Image Distance Research Articles

How textual and picture cues influence motor skill learning in short instructional videos: Evidence from eye-tracking experiments

Three eye-tracking experiments were designed to determine the influence of textual and graphic cues on motor skill learning in short instructional videos. Experiment 1 (n = 45) manipulated the colour of picture cues (red arrows, white arrows, and without cue). Experiment 2 (n = 49) manipulated the position of textual cues (image adjacency, image distance, and without textual cues). Experiment 3 (n = 67) examined the effect of combined textual-picture cues and conducted a 2 (Picture Cue: red arrow vs. white arrow) × 2 (Text Cue: image adjacency, vs. image distance) between-subjects design. Results showed that adding picture cues and textual cues helped learners do better with knowledge tests, reduced cognitive load, and spent more time focusing on the instructional video. Moreover, providing both red arrows and text cues away from the image proved to be an effective combination that achieved better learning outcomes.

Exploring structural variances in monatomic metallic glasses using machine learning and molecular dynamics simulation.

BCC and FCC metals have different glass-forming abilities (GFA) and exhibit different characteristics during the glass transition. However, the structural origin of their different GFAs is still not clear. Here, we explored the structures of eight monatomic metallic glasses by combining molecular dynamics (MD) simulations and machine learning (ML). Our findings reveal that, despite their common long-range disordered atomic structure, metallic glasses can be further classified into two distinct categories indicating an underlying structural order within the disorder. Using machine learning, we found that BCC liquids can sample more diverse glass states than FCC liquids. Furthermore, glasses formed from BCC metals (GFFBs) exhibit a higher degree of disorder than glasses formed from FCC metals (GFFFs). These findings highlight the inherent differences between GFFFs and GFFBs, which help explain the different glass-forming abilities of FCC and BCC metals. Additionally, our results demonstrate the promising potential of computer vision and ML methods in exploring material structures. Classical molecular dynamics simulations were employed to generate configurations of GFFBs and GFFFs, and the simulations were performed using the LAMMPS code. Inter-atomic interactions were described using a classical embedded atom model (EAM) potential. The initial configuration of the model consists of 32,000 atoms in a three-dimensional (3D) cubic box with periodic boundary conditions applied in all three directions. For machine learning, we utilized an unsupervised machine learning method along with MobileNetV2 for classifying glass structures. Image entropy and image distances were used to measure the structural differences of the metallic glasses.

SpaInGNN: Enhanced clustering and integration of spatial transcriptomics based on refined graph neural networks

Recent developments in spatial transcriptomics (ST) technology have markedly enhanced the proposed capacity to comprehensively characterize gene expression patterns within tissue microenvironments while crucially preserving spatial context. However, the identification of spatial domains at the single-cell level remains a significant challenge in elucidating biological processes. To address this, SpaInGNN was developed, a sophisticated graph neural network (GNN) framework that accurately delineates spatial domains by integrating spatial location data, histological information, and gene expression profiles into low-dimensional latent embeddings. Additionally, to fully leverage spatial coordinate data, spatial integration using graph neural network (SpaInGNN) refines the graph constructed for spatial locations by incorporating both tissue image distance and Euclidean distance, following a pre-clustering of gene expression profiles. This refined graph is then embedded using a self-supervised GNN, which minimizes self-reconfiguration loss. By applying SpaInGNN to refined graphs across multiple consecutive tissue slices, this study mitigates the impact of batch effects in data analysis. The proposed method demonstrates substantial improvements in the accuracy of spatial domain recognition, providing a more faithful representation of the tissue organization in both mouse olfactory bulb and human lateral prefrontal cortex samples

Benefits of front coating crystalline scintillator screens for phase-contrast synchrotron micro-tomography

Transparent crystalline scintillators such as cerium-doped YAG or LuAG are widely used in X-ray imaging for the indirect detection of X-rays. The application of reflective coatings on the front side to improve the optical gain is common practice for flat panel detectors with CsI or Gd2O2S powder scintillators but still largely unknown for crystalline scintillators such as LuAG. This work shows experimentally and quantitatively how a black and reflective coating on the X-ray side of a 2 mm LuAG:Ce scintillator improves the image quality compared to a 2 mm LuAG:Ce scintillator without a coating. The measurements have been done for two different distances, with 2 m and 29.7 m on the BM18 beamline of the European Synchrotron. The Modulation Transfer Function (MTF) and the Signal-to-Noise-Ratio (SNR2) power spectrum as well as contrast-to-noise ratio are used for comparing image quality. Propagation-based phase contrast strongly enhances the SNR2 amplitudes (gain ≈10 from 2 m to 29.7 m object-detector distance) of the raw images’ spectrum independent of the scintillator coating. For both detector positions, the reflective coating is able to raise SNR2 by up to 80% through the improved optical gain, while black coating does the opposite (decrease SNR2 by 20%) with respect to no coating. With the tested optical setups, changes in MTF /sharpness between the coatings are minor. Comparing CNR2 in CT scans of a multi-material sample, in this case an electric motor, we observe the reflective coating yielding better material contrast for plastic and air. Application and effect of Wiener-deconvolution, along with Paganin-type phase retrieval, are also discussed in the context of CT image quality.

Mirror-centered representation of a focusing hyperbolic mirror for X-ray beamlines.

Conic sections are commonly used in reflective X-ray optics. Hyperbolic mirrors can focus a converging light source and are frequently paired with elliptical or parabolic mirrors in Wolter type configurations. This paper derives the closed-form expression for a mirror-centered hyperbolic shape, with zero-slope at the origin. Combined with the slope and curvature, such an expression facilitates metrology, manufacturing and mirror-bending calculations. Previous works consider ellipses, parabolas, magnifying hyperbolas or employ lengthy approximations. Here, the exact shape function is given in terms of the mirror incidence angle and the source and image distances.

Design of a dual-focal-plane head-up display system using double freeform mirrors

This paper presents the design of a dual-focal-plane head-up display (HUD) system utilizing double freeform mirrors. The system is specifically developed for automotive applications, aiming to enhance driving safety and experience by projecting vehicle information within the driver’s line of sight. Compared to the traditional WHUD, the ARHUD offers a broader field of view and a greater virtual image distance. If the ARHUD system has double focal planes, it can separately display basic and interactive driving information to drivers, which is currently a significant research direction in the ARHUD system design. The proposed design only uses double freeform mirrors and uses a single display source for both focal planes, thus reducing the overall system complexity, assembly difficulty, and cost. Detailed design methods and results are discussed, showcasing the system’s good imaging performance and compact structure, which provide valuable insights for the development of next-generation dual-focal-plane HUDs for vehicles. The proposed system demonstrates significant advancements in reducing fabrication and testing difficulties compared to traditional dual-focal-plane HUD designs. The study’s findings contribute to the future development of dual-focal-plane head-up display systems.

Image formation near hyperbolic umbilic in strong gravitational lensing

Hyperbolic umbilic (HU) is a point singularity of the gravitational lens equation, giving rise to a ring-shaped image formation made of four highly magnified images, off-centred from the lens centre. Recent observations have revealed new strongly lensed image formations near HU singularities, and many more are expected in ongoing and future observations. Like fold/cusp, image formations near HU also satisfy magnification relation (), i.e., the signed magnification sum of the four images equals zero. Here, we study how deviates from zero as a function of area () covered by the image formation near HU and the distance ( d) of the central maxima image (which is part of the HU image formation) from the lens centre for ideal single- and double-component cluster-scale lenses. For lens ellipticity values ≥0.3, the central maxima image will form sufficiently far from the lens centre ( d≳5″), similar to the observed HU image formations with . We also find that, in some cases, double-component and actual cluster-scale lenses can lead to large cross-sections for HU image formations for sources at z≳5, effectively increasing the chances to observe HU image formation at high redshifts. Finally, we study the time delay distribution in the observed HU image formation, finding that not only are these images highly magnified, but the relative time delay between various pairs of HU characteristic image formation has a typical value of ∼100 days, an order of magnitude smaller than generic five-image formations in cluster lenses, making such image formations optimal targets for time delay cosmography studies.

Imaging distance of an aquatic image formed by an aquatic display obtained by paraxial approximation

Imaging distance of an aquatic image formed by an aquatic display obtained by paraxial approximation

Personalised High Tibial Osteotomy Surgery Is Accurate: An Assessment Using 3D Distance Mapping

Early-stage knee osteoarthritis is often suitable for treatment with high tibial osteotomy (HTO). This is an effective joint-preserving treatment, resulting in good postoperative outcomes. To overcome the limitations of traditional HTO, the surgical technique and correction accuracy can be enhanced by personalised procedures using three-dimensional digital planning and metal additive manufacturing, The purpose of this clinical trial study was to evaluate the three-dimensional accuracy of a new personalised HTO procedure, using modern imaging techniques, 3D modelling, and distance map analysis (DMA). Twenty-five patients were treated with the personalised HTO procedure. Before surgery and after 6 months, they underwent clinical evaluation scoring, radiographic imaging, and computed-tomography scanning to generate morphological models. Specifically, preoperative tibia models were used to plan the tibia correction and the design and position of the fixation plate. Preoperative, planned, and postoperative models were imported in computer-aided and designing software (Geomagic ControlTM 2014, 3D Systems, Rock Hill, SC, USA) for DMA implementation to assess geometrical differences between model surfaces. A very good reproduction of the planned tibia morphology was achieved postoperatively (average differences between −0.9 mm and 1.4 mm). DMA values associated with fixation-plate deformation were less than 1 mm, similar to those for plate-to-tibia surface-contour matching. Overall, personalised digitally planned HTO utilising three-dimensional printed surgical guides and plates enables accurate planned correction and plate placement.

A transcrestal sinus floor elevation strategy based on a haptic robot system: An in vitro study.

To reveal the force profiles recorded by haptic autonomous robotic force feedback during the transcrestal sinus floor elevation (TSFE) process, providing a reference for the surgery strategy during TSFE. A total of 42 maxillary sinus models with different angles of the sinus floor (30°, 40°, 50°, 60°, 70°, 80°, and 90°, compared to vertical plane) were 3D printed. Implant site preparation was performed using a robotic system, and the total force (Ft) and axial force along the drill (Fz) during the surgery were recorded by the haptic robotic arm. The actual initial breakthrough point (drill contacting sinus floor) and complete breakthrough point (drill penetrating the sinus floor) were defined visually (the actual IBP and the actual CBP). The theoretical initial breakthrough point (the theoretical IBP) and the theoretical complete breakthrough point (the theoretical CBP) defined by the robot-guided system and the CBCT were determined by real-time force feedback and imaging distance measurement, respectively. The distance from the bottom of the resin model to the actual IBP and the actual CBP was defined as Di and Dt, respectively. The difference in Fz began to increase significantly at 70°, while the difference in Ft became significant at 60°. When the angle was greater than 70°, there was no significant difference in the discrepancy between the actual and theoretical perforation points. Compared to judging the breakthrough point by CBCT, real-time force feedback TSFE under robotic surgery achieved more accurate initial breakthrough point detection. The smaller the angle, the larger the breakthrough force for the drill. The real-time force feedback of haptic robotic system during TSFE could provide reliable reference for dentists. More clinical studies are needed to further validate the application of robotic surgery assisted TSFE.

A few-shot learning framework for the diagnosis of osteopenia and osteoporosis using knee X-ray images.

We developed a few-shot learning (FSL) framework for the diagnosis of osteopenia and osteoporosis in knee X-ray images. Computer vision models containing deep convolutional neural networks were fine-tuned to enable generalization from natural images (ImageNet) to chest X-ray images (normal vs. pneumonia, base images). Then, a series of automated machine learning classifiers based on the Euclidean distances of base images were developed to make predictions for novel images (normal vs. osteopenia vs. osteoporosis). The performance of the FSL framework was compared with that of junior and senior radiologists. In addition, the gradient-weighted class activation mapping algorithm was used for visual interpretation. In Cohort #1, the mean accuracy (0.728) and sensitivity (0.774) of the FSL models were higher than those of the radiologists (0.512 and 0.448). A diagnostic pipeline of FSL model (first)-radiologists (second) achieved better performance (0.653 accuracy, 0.582 sensitivity, and 0.816 specificity) than radiologists alone. In Cohort #2, the diagnostic pipeline also showed improved performance. The FSL framework yielded practical performance with respect to the diagnosis of osteopenia and osteoporosis in comparison with radiologists. This retrospective study supports the use of promising FSL methods in computer-aided diagnosis tasks involving limited samples.

Research on the safety risks of microwave irradiation on motion balance perception in electric power environments

To the microwave irradiation safety hazards caused by the co-construction of towers in smart grids, this paper investigates the effects of microwave irradiation in the power environment on the biological motion balance perception function. Firstly, simulation of microwave signals in the electric power environment, i.e., low-frequency harmonics and high-frequency carriers, were realized by signal modulation and applied in four types of behavior testing scenarios. Then, determining rats as target organisms to replace workers and randomly dividing into groups in proportion: open field, rotating rod fatigue, beam walking and forced swimming. Configuring radar with various parameters to match the electric power irradiation scene and stimulate rats, monitoring the abnormal behavior by image processing module, including posture, motion trajectory, distance, and other features. The experimental result showed that exposed to microwaves induce rats motor ability decline, balance perception imbalance, together with paralysis within long-term exposure, and its locomotor activity, coordination, posture control and reaction time all exhibit varying degrees of weakening. These findings indicate that microwave irradiation in electric power environment may pose significant health and safety risks for worker.

A correction method for calculating sky view factor in urban canyons using fisheye images

The Sky View Factor (SVF) is a critical parameter for studying the thermal environment of buildings theoretically. However, there are two main definitions of SVF, one based on the visible area ratio and the other based on the view factor. When utilizing fisheye images for SVF calculation, various issues often arise, such as conceptual confusion and algorithmic errors, significantly impeding the accurate scientific analysis of thermal environmental problems. Therefore, in this study, we employed methods such as theoretical model deduction, field analysis, and software simulation and validation to investigate the calculation methods for fisheye images with equidistant projection, equisolid angle projection, and stereographic projection. We proposed the Image Distance Ratio Transformation Method (IDRTM), a correction method for calculating the SVF of fisheye images with the three projection types and corresponding calculation formulas. Finally, two cases demonstrate the computational advantages of simplicity and high accuracy associated with the orthogonal transformation formulas applied to the three types of fisheye images. In this study, the error rates of SVF calculation using three non-orthographic fisheye images directly are all higher than 29 %. After applying IDRTM, the average error rates of the transformed IDRs of three types of fisheye images are all less than 0.58 %, and the error rates of the corrected SVF of three types of fisheye images are all less than 1 %. This study elucidates the scientific definition and algorithm of SVF and provides a scientific basis for the accurate selection of SVF calculation methods corresponding to fisheye image types.

Passive imaging through inhomogeneous scattering media

According to the atmospheric scattering model (ASM), the object signal’s attenuation diminishes exponentially as the imaging distance increases. This imposes limitations on ASM-based methods in situations where the scattering medium one wish to look through is inhomogeneous. Here, we extend ASM by taking into account the spatial variation of the medium density, and propose a two-step method for imaging through inhomogeneous scattering media. In the first step, the proposed method eliminates the direct current component of the scattered pattern by subscribing to the estimated global distribution (background). In the second step, it eliminates the randomized components of the scattered light by using threshold truncation, followed by the histogram equalization to further enhance the contrast. Outdoor experiments were carried out to demonstrate the proposed method.

Seracam: characterisation of a new small field of view hybrid gamma camera for nuclear medicine

BackgroundPortable gamma cameras are being developed for nuclear medicine procedures such as thyroid scintigraphy. This article introduces Seracam® – a new technology that combines small field of view gamma imaging with optical imaging – and reports its performance and suitability for small organ imaging.MethodsThe count rate capability, uniformity, spatial resolution, and sensitivity for 99mTc are reported for four integrated pinhole collimators of nominal sizes of 1 mm, 2 mm, 3 mm and 5 mm. Characterisation methodology is based on NEMA guidelines, with some adjustments necessitated by camera design. Two diagnostic scenarios – thyroid scintigraphy and gastric emptying – are simulated using clinically relevant activities and geometries to investigate application-specific performance. A qualitative assessment of the potential benefits and disadvantages of Seracam is also provided.ResultsSeracam’s performance across the measured characteristics is appropriate for small field of view applications in nuclear medicine. At an imaging distance of 50 mm, corresponding to a field of view of 77.6 mm × 77.6 mm, spatial resolution ranged from 4.6 mm to 26 mm and sensitivity from 3.6 cps/MBq to 52.2 cps/MBq, depending on the collimator chosen. Results from the clinical simulations were particularly promising despite the challenging scenarios investigated. The optimal collimator choice was strongly application dependent, with gastric emptying relying on the higher sensitivity of the 5 mm pinhole whereas thyroid imaging benefitted from the enhanced spatial resolution of the 1 mm pinhole. Signal to noise ratio in images was improved by pixel binning. Seracam has lower measured sensitivity when compared to a traditional large field of view gamma camera, for the simulated applications this is balanced by advantages such as high spatial resolution, portability, ease of use and real time gamma-optical image fusion and display.ConclusionThe results show that Seracam has appropriate performance for small organ 99mTc imaging. The results also show that the performance of small field of view systems must be considered holistically and in clinically appropriate scenarios.

Transformer Discharge Carbon-Trace Detection Based on Improved MSRCR Image-Enhancement Algorithm and YOLOv8 Model.

It is difficult to visually detect internal defects in a large transformer with a metal closure. For convenient internal inspection, a micro-robot was adopted, and an inspection method based on an image-enhancement algorithm and an improved deep-learning network was proposed in this paper. Considering the dim environment inside the transformer and the problems of irregular imaging distance and fluctuating supplementary light conditions during image acquisition with the internal-inspection robot, an improved MSRCR algorithm for image enhancement was proposed. It could analyze the local contrast of the image and enhance the details on multiple scales. At the same time, a white-balance algorithm was introduced to enhance the contrast and brightness and solve the problems of overexposure and color distortion. To improve the target recognition performance of complex carbon-trace defects, the SimAM mechanism was incorporated into the Backbone network of the YOLOv8 model to enhance the extraction of carbon-trace features. Meanwhile, the DyHead dynamic detection Head framework was constructed at the output of the YOLOv8 model to improve the perception of local carbon traces with different sizes. To improve the defect target recognition speed of the transformer-inspection robot, a pruning operation was carried out on the YOLOv8 model to remove redundant parameters, realize model lightness, and improve detection efficiency. To verify the effectiveness of the improved algorithm, the detection model was trained and validated with the carbon-trace dataset. The results showed that the MSH-YOLOv8 algorithm achieved an accuracy of 91.80%, which was 3.4 percentage points higher compared to the original YOLOv8 algorithm, and had a significant advantage over other mainstream target-detection algorithms. Meanwhile, the FPS of the proposed algorithm was up to 99.2, indicating that the model computation and model complexity were successfully reduced, which meets the requirements for engineering applications of the transformer internal-inspection robot.

Double vision: 2D and 3D mosquito trajectories can be as valuable for behaviour analysis via machine learning

BackgroundMosquitoes are carriers of tropical diseases, thus demanding a comprehensive understanding of their behaviour to devise effective disease control strategies. In this article we show that machine learning can provide a performance assessment of 2D and 3D machine vision techniques and thereby guide entomologists towards appropriate experimental approaches for behaviour assessment. Behaviours are best characterised via tracking—giving a full time series of information. However, tracking systems vary in complexity. Single-camera imaging yields two-component position data which generally are a function of all three orthogonal components due to perspective; however, a telecentric imaging setup gives constant magnification with respect to depth and thereby measures two orthogonal position components. Multi-camera or holographic techniques quantify all three components.MethodsIn this study a 3D mosquito mating swarm dataset was used to generate equivalent 2D data via telecentric imaging and a single camera at various imaging distances. The performance of the tracking systems was assessed through an established machine learning classifier that differentiates male and non-male mosquito tracks. SHAPs analysis has been used to explore the trajectory feature values for each model.ResultsThe results reveal that both telecentric and single-camera models, when placed at large distances from the flying mosquitoes, can produce equivalent accuracy from a classifier as well as preserve characteristic features without resorting to more complex 3D tracking techniques.ConclusionsCaution should be exercised when employing a single camera at short distances as classifier balanced accuracy is reduced compared to that from 3D or telecentric imaging; the trajectory features also deviate compared to those from the other datasets. It is postulated that measurement of two orthogonal motion components is necessary to optimise the accuracy of machine learning classifiers based on trajectory data. The study increases the evidence base for using machine learning to determine behaviours from insect trajectory data.Graphical

Research on a Machine Vision-based Blade Measurement Method

A high-precision visual measurement method for the geometric dimensions of oil pump blades is proposed, utilizing an auxiliary measurement mechanism to assist in sub-pixel edge positioning. Based on images of calibration boards, the camera distortion is corrected by partitioning the image into regions and establishing a mapping index between pixel coordinates and real-world coordinates. Under fixed object and image distances, images of the oil pump blades and the contact between the blades and the pump are separately captured. The Canny operator and Sigmoid function are employed to fit the edges, extracting sub-pixel coordinates, which are then mapped to real-world coordinates. Finally, a least squares edge fitting is conducted to compute the dimensional parameters. Experimental results indicate that the use of auxiliary measurement mechanisms effectively enhances the precision of blade measurements, as compared to measurements solely based on oil pump blade images and images of the blade in contact with the pump.

Field curvature analysis and optimization method for near-eye display systems

Near-eye display (NED) systems with a large field of view (FOV) and a large pupil are often accompanied by field curvature. In such cases, there is often a significant deviation between the virtual image seen by the human eye and an ideal plane. Currently, there is a lack of precise methods for describing and controlling the shape of virtual images in visual space. In this paper, the system is modeled, and the curvature is controlled through optimization. Under limited conditions, the system’s field curvature is controlled to approach an ideal state. When the system’s field curvature cannot be completely corrected, a method for describing the virtual image surface is introduced. This method helps optical designers effectively predict the degree of curvature and the distance of the virtual image. The image quality of the system with a curved image is evaluated and optimized based on the focusing function of the human eye. Additionally, the field curvature and pupil swim are analyzed jointly. The effectiveness of the method is verified by designing two different types of NED systems.

Assessing the spatial resolution distance of satellite images: SuperDove versus Landsat 8

ABSTRACT The remote sensing community usually confuses the Spatial Resolution Distance (SRD) of satellite images with their Ground Sampling Distance (GSD). This misconception has been highlighted by assessments of Planet’s CubeSat images conducted independently by NASA and ESA, which found that their SRD is about five times larger than their GSD. The discrepancies between different metrics to compute the SRD have contributed to the confusion between SRD and GSD. A recently developed spatial resolution metric computed in terms of the imaging sensor’s Point Spread Function (PSF), named the Spatial Resolution Function (SRF), allows a comprehensive assessment of the SRD as a function of the resolving contrast generated in the image, when there are two-point sources in the scene. The SRF shows under what conditions the GSD is a good approximation to the SRD and provides a tool to assess the predictions of other metrics used to compute the SRD. We use the SRF to compute the SRD of images captured by Landsat 8 and Planet’s SuperDove satellites. The PSF of these sensors is calculated by using the specifications and measurements provided by their operators. The SRD predictions of the SRF metric agree with NASA and ESA findings, showing that the average SRD of SuperDove and Landsat 8 images is 4.9 and 1.4 times larger than their GSD, respectively. The stability of the SRD of these images is assessed by computing the degradation (increase) of SRD resulting from an increase of atmospheric turbulence and spacecraft vibrations. We conclude that the confusion between SRD and GSD has led to the development of CubeSat images with a small GSD whose SRD is several times larger than their GSD. These CubeSat images have an intrinsically unstable SRD in the space environment, a property that explains the numerous radiometric inconsistencies reported by their users.