Camera Image Processing Research Articles

Simulation and Analysis of Imaging Process of Phosphor Screens for X-Ray Imaging of Streak Tube Using Geant4-Based Monte Carlo Method

Ultrafast diagnostic technology has caused breakthroughs in fields such as inertial confinement fusion, particle accelerator research, and laser-induced phenomena. As the most widely used tool for ultrafast diagnostic technology, investigating the characteristics of streak cameras in the imaging process and streak tubes’ complex physical processes is significant for its overall development. In this work, the imaging process of a streak camera is modeled and simulated using Geant4-based Monte Carlo simulations. Based on the selected phosphor screen P43 (Gd2O2S: Tb) and charged coupled device (CCD) sensor parameters, Monte Carlo simulation models of phosphor screens and CCD sensors(We refer to the sensor parameters of the US company onsemi’s KAF-50100 sensor, but some adjustments are made during the simulation), implemented with the toolkit Geant4, are used to study the electron beam to generate fluorescence on phosphor and photoelectrons on CCD sensors. The physical process of a high-energy electron beam hitting a phosphor screen and imaging on the CCD camera is studied. Meanwhile, merits such as the luminous efficiency of the selected phosphor, spatial resolution of the phosphor screen, and spatial resolution of the selected CCD sensor are analyzed. The simulation results show that the phosphor screen and CCD sensor simulation models can accurately simulate the selected components’ performance parameters with the imaging process’ simulation results precisely reflecting the distribution of output electrons in the streak image tube. References for simulation and device selection in the subsequent research on streak cameras can be provided.

Optical Automatic Contour Tracing (O-ACT) - A novel optical image-guided contour tracing method for electron beam shaping.

Electron therapy, vital for treating skin lesions and superficial tumors, demands precise electron cutouts to ensure accurate dose delivery. Traditional manual tracing methods introduce uncertainties and inefficiencies, necessitating innovative solutions for custom block creation. To introduce and validate the Optical Automatic Contour Tracing (O-ACT) method, enhancing electron cutout generation's accuracy and efficiency through optical imaging and software automation. Utilizing a 3D-printed holder, a centrally-mounted charge-coupled device (CCD) camera on the electron applicator secured two distinct perspective images of the skin's contour at varying heights. Through image binarization and skeletonization, we identified the clinical target contour's region-of-interests (ROIs) in each setup image. Employing distances from each ROI's center, assessed at 5-degree intervals from both images, we reconstructed the target contour on the skin. The magnification factor, set at a 95cm source-to-point distance, determined the final cutout shape. We crafted an in-house software in MATLAB for camera calibration and image processing and juxtaposed our results against the standard clinical cutout from the treatment planning system (TPS) using a correlation coefficient based on masked binary images' mutual information. Additionally, we performed dosimetric evaluations using abstract shapes to compare O-ACT with other methods. Our methodology yielded cutout shapes exhibiting remarkable alignment with the TPS clinical cutout. O-ACT demonstrated superior precision in generating cutout shapes that closely align with the contours in TPS, improving upon other methods in terms of adaptability to patient body shapes and contour accuracy. Dosimetric evaluations showed minimal differences between methods, with O-ACT providing slightly more consistent results. Dose profile analyses in penumbra regions indicated O-ACT's improved accuracy compared to conventional methods. Pushing the boundaries of traditional practices, our O-ACT offers a more accurate, efficient, and reproducible method for custom electron cutout creation from clinical setup images. This innovation promises not only to streamline clinical workflow but also to potentially uplift clinical outcomes in radiation oncology by providing more accurate patient-specific treatment accessary.

DESIGN OF A CAMERA-ENABLED MOBILE ROBOT FOR IN-PIPE INSPECTION

The role of pipeline infrastructure is crucial in modern society, as it transports necessary resources such as water, gas, and oil. It is essential to prioritize the maintenance of these pipelines to prevent leaks, environmental harm, and economic losses. Traditional inspection techniques, which involve manual inspection, can be inefficient, laborious, and susceptible to mistakes. To overcome these obstacles, this article introduces a camera-equipped mobile robot specifically created for automated crack detection in pipelines. The robot, which is equipped with a high-resolution camera and advanced image processing algorithms, navigates inside pipelines on its own to capture detailed images of the pipe walls. These images are then processed using computer vision techniques to identify and categorize potential cracks. The algorithms utilize machine learning models that have been trained on a dataset of labelled crack images, enabling accurate and efficient crack detection. This paper provides a comprehensive overview of the robot system's design, development, and validation, detailing the critical hardware components such as the mobility platform, camera system, and sensor integration, as well as the software architecture, which encompasses image processing and machine learning frameworks. Field studies demonstrate the robot's ability to effectively detect cracks across various pipe materials and under diverse environmental conditions. Results indicate that the automated system not only improves inspection efficiency and accuracy but also significantly reduces the need for human intervention, thereby enhancing overall pipeline safety Keywords: pipeline inspection, crack detection, mobile robot, image processing, computer vision.

Structural dynamics of individual plant fibres: a contribution to the understanding of damping properties of bio-based composites

The field of bio-based composites faces challenges in understanding the physical mechanisms providing excellent compromise between stiffness and damping. Potential sources of damping, including the matrix, the fibre, and the interfaces (fibres/fibres and fibres/matrix) are identified in the literature. To better understand damping at the composite scale, it is necessary to understand the dissipation causes at each scale of the material. However, the mechanical characteristics of individual plant fibres, such as modulus and damping, can be uncertain due to the complexity of measuring their properties at such small scale (10 µm diameter, a few mm long). To address this knowledge gap, this work proposes a method based on dynamic response of individual plant fibres. By exciting the fibre with a piezoelectric actuator and using high-speed camera image processing to evaluate displacements along the fibre, it becomes possible to determine intrinsic dynamic properties such as the loss factor and storage modulus. Controlled environment tests are conducted to investigate the influence of external parameters such as temperature and pressure on the measurement results. By utilizing this method, the aim is to obtain a better understanding of the damping properties of plant fibres and ultimately improve design choices for bio-based composite materials.

Properties of biochars derived from different straw at 500℃ pyrolytic temperature: Implications for their use to improving acidic soil water retention

Climate change cause extreme weather effects with temperature increases and drops in humidity, such as drought and heatwaves, which will lead to more evaporation in arid and semi-arid lands. The application of biochar made from crop straw without burning to farmland can effectively improve the water retention capacity of soil. A testing program has been carried out in a climate simulation laboratory to study the effects of different straw biochars on the cracking and evaporation of soils due to drying. Biochar from wheat straw (WS), corn straw (CS) and rice straw (RS) is produced at a pyrolysis temperature of 500℃. Thermogravimetric analysis and elemental analysis are carried out to obtain the properties of the biochar. Five percent of WS, CS and RS biochar by weight is added to acidic soil. Digital camera and digital image processing technology are used to analyze the crack morphology of the samples during evaporation. The results indicate that the RS biochar has the highest ash content (32.5 %), CS biochar has the highest content of volatile solid (25.36 %) and WS biochar has the highest content of fixed carbon (55.38 %). Biochar can effectively improve the water retention capacity of soil. The final water contents of the WS, CS and RS biochar soil samples are 132.3 %, 101.0 %, and 20.7 % respectively higher than that of the soil without biochar. Moreover, biochar can effectively reduce the degree of soil cracking. The addition of WS, CS and RS biochar can reduce soil cracking by 9.21 %, 16.57 %, and 7.46 %, respectively. WS contains more total cellulose than CS and RS, so WS biochar is the best choice to improve soil water retention ability. Therefore, biochar technology helps to optimize soil water retention while avoiding environmental pollution from straw burning.

Verification of Criterion-Related Validity for Developing a Markerless Hand Tracking Device.

Physicians, physical therapists, and occupational therapists have traditionally assessed hand motor function in hemiplegic patients but often struggle to evaluate complex hand movements. To address this issue, in 2019, we developed Fahrenheit, a device and algorithm that uses infrared camera image processing to estimate hand paralysis. However, due to Fahrenheit's dependency on specialized equipment, we conceived a simpler solution: developing a smartphone app that integrates MediaPipe. The objective of this study was to measure hand movements in stroke patients using both MediaPipe and Fahrenheit and to assess their criterion-related validity. The analysis revealed moderate-to-high correlations between the two methods. Consistent results were also observed in the peak angle and velocity comparisons across the severity stages. Because Fahrenheit determines finger recovery status based on these measures, it has the potential to transfer this function to MediaPipe. This study highlighted the potential use of MediaPipe in paralysis estimation applications.

Polar-vision1: A Novel Collinearity Equation of Perspective Projection in Polar Coordinate System

Abstract. Progress has been made in the community of photogrammetry and 3d computer vision in addressing the mathematical challenge posed by the collinearity equation. We introduce a new method for establishing the coordinate reference for 2d pixels and 3d landmarks using 'angular coordinates'. The mathematical relationships required for converting 3d landmarks, expressed in angular coordinates, to the camera framework are presented. The landmarks are then projected using perspective projection to obtain 2d pixels represented in angular coordinates. This framework is formally nominated as the 'Polar-vision1', which has been developed and integrated into the commercial software G3D-Cluster. Its application to pinhole camera image processing has demonstrated superior efficiency and admission rates of tie points, as well as reconstruction detail capabilities, compared to OpenMVG, achieving approximately a 1.4× improvement. The project 'Key Technologies and Tool System for Realistic 3D Modeling through Integration of Multi-Source Information in the Space-Air-Ground Domain' was awarded First Prize at the 2023 Surveying Science and Technology Awards, with Polar-vision1 as one of the innovative points.

Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera

This paper presents a hierarchical baseline stereo-matching framework for depth estimation using a novelly developed light field camera. The imaging process of a micro-lens array-based light field camera is derived. A macro-pixel map is constructed by treating each micro-lens as one macro-pixel in the light field’s raw image. For each macro-pixel, a feature vector is represented by leveraging texture and gradient cues over the surrounding ring of neighboring macro-pixels. Next, the micro-lenses containing edges are detected on the macro-pixel map. Hierarchical baseline stereo-matching is performed by macro-pixel-wise coarse matching and pixel-wise fine matching, effectively eliminating matching ambiguities. Finally, a post-processing step is applied to improve accuracy. The lab-designed light field camera’s imaging performance is evaluated in terms of accuracy and processing speed by capturing real-world scenes under studio lighting conditions. And an experiment using rendered synthetic samples is conducted for quantitative evaluation, showing that depth maps with local details can be accurately recovered.

HIGH-SPEED VIDEO IMAGE ANALYSIS OF LIQUID METAL ATOMIZATION PROCESS

Gas-assisted atomization is used to produce metal powders for additive manufacturing. In these systems the primary atomization governs the powder size distribution, but it is experimentally challenging to investigate. In this study, the primary atomization of a molten metal spray was monitored with a calibrated high-speed camera and digital image processing. The technique enabled the generation of spatio-temporal maps at different distances from the nozzle. Among the characteristics of the spray, mean values and standard deviation of the spray width along the spraying direction, and the frequency of the oscillation of the spray boundaries are presented. Statistically relevant spray asymmetries were identified, which can be caused by faulty spray operation in form of a localized metal solidification at the pouring nozzle. By systematically varying the gas temperature and the gas atomization pressure, it is shown that heating of the gas up to 573 K stabilizes the spraying process and enhances the liquid dispersion in the recirculation zone.

The Determination of Dawn Time through Image Processing Camera

Determining the early time prayer is very fundamental for Muslims as it directly relates to the legal requirements of prayer. Prayers are not performed whenever we want, but rather there is a determination of the beginning and end of the prayer time as a guideline for Muslims to carry it out. The Indonesia government sets standards for Muslims to perform the dawn prayer service, by precisely determining the degree of the emergence of the dawn of Sadiq by -200. This study aims to compare the determination of the government's dawn time using different sensors, specifically drone cameras as image sensors. Drones were chosen due to their several advantages. The data generated by the drone is in the form of photos, which are subsequently processed using digital image processing software, called image-J. The data obtained are in the form of mean and standard deviation. All data collected in 1 day is recorded using Excel to form a graph of data which is then carried out by a polynomial approach to find out the cutoff point as the beginning of the dawn of Sadiq which indicates the start of dawn. The method used in this research is using the 4th order polynomial approach and the Sarrus method and the data obtained is the mean value and standard deviation. The conclusions obtained in the image analysis research are that the government's dawn time is 15 minutes too fast, the standard obtained in this study is -14.98° and unlike 2D SQM data, 3D drone data results in more accurate data analysis.

Reading Assistant for Visually Challenged Peoples with Advance Image Capturing Technique Using Machine Learning

Since blindness prevents a person from learning about their surroundings, it is difficult for them to independently navigate, recognise items, avoid hazards, and read. In this essay, we provide a ground-breaking system for visually impaired people who use assistive technology. The concept incorporates a camera, sensors, and effective image processing algorithms that use Raspberry Pi for object detection and obstacle avoidance. Ultrasonic sensors and the camera both measure the user's distance from the obstruction. The system consists of integrated reading help that first generates an audio response before converting images to text. The complete apparatus is small and light, and it can be easily and inexpensively mounted on a regular pair of eyeglasses. The entire system is affordable, easy to use, and can be attached to a regular pair of eyeglasses. It is also portable and lightweight. Ten people who are completely blind will be used to compare the performance of the suggested device to the traditional white cane. The evaluations are conducted in controlled environments intended to mimic day-to-day activities for blind persons. The findings show that the proposed device provides more accessibility, comfort, and simplicity of navigation for the blind when compared to the white cane.

Adaptive optimal exposure selection based on time cost function for 3D reconstruction of high dynamic range surfaces

High dynamic range surfaces are prone to overexposure and underexposure in the camera imaging process, which poses a challenging issue for three-dimensional (3D) reconstruction. Capturing multiple images of high dynamic range surfaces at different exposure times is widely used for 3D reconstruction, and the exposure sequence can significantly affect reconstruction performance. However, most existing methods select the exposure sequence based on the overall performance of the image or groups of image pixels, without considering individual pixels in the image. To circumvent the problem, this paper presents the adaptive optimal exposure sequence selection based on time cost function for the 3D reconstruction of high dynamic range surfaces. Different from existing methods that use the overall performance, the acceptable exposure time range for each pixel in the image is determined independently. Furthermore, the time cost function is established to evaluate the priority of each exposure time and used to select the optimal exposure sequence iteratively. Experimental results verify both the reconstruction performance and suitability of the proposed method.

Assessment of adulteration in honey by artificial sweeteners using dynamic laser speckle technique

Our research work uses the dynamic laser speckle technique for the first time to analyze the adulteration of “Dabur Honey” with two golden syrups Dhampur Green Golden Syrup (DGGS) and Solar Golden Syrup (SGS) as adulterate. Identifying 25% adulteration in honey by sweeteners is challenging due to several reasons, such as the complexity of honey composition and the similarity of some sweeteners to honey. A spatially filtered He-Ne laser beam is made to pass through adulterated samples. Forwarding scattered light is captured using a charge-coupled device (CCD) camera and Digital Image Processing is used to analyze the images. Seven textures and six features of images are evaluated. For 20% adulteration in honey by both the adulterates, a significant percentage change of 17%, 16%, 16%, 13%, and 11% is observed in Angular Second Moment, Covariance, Autocorrelation, Inertia, and Kurtosis respectively. Additionally, for a 20% adulteration in honey by DGGS and SGS separately, a notable percentage change of 3.8% and 6.6% respectively in the value of Inertia Moment (IM) is obtained. Thus, it is concluded that the above image processing algorithms can be used to detect 20% adulteration in pure honey.

An autonomous system design for mold loading on press brake machines using a camera platform, deep learning, and image processing

An autonomous system design for mold loading on press brake machines using a camera platform, deep learning, and image processing

Efficient Roundabout Supervision: Real-Time Vehicle Detection and Tracking on Nvidia Jetson Nano

In recent years, a significant number of people in Morocco have been commuting daily to Casablanca, the country’s economic capital. This heavy traffic flow has led to congestion and accidents during certain times of the day as the city’s roads cannot handle the high volume of vehicles passing through. To address this issue, it is essential to expand the infrastructure based on accurate traffic-flow data. In collaboration with the municipality of Bouskoura, a neighboring city of Casablanca, we proposed installing a smart camera on the primary route connecting the two cities. This camera would enable us to gather accurate statistics on the number and types of vehicles crossing the road, which can be used to adapt and redesign the existing infrastructure. We implemented our system using the YOLOv7-tiny object detection model to detect and classify the various types of vehicles (such as trucks, cars, motorcycles, and buses) crossing the main road. Additionally, we used the Deep SORT tracking method to track each vehicle appearing on the camera and to provide the total number of each class for each lane, as well as the number of vehicles passing from one lane to another. Furthermore, we deployed our solution on an embedded system, specifically the Nvidia Jetson Nano. This allowed us to create a compact and efficient system that is capable of a real-time processing of camera images, making it suitable for deployment in various scenarios where limited resources are required. Deploying our solution on the Nvidia Jetson Nano showed promising results, and we believe that this approach could be applied in similar traffic-surveillance projects to provide accurate and reliable data for better decision-making.

Object Grasp Control of a 3D Robot Arm by Combining EOG Gaze Estimation and Camera-Based Object Recognition.

The purpose of this paper is to quickly and stably achieve grasping objects with a 3D robot arm controlled by electrooculography (EOG) signals. A EOG signal is a biological signal generated when the eyeballs move, leading to gaze estimation. In conventional research, gaze estimation has been used to control a 3D robot arm for welfare purposes. However, it is known that the EOG signal loses some of the eye movement information when it travels through the skin, resulting in errors in EOG gaze estimation. Thus, EOG gaze estimation is difficult to point out the object accurately, and the object may not be appropriately grasped. Therefore, developing a methodology to compensate, for the lost information and increase spatial accuracy is important. This paper aims to realize highly accurate object grasping with a robot arm by combining EMG gaze estimation and the object recognition of camera image processing. The system consists of a robot arm, top and side cameras, a display showing the camera images, and an EOG measurement analyzer. The user manipulates the robot arm through the camera images, which can be switched, and the EOG gaze estimation can specify the object. In the beginning, the user gazes at the screen's center position and then moves their eyes to gaze at the object to be grasped. After that, the proposed system recognizes the object in the camera image via image processing and grasps it using the object centroid. The object selection is based on the object centroid closest to the estimated gaze position within a certain distance (threshold), thus enabling highly accurate object grasping. The observed size of the object on the screen can differ depending on the camera installation and the screen display state. Therefore, it is crucial to set the distance threshold from the object centroid for object selection. The first experiment is conducted to clarify the distance error of the EOG gaze estimation in the proposed system configuration. As a result, it is confirmed that the range of the distance error is 1.8-3.0 cm. The second experiment is conducted to evaluate the performance of the object grasping by setting two thresholds from the first experimental results: the medium distance error value of 2 cm and the maximum distance error value of 3 cm. As a result, it is found that the grasping speed of the 3 cm threshold is 27% faster than that of the 2 cm threshold due to more stable object selection.

Single-image HDR reconstruction by dual learning the camera imaging process

Single-image HDR reconstruction by dual learning the camera imaging process

In-situ 3D contour measurement for laser powder bed fusion based on phase guidance

In-situ layerwise imaging measurement of laser powder bed fusion (LPBF) provides a wealth of forming and defect data which enables monitoring of components quality and powder bed homogeneity. Using high-resolution camera layerwise imaging and image processing algorithms to monitor fusion area and powder bed geometric defects has been studied by many researchers, which successfully monitored the contours of components and evaluated their accuracy. However, research for the methods of in-situ 3D contour measurement or component edge warping identification is rare. In this study, a 3D contour measurement method combining gray intensity and phase difference is proposed, and its accuracy is verified by designed experiments. The results show that the high-precision of the 3D contours can be achieved by the constructed energy minimization function. This method can detect the deviations of common geometric features as well as warpage at LPBF component edges, and provides fundamental data for in-situ quality monitoring tools.

Time-Series Fusion-Based Multicamera Self-Calibration for Free-View Video Generation in Low-Texture Sports Scene

Multicamera calibration is an important technique for generating free-view videos. By arranging multiple cameras in a scene after camera calibration and image processing, a multidimensional viewing experience can be presented to the audience. To address the problem that low texture cannot be robustly self-calibrated in common sports scenes when placing artificial markers or towers in the calibration process is impractical, this article proposes a robust multicamera calibration method based on sequence feature matching and fusion. Additionally, to validate the effectiveness of the proposed calibration algorithm, a virtual axis fast bullet-time synthesis algorithm is proposed for generating a free-view video. First, camera self-calibration is performed in low-texture situations by fusing dynamic objects in time series to enrich geometric constraints in scenes without the use of calibration panels or additional artificial markers. Second, a virtual-axis bullet-time video synthesis method based on the calibration result is proposed. In the calibrated multicamera scenario, a fast bullet-time video is generated by constructing a virtual axis. Qualitative and quantitative experiments in comparison with a state-of-the-art calibration method demonstrate the validity and robustness of the proposed calibration algorithm for free-view video synthesis tasks.

Missing Recovery: Single Image Reflection Removal based on Auxiliary Prior Learning.

Photographs taken through a glass window are susceptible to disturbances due to reflection. Therefore, single image reflection removal is crucial to image quality enhancement. In this paper, a novel learning architecture that can address this ill-posed problem is proposed. First, a novel reflection removal pipeline was designed to reconstruct the missing information caused by the camera imaging process using the proposed missing recovery network. Second, to address the issues in existing reflection removal strategies, we revisit several auxiliary priors and integrate them by defining an energy function. To solve the energy function, a convolutional neural network-based optimization scheme was proposed. Finally, we investigated the dark channel responses of reflection and clean images and found an interesting way to distinguish between these two types of images. We prove this property mathematically and propose a novel loss function called dark channel loss to improve performance. Experiments show that the proposed method outperforms state-of-the-art reflection removal methods both quantitatively and qualitatively.