameter settings are even able to deal with quasi texture-less images. This enables the simultaneous processing of various kind of mono-temporal and multi-temporal data handled in a single workflow from data preparation over feature matching through to camera parameter estimation and the generation of a sparse point cloud. It outperforms conventional strategies in the number of correct feature matches, number of registered images and calculated 3D points and allows the generation of multi-temporal DSMs with high quality. With the flexibility of the method, it enables the automatic processing of formerly unusable or only to be interactively processed data, e.g. aerial images where the flight route is unknown, or with difficult radiometric properties. This makes it possible to go back even further in time, where the data quality usually decreases, and enables a holistic monitoring and comparison of environments of high interest. The code is made publicly available at https://github.com/tudipffmgt/HAI-SFM. [Display omitted] • Comprehensive feature matching of historical aerial images. • Adaptive parameter settings for synergetic AI-based image matching. • 3D reconstruction using historical aerial images with limited camera information. • 3D model generation from mono-temporal and multi-temporal data. [ABSTRACT FROM AUTHOR]

To confront the difficulties for moving vehicles in measuring the actual heights at various highway height limit facilities, this paper proposes a binocular camera height limit target real-time monitoring algorithm. First, an analysis of the height limit scenarios and facilities is made and a large- scale multi-scenario highway height limit dataset is built. Second, the target region is obtained by the detection and tracking algorithm based on deep learning. The binocular disparity is processed by a multi-step and multi-scale filtering algorithm to obtain the high-confidence height limit facility disparity data and thus calculate the distance and height information. Finally, the robust output strategy is adopted and alarms will be activated based on the real-time collected distance and height results. After multi-scenario height limit experiments, the target detection accuracy stands at 98.77%, the missed detection rateat 0. 87%, the false detection rate at 0. 36%, and the average height measurement error within 60 m below 4%. The results show that our algorithm achieves automatic detection and computation of highway height limit targets, as well as real-time and robust delivery of distance and height measurements and early warnings. [ABSTRACT FROM AUTHOR]

The environmental perception of intelligent vehicles is an important part of achieving autonomous driving, and object detection is one of the crucial functions of the environmental perception systems. Accurate and rapid detection of targets nearby is essentail for the smart vehicles. The faster and more accurately the detection algorithm can detect surrounding targets, the better. However, the detection speed and accuracy of existing perception algorithms still need improving. The distance information between the surrounding objects and the vehicle itself during its operation should also be obtained. The widely used YOLO series of algorithms, as representatives of one-stage object detection methods, have achieved a satisfactory balance between speed and detection accuracy, but there is stiil great potential for further improvement. This paper proposes an end-to-end object detection algorithm based on YOLO algorithm improvement-dual YOLO, which detects and measures the major participants in road traffic ( cars, pedestrians, cyclists) . The attention mechanism acts in the same way as humans observe things, focusing on more important information. The basic idea of combining attention mechanism with convolutional neural networks is to allow the network to concentrate on effective feature information in the image and ignore invalid information, thus improving the overall detection precision. Its mechanism of action is achieved by changing the weights of neurons in the network. This paper is based on the improvement of YOLOV5 algorithm, adding a channel attention mechanism to the backbone network to increase the learning weight of the neural network for effective features, thereby enhancing detection accuracy. In the meantime, a distance measurement module for binocular cameras is introduced to obtain target distance information. The basic principle of binocular camera ranging is triangulation, which employs the visual difference between two cameras to determine the distance of an object. Firstly, the camera is calibrated and distortion is corrected, and then stereo matching using the Semi Global Block Matching (SGBM) algorithm is performed to obtain disparity. Finally, the distance information of the target is calculated. The test results show the dual YOLO object detection algorithm achieves a detection speed of 60 fps and an average accuracy of 85. 99% in identifying the major participants in road traffic, which are far more superior to other algorithms in detecting cyclists and pedestrians. The proposed algorithm well meets the real-time detection and distance measurement requirements of intelligent vehicles. [ABSTRACT FROM AUTHOR]

Purpose: A macular lesion preventing the foveal fixation could lead to the fixation from eccentric points in age-related macular degeneration (AMD). There is a lack of knowledge about the binocular function of these patients and the role of preferred retinal locus in binocularity. This study aims to examine binocular fusion and stereopsis in a unique group of patients who have unilateral choroidal neovascular membrane (CNVM) involving the fovea. Methods: Twenty-five patients with the diagnosis of the CNVM in one eye and type I or type II drusen in the other eye were examined. The Bagolini test was performed to determine binocular fusion. The Stereo Butterfly test was used for stereo acuity determination. CNVM measurements were done with optical coherence tomography. Results: In the Bagolini test, 12 patients saw two lines with break in one of the lines. Eleven patients saw two lines crossing at higher or lower than the center. Two patients saw only one line. One of 25 patients had gross stereopsis (2500 s of arc). The area of the CNVM was extending to the perifovea in 2 patients suppressing the other eye. In remaining 23 patients, CNVM was located in fovea or extended up to the parafovea. Conclusion: Binocular fusion is possible if the CNVM lesion size and location allow usage of the fovea-parafovea visual angle. Our study results support that the binocular function of patients with neovascular AMD depends on the corresponding retinal areas and the fusional limit of non-corresponding points. [ABSTRACT FROM AUTHOR]

(1) Background: In real road scenarios, various complex environmental conditions may occur, including bright lights, nighttime, rain, and snow. In such a complex environment for detecting pole-shaped obstacles, it is easy to lose the feature information. A high rate of leakage detection, false positives, and measurement errors are generated as a result. (2) Methods: The first part of this paper utilizes the improved YOLOv5 algorithm to detect and classify pole-shaped obstacles. Then, the identified target frame information is combined with binocular stereo matching to obtain more accurate distance information. (3) Results: The experimental results demonstrate that this method achieves a mean average precision (mAP) of 97.4% for detecting pole-shaped obstacles, which is 3.1% higher than the original model. The image inference time is only 1.6 ms, which is 1.8 ms faster than the original algorithm. Additionally, the model size is only 19.0 MB. Furthermore, the range error of this system is less than 7% within the range of 3–15 m. (4) Conclusions: Therefore, the algorithm not only achieves real-time and accurate identification and classification but also ensures precise measurement within a specific range. Meanwhile, the model is lightweight and better suited for deploying sensing systems. [ABSTRACT FROM AUTHOR]

In the realm of non-cooperative space security and on-orbit service, a significant challenge is accurately determining the pose of abandoned satellites using imaging sensors. Traditional methods for estimating the position of the target encounter problems with stray light interference in space, leading to inaccurate results. Conversely, deep learning techniques require a substantial amount of training data, which is especially difficult to obtain for on-orbit satellites. To address these issues, this paper introduces an innovative binocular pose estimation model based on a Self-supervised Transformer Network (STN) to achieve precise pose estimation for targets even under poor imaging conditions. The proposed method generated simulated training samples considering various imaging conditions. Then, by combining the concepts of convolutional neural networks (CNN) and SIFT features for each sample, the proposed method minimized the disruptive effects of stray light. Furthermore, the feedforward network in the Transformer employed in the proposed method was replaced with a global average pooling layer. This integration of CNN's bias capabilities compensates for the limitations of the Transformer in scenarios with limited data. Comparative analysis against existing pose estimation methods highlights the superior robustness of the proposed method against variations caused by noisy sample sets. The effectiveness of the algorithm is demonstrated through simulated data, enhancing the current landscape of binocular pose estimation technology for non-cooperative targets in space. [ABSTRACT FROM AUTHOR]

Aims: To evaluate the impact of smartphone gaming on the vergence system of the eye. Settings and Design: A 5-month (from March 2023 to August 2023) comparative and experimental research was conducted. Materials and Methods: Eighty-two participants with a mean age of 21.98 ± 2.26 years were present in the study. Prior to assessing accommodation and vergence system characteristics, participants underwent a comprehensive eye examination. The participants were asked to play a shooting game on a smartphone for 30 minutes at a 40 cm distance. Measurements of the vergence parameters were taken before and after the activity and afterwards were compared. Statistical Analysis: Non-parametric tests were used to compare pre- and post-task measurements. The Wilcoxon signed-rank test was used to compare the variables: Negative fusional vergence (NFV), Positive fusional vergence (PFV), Near point of convergence (NPC), and Vergence Facility (VF), with the alpha error set at 5%. Results: The mean age of the participants was 21.98 ± 2.26 years. Post-task, the vergence parameters: NPC (p < 0.001), NFV for near distance (p < 0.001), PFV for near distance (p < 0.001), and VF (p < 0.001) showed significant decrease in vergence parameters. Conclusions: The study shows smartphone gaming for 30 minutes affects the vergence system, leading to binocular vision anomalies in young individuals.

While the prevailing treatment for corneal abrasions outside the athletic sphere is the application of a bandage contact lens (BCL), which lessens pain and allows for the maintenance of binocular vision, this is not the case during athletic play. This brief report highlights the advantage of BCLs in treating in-game corneal abrasions, ultimately allowing for an immediate return to play. Additionally, this report summarizes the mechanisms of bandage contact lenses, differentiates them from standard hydrogel contact lenses, and highlights the significant steps necessary to apply the bandage contact lens during an in-game corneal abrasion event. Overall, we link modern ophthalmology clinical practice and sports medicine, allowing for the attenuation of acutely-induced ocular pain to a manageable state.

The mouse primary visual cortex is a model system for understanding the relationship between cortical structure, function, and behavior (Seabrook et al., 2017; Chaplin and Margrie, 2020; Hooks and Chen, 2020; Saleem, 2020; Flossmann and Rochefort, 2021). Binocular neurons in V1 are the cellular basis of binocular vision, which is required for predation (Scholl et al., 2013; Hoy et al., 2016; La Chioma et al., 2020; Berson, 2021; Johnson et al., 2021). The normal development of binocular responses, however, has not been systematically measured. Here, we measure tuning properties of neurons to either eye in awake mice of either sex from eye opening to the closure of the critical period. At eye opening, we find an adult-like fraction of neurons responding to the contralateral-eye stimulation, which are selective for orientation and spatial frequency; few neurons respond to ipsilateral eye, and their tuning is immature. Fraction of ipsilateral-eye responses increases rapidly in the first few days after eye opening and more slowly thereafter, reaching adult levels by critical period closure. Tuning of these responses improves with a similar time course. The development and tuning of binocular responses parallel that of ipsilateral-eye responses. Four days after eye opening, monocular neurons respond to a full range of orientations but become more biased to cardinal orientations. Binocular responses, by contrast, lose their cardinal bias with age. Together, these data provide an in-depth accounting of the development of monocular and binocular responses in the binocular region of mouse V1 using a consistent set of visual stimuli and measurements.SIGNIFICANCE STATEMENT In this manuscript, we present a full accounting of the emergence and refinement of monocular and binocular receptive field tuning properties of thousands of pyramidal neurons in mouse primary visual cortex. Our data reveal new features of monocular and binocular development that revise current models on the emergence of cortical binocularity. Given the recent interest in visually guided behaviors in mice that require binocular vision (e.g., predation), our measures will provide the basis for studies on the emergence of the neural circuitry guiding these behaviors.

ObjectiveThe width of maize stalks is an important indicator affecting the lodging resistance of maize. The measurement of maize stalk width has many problems, such as cumbersome manual collection process and large errors in the accuracy of automatic equipment collection and recognition, and it is of great application value to study a method for in-situ detection and high-precision identification of maize stalk width.MethodsThe ZED2i binocular camera was used and fixed in the field to obtain real-time pictures from the left and right sides of maize stalks together. The picture acquisition system was based on the NVIDIA Jetson TX2 NX development board, which could achieve timed shooting of both sides view of the maize by setting up the program. A total of maize original images were collected and a dataset was established. In order to observe more features in the target area from the image and provide assistance to improve model training generalization ability, the original images were processed by five processing methods: image saturation, brightness, contrast, sharpness and horizontal flipping, and the dataset was expanded to 3500 images. YOLOv8 was used as the original model for identifying maize stalks from a complex background. The coordinate attention (CA) attention mechanism can bring huge gains to downstream tasks on the basis of lightweight networks, so that the attention block can capture long-distance relationships in one direction while retaining spatial information in the other direction, so that the position information can be saved in the generated attention map to focus on the area of interest and help the network locate the target better and more accurately. By adding the CA module multiple times, the CA module was fused with the C2f module in the original Backbone, and the Bottleneck in the original C2f module was replaced by the CA module, and the C2fCA network module was redesigned. Replacing the loss function Efficient IoU Loss(EIoU) splits the loss term of the aspect ratio into the difference between the predicted width and height and the width and height of the minimum outer frame, which accelerated the convergence of the prediction box, improved the regression accuracy of the prediction box, and further improved the recognition accuracy of maize stalks. The binocular camera was then calibrated so that the left and right cameras were on the same three-dimensional plane. Then the three-dimensional reconstruction of maize stalks, and the matching of left and right cameras recognition frames was realized through the algorithm, first determine whether the detection number of recognition frames in the two images was equal, if not, re-enter the binocular image. If they were equal, continue to judge the coordinate information of the left and right images, the width and height of the bounding box, and determine whether the difference was less than the given Ta. If greater than the given Ta, the image was re-imported; If it was less than the given Ta, the confidence level of the recognition frame of the image was determined whether it was less than the given Tb. If greater than the given Tb, the image is re-imported; If it is less than the given Tb, it indicates that the recognition frame is the same maize identified in the left and right images. If the above conditions were met, the corresponding point matching in the binocular image was completed. After the three-dimensional reconstruction of the binocular image, the three-dimensional coordinates (Ax, Ay, Az) and (Bx, By, Bz) in the upper left and upper right corners of the recognition box under the world coordinate system were obtained, and the distance between the two points was the width of the maize stalk. Finally, a comparative analysis was conducted among the improved YOLOv8 model, the original YOLOv8 model, faster region convolutional neural networks (Faster R-CNN), and single shot multiBox detector (SSD)to verify the recognition accuracy and recognition accuracy of the model.Results and DiscussionsThe precision rate (P)、recall rate (R)、average accuracy mAP0.5、average accuracy mAP0.5:0.95 of the improved YOLOv8 model reached 96.8%、94.1%、96.6% and 77.0%. Compared with YOLOv7, increased by 1.3%、1.3%、1.0% and 11.6%, compared with YOLOv5, increased by 1.8%、2.1%、1.2% and 15.8%, compared with Faster R-CNN, increased by 31.1%、40.3%、46.2%、and 37.6%, and compared with SSD, increased by 20.6%、23.8%、20.9% and 20.1%, respectively. Respectively, and the linear regression coefficient of determination R2, root mean square error RMSE and mean absolute error MAE were 0.373, 0.265 cm and 0.244 cm, respectively. The method proposed in the research can meet the requirements of actual production for the measurement accuracy of maize stalk width.ConclusionsIn this study, the in-situ recognition method of maize stalk width based on the improved YOLOv8 model can realize the accurate in-situ identification of maize stalks, which solves the problems of time-consuming and laborious manual measurement and poor machine vision recognition accuracy, and provides a theoretical basis for practical production applications.

In underwater building-surface defect detection, high pressure, cold and other underwater factors will cause harm to frogmen, sonar and other detection techniques contain limited information and accuracy is difficult to meet the demand. In view of the above disadvantages, a sine stripe-assisted visual detection method of underwater building surface-defects is proposed. By generating a fixed sine stripe by program and projected it on the surface of the underwater building, and then control a binocular CCD to acquire six frames of sinusoidal stripes with different phases in one sampling period. Experiment results show that the error of our method does not exceed 0.5 mm and the reconstruction efficiency is not less than 5 m2/s in the 3D dimensional measurement of the defects on the surface of the submerged building body.

The role of postnatal experience in sculpting cortical circuitry, while long appreciated, is poorly understood at the level of cell types. We explore this in the mouse primary visual cortex (V1) using single-nucleus RNA sequencing, visual deprivation, genetics, and functional imaging. We find that vision selectively drives the specification of glutamatergic cell types in upper layers (L) (L2/3/4), while deeper-layer glutamatergic, GABAergic, and non-neuronal cell types are established prior to eye opening. L2/3 cell types form an experience-dependent spatial continuum defined by the graded expression of ∼200 genes, including regulators of cell adhesion and synapse formation. One of these genes, Igsf9b, a vision-dependent gene encoding an inhibitory synaptic cell adhesion molecule, is required for the normal development of binocular responses in L2/3. In summary, vision preferentially regulates the development of upper-layer glutamatergic cell types through the regulation of cell-type-specific gene expression programs.

This article discusses Streff syndrome, a condition characterized by functional vision loss in children. The syndrome is often misperceived as a visual impairment but is actually a manifestation of psychological stress. The article emphasizes the need for a holistic approach to diagnosis and treatment, considering social, environmental, and psychological factors. The case report presented in the article highlights the importance of interdisciplinary collaboration between healthcare professionals and educators in supporting children with Streff syndrome. Alternative therapies, such as psychotherapy and photonic stimulation, may also be beneficial in managing the condition. [Extracted from the article]