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The results of the study of radiation produced by the electrical explosion of Al foil with a thickness of 4 μm with an artificial periodic structure of periodic holes with 50–400 μm steps created by laser engraving are presented. The experiments were carried out on a KING pulse high-current generator (220 kA, 40 kV, 300 ns). Images of exploded foils were recorded using pinhole cameras with a 20 μm aperture on a four-frame microchannel plate camera with 5 ns temporal resolution. In experiments on the electrical explosion of thin foils, it was shown that the application of an artificial periodic structure to the foil leads to a much more uniform radiation in the UV spectrum range along the entire length and width of the foil. Uniform radiation was observed from 20 to 70 ns. It is shown that such radiation can be used for the imaging of test objects or plasma objects like exploded wires. [ABSTRACT FROM AUTHOR]

Magnetized Liner Inertial Fusion experiments have been performed at the Z facility at Sandia National Laboratories. These experiments use deuterium fuel, which produces 2.45 MeV neutrons on reaching thermonuclear conditions. To study the spatial structure of neutron production, the one-dimensional imager of neutrons diagnostic was fielded to record axial resolved neutron images. In this diagnostic, neutrons passing through a rolled edge aperture form an image on a CR-39-based solid state nuclear track detector. Here, we present a modified generalized expectation–maximization algorithm to reconstruct an axial neutron emission profile of the stagnated fusion plasma. We validate the approach by comparing the reconstructed neutron emission profile to an x-ray emission profile provided by a time-integrated pinhole camera. [ABSTRACT FROM AUTHOR]

The pinhole gamma camera is a simple and useful device for determining the radiation distribution in a certain region. Using this device, we developed a method to measure the distribution of 137Cs contamination density on surfaces using the total energy absorption peak count rate of gamma rays, where each camera pixel was projected onto the surface to determine the corresponding measured area and distance to the surface. We applied this method to measure the 137Cs contamination density of the wall, ceiling, and floor of the Unit 2 Operation Floor at the Fukushima Daiichi Nuclear Power Station in 2020 and 2022 and compared the results obtained in 2020 to those of a robot-operated, conventional, high-dose-area smear test. We found a pinhole gamma camera with the proposed method is useful for obtaining contamination density distribution results quickly, without the complexities of using a robot. [ABSTRACT FROM AUTHOR]

We introduce an atlas of algebro-geometric objects associated with image formation in pinhole cameras. The nodes of the atlas are algebraic varieties or their vanishing ideals related to each other by projection or elimination and restriction or specialization, respectively. This atlas offers a unifying framework for the study of problems in 3D computer vision. We initiate the study of the atlas by completely characterizing a part of the atlas stemming from the triangulation problem. We conclude with several open problems and generalizations of the atlas. [ABSTRACT FROM AUTHOR]

Achieving precise 6 degrees of freedom (6D) pose estimation of rigid objects from color images is a critical challenge with wide-ranging applications in robotics and close-contact aircraft operations. This study investigates key techniques in the application of YOLOv5 object detection convolutional neural network (CNN) for 6D pose localization of aircraft using only color imagery. Traditional object detection labeling methods suffer from inaccuracies due to perspective geometry and being limited to visible key points. This research demonstrates that with precise labeling, a CNN can predict object features with near-pixel accuracy, effectively learning the distinct appearance of the object due to perspective distortion with a pinhole camera. Additionally, we highlight the crucial role of knowledge about occluded features. Training the CNN with such knowledge slightly reduces pixel precision, but enables the prediction of 3 times more features, including those that are not initially visible, resulting in an overall better performing 6D system. Notably, we reveal that the data augmentation technique of scale can interfere with pixel precision when used during training. These findings are crucial for the entire system, which leverages the Solve Perspective-N-Point (Solve-PnP) algorithm, achieving 6D pose accuracy within 1 ∘ and 7 cm at distances ranging from 7.5 to 35 m from the camera. Moreover, this solution operates in real-time, achieving sub-10ms processing times on a desktop PC. [ABSTRACT FROM AUTHOR]

This article explores issues of what is seen and not seen, recorded and disregarded, as they relate to the author's practical experimentations with alternate uses/forms of the camera. These alternates include the slit-scan camera and a little-known form called the cylinder pinhole camera, which was originally designed and tested by the photo historian Joel Snyder. What do these cameras tell us, the author asks, about the center and periphery of an image as it exists inside a camera before that image is recorded on film? The prioritization of content at the center of an image, the article argues, is consistent with the West's cultural bias toward making two-dimensional images on rectangular surfaces, such as drawings, paintings, and photographs. But we see something very different by looking into the black box of the camera, something not so rectangular. The figures included here, then, are meant to disrupt the center-periphery binary that has ruled photography in the West. These photos offer new insight into social expectations about the capacity of the camera, of panoramic images, and about strategies for analysis of art-making practices in general. [ABSTRACT FROM AUTHOR]

To study the spatial distribution of the soft X-ray radiation intensity of a laser-produced plasma, a new type of coded aperture was used. This aperture is a structure with a mutually perpendicular arrangement of opaque strips, the distances between which and their widths were calculated with the use of a random number generator. The radiation transmitted through such aperture gives an intricate pattern of the encoded image, which was recorded on a Fuji TR imaging plate without a protective coating. To reconstruct the spatial distribution of the plasma radiation intensity from this pattern, a mathematical algorithm was developed that is an iterative method for solving an ill-posed problem—the Fredholm integral equation of the first kind. It has been shown that the use of the coded aperture not only enhances the efficiency of the registration system by many times but also allows one to get a spatial resolution no worse than in the case of the use of a pinhole camera. It is established that the developed iterative method can be used both for sources which differ little from point sources and for spatially extended objects. [ABSTRACT FROM AUTHOR]

Monocular depth estimation is a task aimed at predicting pixel-level distances from a single RGB image. This task holds significance in various applications including autonomous driving and robotics. In particular, the recognition of surrounding environments is important to avoid collisions during autonomous parking. Fisheye cameras are adequate to acquire visual information from a wide field of view, reducing blind spots and preventing potential collisions. While there have been increasing demands for fisheye cameras in visual-recognition systems, existing research on depth estimation has primarily focused on pinhole camera images. Moreover, depth estimation from fisheye images poses additional challenges due to strong distortion and the lack of public datasets. In this work, we propose a novel underground parking lot dataset called JBNU-Depth360, which consists of fisheye camera images and their corresponding LiDAR projections. Our proposed dataset was composed of 4221 pairs of fisheye images and their corresponding LiDAR point clouds, which were obtained from six driving sequences. Furthermore, we employed a knowledge-distillation technique to improve the performance of the state-of-the-art depth-estimation models. The teacher–student learning framework allows the neural network to leverage the information in dense depth predictions and sparse LiDAR projections. Experiments were conducted on the KITTI-360 and JBNU-Depth360 datasets for analyzing the performance of existing depth-estimation models on fisheye camera images. By utilizing the self-distillation technique, the AbsRel and SILog error metrics were reduced by 1.81% and 1.55% on the JBNU-Depth360 dataset. The experimental results demonstrated that the self-distillation technique is beneficial to improve the performance of depth-estimation models. [ABSTRACT FROM AUTHOR]

Background: The location and proximity to the spinal cord in spinal osteoid osteoma can increase the likelihood of an incomplete resection. Intraoperative bone scintigraphy (IOBS) can be used to verify location and complete surgical resection. Objective: To review our experience using IOBS for resection of intraspinal osteoid osteoma. Methods: IRB approved, retrospective review of IOBS-guided resection over 10 years. Patients underwent injection of 200 uCi/kg (1–20 mCi) 99mTc-MDP 3–4 h prior surgery. Portable single-headed gamma camera equipped with a pinhole collimator (3- or 4-mm aperture) was used. Images were obtained pre-operatively, at the start of the procedure, and intraoperatively. Operative notes were reviewed. Evaluation of recurrence and clinical follow-up was performed. Results: Twenty IOBS-guided resections were performed in 18 patients (median age 13.5 years, 6–22 years, 12 males). Size ranged 5–16 mm, with 38.9% (7/18) cervical, 22.2% (4/18) thoracic, 22.2% (4/18) lumbar, and 16.7% (3/18) sacral. In all cases, IOBS was able to localize the lesion. After suspected total excision, IOBS altered the surgical plan in 75% of cases (15/20), showing residual activity prompting further resection. Median length of follow-up was 6 months (range 1–156 months) with 90% (18/20) showing complete resection without recurrence. Two patients had osteoid osteoma recurrence at 7 and 10 months following the original resection, requiring re-intervention. Conclusions: IOBS is a useful tool for real-time localization and assessment of spinal osteoid osteoma resection. In all cases, IOBS was able to localize the lesion and changed surgical planning in 75% of cases. Ninety percent of patients achieved complete resection and remain recurrence free. [ABSTRACT FROM AUTHOR]

Train distance estimation in a turnout area is an important task for the autonomous driving of urban railway transit, since this function can assist trains in sensing the positions of other trains within the turnout area and prevent potential collision accidents. However, because of large incident angles on object surfaces and far distances, Lidar or stereo vision cannot provide satisfactory precision for such scenarios. In this paper, we propose a method for train distance estimation in a turnout area based on monocular vision: firstly, the side windows of trains in turnout areas are detected by instance segmentation based on YOLOv8; secondly, the vertical directions, the upper edges and lower edges of side windows of the train are extracted by feature extraction; finally, the distance to the target train is calculated with an appropriated pinhole camera model. The proposed method is validated by practical data captured from Hong Kong Metro Tsuen Wan Line. A dataset of 2477 images is built to train the instance segmentation neural network, and the network is able to attain an MIoU of 92.43% and a MPA of 97.47% for segmentation. The accuracy of train distance estimation is then evaluated in four typical turnout area scenarios with ground truth data from on-board Lidar. The experiment results indicate that the proposed method achieves a mean RMSE of 0.9523 m for train distance estimation in four typical turnout area scenarios, which is sufficient for determining the occupancy of crossover in turnout areas. [ABSTRACT FROM AUTHOR]

A camera equipped with a transparent shield can be modeled using the pinhole camera model and residual error vectors defined by the difference between the estimated ray from the pinhole camera model and the actual three-dimensional (3D) point. To calculate the residual error vectors, we employ sparse calibration data consisting of 3D points and their corresponding 2D points on the image. However, the observation noise and sparsity of the 3D calibration points pose challenges in determining the residual error vectors. To address this, we first fit Gaussian Process Regression (GPR) operating robustly against data noise to the observed residual error vectors from the sparse calibration data to obtain dense residual error vectors. Subsequently, to improve performance in unobserved areas due to data sparsity, we use an additional constraint; the 3D points on the estimated ray should be projected to one 2D image point, called the ray constraint. Finally, we optimize the radial basis function (RBF)-based regression model to reduce the residual error vector differences with GPR at the predetermined dense set of 3D points while reflecting the ray constraint. The proposed RBF-based camera model reduces the error of the estimated rays by 6% on average and the reprojection error by 26% on average. [ABSTRACT FROM AUTHOR]

A multichannel soft X-ray (SXR) array has been developed to measure the electron temperature in the Versatile Experiment Spherical Torus (VEST). To estimate electron temperature using the two-filter method applied to SXR intensity, we designed a pinhole camera that has two photodiode arrays with different metallic filters. We also adopted a filter wheel and tested various filter parameters to find the optimal filter set. Through tests, the combination of aluminum and beryllium was found to be the most suitable for the current experimental conditions in VEST. The filtered SXR signals were acquired with a low-noise preamplifier, exhibiting sufficient signal-to-noise ratios for electron temperature estimation based on the intensity ratio of two signals obtained with different filters. The estimated electron temperature from the developed two-filter SXR array showed reasonably matched levels and consistent trends with Thomson scattering measurements. Error contribution from impurity line emission is also discussed. [ABSTRACT FROM AUTHOR]

Hera mission is the European Space Agency's contribution to the international collaboration with NASA for the planetary defence, i.e. Asteroid Impact Deflection Assessment aiming to deflect the trajectory of its target binary asteroid system (65803) Didymos. The Early Characterization Phase and the Detailed Characterization Phase of Hera mission are two phases of the proximity operations with the objective to physically and dynamically characterize Didymos. During these phases, an Image Processing algorithm is required to estimate the position of the centroid of the primary to enable Line of Sight navigation. However, the performance of standard Image Processing algorithms is affected by the disturbances of the image, such as poor illumination conditions, the presence of external bodies and the irregular shape of the target. This research addresses this challenge by developing a robust Convolutional Neural Networks-based Image Processing algorithm to estimate the position of the centroids of Didymos and its moon Dimorphos, the pseudorange from the primary and the Sun phase angle. The training, validation and testing datasets are generated with the software Planet and Asteroid Natural scene Generation Utility using the Early Characterization Phase and the Detailed Characterization Phase trajectories as case scenario. The position in the image of the centroids of Didymos and Dimorphos is estimated using their respective position vectors. To estimate the pseudorange, the developed algorithm regresses a set of keypoints on the visible border of Didymos and evaluates its apparent radius. For the Sun phase angle, the pixel position of the subsolar point of the primary is leveraged. The High-Resolution Network is the Convolutional Neural Network architecture applied to detect keypoints with superior spatial precision. Even with the considered disturbances, the analysis shows that the proposed algorithm is able to provide an accurate estimation of the mentioned outputs for all the Early Characterization Phase trajectory and for 77.33% of the Detailed Characterization Phase trajectory, improving the robustness and autonomy of the mission navigation. • The Image Processing of Hera is investigated using Convolutional Neural Networks. • The centroid regression of both Didymos and Dimorphos is solved. • The range and the Sun phase angle are estimated using the pinhole camera model. • The subsolar point position on the surface of Didymos is estimated. [ABSTRACT FROM AUTHOR]

Spatial frequency domain imaging (SFDI) is well established in biology and medicine for non-contact, wide-field imaging of optical properties and 3D topography. Especially for turbid media with displaced, tilted or irregularly shaped surfaces, the reliable quantitative measurement of diffuse reflectance requires efficient calibration and correction methods. In this work, we present the implementation of a generic and hardware independent calibration routine for SFDI setups based on the so-called pinhole camera model for both projection and detection. Using a two-step geometric and intensity calibration, we obtain an imaging model that efficiently and accurately determines 3D topography and diffuse reflectance for subsequently measured samples, taking into account their relative distance and orientation to the camera and projector, as well as the distortions of the optical system. Derived correction procedures for position- and orientation-dependent changes in spatial frequency and intensity allow the determination of the effective scattering coefficient μ s ′ and the absorption coefficient μ a when measuring a spherical optical phantom at three different measurement positions and at nine wavelengths with an average error of 5% and 12%, respectively. Model-based calibration allows the characterization of the imaging properties of the entire SFDI system without prior knowledge, enabling the future development of a digital twin for synthetic data generation or more robust evaluation methods. [ABSTRACT FROM AUTHOR]

The velocity fields measured by experiments or determined through simulations are essential in advancing our understanding of the complex atomization process of impinging jets. However, existing methods are expensive and time-consuming. In this study, we apply deep learning to the estimation of the three-dimensional velocity fields produced by the atomization of two impinging jets. Two deep learning models are developed, namely, a liquid volume fraction (LVF) estimation model based on the Swin Transformer architecture and a three-dimensional velocity field estimation model based on four-dimensional convolution (4D-Conv). The dataset for training the models is generated by direct numerical simulations (DNS). To train the LVF model, we utilize two gray images generated by a pinhole camera model, mimicking the acquisition of experimental images. We then introduce a mask generated by binocular vision techniques into the LVF model. The LVF fields estimated with the mask are in better agreement with the reference DNS data. We further utilize the estimated LVF fields to train the 4D-Conv-based model. The mean absolute percentage error compared with the results of a full-flow test is found to be less than 5%. The results indicate that the proposed approach has the potential to accurately reconstruct volume velocity data from two-dimensional images. [ABSTRACT FROM AUTHOR]

7) should be substituted with a spherical angle symbol : For example, the top left pixel gets a coordinate of HT ht for a pinhole camera model with a field of view of HT ht and HT ht horizontally and vertically, respectively. 8) should be substituted with HT ht : We use an absolute positional encoding, i.e. HT ht Graph where the positional encoding HT ht and the subimage feature vector HT ht are superimposed. [Extracted from the article]

We present a new and efficient solution for the forward world to image projection of a pinhole camera with distortions placed behind a planar refractive slab. Firstly, we introduce a novel way to compute the projection by reducing the problem to finding a real quantity called a slab shift. We characterize a physically meaningful slab shift as the unique solution of a fixed point equation on the one hand and as a specific uniquely defined root of a quartic polynomial in the unknown slab shift on the other. In the latter case we obtain a closed-form formula, which provides the unique physically meaningful projection. Secondly, we develop an approximation of the projection through the slab that reaches single-precision floating point accuracy for practically relevant problem instances with considerably lower computational costs compared to the exact solution. We demonstrate the accuracy and the efficiency of our method with realistic synthetic experiments. We demonstrate with real experiments that our method enables efficient camera calibration behind the windshield for automotive industry applications. [ABSTRACT FROM AUTHOR]

This text is an analysis of a series of pinhole photographs, by Anne Peschken and Marek Pisarsky (Urban Art), entitled East Side Story I (Myślibórz). Photo research on migration and arrival stories, 2019 on-going. The main thesis is that these photographs are a model example of images which, while addressing the theme of migration in the representational layer, also activate the processual and migratory nature of visual forms themselves. In order to substantiate this thesis, the East Side Story project is examined in the following contexts: critical border (art) studies; H. Belting's anthropology of the image; memory studies; re-enactment; the blurriness of images made with a pinhole camera; A. Berleant's re-thinking aesthetics and the notion of aesthetic embodiment. Reflecting on the tension between history, memory, identity and politics and activating the critical potential of borderscaping, Peschken and Pisarsky transform the landscape of the Polish-German borderland into an anachronistic narrative agent. The photographs from the East Side Story series are thus transgenerational corpographies of memory, showing that migration is a key and inalienable element of Polish history. [ABSTRACT FROM AUTHOR]

Central catadioptric cameras with a single effective viewpoint contain both mirrors and pinhole cameras that increase the imaging field of view. In this study, the common pole–polar properties of central catadioptric sphere or line images are investigated and used for camera calibration. From these properties, the pole and polar with respect to the image of absolute conic and the modified image of absolute conic, respectively, can be recovered according to the generalized eigenvalue decomposition. Moreover, these techniques are valid for paracatadioptric sensors with the degenerate conic dual to the circular points being considered. At least three images of spheres or lines are required to completely calibrate any central catadioptric camera. The intrinsic parameters of the camera, the shape of reflective mirror, and the distortion parameters can be linearly estimated using the algebraic and geometric constraints of the sphere or line images obtained by the central catadioptric camera. The obtained experimental results demonstrate the effectiveness and feasibility of the proposed calibration algorithm. [ABSTRACT FROM AUTHOR]

Cherry tomatoes are a small variety of tomatoes with a shape size of less than 2.5 cm in large clusters. Furthermore, the bunches of cherry tomatoes also grow in variable postures. These growth conditions have posed a great challenge to the harvesting robot at a fixed angle. Once the robots automatically perform single-fruit harvesting operations, the stems can be found to usually interfere with the end-effectors, resulting in low picking efficiency. The reason can be that the picking robots cannot move towards commercialization. Particularly, not all fruits in a tomato bunch grow and ripen simultaneously. It is very necessary to pick the ripe fruits on time, in order to ensure a fresh taste with high economic profits. Therefore, a robotic vision system is highly required to rapidly and accurately identify fruit ripeness. In this study, a cascaded vision inspection was proposed to harvest the single tomatoes from the robotic spikes. The processing procedure included three key aspects: the detection of the harvesting target, the determination of target maturity, and the fruit-stalk position relationship. Firstly, the YOLOv5 model of target detection was introduced to detect the tomato fruits and bunches. The tomato fruits were labelled into four categories using agronomic growing and harvesting requirements, including green, turning, ripe, and fully ripe fruit. It was totally difference from the simply classified ripeness than before. Among them, the ripe, and fully ripe fruit were targeted for robotic harvesting. The overlap of visual features was then fully considered for ripeness determination and target detection. The original YOLOv5 was improved for ripeness detection using multi-task learning. The robot was confined to only picking the tomatoes on both sides of the culture rack, due mainly to the structure of the greenhouse facility. The target detection was then filtered out for the targets beyond the execution range of the robot. The distance was also set as 1.55m between the culture racks in this case. The region of interest (ROI) of the target fruit was then approximated as a sphere with a diameter of about 2.5 cm. The pinhole camera model was used to calculate the ROI picking range. Specifically, the tomatoes growing on the incubator outside the working range of the robot were mostly smaller than the 10 pixel×10 pixel region in the 640 pixel×640 pixel RGB image. At the same time, a large number of feature layers were cropped to choose the unlabeled targets in the annotation stage. As such, better performance was achieved to reduce the labor cost, particularly when filtering the targets without being captured. This end-to-end approach was required without post-processing. It was much more adaptable to real scenarios, compared with the traditional approach of filtering targets by the threshold setting. The field experiments show that the fruit stalk interfering with the end-effector was a major cause of robot picking failure or low efficiency. Correspondingly, the optimal angle was one of the most important parameters for the harvesting action. After the screening of targets to be picked, the target rectangle detection box was enlarged by 10 percentage points in length and width, in order to contain the peripheral information, such as pedicels and calyces. The expanded image block was then input into the Mobilenetv3 network model, in order to evaluate the relative position relationship between the target fruit and the fruit stalk. As such, the input was provided for the end-effector to change the picking position, and then choose the direction favorable for the fruit picking, in order to approach the fruit and then perform the action using the pose of the string. A harvesting robot system was also built consisting of a depth camera, a four-degree-of-freedom robot arm, a chassis, and a negative-pressure end-effector. The harvesting system was tested in the greenhouses at different times of the year, particularly for object detection, the prediction of the position relationship between fruit stalks, and fruit harvesting. The results showed that the average detection accuracy of cherry tomato bunches and fruits with different ripeness reached 89.9% with the Intersection over the union threshold of 0.5. The average inference time was 22 ms in the cascade detection system. Furthermore, the harvesting efficiency was improved by 28.7 percentage points, compared with targeting to be picked at a fixed angle. The average time was 10.4 s per fruit for harvesting fruits, indicating the better performance of the improved system. This finding can also provide a strong reference for fruit and vegetable harvesting robots. [ABSTRACT FROM AUTHOR]

The upcoming experiments on heating targets using intense beams of heavy ions to be carried out within the scope of the FAIR (Facility for Antiproton and Ion Research) project require measuring the profile of the beam on the target to estimate the optimal energy uptake by materials exposed and to interpret accurately experimental results. We propose a method to diagnose the transverse distribution of intensity of ions through registering X-rays generated by the target as a result of the incident ion beam. The numerical code Geant4 is used to develop and design a full-scale experimental model to proceed with simulating X-ray diagnostics of the ion beam. The UNILAC accelerator (GSI, Germany) is used to validate the method proposed and compare it with the results of simulation. An Au+26 ion beam (energy of 11.4 MeV/a.m.u.) is passed through a copper target 10 μm thick. The X-ray image of the target is formed on a CsI scintillator using a pinhole camera. The image is recorded using a digital sCMOS camera having a brightness amplifier. [ABSTRACT FROM AUTHOR]

Solar Power Tower technology requires accurate techniques to ensure the optical performance of the heliostats both in commissioning and operation phases. This paper presents a technique based on target reflection to detect and correct canting errors in heliostat facets. A camera mounted on the back of a target heliostat sees an object heliostat and the target facets in reflection. The pixels difference between detected and theoretical borders determines the canting errors. Experiments in a lab scale testbed show that canting errors can be corrected up to an average value of around as low as 0.15 mrad. Experiments were also performed on a real heliostat at Plataforma Solar de Almería. As a result, canting errors (up to 5 mrad) have been reduced below 0.75 mrad. Mirror slope errors, which can be noticeable in large facets, becomes the largest source of inaccuracy in the presented method. [Display omitted] [ABSTRACT FROM AUTHOR]

Each HT ht parameter quantifies twist per unit length; twist increases linearly from the wing tip (no twist) to the wing base (maximum twist). The cameras are back-lit by IR LEDs and tilted upwards by HT ht to reduce wing-wing and body-wing occlusions with respect to a Cartesian camera configuration, which aligns with the lab frame of reference. This degeneracy is observed almost exclusively in the wing pitch angle, HT ht . The volume mutually seen by the cameras is HT ht , which is located at the center of a custom-made 3D-printed cage. [Extracted from the article]

The Gamma Ray Imager (GRI) is a pinhole camera providing 2D imaging of MeV hard x-ray (HXR) bremsstrahlung emission from runaway electrons (REs) over the poloidal cross section of the DIII-D tokamak. We report a series of upgrades to the GRI expanding the access to RE scenarios from the diagnosis of a trace amount of REs to high flux HXR measurements during the RE plateau phase. We present the implementation of novel gamma ray detectors based on LYSO and YAP crystals coupled to multi-pixel photon counters, enabling a count rate in excess of 1 MHz. Finally, we highlight new insights into the RE physics discovered during the current quench and RE plateau phase experiments as the result of these upgrades. [ABSTRACT FROM AUTHOR]

Multi-view stereo remains a popular choice when recovering 3D geometry, despite performance varying dramatically according to the scene content. Moreover, typical pinhole camera assumptions fail in the presence of shallow depth of field inherent to macro-scale scenes; limiting application to larger scenes with diffuse reflectance. However, the presence of defocus blur can itself be considered a useful reconstruction cue, particularly in the presence of view-dependent materials. With this in mind, we explore the complimentary nature of stereo and defocus cues in the context of multi-view 3D reconstruction; and propose a complete pipeline for scene modelling from a finite aperature camera that encompasses image formation, camera calibration and reconstruction stages. As part of our evaluation, an ablation study reveals how each cue contributes to the higher performance observed over a range of complex materials and geometries. Though of lesser concern with large apertures, the effects of image noise are also considered. By introducing pre-trained deep feature extraction into our cost function, we show a step improvement over per-pixel comparisons; as well as verify the cross-domain applicability of networks using largely in-focus training data applied to defocused images. Finally, we compare to a number of modern multi-view stereo methods, and demonstrate how the use of both cues leads to a significant increase in performance across several synthetic and real datasets. [ABSTRACT FROM AUTHOR]

This article presents the results of the low inductance vacuum spark plasma diagnostics obtained with the help of a microchannel plate (MCP) detector. Time-resolved pinhole camera images and extreme-ultraviolet (EUV) spectra were obtained for discharge plasma with a framing time in the nanosecond range. It was found that, in the case of an Al anode, discharge plasma undergoes filamentation with subsequent pinching. In the case of a Mo anode, the discharge plasma displays a stratified structure—a system of plasma layers oriented mostly normal to the discharge axis. The difference of the structure can be connected with the process of the erosion of anode at the stage of the discharge current growth. The EUV spectra of discharge plasma at the pinch stage showed the presence of Al ions up to Al VIII (ionization potential 284.6 eV) and Mo ions up to Mo XVII (ionization potential 646 eV). [ABSTRACT FROM AUTHOR]

Aiming at the problem that adaptive Monte Carlo localization (AMCL) algorithm is difficult to localize in large scenes and similar environments. This paper uses a semantic information-assisted approach to improve the AMCL algorithm. This method realizes the robust localization of the robot in the large scenes and similar environments. Firstly, the 2D grid map created by simultaneous localization and mapping using lidar can obtain highly accurate indoor environmental contour information. Secondly, the semantic object capture is achieved by using a depth camera combined with an instance segmentation algorithm. Then, the semantic grid map is created by mapping the semantic point cloud through the back-projection process of the pinhole camera. Finally, semantic grid maps are used as a priori information to assist in localization, which will be used to improve the initial particle swarm distribution in the global localization of the AMCL algorithm and thus will solve the robot localization problem in this environment. The experimental evidence shows that the semantic grid map solves the environmental information degradation problem caused by 2D lidar as well as improves the robot's perception of the environment. In addition, this paper improves the localization robustness of the AMCL algorithm in large scenes and similar environments, resulting in an average localization success rate of about 90% or even higher, and further reduces the number of iterations. The global localization problem of robots in large scenes and similar environments is effectively solved. [ABSTRACT FROM AUTHOR]

Due to the problems of low recognition accuracy and long recognition time in the traditional vehicle side blind area obstacle recognition methods, a vehicle side blind area obstacle recognition method based on binary tree support vector machine is proposed. First, build the camera pinhole model, collect the vehicle side blind area image, after correcting the image distortion, preprocess the collected vehicle side blind area image through three steps of image denoising, graying and enhancement, then use Roberts operator to detect the edge of the processed image, screen the obstacle area in the vehicle side blind area, and finally use the binary tree support vector machine to filter the obstacle area according to the selected obstacle area, The obstacles in the lateral blind area of the vehicle are classified to identify the obstacles. The simulation results show that the proposed method has higher accuracy and shorter recognition time for vehicle side blind zone obstacle recognition. [ABSTRACT FROM AUTHOR]

This work is devoted to a cost-effective method for the automatic placement of visual sensors within a smart room to ensure the requirements for its design. Various unique conditions make the process of manually placing sensors time consuming and can also lead to a decrease in system efficiency. To automate the design process, we solve a multi-objective optimization problem known as the art gallery problem in 3D, modified as follows. For the specified regions of interest within a smart room, the required pixels per meter level (PPM) should be ensured. The optimization criteria are visibility of the room and the cost of equipment. To meet these criteria, we describe a room model with doors, windows, and obstacles represented in such a way as to consider their impact on the field of view of the sensors. To model sensor placement, a genetic algorithm is used. The optimal solution is selected from the Pareto front by means of the technique for order of preference by similarity to ideal solution (TOPSIS). The developed method's effectiveness has been tested on modeling real premises of various types. The method is flexible because of the assignment of weights to certain aspects when placing sensors. Further, it can be scalable to other types of sensors. [ABSTRACT FROM AUTHOR]

On the OMEGA laser system, the beam-pointing accuracy is verified by irradiating a 4 mm diameter Au-coated spherical target with ∼23 kJ of laser energy. Up to ten x-ray pinhole cameras record the x-ray emission from all 60-beam spots. A new set of algorithms has been developed to improve the accuracy of the pointing evaluation. An updated edge-finding procedure allows one to infer the center of the sphere with subpixel accuracy. A new approach was introduced to back-propagate the pixel locations on the 2D image to the 3D surface of the sphere. A fast Fourier transform-based de-noising method significantly improves the signal-to-noise of the data. Based on the beam-pointing analysis, hard-sphere calculations of the laser-drive illumination uniformity on the target surface and the decomposition of the illumination distribution into lower order modes (1–10) are evaluated. [ABSTRACT FROM AUTHOR]

WEST (tungsten environment in steady-state tokamak) is starting operation for the first time with a water-cooled full tungsten divertor, enabling long pulse operation. Heating is provided by radiofrequency systems, including lower hybrid current drive (LHCD). In this context, a compact multi-energy hard x-ray camera has been installed for energy and space-resolved measurements of the electron temperature, the fast electron tail density produced by LHCD and runaway electrons, and the beam–target emission of tungsten at the target due to fast electron losses interacting with the divertor plates. The diagnostic is a pinhole camera based on a 2D pixel array detector (Pilatus 3 CdTe CMOS Hybrid-Pixel detector produced by DECTRIS). The novelty of this diagnostic technique is the detector's capability of adjusting the threshold energy at pixel level. This innovation provides great flexibility in the energy configuration, allowing simultaneous space and energy-resolved x-ray measurements. This contribution details two important steps in the preparation of the diagnostic operation. First, the in-vessel spatial calibration that was carried out with a radioactive source. Second, the synthetic diagnostic is obtained by the suite of codes ALOHA/C3PO/LUKE/R5-X2, which simulates LH wave propagation and absorption, as well as the fast electron bremsstrahlung production. [ABSTRACT FROM AUTHOR]

High-precision x-ray imaging diagnostics of hotspot at the stagnation stage are essential for regulating implosion asymmetry and retrieving physical implosion parameters. With regard to 10–20 keV energy band imaging, existing diagnostic instruments such as Kirkpatrick–Baez microscopes and pinhole cameras are insufficient in terms of spatial resolution and collection efficiency. The situation is even worse when high-speed, time-resolved imaging diagnostics are performed by coupling framing cameras or line-of-sight imagers. This article presents the basic principles and optical system design of a 17.48 keV modified Wolter x-ray microscope, to resolve the problems encountered in high-energy imaging diagnostics. The proposed optical configuration offers a better spatial resolution, greater depth of field, and preliminary compliance with the requirements of high precision optical processing techniques. The spatial resolution is better than 1 µm in a field range ±150 µm, and is better than 3 µm in a total field of view ∼408 µm in diameter. The geometric solid angle is calculated as 3.0 × 10−5 sr and is estimated to be 1.2 × 10−6 sr, considering the reflectivity of the double mirrors. The proposed microscope is expected to effectively improve spatial resolution and signal-to-noise ratio for high-energy imaging diagnostics. [ABSTRACT FROM AUTHOR]

High-level structure (HLS) extraction recovers 3D elements on human-made surfaces (objects, buildings, ground, etc.). There are several approaches to HLS extraction. However, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds extracted from the camera images. In general, 3D point cloud and multiple views approaches have good performance for certain scenes with video sequences or image sequences, but they need sufficient parallax in order to guarantee accuracy. To address this problem, an alternative is to process a single RGB image seeking to interpret areas of the images where the human-made structure may be observed, thus removing parallax dependency, but adding the challenge of having to interpret image ambiguities correctly. Motivated by the latter, we propose a methodology for 3D volumetric structure extraction from a single image. Our strategy is to divide and simplify the 3D structure extraction process. For that, our methodology has three steps. First, the structure recognition step provides the segmentation, location, and delimitation of the urbanized structures in the scene. Second, we propose a graph analysis to classify and locate the boundaries between the different urbanized structures in the scene. Third, we use a proposed CNN and the pinhole camera model to extract the 3D volumetric structure. On the other hand, we evaluate this methodology in synthetic and public datasets. [ABSTRACT FROM AUTHOR]

• OR-SE module was proposed to realize high-precise amodal segmentation. • Amodal masks were used to estimate size of the oudemansiella raphanipes caps. • The OR-ORCNN was robust for deocclusion and sizing tasks. • Results showed a AP50 = 91.656 % and a MAE = 0.93 mm, respectively. High-throughput acquisition of phenotypic parameters based on machine vision is important for intelligent breeding, digital cultivation and automated harvesting of Oudemansiella raphanipes. However, due to the occlusion among Oudemansiella raphanipes in the growing bed, it is challenging to accurately and rapidly capture their full shape with conventional methods, resulting in a low measurement success rate. The overall goal of this study is to propose a deep learning-based method (Oudemansiella raphanipes Occlusion Region-based Convolutional Neural Networks, OR-ORCNN) to obtain the morphology of occluded Oudemansiella raphanipes precisely in the mushroom growing bed with high efficiency. To accomplish this, a state-of-the-art attention architecture (OR-SE), which consists of CCA (Criss-Cross Attention) and MSF (Multiscale Fusion) modules, was applied to the deocclusion assignment. Then, the predicted amodal mask combined pinhole camera model was used to compute the size of Oudemansiella raphanipes caps in millimeters (mm). The experimental results showed an improvement in the AP50 of 2.78 % and mAR of 5.60 % compared with the baseline model, demonstrating the effectiveness of the OR-SE module in improving the feature information capability of backbone networks. Meanwhile, the Oudemansiella raphanipes cap size estimation results reported an MAE of 0.93 mm and a MAPE of 4.96 %. Furthermore, to confirm the robustness and generalizability of the OR-ORCNN model, we also conducted experiments on Agrocybe cylindraceas and obtained satisfactory results. In summary, the proposed algorithm provides an effective way to recover the shape of the occluded region of Oudemansiella raphanipes with high efficiency and precision, which can be used to help researchers breed intelligently and growers optimize management. [ABSTRACT FROM AUTHOR]

Gas-puff z-pinch implosions are characterized by the formation of a dense annular plasma shell, the sheath, that is driven to the axis by magnetic forces and therefore subject to the magneto-Rayleigh–Taylor instability. Here, the conditions within these sheaths are measured on the 1-MA COBRA generator at Cornell University [Greenly et al., Rev. Sci. Instrum. 79, 073501 (2008)] for various gas species and initial fill densities. The gas-puff loads are initialized by a 7 cm diameter triple-nozzle gas valve assembly with concentric outer and inner annular nozzles and a central gas jet. Thomson scattering and laser interferometry provide spatially resolved flow, temperature, and electron density profiles midway through the implosion, while extreme ultraviolet pinhole cameras record the evolution of the plasma column and photoconducting diodes measure x-ray emission. Analysis of the scattering spectra includes a means of discriminating between thermal and non-thermal broadening to test for the presence of hydrodynamic turbulence. Two types of sheath profiles are observed, those with sharp discontinuities at the leading edge and those with smooth gradients. In both cases, non-thermal broadening is generally peaked at the front of the sheath and exhibits a characteristic decay length that roughly scales with the sheath ion mean free path. We demonstrate that this non-thermal broadening term is inconsistent with laminar velocity gradients and is more consistent with dissipative turbulence driven by unstable plasma waves in a collisionless shock. The resulting differences in sheath profile are then set by the sheath ion collisionality in a manner consistent with recent 1D kinetic simulations [Angus et al., Phys. Plasmas 28, 010701 (2021)]. [ABSTRACT FROM AUTHOR]

The shock ignition (SI) approach to inertial confinement fusion is a promising scheme for achieving energy production by nuclear fusion. SI relies on using a high intensity laser pulse (≈1016 W/cm2, with a duration of several hundred ps) at the end of the fuel compression stage. However, during laser–plasma interaction (LPI), several parametric instabilities, such as stimulated Raman scattering and two plasmon decay, nonlinearly generate hot electrons (HEs). The whole behavior of HE under SI conditions, including their generation, transport, and final absorption, is still unclear and needs further experimental investigation. This paper focuses on the development of an experimental platform for SI-related experiments, which simultaneously makes use of multiple diagnostics to characterize LPI and HE generation, transport, and energy deposition. Such diagnostics include optical spectrometers, streaked optical shadowgraph, an x-ray pinhole camera, a two-dimensional x-ray imager, a Cu Kα line spectrometer, two hot-electron spectrometers, a hard x-ray (bremsstrahlung) detector, and a streaked optical pyrometer. Diagnostics successfully operated simultaneously in single-shot mode, revealing the features of HEs under SI-relevant conditions. [ABSTRACT FROM AUTHOR]

The current highway abnormal event detection efficiency is low, the leakage rate is high, real-time poor, and so on, this paper proposed highway abnormal parking detection method based on space-time relationship. Firstly, it used the traffic flow frequency to quantify the split image and removed the smaller connectivity domain to extract the road surface information, then used the perspective geometry of the pinhole camera to normalize the same scale. It used YOLOv4 network to obtain the exact position of the vehicle target, and established the space-time information matrix. To detect abnormal region and the detection results can output by the update of the space-time matrix. The experimental results show that the accuracy of this method is 95% in long-distance scenes and 93% in crowded scenes. [ABSTRACT FROM AUTHOR]

The High Energy Engineering X-ray Scattering (HEX) beamline at the National Synchrotron Light Source II (NSLS-II) is focused on advancing clean energy technologies and energy storage solutions. It allows for in-situ studies of materials under real operating or manufacturing conditions, including batteries, fuel cells, and engineering assemblies. The beamline utilizes a superconducting multipole wiggler to create X-rays with a continuous energy spectrum, and a double crystal monochromator to deliver a monochromatic beam. Experimental techniques include imaging, tomography, and diffraction. HEX is currently undergoing commissioning and will be available for use in early 2024. [Extracted from the article]

Wide field-of-view (FOV) cameras, which capture a larger scene area than narrow FOV cameras, are used in many applications including 3D reconstruction, autonomous driving, and video surveillance. However, wide-angle images contain distortions that violate the assumptions underlying pinhole camera models, resulting in object distortion, difficulties in estimating scene distance, area, and direction, and preventing the use of off-the-shelf deep models trained on undistorted images for downstream computer vision tasks. Image rectification, which aims to correct these distortions, can solve these problems. In this paper, we comprehensively survey progress in wide-angle image rectification from transformation models to rectification methods. Specifically, we first present a detailed description and discussion of the camera models used in different approaches. Then, we summarize several distortion models including radial distortion and projection distortion. Next, we review both traditional geometry-based image rectification methods and deep learning-based methods, where the former formulates distortion parameter estimation as an optimization problem and the latter treats it as a regression problem by leveraging the power of deep neural networks. We evaluate the performance of state-of-the-art methods on public datasets and show that although both kinds of methods can achieve good results, these methods only work well for specific camera models and distortion types. We also provide a strong baseline model and carry out an empirical study of different distortion models on synthetic datasets and real-world wide-angle images. Finally, we discuss several potential research directions that are expected to further advance this area in the future. [ABSTRACT FROM AUTHOR]

Two misconceptions about the mechanism of image formation in the human eye are common among students and even in textbooks and other teaching materials. The first attributes all refraction to the eye lens; the second treats the eye as a pinhole camera. To reduce these persistent conceptions of students, a series of simple experiments is presented using a water-filled fishbowl as a model of the eye. The model is used to demonstrate the propagation of light through the eye and the image formation on the retina in normal vision, nearsightedness, and farsightedness. The model also allows visualization of the corrections of malfunctions of the eye. [ABSTRACT FROM AUTHOR]

A custom Python-based Monte Carlo Simulation Tool (PMST) has been developed to assist in the development of portable gamma cameras or the generation of simulated gamma image datasets, e.g. for deep learning. The simulation tool has been validated by comparison between theoretical and experimental results. PMST includes common attenuation interactions such as the photoelectric effect and Compton and Rayleigh scattering and fluorescence and non-radiative secondary effects. It allows users to simulate and visualise a gamma imaging system with multiple absorbers, flexible geometry, and different materials. [ABSTRACT FROM AUTHOR]

• Mode shape-aided cable force estimation method is proposed for a double-hanger cable system. • A target-free method is studied to determine the spatial position of measured points. • The scaling factor considering camera title angles is theoretically derived. • A real-time vision-based monitoring system is developed. Hangers with intermediate elastic supports are widely used in bridge engineering but research work about tension estimation in these cables is rare in literature. The presence of the dampers divides the cable into two or three segments, which poses difficulties in determining an accurate vibration length to reflect the actual behavior of hangers. This paper proposes a novel mode shape-based technique to identify the tension force of a double-hanger cable system tied by a lateral clamp. The dynamic behavior of the double-hanger cable system is studied through a theoretical method and a mode shape-based method is suggested to estimate the tension force. Three major contributions are illustrated below: 1) Efforts have been made to construct the mechanical model of a double-hanger system and a mode shape-based cable force identification method is proposed via a vision system; 2) Due to the challenge of determining the position of the surface feature along the cable, a target-free method is studied in this paper to identify the spatial positions of measured points by making full use of the clamps' geometric dimensions; 3) Concerning the practical applications of the vision-based monitoring system, a theoretical model is investigated to decide the scaling factor with camera title angles based on the pinhole camera model. Both a numerical example and a laboratory test are conducted to reveal the effectiveness and accuracy of the proposed method. With the aid of a long-term vision-based monitoring system, the proposed cable force estimation method has great application potential in practical bridge maintenance. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel 6-DOF camera motion correction method is proposed using only IMU sensors. • The accuracy and robustness are verified under different distances and focal lengths. • The motion correction ratio is statistically analysed and reaches approximately 80 %. • Pixel movements are caused mainly by camera rotation, especially at long distances. • Translation-induced pixel movement is inversely correlated with the object distance. Environmental conditions such as wind and ground traffic introduce motion in camera measurement systems and affect measurement accuracy. Conventional camera motion correction methods track static reference points with one or multiple cameras, reducing applicability. This study proposes a novel 6-degree-of-freedom (DOF) camera motion correction method using only an inertial measurement unit (IMU) sensor. A Kalman filter is adopted as a data fusion method to estimate the camera orientation and translation using IMU data. Six pinhole camera models are built to evaluate and correct 6-DOF camera motions. The motion correction efficiency and robustness are tested for different object distances and focal lengths of optical lenses. The motion correction ratio is statistically analysed and reaches approximately 80%. The object distance has little effect on the motion correction ratio. The rotation-induced pixel movement is independent of the object distance. More than 90% of the pixel movement noise is caused by camera rotation. The translation-induced pixel movement is inversely correlated with the object distance. [ABSTRACT FROM AUTHOR]

We present a method for precision intrinsic calibration of pinhole-model cameras. We refer to this method as "3-Axis." The algorithm employs a target moving with known displacements along three perpendicular axes to decrease the number of unknown terms that must be determined. This method along with new evaluation strategies for comparing the accuracy of pinhole camera calibration algorithms is described in detail. The 3-Axis approach is then compared with the current state-of-the art algorithms in simulated and physical settings and is shown to exceed them in accuracy and computational performance assuming an equal number of samples acquired. [ABSTRACT FROM AUTHOR]

Underwater cameras are typically placed behind glass windows to protect them from the water. Spherical glass, a dome port, is well suited for high water pressures at great depth, allows for a large field of view, and avoids refraction if a pinhole camera is positioned exactly at the sphere's center. Adjusting a real lens perfectly to the dome center is a challenging task, both in terms of how to actually guide the centering process (e.g. visual servoing) and how to measure the alignment quality, but also, how to mechanically perform the alignment. Consequently, such systems are prone to being decentered by some offset, leading to challenging refraction patterns at the sphere that invalidate the pinhole camera model. We show that the overall camera system becomes an axial camera, even for thick domes as used for deep sea exploration and provide a non-iterative way to compute the center of refraction without requiring knowledge of exact air, glass or water properties. We also analyze the refractive geometry at the sphere, looking at effects such as forward- vs. backward decentering, iso-refraction curves and obtain a 6th-degree polynomial equation for forward projection of 3D points in thin domes. We then propose a pure underwater calibration procedure to estimate the decentering from multiple images. This estimate can either be used during adjustment to guide the mechanical position of the lens, or can be considered in photogrammetric underwater applications. [ABSTRACT FROM AUTHOR]

To improve the performance of environment perception of autonomous vehicles and enhance the ability of monocular cameras to perceive obstacle 3D and boundary information, this paper proposed a ground-aware 3D object detection algorithm. Based on an optimized Center Net model, it used DLA as the backbone network to increase the redundant information prediction of 3 D bounding box center. According to the ground-aware information of the autonomous driving scenario and combined with the pinhole camera model, it obtained the object 3D center depth information to optimize the deep network learning effect. This paper evaluated the algorithm performance using the KITTI 3 D dataset. The results show that the algorithm runs at 20 fps on the basis of ensuring the accuracy of 2D object detection, which meets the real-time requirements of autonomous driving perception. At the same time, compared with the Center Net model, there are performance improvements of 4. 4 and 4. 4% in the average orientation score and the bird eye view AP, respectively. Thus, the algorithm can improve the ability of autonomous vehicles to perceive obstacle 3D and boundary information. [ABSTRACT FROM AUTHOR]

Trapping and transmission of waves through an acoustic waveguide with a set of perforated cross-walls are studied. Eigenvalues of the corresponding spectral Neumann problem for the Laplace operator are found under geometric symmetry conditions. Almost complete transmission of the piston wave through a system of small holes (an inverted Weinstein anomaly) is achieved by fine tuning of the distance between the cross-walls with a diverse configuration of the connecting holes. A criterion for the possibility of this anomaly is obtained. Related topics—in particular, primitive wave filters and the pinhole-camera effect—are discussed. [ABSTRACT FROM AUTHOR]

The use of consumer cameras fitted with extreme wide‐angle (EWA) lenses for photogrammetric measurement is increasing. Conventional modelling of EWA systems relies on the pinhole camera model and up to five radial lens distortion terms. Aiming to reduce model complexity, this paper reports on an investigation into an alternate approach using fisheye lens models for EWA systems, despite them not falling strictly into the fisheye category. Four fisheye models were tested on four different cameras under laboratory conditions. The self‐calibration results show superior model fit for all fisheye models over the pinhole‐plus‐radial model in terms of residual RMS. The number of radial distortion terms required for the fisheye models was lower in all cases, so model complexity was reduced. Independent assessment revealed very similar 3D reconstruction accuracy for all models. The results suggest that fisheye modelling is an advantageous alternative for EWA lens systems. [ABSTRACT FROM AUTHOR]