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Pedestrian detection holds significant importance in advanced driver assistance systems (ADAS) applications, and presents a challenging task in this field. While the advent of deep learning has facilitated the introduction of various pedestrian detectors characterized by accuracy and low inference speed, there persists a need for further improvements. Notably, ADAS requires accurate detection of pedestrians in various environmental conditions that can adversely impact the model's performance, such as poor lighting, and bad weather. Furthermore, an imperative requirement involves the incorporation of distance estimation in conjunction with pedestrian detection, with an extension of detection capabilities to encompass cyclists and riders, who are equally crucial for ensuring road safety. Therefore, this paper introduces a stereovision system designed for the detection of pedestrians, cyclists, and riders. The initial phase, involves improving the performance of you only look once version 5 (YOLOv5s) through a finetuning process with a custom dataset integrating augmentation techniques to common objects in context (COCO) dataset. The detector is trained using Google Colab, and tested in real-time with a Raspberry Pi 4 model B, 8 G RAM. A comparative analysis is conducted between the YOLOv5s and the fine-tuned model to prove the accuracy of our approach. The results showcase a high performance of the detector reaching an accuracy exceeding 79%. [ABSTRACT FROM AUTHOR]

BackgroundAmblyopia is the most common developmental vision disorder in children. The initial treatment consists of refractive correction. When insufficient, occlusion therapy may further improve visual acuity. However, the challenges and compliance issues associated with occlusion therapy may result in treatment failure and residual amblyopia. Virtual reality (VR) games developed to improve visual function have shown positive preliminary results. The aim of this study is to determine the efficacy of these games to improve vision, attention, and motor skills in patients with residual amblyopia and identify brain-related changes. We hypothesize that a VR-based training with the suggested ingredients (3D cues and rich feedback), combined with increasing the difficulty level and the use of various games in a home-based environment is crucial for treatment efficacy of vision recovery, and may be particularly effective in children.MethodsThe AMBER study is a randomized, cross-over, controlled trial designed to assess the effect of binocular stimulation (VR-based stereoptic serious games) in individuals with residual amblyopia (n = 30, 6-35 years of age), compared to refractive correction on vision, selective attention and motor control skills. Additionally, they will be compared to a control group of age-matched healthy individuals (n = 30) to account for the unique benefit of VR-based serious games. All participants will play serious games 30 min per day, 5 days per week, for 8 weeks. The games are delivered with the Vivid Vision Home software. The amblyopic cohort will receive both treatments in a randomized order according to the type of amblyopia, while the control group will only receive the VR-based stereoscopic serious games. The primary outcome is visual acuity in the amblyopic eye. Secondary outcomes include stereoacuity, functional vision, cortical visual responses, selective attention, and motor control. The outcomes will be measured before and after each treatment with 8-week follow-up.DiscussionThe VR-based games used in this study have been conceived to deliver binocular visual stimulation tailored to the individual visual needs of the patient, which will potentially result in improved basic and functional vision skills as well as visual attention and motor control skills.Trial registrationThis protocol is registered on ClinicalTrials.gov (identifier: NCT05114252) and in the Swiss National Clinical Trials Portal (identifier: SNCTP000005024).

The Rashid-1 rover, being the main component of the Emirates Lunar Mission (ELM) developed by the Mohammed Bin Rashid Space Center (MBRSC), was launched in late 2022 on-board the Hakuto lunar lander. The scientific objectives of the rover were to investigate the regolith formations of our natural satellite and analyze the geology of its landing area. To perform its investigations, Rashid-1 carried two wide-angle cameras (CAM-1 and CAM-2) and a narrow-angle microscope camera (CAM-M) equipped with Bayer color filter arrays. The mission was however unsuccessful due to a lander failure during lunar descent which caused the loss of the payload. Despite this drawback, significant insights into exploration rover imaging have been gained through the extensive work done prior to launch to achieve the highest image quality from the on-board cameras, and the resulting CASPIP software package will be used for upcoming rover missions. This paper presents the CAM-1 and CAM-2 instruments and the planned advanced image products. In the first part, we provide a detailed presentation of the calibration of the instruments including their optical parameters, characterization of the detector in term of flat-field, optical ghosts, dark current, spectral sensitivities and distortion. The second part of the paper deals with colorimetry, focusing on the colorimetric characterization of the instrument as well as a description of the correction applied to obtain images expressed in the standard color space CIE XYZ and displayed properly in sRGB. Finally, the third part describes advanced image products developed to assist experts (geologists, navigation engineers). It includes a tool for distance visualization directly inside an image, and the creation of 3D images through stereoscopic reconstruction. [ABSTRACT FROM AUTHOR]

The correct workability of concrete is an essential parameter for its placement and compaction. However, an absence of automatic and transparent measurement methods to estimate the workability of concrete hinders the adaptation from laborious traditional methods such as the slump test. In this paper, we developed a machine-learning framework for estimating the slump class of concrete in the mixer using a stereovision camera. Depth data from five different slump classes was transformed into Haralick texture features to train several machine-learning classifiers. The best-performing classifier achieved a multiclass classification accuracy of 0.8179 with the XGBoost algorithm. Furthermore, we found through statistical analysis that while the denoising of depth data has little effect on the accuracy, the feature extraction of mixer blades and the choice of region of interest significantly increase the accuracy and the efficiency of the classifiers. The proposed framework shows robust results, indicating that stereovision is a competitive solution to estimate the workability of concrete during concrete production. [ABSTRACT FROM AUTHOR]

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

Obstacle detection algorithm of a wide baseline stereovision system (WBSS) is proposed using deep learning for unmanned surface vehicle (USV). The WBSS is designed and constructed for a 7 m-class USV, which contains two fixed-focus cameras with a baseline length for 2 m. The obstacle detection algorithm contains three parts: firstly the sea-sky line is acquired by Hough transformation in an image, then the single shot multi-box detector (SSD) is adopted to detect the obstacles around the sea-sky line, and lastly F-KAZE algorithm is used for binocular image's feature matching in a nonlinear scale space. The performance of the obstacle detection for the WBSS is tested at Jiaozhou bay in Qingdao city, and the sea experiment results prove availability and accuracy of the proposed technology. [ABSTRACT FROM AUTHOR]

Abstract Background The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking temporal and three-dimensional (3D) spatial information. This paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of

Pedestrian detection is a vital aspect of Advanced Driver Assistance Systems (ADAS), crucial for ensuring driving safety and minimizing collision risks. While detecting pedestrians is important, it must be paired with precise distance estimation to create a robust safety solution. Stereovision cameras are well-regarded for their effectiveness and affordability in measuring depth through disparity between two images. Despite this, research on pedestrian distance estimation using only stereovision remains sparse, with many studies relying on computationally heavy dense depth maps. This paper proposes an innovative method for computing object-level disparity specifically for pedestrian detection using stereo cameras. The approach integrates Canny edge detection with ORB (Oriented FAST and Rotated BRIEF) feature matching to efficiently identify and track keypoints within pedestrian bounding boxes. This method not only improves the accuracy of distance estimation but also reduces computational demands, making it suitable for real-time applications. The approach was thoroughly tested on a Raspberry Pi 4, a resource-constrained device, and achieved promising results, demonstrating its potential for practical use in ADAS.

Masking experiments, using vertical and horizontal sinusoidal depth corrugations, have suggested the existence of more than two spatial‐frequency disparity mechanisms. This result was confirmed through an individual differences approach. Here, using factor analytic techniques, we want to investigate the existence of independent temporal mechanisms in frontoparallel stereoscopic (cyclopean) motion. To construct stereomotion, we used sinusoidal depth corrugations obtained with dynamic random‐dot stereograms. Thus, no luminance motion was present monocularly. We measured disparity thresholds for drifting vertical (up‐down) and horizontal (left‐right) sinusoidal corrugations of 0.4 cyc/deg at 0.25, 0.5, 1, 2, 4, 6, and 8 Hz. In total, we tested 34 participants. Results showed a small orientation anisotropy with lower thresholds for horizontal corrugations. Disparity thresholds as a function of temporal frequency were almost constant from 0.25 up to 1 Hz, and then they increased monotonically. Principal component analysis uncovered two significant factors for vertical and two for horizontal corrugations. Varimax rotation showed that one factor loaded from 0.25 to 1–2 Hz and a second factor from 2 to 4 to 8 Hz. Direct Oblimin rotation indicated a moderate intercorrelation of both factors. Our results suggest the possible existence of two somewhat interdependent temporal mechanisms involved in frontoparallel stereomotion. [ABSTRACT FROM AUTHOR]

The surge of haptic technology has greatly impacted Robotic-assisted surgery in recent years due to its inspirational advancement in the field. Delivering tactile feedback to the surgeon has a significant role in improving the user experience in RAMIS. This work proposes a Modified inception ResNet network along with dimensionality reduction to regenerate the variable force produced during the surgical intervention. This work collects the relevant dataset from two ex vivo porcine skins and one ex vivo artificial skin for the validation of the results. The proposed framework is used to model both spatial and temporal data collected from the sensors, tissue, manipulators, and surgical tools. The evaluations are based on three distinct datasets with modest variations in tissue properties. The results of the proposed framework show an improvement of force prediction accuracy by 10.81% over RNN, 6.02% over RNN + LSTM, and 3.81% over the CNN + LSTM framework, and torque prediction accuracy by 12.41% over RNN, 5.75% over RNN + LSTM, and 3.75% over CNN + LSTM. The sensitivity study demonstrates that features such as torque (96.93%), deformation (94.02%), position (93.98%), vision (92.12%), stiffness (87.95%), tool diameter (89.24%), rotation (65.10%), and orientation (62.51%) have respective influences on the anticipated force. It was observed that the quality of the predicted force improved by 2.18% when performing feature selection and dimensionality reduction on features collected from tool, manipulator, tissue, and vision data and processing them simultaneously in all four architectures. The method has potential applications for online surgical tasks and surgeon training. [ABSTRACT FROM AUTHOR]

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The contact wire wear is an important parameter to ensure the safety operation of electric railways. The contact wire may break if the wear is serious, which leads to transportation interruptions. This study proposes an optical measurement method of contact wire wear, using stereovision technology. The matching method of stereovision based on line-scan cameras is proposed. A lookup-table method is developed to exactly determine the image resolution caused by the contact wire being in different spatial positions. The wear width of the contact wire is extracted from catenaries' images, and the residual thickness of the contact wire is calculated. The method was verified by field tests. The round-robin tests of the residual thickness at the same location present excellent measurement repetitiveness. The maximum difference value between dynamic test results and ground measurement results is 0.13 mm. This research represents a potential way to implement condition-based maintenance for contact wire wear in the future in order to improve the maintenance efficiency and ensure the safety of catenary infrastructure. [ABSTRACT FROM AUTHOR]

Nowadays, research focusing on robotics, autonomous vehicles, and scene analysis shows a clear need for the ability to accurately reconstruct three-dimensional environments. One of the methods allowing to conduct such a reconstruction is to use a set of cameras and image processing techniques. This is a passive method. Despite being, in general, less accurate than its active counterparts, it offers significant advantages in numerous applications in which active systems cannot be deployed due to limited performance. This paper provides a theoretical analysis of the accuracy of estimating 3D positions of objects present at a given scene, based on images from a set of cameras. The analysis assumes a known geometrical configuration of the camera system. The important limiting factor in the considered scenario is the physical resolution of sensors-especially in the case of systems that are supposed to work in real time, with a high FPS rate, as the use of high-resolution cameras is difficult in such circumstances. In the paper, the influence of the geometric arrangement of the cameras is studied and important conclusions about the potential of three-camera configurations are drawn. The analysis performed and the formulas derived help predict the boundary accuracy values of any system using a digital camera. The results of an experiment that confirm the theoretical conclusions are presented as well. [ABSTRACT FROM AUTHOR]

Background: The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking temporal and three-dimensional (3D) spatial information. This paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results: The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of < 1 mm between computed and manually measured root length. Conclusions: The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analysing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity. [ABSTRACT FROM AUTHOR]

Nowadays, research focusing on robotics, autonomous vehicles, and scene analysis shows a clear need for the ability to accurately reconstruct three-dimensional environments. One of the methods allowing to conduct such a reconstruction is to use a set of cameras and image processing techniques. This is a passive method. Despite being, in general, less accurate than its active counterparts, it offers significant advantages in numerous applications in which active systems cannot be deployed due to limited performance. This paper provides a theoretical analysis of the accuracy of estimating 3D positions of objects present at a given scene, based on images from a set of cameras. The analysis assumes a known geometrical configuration of the camera system. The important limiting factor in the considered scenario is the physical resolution of sensors - especially in the case of systems that are supposed to work in real time, with a high FPS rate, as the use of high-resolution cameras is difficult in such circumstances. In the paper, the influence of the geometric arrangement of the cameras is studied and important conclusions about the potential of three-camera configurations are drawn. The analysis performed and the formulas derived help predict the boundary accuracy values of any system using a digital camera. The results of an experiment that confirm the theoretical conclusions are presented as well.

Accurately estimating the pose of spacecraft is indispensable for space applications. However, such targets are generally non-cooperative, i.e., no markers are mounted on them, and they include no parts for operation. Therefore, the detection and measurement of a non-cooperative target is very challenging. Stereovision sensors are important solutions in the near field. In this paper, a rectangular natural feature recognition and pose measurement method for non-cooperative spacecraft is proposed. Solar panels of spacecraft were selected as detection objects, and their image features were captured via stereo vision. These rectangle features were then reconstructed in 3D Cartesian space through parallelogram fitting on the image planes of two cameras. The vertexes of rectangle features were detected and used to solve the pose of a non-cooperative target. An experimental system was built to validate the effectiveness of the algorithm. The experimental results show that the average position measurement error of the algorithm is about 10 mm and the average attitude measurement error is less than 1°. The results also show that the proposed method achieves high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Stereovision is the ability to perceive fine depth variations from small differences in the two eyes' images. Using adaptive optics, we show that even minute optical aberrations that are not clinically correctable, and go unnoticed in everyday vision, can affect stereo acuity. Hence, the human binocular system is capable of using fine details that are not experienced in everyday vision. Interestingly, stereo acuity varied considerably across individuals even when they were provided identical perfect optics. We also found that individuals' stereo acuity is better when viewing with their habitual optics rather than someone else's (better) optics. Together, these findings suggest that the visual system compensates for habitual optical aberrations through neural adaptation and thereby optimizes stereovision uniquely for each individual. Thus, stereovision is limited by small optical aberrations and by neural adaptation to one's own optics.

Visual signals are the upmost important source for robots, vehicles or machines to achieve human-like intelligence. Human beings heavily depend on binocular vision to understand the dynamically changing world. Similarly, intelligent robots or machines must also have the innate capabilities of perceiving knowledge from visual signals. Until today, one of the biggest challenges faced by intelligent robots or machines is the matching in stereovision. In this paper, we present the details of a new principle toward achieving a robust matching solution which leverages on the use and integration of top-down image sampling strategy, hybrid feature extraction, and Restricted Coulomb Energy (RCE) neural network for incremental learning (i.e., cognition) as well as robust match-maker (i.e., recognition). A preliminary version of the proposed solution has been implemented and tested with data from Maritime RobotX Challenge. The contribution of this paper is to attract more research interest and effort toward this new direction which may eventually lead to the development of robust solutions expected by future stereovision systems in intelligent robots, vehicles, and machines. [ABSTRACT FROM AUTHOR]

The detection of small objects has always been one of the key challenges in vehicle detection. In this work, a standard for dividing the object more accurately than traditional methods is presented. Based on the division standard of disparity segmentation, we propose a novel multi-scale detection network aiming to reduce the transmission of redundant information between each scale. We divide the objects by depth, which is the distance from the object to the viewpoint. Then, a multi-branch architecture providing specialized detection for objects of each scale separately is constructed. Through ablation experiments, our method achieves an increase of 1.63 to 2.01 mAP compared with the baseline method. On the KITTI dataset, our method combined with state-of-arts achieves an increase of 3.54 mAP for small objects and 0.79 mAP for medium objects. [ABSTRACT FROM AUTHOR]

Accurately estimating the pose of spacecraft is indispensable for space applications. However, such targets are generally non-cooperative, i.e., no markers are mounted on them, and they include no parts for operation. Therefore, the detection and measurement of a non-cooperative target is very challenging. Stereovision sensors are important solutions in the near field. In this paper, a rectangular natural feature recognition and pose measurement method for non-cooperative spacecraft is proposed. Solar panels of spacecraft were selected as detection objects, and their image features were captured via stereo vision. These rectangle features were then reconstructed in 3D Cartesian space through parallelogram fitting on the image planes of two cameras. The vertexes of rectangle features were detected and used to solve the pose of a non-cooperative target. An experimental system was built to validate the effectiveness of the algorithm. The experimental results show that the average position measurement error of the algorithm is about 10 mm and the average attitude measurement error is less than 1°. The results also show that the proposed method achieves high accuracy and efficiency.

Automated 3D image‐based tracking systems are new and promising devices to investigate the foraging behavior of flying animals with great accuracy and precision. 3D analyses can provide accurate assessments of flight performance in regard to speed, curvature, and hovering. However, there have been few applications of this technology in ecology, particularly for insects. We used this technology to analyze the behavioral interactions between the Western honey bee Apis mellifera and its invasive predator the Asian hornet, Vespa velutina nigrithorax. We investigated whether predation success could be affected by flight speed, flight curvature, and hovering of the Asian hornet and honey bees in front of one beehive. We recorded a total of 603,259 flight trajectories and 5175 predator–prey flight interactions leading to 126 successful predation events, representing 2.4% predation success. Flight speeds of hornets in front of hive entrances were much lower than that of their bee prey; in contrast to hovering capacity, while curvature range overlapped between the two species. There were large differences in speed, curvature, and hovering between the exit and entrance flights of honey bees. Interestingly, we found hornet density affected flight performance of both honey bees and hornets. Higher hornet density led to a decrease in the speed of honey bees leaving the hive, and an increase in the speed of honey bees entering the hive, together with more curved flight trajectories. These effects suggest some predator avoidance behavior by the bees. Higher honey bee flight curvature resulted in lower hornet predation success. Results showed an increase in predation success when hornet number increased up to 8 individuals, above which predation success decreased, likely due to competition among predators. Although based on a single colony, this study reveals interesting outcomes derived from the use of automated 3D tracking to derive accurate measures of individual behavior and behavioral interactions among flying species. [ABSTRACT FROM AUTHOR]

Detailed characterization of microvascular alterations requires high‐resolution 3D imaging methods capable of providing both morphological and functional information. Existing optical microscopy tools are routinely used for microangiography, yet offer suboptimal trade‐offs between the achievable field of view and spatial resolution with the intense light scattering in biological tissues further limiting the achievable penetration depth. Herein, a new approach for volumetric deep‐tissue microangiography based on stereovision combined with super‐resolution localization imaging is introduced that overcomes the spatial resolution limits imposed by light diffusion and optical diffraction in wide‐field imaging configurations. The method capitalizes on localization and tracking of flowing fluorescent particles in the second near‐infrared window (NIR‐II, ≈1000–1700 nm), with the third (depth) dimension added by triangulation and stereo‐matching of images acquired with two short‐wave infrared cameras operating in a dual‐view mode. The 3D imaging capability enabled with the proposed method facilitates a detailed visualization of microvascular networks and an accurate blood flow quantification. Experiments performed in tissue‐mimicking phantoms demonstrate that high resolution is preserved up to a depth of 4 mm in a turbid medium. Transcranial microangiography of the entire murine cortex and penetrating vessels is further demonstrated at capillary level resolution. [ABSTRACT FROM AUTHOR]

This paper presents a concept of a 4D multimodal speaker model (4D-MSM) for asynchronous remote speech diagnosis. Recording and archiving diagnostically significant articulation material remain an issue in computer-aided speech diagnosis. Therefore, we propose a workflow for preparing and storing reliable and easily interpretable multimodal data regarding pronunciation. According to our assumptions, data acquisition should be non-invasive, comfortable for both the patient and therapist, not interfere with the articulation process, and provide essential data of high quality. We developed and employed a dedicated device, obtaining a 15-channel spatially distributed audio signal and stable stereovision stream from two cameras focused on the lower part of the face. Our framework for data preprocessing covers digital beamforming of the multichannel audio signal, audio-video synchronization, and segmentation of words in the audio signal. Then, we use stereo data to calculate and adjust the depth map and prepare point clouds. Simultaneously, we delineate the mouth in video frames using a dedicated semi-automated segmentation algorithm. The point clouds are then textured with the camera images with superimposed mouth regions. Finally, we add the audio track to constitute the 4D-MSM. In the paper, we show the concept and detailed specification of the model and present experiments to justify the methodology. Proposed 4D-MSMs may be employed in remote speech diagnosis for objectifying and archiving diagnoses, conducting asynchronous consultations, and documenting the progress in therapy.

Abstract Coral reefs are under increasing threat, and the loss of reef‐associated fishes providing valuable ecosystem services is accelerating. The monitoring of such rapid changes has become a challenge for ecologists and ecosystems managers using traditional approaches like scuba divers performing underwater visual censuses (UVC) or diver operated video recording (DOV). However, the use of small, low‐cost robots could help tackle the challenge of such monitoring, provided that they perform at least as well as diver‐based methods. To address this question, tropical fish assemblages from 13 fringing reefs around Mayotte Island (Indian Ocean) were monitored along 50 m‐long transects using stereo videos recorded by a semi‐autonomous underwater vehicle (SAUV) and by a scuba diver (Diver Operated stereo Video system, DOV). Differences between the methods were tested for complementary fish assemblage metrics (species richness, total biomass, total density, Shannon diversity and Pielou evenness) and for the number and size of nine targeted species. SAUV recorded on average 35% higher biomass than DOV which in turn recorded on average 12% higher species richness. Biomass differences were found to be due to SAUV monitoring larger fishes than DOV, a potential marker of human‐related fish avoidance behaviour. This study demonstrates that SAUV provides accurate metrics of coral reef fish biodiversity compared to diver‐based procedures. Given their ability to conduct video transects at high frequency, 100 m depth range and at a moderate cost, SAUV is a promising tool for monitoring fish assemblages in coral reef ecosystems.

The work is devoted to one of the actual problems of modern strabismology — the study of the ability to stereo perception in children with non-paralytic strabismus.Purpose: to study the capability to stereovision with alternating presentation of stereostimuli in children with functional scotoma in non-paralytic strabismus.Patients and methods. 113 children with functional scotoma (FS) in non-paralytic strabismus were observed. We used stereostimuli with different characteristics in the following regimes of presentation: 1) the regime of simple monocular alternating (alternate presentation of an image for the right eye and the left eye); 2) the regime having an “empty” interval (black background) between monocular phases; 3) the regime having a binocular phase (a binocular image containing details corresponding to the stimuli for the right eye and the left eye) between monocular phases.Results. It was found that in 23 (20,3 %) children, the ability to stereo perception was completely absent. All these children had stable total FS (monocular vision). In the remaining 90 children (with unstable or regional FS), the ability to stereo perception was shown with some stimuli in some modes of their alternating presentation. For stimuli with a central arrangement of linear parts, the stereo effect was possible when they were presented in an alternating mode with an “empty” interval lasting from 20 to 70 ms in combination with the duration of monocular phases from 30 to 90 ms. For stimuli with a peripheral arrangement of linear elements, 22.1 % of children were able to stereo perception not only in the “empty” interval mode, but also in the simple alternation mode. At the same time, the greatest number of children capable of stereo perception was detected when using the mode with an “empty” interval of 30–60 ms and a duration of monocular phases of 40–60 ms. With random-dot stimuli, none of the children in this group were capable of stereo perception.Conclusion. Our results suggest that even in patients with FS in non-paralytic strabismus, stereo perception is possible under the conditions of alternating presentation of stimuli with certain characteristics. In this case, the most likely appearance of a stereo effect with stimuli containing peripheral linear elements that create a stereo effect when presented in an alternating mode with an empty interval between monocular phases.

The work is devoted to one of the actual problems of current ophthalmology — creating effective methods of studying stereovision.The purpose — comparative analysis of the capability of stereoperception under conditions of using different regimes of alternating presentation of stereo stimuli with different characteristics in children with strabismus and in children without ophthalmopathology.Patients and methods. 294 school children — 167 children of the control group (without ophthalmopathology) and 127 children with non-paralytic strabismus without functional scotoma (FSS) were observed. We used stereostimuli with different characteristics in the following regimes of presentation: 1) the regime of simple monocular alternating (alternate presentation of an image for the right and left eye); 2) the regime having an “empty” interval (black background) between monocular phases; 3) the regime having a binocular phase (a binocular image containing details corresponding to the stimuli for the right eye and the left eye) between monocular phases.Results. It was found that the majority of children with non-paralytic strabismus, who are incapable of stereoperception with the classic Fly-test and Lang-test, can perceive the stereoeffect with alternating presentation of stereostimuli within individual ranges of durations of monocular phases, a binocular phase and an “empty” interval. In children of the control group when switching from the simple alternation regime to the “empty” interval regime the maximal durations of monocular phases, which preserved the stereoeffect, decreased and when switching to the binocular phase regime they significantly increased. In children with strabismus linear images are simpler for stereoperception than random-dot images as well as in children of the control group (p < 0.001); stimuli creating the effect of the frontoparallel separation of details get perceived better than those creating the decline effect or the turning effect (p < 0.001); stimuli creating the effect of the vertical stripes decline get perceived better than those creating the effect of the horizontal stripes turning (p < 0.001). However, as opposed to the children of the control group, in children with strabismus the stereoeffect gets formed better under conditions of the peripheral localization of linear details than under conditions of the central one.Conclusion. Using computer programs with different regimes of alternating presentation of stereostimuli with certain characteristics allows to effectively evaluate individual capability of stereoperception which is necessary for the personalized approach to the selection of visual stimuli and stimuli presentation regimes in functional treatment of patients with non-paralytic strabismus.

The stereometry of the working units of mining machines is optimized at the design stage, in terms of selected criteria based on computer simulations of the mining process. The recovered bodies of cutting heads or drums used in manufacturing are regenerated in the overhaul process. Ensuring that their dimensions comply with the nominal ones is labor-intensive and raises production costs. However, deviations of these components from the nominal shape can make it difficult to position the pick holders (which can cause collisions) or make welding them impossible (which results from too large a distance between the pick holders' base and the side surface of the working unit). This applies especially to robotic technologies. By utilizing automatic (online) measurements of the distribution of the actual distances of the pick holders' bases from the side surface of the working unit (taken during their positioning using a robot), it is possible to correct their positions without changing the setting of the pick itself. This measurement can be carried out using the non-contact stereovision method. This paper presents a method of matching raster images obtained from a stereovision system installed on an experimental robotic station. The results are presented of the numerical research carried out to select a mathematical model of the geometric transformation of images, for ensuring the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for estimating the reaction force, the Stereovision Based Force Estimation Method (SBFEM), with deep learning techniques to predict the interaction force of the surgical procedures. The interaction force is estimated through SBFEM combined with computer vision and neural networks instead of using direct force sensors due to the difficulty of adapting them to tools due to biocompatibility, sterilizability, and integration issues. The proposed model processes both spatial and temporal information acquired from the vision and tool data. The LSTM-RNN framework, along with dimensionality reduction, is trained with In-Vivo-experimental data of porcine skin, and the cyclical learning rate method is suggested for fine-tuning the network. The analyses are based on three distinct datasets, each with three cases to validate the result. The proposed method, RNN-LSTM + DR + CLR, outperforms the RNN and RNN-LSTM without dimensionality reduction by 8.46% and 3.98% in force prediction accuracy, respectively. Interestingly, this work reports an average RMSE of 0.01 N in the force component and 0.03Nm in the torque component in the applied force direction. The result shows that estimated force quality is better when reducing dimensionality on extracted features and processing both tool and vision data together. The network performed better when optimized with the loss function, root mean square error, and the cyclical learning rate method as an optimizer for fewer datasets with a minimum computational cost. Finally, the Mann–Whitney U test shows that the predicted force components are adaptable to any dataset. [ABSTRACT FROM AUTHOR]

Neural mechanisms behind stereopsis, which requires simultaneous disparity inputs from two eyes, have remained mysterious. Here we show how ultrafast mirrorsymmetric photomechanical contractions in the frontal forward-facing left and right eye photoreceptors give Drosophila superresolution three-dimensional (3D) vision. By interlinking multiscale in vivo assays with multiscale simulations, we reveal how these photoreceptor microsaccades—by verging, diverging, and narrowing the eyes’ overlapping receptive fields—channel depth information, as phasic binocular image motion disparity signals in time. We further show how peripherally, outside stereopsis, microsaccadic sampling tracks a flying fly’s opticflow field to better resolve the world in motion. These results change our understanding of how insect compound eyes work and suggest a general dynamic stereo-information sampling strategy for animals,robots, and sensors. [ABSTRACT FROM AUTHOR]

Vegetation encroaching on overhead power lines can cause short circuit faults and pose a major threat to the security and stability of power grids. Therefore, establishing an effective visual detection algorithm to oversee potential circuit failures of the power lines is critical to the ongoing inspection of vegetation encroachment. This paper establishes a deep learning-based detection framework that utilizes the images obtained from vision sensors mounted on power transmission towers. The proposed detection framework includes three cascaded modules: (1) detection of vegetation regions based on the Faster Region Convolution Neural Network (Faster R-CNN), (2) detection of power lines based on the Hough transform, and (3) detection of vegetation encroachment based on an advanced stereovision (SV) algorithm. In particular, the proposed SV algorithm converts the detected two-dimensional (2D) image data of the vegetation and power lines to three-dimensional (3D) height and location results in order to obtain precise geographical locations. Case studies using field captured images provided by a Transmission System Operator (TSO) demonstrate the effectiveness of the proposed framework in detecting vegetation failures, thus improving overall reliability and reducing economic loss. [ABSTRACT FROM AUTHOR]

Coral reefs are under increasing threat, and the loss of reef‐associated fishes providing valuable ecosystem services is accelerating. The monitoring of such rapid changes has become a challenge for ecologists and ecosystems managers using traditional approaches like scuba divers performing underwater visual censuses (UVC) or diver operated video recording (DOV). However, the use of small, low‐cost robots could help tackle the challenge of such monitoring, provided that they perform at least as well as diver‐based methods. To address this question, tropical fish assemblages from 13 fringing reefs around Mayotte Island (Indian Ocean) were monitored along 50 m‐long transects using stereo videos recorded by a semi‐autonomous underwater vehicle (SAUV) and by a scuba diver (Diver Operated stereo Video system, DOV). Differences between the methods were tested for complementary fish assemblage metrics (species richness, total biomass, total density, Shannon diversity and Pielou evenness) and for the number and size of nine targeted species. SAUV recorded on average 35% higher biomass than DOV which in turn recorded on average 12% higher species richness. Biomass differences were found to be due to SAUV monitoring larger fishes than DOV, a potential marker of human‐related fish avoidance behaviour. This study demonstrates that SAUV provides accurate metrics of coral reef fish biodiversity compared to diver‐based procedures. Given their ability to conduct video transects at high frequency, 100 m depth range and at a moderate cost, SAUV is a promising tool for monitoring fish assemblages in coral reef ecosystems. [ABSTRACT FROM AUTHOR]