Characteristics Of Camera Research Articles

Metoda pomiaru parametrów kamer termowizyjnych za pomocą zautomatyzowanego stanowiska pomiarowego

To achieve precise and repeatable measurements of thermal camera’s parameters, it is essential to eliminate human error. For this purpose, an automated test bench has been developed that allows fully automatic, semi-automatic and manual control of the measurement process. The designed stand supports remote control via Ethernet interface or USB connection. The developed software enables control, data recording and parameter determination of thermal cameras. The test bench ensures high quality and reliability of thermal measurements, enabling controlled examination of camera characteristics with minimal operator involvement.

Event Vision-based Corner Detection with Count-normalized Multi-Layer Perceptron and Throughput Indicator

The advancement of event cameras has sparked a revolution in imaging technology, presenting exciting opportunities for vision-based measurement tasks. Event cameras operate on an innovative asynchronous imaging principle, which offers several advantages over traditional cameras, including ultra-high dynamic range and exceptional temporal resolution. As a result, event cameras excel in challenging environments characterized by motion blur, overexposure, or underexposure, outperforming conventional frame-based cameras. However, the asynchronous signal streams generated by event cameras pose compatibility challenges with existing vision-based measurement algorithms. This paper focuses specifically on corner detection, a critical vision-based measurement task, tailored for event cameras. To address this challenge, we propose novel corner detectors that leverage advanced optimization techniques, including enhanced time surface representations, multi-layer perceptron classifiers, and an innovative throughput mechanism. Through rigorous experimentation, our method consistently shows lower projection errors compared to state-of-the-art methods across all datasets while also maintaining longer tracking times in low-textured scenarios. Specifically, our CMLP and CMLP-T methods achieve an average valid tracking rate of 83.38% and 84.65%, respectively, on the DAVIS240C dataset collection, surpassing all existing methods. We validate the effectiveness of our proposed corner detectors by demonstrating their enhanced performance compared to state-of-the-art methods. Furthermore, our work contributes to the application of machine learning in event signal processing for vision-based measurement tasks, providing insights into optimizing models for the unique characteristics of event cameras.

Enhancing robustness in asynchronous feature tracking for event cameras through fusing frame steams

Event cameras produce asynchronous discrete outputs due to the independent response of camera pixels to changes in brightness. The asynchronous and discrete nature of event data facilitate the tracking of prolonged feature trajectories. Nonetheless, this necessitates the adaptation of feature tracking techniques to efficiently process this type of data. In addressing this challenge, we proposed a hybrid data-driven feature tracking method that utilizes data from both event cameras and frame-based cameras to track features asynchronously. It mainly includes patch initialization, patch optimization, and patch association modules. In the patch initialization module, FAST corners are detected in frame images, providing points responsive to local brightness changes. The patch association module introduces a nearest-neighbor (NN) algorithm to filter new feature points effectively. The patch optimization module assesses optimization quality for tracking quality monitoring. We evaluate the tracking accuracy and robustness of our method using public and self-collected datasets, focusing on average tracking error and feature age. In contrast to the event-based Kanade–Lucas–Tomasi tracker method, our method decreases the average tracking error ranging from 1.3 to 29.2% and boosts the feature age ranging from 9.6 to 32.1%, while ensuring the computational efficiency improvement of 1.2–7.6%. Thus, our proposed feature tracking method utilizes the unique characteristics of event cameras and traditional cameras to deliver a robust and efficient tracking system.

A Binocular Color Line-Scanning Stereo Vision System for Heavy Rail Surface Detection and Correction Method of Motion Distortion.

Thanks to the line-scanning camera, the measurement method based on line-scanning stereo vision has high optical accuracy, data transmission efficiency, and a wide field of vision. It is more suitable for continuous operation and high-speed transmission of industrial product detection sites. However, the one-dimensional imaging characteristics of the line-scanning camera cause motion distortion during image data acquisition, which directly affects the accuracy of detection. Effectively reducing the influence of motion distortion is the primary problem to ensure detection accuracy. To obtain the two-dimensional color image and three-dimensional contour data of the heavy rail surface at the same time, a binocular color line-scanning stereo vision system is designed to collect the heavy rail surface data combined with the bright field illumination of the symmetrical linear light source. Aiming at the image motion distortion caused by system installation error and collaborative acquisition frame rate mismatch, this paper uses the checkerboard target and two-step cubature Kalman filter algorithm to solve the nonlinear parameters in the motion distortion model, estimate the real motion, and correct the image information. The experiments show that the accuracy of the data contained in the image is improved by 57.3% after correction.

Image-Aided LiDAR Extraction, Classification, and Characterization of Lane Markings from Mobile Mapping Data

The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%.

Research on the Deblurring Algorithm for High Frame Rate Cameras

With the rapid development of technology, high frame rate cameras have been widely used in various fields. However, due to the characteristics of high frame rate cameras, the corresponding deblur algorithm has become the focus of this research. This paper aims to propose an efficient and accurate idea to improve the imaging quality and application performance of high frame rate cameras. However, the current idea still has some limitations, and it is hoped that future studies can realize the algorithm optimization and improve the practicality and performance of the algorithm to provide better support for the application of high frame rate cameras.

The Uncertainty Assessment by the Monte Carlo Analysis of NDVI Measurements Based on Multispectral UAV Imagery.

This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry-clear, humid-hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh).

Theoretical and experimental study on spatial characteristics of a large imaging area streak camera developed for ICF precise diagnostics

The Modulation Transfer Function (MTF) offers an objective measure to assess the imaging performance of streak cameras. This study aims to enhance the diagnostic precision of streak camera used in laser-driven inertial confinement fusion research. We introduce a novel method for evaluating the MTF of a recently developed large imaging area streak tube. This method integrates the electron optical MTF theory and the production of a reticle plate-type photocathode. Using ultraviolet and femtosecond lasers, we established two calibration platforms for the streak camera in both static and dynamic modes. We measured the radius of the electrons reaching fluorescent screen and obtained MTFs. Our findings reveal that the ultimate spatial resolutions at off-axis distances of 1.2, 4.7, 8.2, and 11.7mm are 28.5, 27.2, 26.4, 24.9 lp/mm (static) and 25.0, 22.8, 23.0, 21.2 lp/mm (dynamic). Notably, we successfully charted the ultimate spatial resolution distributions across the streak camera's full imaging area in both modes.

Experimental Study on Reducing Wavelength Dependency of Spatial Resolution Characteristics of a Digital Camera by MTF Correction

Experimental Study on Reducing Wavelength Dependency of Spatial Resolution Characteristics of a Digital Camera by MTF Correction

An Anti-Noise-Designed Residual Phase Unwrapping Neural Network for Digital Speckle Pattern Interferometry

DSPI (Digital Speckle Pattern Interferometry) is a non-destructive optical measurement technique that obtains phase information of an object through phase unwrapping. Traditional phase unwrapping algorithms depend on the quality of the images, which demands preprocessing such as filtering and denoising. Moreover, the unwrapping time is highly influenced by the size of the images. In this study, we proposed a new deep learning-based phase unwrapping algorithm combining the residual network and U-Net network. Additionally, we incorporated an improved SSIM function as the loss function based on camera characteristics. The experimental results demonstrated that the proposed method achieved higher quality in highly noisy phase unwrapping maps compared to traditional algorithms, with SSIM values consistently above 0.98. In addition, we applied image stitching to the network to process maps of various sizes and the unwrapping time remained around 1 s even for larger images. In conclusion, our proposed network is able to achieve efficient and accurate phase unwrapping.

Analysis of phase calculation error introduced by the extinction ratio in instantaneous phase shifting interference.

Instantaneous phase shifting interferometry technology, the core component of which is the pixel micropolarizer camera, has been widely used in commercial interferometers. This technology has the superiority of single-frame acquisition, vibration insensitivity, and no need for phase shifting devices. However, due to manufacturing defects and accuracy limitations, the extinction ratios (ER) of the micropolarizer array are different and fairly small, directly affecting the phase calculation accuracy. This paper initially derives a theoretical expression for the phase calculation error introduced by the extinction ratio (ER) and proposes the error correction model to reduce phase calculation errors caused by the extinction ratio. The theoretical analysis can serve as an important basis for accurately assessing the polarization characteristics of a pixel micropolarizer camera. Quantifying the impact of the extinction ratios provides significant support for the selection of polarization equipment. In addition, the paper proposes a calibration model to improve measurement accuracy, which can serve as an effective means to reduce the impact of the extinction ratio (ER). The innovative research content revealed the influence of extinction ratio (ER), serving as a valuable complement to the existing analysis and research on extinction ratio (ER).

Application of Digital Image Correlation to Field Monitoring of Masonry Arch Bridges

Many masonry bridges constructed in Europe during the Industrial Revolution are still in service. For over a century, continuous operation and weathering effects have contributed to material degradation in the main bridge components. To ensure that old masonry bridges remain safe even under increasing traffic loading, it is crucial to identify potential deterioration in bridge performance before the onset of critical irreversible damage. This task can be accomplished via periodic monitoring of the bridge response under traffic loading. Digital Image Correlation (DIC) is an effective non-contact method to measure displacements and strains. Most DIC applications have been in laboratory settings, and DIC techniques have yet to be extensively applied in the field. This paper showcases the potential of DIC monitoring for serviceability assessment of masonry bridges. Some results of an extensive monitoring program on railway viaducts in the UK are presented. Structures with different geometrical characteristics, including pier heights, span lengths, and span-to-rise ratios, have been investigated. The results obtained using specific targets attached to the monitored structures or using the actual masonry texture as natural targets have been compared; consideration is also given to the influence of camera characteristics and settings. A significant impact of the camera depth of field has been established, especially when measuring deflections at different locations on the intrados of the arch barrel.

Investigating image-based fallow weed detection performance on Raphanus sativus and Avena sativa at speeds up to 30 km h−1

Site-specific weed control (SSWC) can provide considerable reductions in weed control costs and herbicide usage. Despite the promise of machine vision for SSWC systems and the importance of ground speed in weed control efficacy, there has been little investigation of the role of ground speed and camera characteristics on weed detection performance. Here, we compare the performance of four camera/software combinations using the open-source OpenWeedLocator platform – (1) default settings on a Raspberry Pi HQ camera (HQ1); (2) optimised software settings on a HQ camera (HQ2), (3) optimised software settings on the Raspberry Pi v2 camera (V2); and (4) a global shutter Arducam AR0234 camera (ARD) – at speeds ranging from 5 km h−1 to 30 km h−1. A combined excess green (ExG) and hue, saturation, value (HSV) thresholding algorithm was used for testing under fallow conditions using tillage radish (Raphanus sativus) and forage oats (Avena sativa) as representative broadleaf and grass weeds, respectively. ARD demonstrated the highest recall among camera systems, with up to 100 % recall for tillage radish and 91.6 % for forage oats at 5 km h−1. At 30 km h−1 ARD recall for forage oats declined (P < 0.05) by 12.4 %. There was no significant change for tillage radish. All cameras experienced a decrease in recall as speed increased. The highest rate of decrease was observed for HQ1 with 1.12 % and 0.90 % reductions in recall for every km/h increase in speed for tillage radish and forage oats, respectively. Detection of the ‘grassy’ forage oats was worse (P < 0.05) than the broadleaved tillage radish for all cameras. Despite the variations in recall, HQ1, HQ2, and V2 maintained near-perfect precision at all tested speeds. The variable effect of ground speed and camera system on detection performance of grass and broadleaf weeds, indicates that careful hardware and software considerations must be made when developing SSWC systems.

High dynamic range 3D shape measurement based on crosstalk characteristics of a color camera.

Fringe projection profilometry (FPP) has been widely used in many fields due to its fast speed, high accuracy and full-field characteristics. However, it is still a challenging problem to deal with high dynamic range (HDR) objects for traditional FPP, which utilizes a single exposure time or a single projection intensity. Overexposure will occur in areas with large reflectivity, which exceeds the maximum capturing capacity of camera sensors, resulting in the failure to obtain the accurate intensity, absolute phase and three-dimensional (3D) data. In this paper, a uniform blue image is projected to divide object surface into three areas with different reflectivity by using different intensity responses of RGB channels of color images. Crosstalk coefficient function is applied to obtain intensity of overexposed areas, and then the optimal exposure time of areas is calculated by the linear photometric response of the camera. Finally, three sets of blue fringe patterns with optimal exposure time are synthesized into the fused HDR images to calculate the absolute phase. Experimental results confirm that the proposed method can accurately measure HDR objects with large variation range of reflectivity.

Ground Testing of the Landing Platform Television System of the Exomars-2022 Spacecraft

Ground testing results are presented for the landing platform television system (TSPP) within the complex of scientific payloads onboard the ExoMars-2022 spacecraft. In the course of the ground testing, the different operation modes have been checked and the camera characteristics have been measured and calibrated. The Space Research Institute (IKI RAS) has obtained photographic material from each camera, with the cameras being installed on a stand that simulates in full scale the ExoMars-2022 landing platform. In addition, special measurements have been collected for the cameras’ most important characteristics: horizontal and vertical angular field of view, distortion, focal length, resolution, dynamic range, vignetting coefficient, and absolute sensitivity.

Personalized Ambient Pollution Estimation Based on Stationary-Camera-Taken Images Under Cross-Camera Information Sharing in Smart City

Timely and high-density air quality monitoring is essential for the development of future smart cities. The images captured from widely deployed stationary cameras can be transferred quickly via the Internet of Things (IoT) to facilitate ambient pollution estimation anytime anywhere. Imaged-based air pollution estimation is normally formulated as a supervised learning problem, relying on an extended number of image samples. However, individual stationary cameras can offer only very limited samples and scenes, whilst locally trained estimation models can easily overfit. A global method was proposed to address this challenge. The global model was trained via images captured from different cameras. However, such a model is less effective in extracting local features from scenes. A personalized method is therefore proposed to improve not only the generalization of the estimation model but also to preserve the local characteristics of individual cameras. Our personalized method consists of a two-stage architecture: (1) images from different cameras are used to train the global estimation model to avoid overfitting due to fixed scenes and small sample size; (2) the global model is further refined by images captured from individual cameras separately for adapting local characteristics. To evaluate our proposed personalized method, a large dataset was constructed, based on stationary camera-taken images captured in Hong Kong, consisting of different pollution measurements, including PM2.5, PM10, NO2 and O3. As compared to the local model, our proposed personalized model has reduced average MAE by 5.68% and average SMAPE by 6.82%, and improved average r by 4.69%.

Design and testing of star tracker algorithms for autonomous optical line-of-sight deep-space navigation.

This paper aims to investigate the capabilities of exploiting optical line-of-sight navigation using star trackers. First, a synthetic image simulator is developed to generate realistic images, which is later exploited to test the star tracker's performance. Then, generic considerations regarding attitude estimation are drawn, highlighting how the camera's characteristics influence the accuracy of the estimation. The full attitude estimation chain is designed and analyzed in order to maximize the performance in a deep-space cruising scenario. After that, the focus is shifted to the actual planet-centroiding algorithm, with particular emphasis on the illumination compensation routine, which is shown to be fundamental to achieving the required navigation accuracy. The influence of the center of the planet within the singular pixel is investigated, showing how this uncontrollable parameter can lower performance. Finally, the complete algorithm chain is tested with the synthetic image simulator in a wide range of scenarios. The final promising results show that with the selected hardware, even in the higher noise condition, it is possible to achieve a direction's azimuth and elevation angle error in the order of 1-2arc sec for Venus, and below 1arc sec for Jupiter, for a spacecraft placed at 1AU from the Sun. These values finally allow for a positioning error below 1000km, which is in line with the current non-autonomous navigation state-of-the-art.

An effective stereo SLAM with high-level primitives in underwater environment

Visual simultaneous localization and mapping (SLAM) algorithms face challenges in complex underwater scenarios, such as turbidity, dynamism, and low texture, where point features are unreliable and can lead to weakened or even failed systems. To overcome these issues, high-level object features are considered due to their accuracy and robustness. In this paper, we introduce an effective object-level SLAM method that employs a stereo camera to enhance the navigation robustness of autonomous underwater vehicles and generates a detailed semantic map. Point features and object features are integrated to serve the proposed approach. We begin by detecting 2D objects in images using a state-of-the-art neural network, followed by obtaining 3D objects described by the general model through the principle of multi-view geometry and eventually constructing semantic landmarks. To account for object data association, we present an object match method that takes into consideration the stereo camera characteristics in a single stereo frame and a filter-based approach to track the landmarks in odometry. Experiments are also conducted using the KITTI dataset and our sequences collected from the pool and coast. The evaluation results indicate that the proposed method can improve the performance of ORBSLAM2 in terms of both navigation robustness and mapping information in underwater scenarios.

Determining the Feasibility of Applying Existing Criteria for Solving Operational Problems in the Design of CCTV Information Systems

Comparative analysis of the criteria for designing video surveillance information systems was conducted based on international and national CCTV standards. Their greatest differences concern identification and recognition tasks, and the smallest -– monitoring and detection. A comprehensive analysis of the impact of the technical characteristics of video cameras on the results of solving CCTV operational problems has not been carried out at this time. Therefore, the current direction of research is to determine the maximum feasibility of using the formed set of criteria. Increasing the resolution of video cameras leads to an increase in the number of pixels per meter of linear field of view. This in turn improves the display quality of the surveillance object at a greater distance from the camera. With the emergence of new information technologies, the resolution of matrices in video surveillance systems will increase. It will lead to qualitative and quantitative changes in the established set of criteria for designing of CCTV information systems. The methodology of estimation of spatial resolution of images of video surveillance information systems (CCTV) is presented in this work. The theoretical foundations for analyzing a set of technical characteristics of video cameras are confirmed by the results of computer modeling using specialized software "IP Video System Design Tool". The overall discrepancy between the calculated and modeled data does not exceed 3%. As a result, it was found that the use of a 25 MP video camera satisfies and exceeds all currently existing criteria for designing CCTV information systems for solving various types of operational tasks and the possibility of human vision. Therefore, with the cheapening and widespread use of video cameras with a resolution of over 25 MP, it will be necessary to review the feasibility of using existing criteria for designing CCTV information systems.

A Study of Using Synthetic Data for Effective Association Knowledge Learning

Association, aiming to link bounding boxes of the same identity in a video sequence, is a central component in multi-object tracking (MOT). To train association modules, e.g., parametric networks, real video data are usually used. However, annotating person tracks in consecutive video frames is expensive, and such real data, due to its inflexibility, offer us limited opportunities to evaluate the system performance w.r.t. changing tracking scenarios. In this paper, we study whether 3D synthetic data can replace real-world videos for association training. Specifically, we introduce a large-scale synthetic data engine named MOTX, where the motion characteristics of cameras and objects are manually configured to be similar to those of real-world datasets. We show that, compared with real data, association knowledge obtained from synthetic data can achieve very similar performance on real-world test sets without domain adaption techniques. Our intriguing observation is credited to two factors. First and foremost, 3D engines can well simulate motion factors such as camera movement, camera view, and object movement so that the simulated videos can provide association modules with effective motion features. Second, the experimental results show that the appearance domain gap hardly harms the learning of association knowledge. In addition, the strong customization ability of MOTX allows us to quantitatively assess the impact of motion factors on MOT, which brings new insights to the community.