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Several miniaturized viscometers, or microviscometers, have been developed exploiting numerous rapid prototyping techniques. Among them, paper microstrips, famously known as microfluidic paper-based analytical devices ($\mu $ PADs), have become popular due to their cost-efficacy, simple fabrication, fast response, and easily disposable. Many fabrication methods are existing to develop paper microstrips. Herein, an alternative fabrication method is proposed where fused deposition modeling (FDM)-based 3-D printer (3DP) has been employed using polycaprolactone (PCL) filament. F, image processing has been utilized to measure viscosity in such microfluidic domain. Viscosity was calculated by measuring the time taken by the fluid to cover a fixed length between two spots in the microchannel based on the programed and color-coded regions-of-interest. The image processing program was developed considering the change in the gray scale in the virtual region of interests (ROIs) in the microchannel during the fluid flow in the paper microstrips. A 3-D printed handheld platform, containing raspberry pi with on-board camera and display, was developed to execute the image processing and automate the entire work flow. In the proposed device, the accuracy was measured to be >92%. [ABSTRACT FROM AUTHOR]

A route positioning system is a technology that identifies the current route when driving from one stop to the next, commonly found in public transportation systems such as shuttle buses that follow fixed routes. This is especially useful for smaller-scale services, such as shuttle buses, where using expensive technology and sensors for location tracking might not be feasible. Particularly in urban areas with tall buildings or mountainous regions with lots of trees, relying solely on GPS can lead to many errors. Therefore, this paper suggests a cost-effective solution that uses just one camera sensor to accurately determine the location of small-scale transportation services on fixed routes. For this, this paper uses a single-stage detection network that quickly identifies objects and tracks them using a simple algorithm. These detected features are compiled into a "codebook" using the bag-of-visual-words technique. During actual trips, this pre-created codebook is compared with landmarks that the camera sees. This comparison helps to determine the route currently being traveled. To test the effectiveness of this approach, this paper used the route of a shuttle bus on the Gachon University campus, which is similar to a downtown area with tall buildings or a wooded mountainous area. The results showed that the shuttle bus's route was recognized with an accuracy of 0.60. Areas with distinct features were recognized with an accuracy of 0.99, while stops with simple, nondescript structures were recognized with an accuracy of 0.29. Additionally, applying the SORT algorithm to enhance performance slightly improved the accuracy from 0.60 to 0.61. This demonstrates that our proposed method can effectively perform location recognition using only cameras in small shuttle buses. [ABSTRACT FROM AUTHOR]

The ability to make informed decisions in complex scenarios is crucial for intelligent automotive systems. Traditional expert rules and other methods often fall short in complex contexts. Recently, reinforcement learning has garnered significant attention due to its superior decision-making capabilities. However, there exists the phenomenon of inaccurate target network estimation, which limits its decision-making ability in complex scenarios. This paper mainly focuses on the study of the underestimation phenomenon, and proposes an end-to-end autonomous driving decision-making method based on an improved TD3 algorithm. This method employs a forward camera to capture data. By introducing a new critic network to form a triple-critic structure and combining it with the target maximization operation, the underestimation problem in the TD3 algorithm is solved. Subsequently, the multi-timestep averaging method is used to address the policy instability caused by the new single critic. In addition, this paper uses Carla platform to construct multi-vehicle unprotected left turn and congested lane-center driving scenarios and verifies the algorithm. The results demonstrate that our method surpasses baseline DDPG and TD3 algorithms in aspects such as convergence speed, estimation accuracy, and policy stability. [ABSTRACT FROM AUTHOR]

In autonomous driving, the fusion of multiple sensors is considered essential to improve the accuracy and safety of 3D object detection. Currently, a fusion scheme combining low-cost cameras with highly robust radars can counteract the performance degradation caused by harsh environments. In this paper, we propose the IRBEVF-Q model, which mainly consists of BEV (Bird's Eye View) fusion coding module and an object decoder module.The BEV fusion coding module solves the problem of unified representation of different modal information by fusing the image and radar features through 3D spatial reference points as a medium. The query in the object decoder, as a core component, plays an important role in detection. In this paper, Heat Map-Guided Query Initialization (HGQI) and Dynamic Position Encoding (DPE) are proposed in query construction to increase the a priori information of the query. The Auxiliary Noise Query (ANQ) then helps to stabilize the matching. The experimental results demonstrate that the proposed fusion model IRBEVF-Q achieves an NDS of 0.575 and a mAP of 0.476 on the nuScenes test set. Compared to recent state-of-the-art methods, our model shows significant advantages, thus indicating that our approach contributes to improving detection accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm of discovering the structure of a kaleidoscopic imaging system that consists of multiple planar mirrors and a camera. The kaleidoscopic imaging system can be recognized as the virtual multi-camera system and has strong advantages in that the virtual cameras are strictly synchronized and have the same intrinsic parameters. In this paper, we focus on the extrinsic calibration of the virtual multi-camera system. The problems to be solved in this paper are two-fold. The first problem is to identify to which mirror chamber each of the 2D projections of mirrored 3D points belongs. The second problem is to estimate all mirror parameters, i.e., normals, and distances of the mirrors. The key contribution of this paper is to propose novel algorithms for these problems using a single 3D point of unknown geometry by utilizing a kaleidoscopic projection constraint, which is an epipolar constraint on mirror reflections. We demonstrate the performance of the proposed algorithm of chamber assignment and estimation of mirror parameters with qualitative and quantitative evaluations using synthesized and real data. [ABSTRACT FROM AUTHOR]

This paper presents a smart and embedded multi-touch system for consumer electronics. The infrared (IR) camera is utilized to capture the coordinate of the IR blob from IR stylus or human finger in an emitted IR field. This system allows the consumer to easily convert large surface, projector screen, or conference table into multi-touch interface. The keystone distortion of IR camera is compensated by using homography. To improve the user experience, the calibration of homography is automatically triggered by accelerometer sensor. The special purpose (SP) processor with the parallel COordinate Rotation Digital Computer (CORDIC) architecture computes the observation-to-track association, and the computation result updates the motion status of each tracked trajectory. These motion data support the multi-touch tracking system to evaluate the sliding window of each tracked trajectory for reducing the tracking error. Additionally, the motion data are reused by the adaptive trajectory estimation (TE) system to smooth the touch trajectory and mitigate the measurement noise. This research presents multiple autonomous functions to provide a user-friendly multi-touch interface for consumer electronics. These include the automatic calibration system, the multi-touch tracking architecture with motion estimation system, and the adaptive TE system. [ABSTRACT FROM AUTHOR]

One of the key challenges in Multi-Spectral Automatic Diagnostic (MAD) robot design is the precise targeting of narrow-angle cameras on a specific part of the equipment. The paper shows that a low-cost MAD robot, whose navigation system is based on open-source ArduRover firmware and a pair of low-cost Ublox F9P GNSS receivers, can inspect the 8 × 4 degree ultraviolet camera bounding the targeting error within 0.5 degrees. To achieve this result, we propose a new targeting procedure that can be implemented without any modifications in ArduRover firmware and outperforms more expensive solutions based on LiDAR SLAM and UWB. This paper will be interesting to the developers of robotic systems for power equipment inspection because it proposes a simple and effective solution for MAD robots' camera targeting and provides the first quantitative analysis of the GNSS reception conditions during power equipment inspection. This analysis is based on the experimental results collected during the inspection of the overhead power transmission lines and equipment inspections on the open switchgear of different power plants. Moreover, it includes not only satellite, dilution of precision, and positioning/heading estimation accuracy but also the direct measurements of angular errors that could be achieved on operating power plants using GNSS-only camera targeting. [ABSTRACT FROM AUTHOR]

Transparent objects are encountered frequently in our daily lives, yet recognizing them poses challenges for conventional vision sensors due to their unique material properties, not being well perceived from RGB or depth cameras. Overcoming this limitation, thermal infrared cameras have emerged as a solution, offering improved visibility and shape information for transparent objects. In this paper, we present TRansPose, the first large-scale multispectral dataset that combines stereo RGB-D, thermal infrared (TIR) images, and object poses to promote transparent object research. The dataset includes 99 transparent objects, encompassing 43 household items, 27 recyclable trashes, 29 chemical laboratory equivalents, and 12 non-transparent objects. It comprises a vast collection of 333,819 images and 4,000,056 annotations, providing instance-level segmentation masks, ground-truth poses, and completed depth information. The data was acquired using an FLIR A65 thermal infrared camera, two Intel RealSense L515 RGB-D cameras, and a Franka Emika Panda robot manipulator. Spanning 87 sequences, TRansPose covers various challenging real-life scenarios, including objects filled with water, diverse lighting conditions, heavy clutter, non-transparent or translucent containers, objects in plastic bags, and multi-stacked objects. Supplementary material can be accessed from the following link: https://sites.google.com/view/transpose-dataset. [ABSTRACT FROM AUTHOR]

For processing streaming events from a Dynamic Vision Sensor camera, two types of neural networks can be considered. One are spiking neural networks, where simple spike-based computation is suitable for low-power consumption, but the discontinuity in spikes can make the training complicated in terms of hardware. The other one are digital Complementary Metal Oxide Semiconductor (CMOS)-based neural networks that can be trained directly using the normal backpropagation algorithm. However, the hardware and energy overhead can be significantly large, because all streaming events must be accumulated and converted into histogram data, which requires a large amount of memory such as SRAM. In this paper, to combine the spike-based operation with the normal backpropagation algorithm, memristor–CMOS hybrid circuits are proposed for implementing event-driven neural networks in hardware. The proposed hybrid circuits are composed of input neurons, synaptic crossbars, hidden/output neurons, and a neural network's controller. Firstly, the input neurons perform preprocessing for the DVS camera's events. The events are converted to histogram data using very simple memristor-based latches in the input neurons. After preprocessing the events, the converted histogram data are delivered to an ANN implemented using synaptic memristor crossbars. The memristor crossbars can perform low-power Multiply–Accumulate (MAC) calculations according to the memristor's current–voltage relationship. The hidden and output neurons can convert the crossbar's column currents to the output voltages according to the Rectified Linear Unit (ReLU) activation function. The neural network's controller adjusts the MAC calculation frequency according to the workload of the event computation. Moreover, the controller can disable the MAC calculation clock automatically to minimize unnecessary power consumption. The proposed hybrid circuits have been verified by circuit simulation for several event-based datasets such as POKER-DVS and MNIST-DVS. The circuit simulation results indicate that the neural network's performance proposed in this paper is degraded by as low as 0.5% while saving as much as 79% in power consumption for POKER-DVS. The recognition rate of the proposed scheme is lower by 0.75% compared to the conventional one, for the MNIST-DVS dataset. In spite of this little loss, the power consumption can be reduced by as much as 75% for the proposed scheme. [ABSTRACT FROM AUTHOR]

The modulation transfer function (MTF) serves as a crucial technical index for assessing the imaging quality of remote sensing cameras, which is integral throughout their entire operational cycle. Currently, the MTF evaluation of remote sensing cameras primarily relies on the slanted-edge method. The factors influencing the slanted-edge method's effectiveness are broadly classified into two categories: algorithmic factors and image factors. This paper innovatively comprehensively analyzes the influencing factors of the slanted-edge method and proposes an improved slanted-edge method to calculate the MTF testing method of remote sensing cameras, which is applied to the MTF testing of remote sensing cameras. Since the traditional algorithm can only be applied in the small angle situation, this paper proposes a new method of slanted-edge method test calculation based on the optimal oversampling rate (OSR) adaptive model of the slanted edge and uses simulation experiments to verify the reliability of the algorithm model through the deviation of the slanted-edge angle calculation and MTF measurement, and the results show that the algorithm improves the accuracy of the MTF measurement compared with the ISO-cos and OMINI-sine methods. Then, the effects of the slanted-edge angle, image region of interest (ROI), as well as image contrast and signal-to-noise ratio (SNR) on the accuracy of the MTF calculation by the slanted-edge method were quantitatively analyzed as the constraints of the slanted-edge method test. Based on the laboratory target experiment, the algorithm flow and various influencing factors obtained in the simulation stage are verified, and the experimental results are more consistent with the various test results obtained in the simulation stage. Consequently, the slanted-edge method introduced in this paper is applicable for future remote sensing camera MTF testing. This approach offers a valuable reference for on-orbit focusing, satellite operational condition monitoring, lifespan estimation, and image restoration. [ABSTRACT FROM AUTHOR]

Bayer filter color cameras are more and more widely used in the field of aerospace remote sensing, but the Bayer filter causes great degradation in image quality; therefore, obtaining a means of achieving the high-precision measurement of the modulation transfer function (MTF) of Bayer filter color cameras is an urgent problem. In order to solve this problem, this paper develops a slanted-edge method via three steps: the detection of the slanted edge, the acquisition and processing of the edge spread function (ESF), and the acquisition and processing of the line spread function (LSF). A combination of the Canny operator and Hough transform is proposed for the detection of the slanted edge, which improves the fitting accuracy and anti-interference ability of the algorithm. Further, the Canny operator is improved by constructing an adaptive filter function and introducing the Otsu method, which can more effectively smooth the image and remove its false edges. A method of processing ESF data by combining cubic spline interpolation and Savitzky–Golay (SG) filtering is proposed, which reduces the effects of noise and the non-uniform sampling of ESF on MTF. A method of LSF processing using Gaussian function fitting is proposed to further reduce the effect of noise on MTF. The improved algorithm is verified by the MTF measurement test applied to a specific type of Bayer filter color space camera. The simulation and test results show that the improved slanted-edge method discussed in this paper has greater precision and a better anti-interference ability, and it can effectively solve the difficult problem associated with MTF detection in Bayer filter color space cameras. [ABSTRACT FROM AUTHOR]

Sitting work posture is often used in office work. Work position failures when sitting can cause Musculoskeletal Disorders. Therefore, ergonomists are necessary for the workplace to assist workers' posture. However, these needs cannot be met due to the limited number of ergonomists and costs. So a practical and inexpensive semi-automatic application is needed. One method for assessing upper body work posture is RULA. This study aims to develop a posture assessment application for sitting work and suggest improvement. In addition, the effect of changes in room brightness, the variability of chair height, and types of office-work tasks were explored in this study to determine the accuracy level of the depth camera to measure the worker's posture. Research design uses empirical research with independent variables: room illuminance, chair height, and four types of office-work tasks. The two levels of room illuminance are 32 lux and 60 lux; the seat height consists of three levels, i.e., 40, 45, and 50cm, while four types of tasks include writing, typing, writing with a cheat sheet, and typing with a cheat sheet beside the Subject. The experiment tests the difference between the application's RULA estimation results and the ergonomist's RULA value. The experiment results showed that the proposed application successfully assessed RULA with the same results as the calculations by the ergonomist. This study makes posture assessment easier for sitting work and provides suggestions for posture improvement. In addition, automation of posture assessment using a depth camera helps provide evaluations and recommendations for improving posture while working sitting. [ABSTRACT FROM AUTHOR]

The relative rotation angle between two cabins should be automatically and precisely obtained during automated assembly processes for spacecraft and aircraft. This paper introduces a method to solve this problem based on distributed vision, where two groups of cameras are employed to take images of mating features, such as dowel pins and holes, in oblique directions. Then, the relative rotation between the mating flanges of two cabins is calculated. The key point is the registration of the distributed cameras; thus, a simple and practical registration process is designed. It is assumed that there are rigid and scaling transformations among the world coordinate systems (WCS) of each camera. Therefore, the rigid-correct and scaling-correct matrices are adopted to register the cameras. An auxiliary registration device with known features is designed and moved in the cameras' field of view (FOV) to obtain the matrix parameters so that each camera acquires traces of every feature. The parameters can be solved using a genetic algorithm based on the known geometric relationships between the trajectories on the registration devices. This paper designs a prototype to verify the method. The precision reaches 0.02° in the measuring space of 340 mm. [ABSTRACT FROM AUTHOR]

The accuracy and real-time performance of 3D object detection are key factors limiting its widespread application. While cameras capture detailed color and texture features, they lack depth information compared to LiDAR. Multimodal detection combining both can improve results but incurs significant computational overhead, affecting real-time performance. To address these challenges, this paper presents a real-time multimodal fusion model called Fast Transfusion that combines the benefits of LiDAR and camera sensors and reduces the computational burden of their fusion. Specifically, our Fast Transfusion method uses QConv (Quick Convolution) to replace the convolutional backbones compared to other models. QConv concentrates the convolution operations at the feature map center, where the most information resides, to expedite inference. It also utilizes deformable convolution to better match the actual shapes of detected objects, enhancing accuracy. And the model incorporates EH Decoder (Efficient and Hybrid Decoder) which decouples multiscale fusion into intra-scale interaction and cross-scale fusion, efficiently decoding and integrating features extracted from multimodal data. Furthermore, our proposed semi-dynamic query selection refines the initialization of object queries. On the KITTI 3D object detection dataset, our proposed approach reduced the inference time by 36 ms and improved 3D AP by 1.81% compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Estimating the pose of a large set of fixed indoor cameras is a requirement for certain applications in augmented reality, autonomous navigation, video surveillance, and logistics. However, accurately mapping the positions of these cameras remains an unsolved problem. While providing partial solutions, existing alternatives are limited by their dependence on distinct environmental features, the requirement for large overlapping camera views, and specific conditions. This paper introduces a novel approach to estimating the pose of a large set of cameras using a small subset of fiducial markers printed on regular pieces of paper. By placing the markers in areas visible to multiple cameras, we can obtain an initial estimation of the pair-wise spatial relationship between them. The markers can be moved throughout the environment to obtain the relationship between all cameras, thus creating a graph connecting all cameras. In the final step, our method performs a full optimization, minimizing the reprojection errors of the observed markers and enforcing physical constraints, such as camera and marker coplanarity and control points. We validated our approach using novel artificial and real datasets with varying levels of complexity. Our experiments demonstrated superior performance over existing state-of-the-art techniques and increased effectiveness in real-world applications. Accompanying this paper, we provide the research community with access to our code, tutorials, and an application framework to support the deployment of our methodology. [ABSTRACT FROM AUTHOR]

The Amazon forest has the highest biodiversity on Earth. However, information on Amazonian vertebrate diversity is still deficient and scattered across the published, peer‐reviewed, and gray literature and in unpublished raw data. Camera traps are an effective non‐invasive method of surveying vertebrates, applicable to different scales of time and space. In this study, we organized and standardized camera trap records from different Amazon regions to compile the most extensive data set of inventories of mammal, bird, and reptile species ever assembled for the area. The complete data set comprises 154,123 records of 317 species (185 birds, 119 mammals, and 13 reptiles) gathered from surveys from the Amazonian portion of eight countries (Brazil, Bolivia, Colombia, Ecuador, French Guiana, Peru, Suriname, and Venezuela). The most frequently recorded species per taxa were: mammals: Cuniculus paca (11,907 records); birds: Pauxi tuberosa (3713 records); and reptiles: Tupinambis teguixin (716 records). The information detailed in this data paper opens up opportunities for new ecological studies at different spatial and temporal scales, allowing for a more accurate evaluation of the effects of habitat loss, fragmentation, climate change, and other human‐mediated defaunation processes in one of the most important and threatened tropical environments in the world. The data set is not copyright restricted; please cite this data paper when using its data in publications and we also request that researchers and educators inform us of how they are using these data. [ABSTRACT FROM AUTHOR]

Monitoring the deformation of long-span bridges is essential for assessing structural health and safety. The existing single-camera deformation measurement method is unable to meet the high-precision measurement requirement for the deformation of large-span bridges. This paper proposes a real-time deformation measurement method for the long-span bridge using multi-inertial camera system. The method is based on visual measurement which the target deformation is measured by multiple cameras. Meanwhile, inertial measurement is used to estimate the relative pose of the camera to compensate for camera motion errors. It is applied to the health monitoring system of a long-span bridge. The experimental results show that the method proposed in this paper is highly consistent with the hydrostatic leveling measurement, with a RMSE of 4.83mm. The method can accurately and real-time measure the deformation of large-span bridges with a promising engineering value. [ABSTRACT FROM AUTHOR]

Scientific-grade cameras are frequently employed in industries such as spectral imaging technology, aircraft, medical detection, and astronomy, and are characterized by high precision, high quality, fast speed, and high sensitivity. Especially in the field of astronomy, obtaining information about faint light often requires long exposure with high-resolution cameras, which means that any external factors can cause the camera to become unstable and result in increased errors in the detection results. This paper aims to investigate the effect of displacement introduced by various vibration factors on the imaging of an astronomical camera during long exposure. The sources of vibration are divided into external vibration and internal vibration. External vibration mainly includes environmental vibration and resonance effects, while internal vibration mainly refers to the vibration caused by the force generated by the refrigeration module inside the camera during the working process of the camera. The cooling module is divided into water-cooled and air-cooled modes. Through the displacement and vibration experiments conducted on the camera, it is proven that the air-cooled mode will cause the camera to produce greater displacement changes relative to the water-cooled mode, leading to blurring of the imaging results and lowering the accuracy of astronomical detection. This paper compares the effects of displacement produced by two methods, fan cooling and water-circulation cooling, and proposes improvements to minimize the displacement variations in the camera and improve the imaging quality. This study provides a reference basis for the design of astronomical detection instruments and for determining the vibration source of cameras, which helps to promote the further development of astronomical detection. [ABSTRACT FROM AUTHOR]

To satisfy the requirement of quality control in printing and packaging industry, a sheet counting apparatus is developed, which adopts a line-scan camera to image the fringes of sheet stack and is able to provide a real-time and noncontact measurement of their quantity. With a brief introduction of the system architecture, our main work focuses on the sheet counting algorithms. The basic principle is to identify each sheet profile from the 1-D image with a robust ridge strength measurement. First, a multiscale bi-Gaussian ridge likelihood measurement and a ridge-valley descriptor are utilized to improve adjacent objects detection by increasing local contrast around sheet fringes. Then, a sheet recognition scheme, which integrates a peak detection algorithm and the ridge region criteria for verification, is proposed to discriminate true sheets from the obtained ridgeness measure. According to experiments and tests in real production lines, our apparatus can reach a very high measuring accuracy for printing papers or cards with a thickness not <0.2 mm. [ABSTRACT FROM PUBLISHER]

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs' localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a leader-follower formation control of multiple mobile robots by position-based method using a fisheye camera. A fisheye camera has a wide field of view and recognizes a wide range of objects. In this paper, the fisheye camera is first modeled on spherical coordinates and then a position estimation technique is proposed by using an AR marker based on the spherical model. This paper furthermore presents a method for estimating the velocity of a leader robot based on a disturbance observer using the obtained position information. The proposed techniques are combined with a formation control based on the virtual structure. In this paper, the formation controller and velocity estimator can be designed independently, and the stability analysis of the total system is performed by using Lyapunov theorem. The effectiveness of the proposed method is demonstrated by simulation and experiments using two real mobile robots. [ABSTRACT FROM AUTHOR]

This paper presents a leader–follower mobile robot control approach using onboard sensors. The follower robot is equipped with an Intel RealSense camera mounted on a rotating platform. Camera observations and ArUco markers are used to localize the robots to each other and relative to the workspace. The rotating platform allows the expansion of the perception range. As a result, the robot can use observations that are not within the camera's field of view at the same time in the localization process. The decision-making process associated with the control of camera rotation is implemented using behavior trees. In addition, measurements from encoders and IMUs are used to improve the quality of localization. Data fusion is performed using the EKF filter and allows the user to determine the robot's poses. A 3D-printed cuboidal tower is added to the leader robot with four ArUco markers located on its sides. Fiducial landmarks are placed on vertical surfaces in the workspace to improve the localization process. The experiments were performed to verify the effectiveness of the presented control algorithm. The robot operating system (ROS) was installed on both robots. [ABSTRACT FROM AUTHOR]

There is a growing demand for rapid and sensitive detection approaches for pathogenic bacteria that can be applied by non-specialists in non-laboratory field settings. Here, the detection of the typical E. coli enzyme β-glucuronidase using a chitosan-based sensing hydrogel-coated paper sensor and the detailed analysis of the reaction kinetics, as detected by a smartphone camera, is reported. The chromogenic reporter unit affords an intense blue color in a two-step reaction, which was analyzed using a modified Michaelis–Menten approach. This generalizable approach can be used to determine the limit of detection and comprises an invaluable tool to characterize the performance of lab-in-a-phone type approaches. For the particular system analyzed, the ratio of reaction rate and equilibrium constants of the enzyme–substrate complex are 0.3 and 0.9 pM−1h−1 for β-glucuronidase in phosphate buffered saline and lysogeny broth, respectively. The minimal degree of substrate conversion for detection of the indigo pigment formed during the reaction is 0.15, while the minimal time required for detection in this particular system is ~2 h at an enzyme concentration of 100 nM. Therefore, this approach is applicable for quantitative lab-in-a-phone based point of care detection systems that are based on enzymatic substrate conversion via bacterial enzymes. [ABSTRACT FROM AUTHOR]

To effectively mine the contents embedded in web images, it is useful to classify the images into different types so that they can be fed to different procedures for detailed analysis. The authors herein propose a hierarchical algorithm for efficiently classifying web images into four classes. Their algorithm consists of two stages: the first stage extracts global features reflecting the distributions of color, edge and gradient, and uses a support vector machine (SVM) classifier for preliminary classification. Images assigned low confidence by the first stage classifier are processed by the second stage, which further extracts local texture features represented in the bag-of-words framework and uses another SVM classifier for final classification. In addition, they design two fusion strategies to train the second-stage classifier and generate the final prediction depending on the usage of local features in the second stage. To validate the effectiveness of proposed method, they built a database containing more than 55,000 images from various sources. On their test image set, they obtained an overall classification accuracy of 98.4% and the processing speed is over 27 fps on an Intel(R) Xeon(R) central processing unit (2.90 GHz).

In this paper, we explore video blockchain for establishing connectivity among vehicles in a smart city through utilizing blockchain technology. By leveraging intelligent vehicular systems that provide location-based visualization through multiple deployed cameras in vehicles, we expand the scope of collecting video surveillance data for observation, thereby enhancing overall situational awareness. We utilize the decentralized nature of blockchain to implement a vehicle-based surveillance system across a smart city. To ensure reliability, the integration of two cryptographic functions, hashing and signing, with the blockchain is employed. This integration ensures secure and tamper-proof solutions for the existing intelligent surveillance system. In this paper, our primary focus is on combining blockchain technology to achieve sustainable and robust smart solutions for intelligent vehicular distributed video networks while eliminating the need for third-party intermediaries. Through extensive experiments and analysis, we demonstrate the effectiveness and feasibility of our proposed video blockchain approach. The results indicate that this innovative framework provides enhanced security, privacy, and scalability for intelligent vehicular distributed networks in smart cities, paving the way for a connected and efficient urban environment. [ABSTRACT FROM AUTHOR]

In scenarios of indoor localization of mobile robots, Global Positioning System (GPS) signals are prone to loss due to interference from urban building environments and cannot meet the needs of robot localization. On the other hand, traditional indoor localization methods based on wireless signals such as Bluetooth and WiFi often require the deployment of multiple devices in advance, and these methods can only obtain distance information and are unable to obtain the attitude of the positioning target in space. This paper proposes a method for the indoor localization of mobile robots based on a depth camera. Firstly, we extracted ORB feature points from images captured by a depth camera and performed homogenization processing. Then, we performed feature matching between adjacent two frames of images, and the mismatched points are eliminated to improve the accuracy of feature matching. Finally, we used the Iterative Closest Point (ICP) algorithm to estimate the camera's pose, thus achieving the localization of mobile robots in indoor environments. In addition, an experimental evaluation was conducted on the TUM dataset of the Technical University of Munich to validate the feasibility of the proposed depth-camera-based indoor localization system for mobile robots. The experimental results show that the average localization accuracy of our algorithm on three datasets is 0.027 m, which can meet the needs of indoor localization for mobile robots. [ABSTRACT FROM AUTHOR]

The rapid development of computer vision technology provides a basic guarantee for public security reliance on video surveillance. In current video surveillance based on static cameras, accurate and quick extractions of foreground regions of moving objects enable quicker analysis of the behavior of meaningful objects and thus improve the intelligent analysis level of video surveillance. However, there would always occur false detection in the extraction of foreground regions, because of the shaking of tree branches and leaves in the scene and the "ghosting" area caused by the delayed updating of the background model. To solve this problem, this paper proposes a method for the extraction of foreground regions by using spatio-temporal information. This method can accurately extract foreground regions of moving objects by utilizing the difference and complementarity between spatial domain methods and temporal domain methods and further in combination with image processing technology. Specifically, the foreground regions of moving objects can be extracted by the morphological processing of the combination of the spatial information and the morphologically processed temporal information in the video. The experimental results show that the proposed method for the extraction of foreground regions of moving objects in view of the spatio-temporal information can reduce false detections caused by the shaking of tree branches and leaves, and thus effectively extract foreground regions of moving objects. [ABSTRACT FROM AUTHOR]

Background: High-speed strain field measurement based on digital image correlation (DIC) is limited by high equipment cost and large transmission bandwidth requirements for high-speed cameras. Emerging event-based cameras offer microsecond time resolution and low power consumption, generate events by asynchronously detecting illumination intensity changes at each pixel, have potential for applications in high-speed strain field measurements as a low-cost solution. Objective: Using an event camera to directly capture a deformation process has some limitations, including motion blur, unclear images, and uneven gray scale quantization. This paper proposes a new method to avoid the above limitations. Methods: A strobe light is used to assist image reconstruction for event cameras. Event cameras can generate events using a strobe light to illuminate the object with white speckle on black background, to obtain a speckle image at a specific moment, and then use DIC to obtain the displacement and strain fields. Results: Validation experiments were performed, capturing 2D displacement and strain fields at 1000 frames per second with 1280 × 800 pixel resolution, and DIC matching error = 0.4 pixels. Conclusions: This paper introduces a novel using strobe lighting to assist image reconstruction for event cameras. This technique presents a cost-effective alternative for high-speed deformation measurements, bypassing the constraints of directly capturing the deformation process with an event camera. The proposed method exhibits remarkable adaptability to the motion speed of the object being measured, while maintaining high temporal resolution and low data redundancy. [ABSTRACT FROM AUTHOR]

In this paper, we focus on investigating the potential of advanced Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting for 3D scene reconstruction from aerial imagery obtained via sensor platforms with an almost nadir-looking camera. Such a setting for image acquisition is convenient for capturing large-scale urban scenes, yet it poses particular challenges arising from imagery with large overlap, very short baselines, similar viewing direction and almost the same but large distance to the scene, and it therefore differs from the usual object-centric scene capture. We apply a traditional approach for image-based 3D reconstruction (COLMAP), a modern NeRF-based approach (Nerfacto) and a representative for the recently introduced 3D Gaussian Splatting approaches (Splatfacto), where the latter two are provided in the Nerfstudio framework. We analyze results achieved on the recently released UseGeo dataset both quantitatively and qualitatively. The achieved results reveal that the traditional COLMAP approach still outperforms Nerfacto and Splatfacto approaches for various scene characteristics, such as less-textured areas, areas with high vegetation, shadowed areas and areas observed from only very few views. [ABSTRACT FROM AUTHOR]

There is a gap between mechanical measurement methods under laboratory conditions and those under real conditions of structural monitoring. This paper proposes a method that applies well-established computer vision developments to photomechanics in difficult conditions. It is therefore a technique that is flexible and versatile while maintaining satisfying accuracy. It consists in marker tracking using ArUco fiducial markers as measurement points. It allows locating markers in non-optimal conditions of camera orientation and position. A homography process was used to analyse pictures taken with a view angle. The accuracy of the method was estimated, especially in case of out-of-plane motions, and the impact of the view angle and of the distance between camera and markers on the location error was studied. The method was applied in creep tests to measure crack parameters as well as the transverse expansion of wooden beams. In the application example presented, it enabled to compute distances between markers with only 0.28% of relative error and hence to measure the crack parameters and the long-term shrinkage-swelling of the wooden beams. However, the impacts of brightness variations and camera parameters have not been estimated. This method is very promising when experimental conditions are variable and when multiple measurements are necessary. [ABSTRACT FROM AUTHOR]

The successful investigation and prosecution of significant crimes, including child pornography, insurance fraud, movie piracy, traffic monitoring, and scientific fraud, hinge largely on the availability of solid evidence to establish the case beyond any reasonable doubt. When dealing with digital images/videos as evidence in such investigations, there is a critical need to conclusively prove the source camera/device of the questioned image. Extensive research has been conducted in the past decade to address this requirement, resulting in various methods categorized into brand, model, or individual image source camera identification techniques. This paper presents a survey of all those existing methods found in the literature. It thoroughly examines the efficacy of these existing techniques for identifying the source camera of images, utilizing both intrinsic hardware artifacts such as sensor pattern noise and lens optical distortion, and software artifacts like color filter array and auto white balancing. The investigation aims to discern the strengths and weaknesses of these techniques. The paper provides publicly available benchmark image datasets and assessment criteria used to measure the performance of those different methods, facilitating a comprehensive comparison of existing approaches. In conclusion, the paper outlines directions for future research in the field of source camera identification. [ABSTRACT FROM AUTHOR]

The rapid evolution of 3D technology in recent years has brought about significant change in the field of agriculture, including precision livestock management. From 3D geometry information, the weight and characteristics of body parts of Korean cattle can be analyzed to improve cow growth. In this paper, a system of cameras is built to synchronously capture 3D data and then reconstruct a 3D mesh representation. In general, to reconstruct non-rigid objects, a system of cameras is synchronized and calibrated, and then the data of each camera are transformed to global coordinates. However, when reconstructing cattle in a real environment, difficulties including fences and the vibration of cameras can lead to the failure of the process of reconstruction. A new scheme is proposed that automatically removes environmental fences and noise. An optimization method is proposed that interweaves camera pose updates, and the distances between the camera pose and the initial camera position are added as part of the objective function. The difference between the camera's point clouds to the mesh output is reduced from 7.5 mm to 5.5 mm. The experimental results showed that our scheme can automatically generate a high-quality mesh in a real environment. This scheme provides data that can be used for other research on Korean cattle. [ABSTRACT FROM AUTHOR]

The problem of 3D human pose estimation (HPE) has been the focus of research in recent years, yet precise estimation remains an under-explored challenge. In this paper, the merits of both multiview images and wearable IMUs are combined to enhance the process of 3D HPE. We build upon a state-of-the-art baseline while introducing three novelties. Initially, we enhance the precision of keypoint localization by substituting Gaussian kernels with Laplacian kernels in the generation of target heatmaps. Secondly, we incorporate orientation regularized network (ORN), which enhances cross-modal heatmap fusion by taking a weighted average of the top-scored values instead of solely relying on the maximum value. This not only improves robustness to outliers but also leads to higher accuracy in pose estimation. Lastly, we modify the limb length constraint in the conventional orientation regularized pictorial structure model (ORPSM) to improve the estimation of joint positions. Specifically, we devise a soft-coded binary term for limb length constraint, hence imposing a flexible and smoothed penalization and reducing sensitivity to hyperparameters. The experimental results using the TotalCapture dataset reveal a significant improvement, with a 10.3% increase in PCKh accuracy at the one-twelfth threshold and a 3.9 mm reduction in MPJPE error compared to the baseline. [ABSTRACT FROM AUTHOR]

Low-light images are prevalent in intelligent monitoring and many other applications, with low brightness hindering further processing. Although low-light image enhancement can reduce the influence of such problems, current methods often involve a complex network structure or many iterations, which are not conducive to their efficiency. This paper proposes a Zero-Reference Camera Response Network using a camera response model to achieve efficient enhancement for arbitrary low-light images. A double-layer parameter-generating network with a streamlined structure is established to extract the exposure ratio K from the radiation map, which is obtained by inverting the input through a camera response function. Then, K is used as the parameter of a brightness transformation function for one transformation on the low-light image to realize enhancement. In addition, a contrast-preserving brightness loss and an edge-preserving smoothness loss are designed without the requirement for references from the dataset. Both can further retain some key information in the inputs to improve precision. The enhancement is simplified and can reach more than twice the speed of similar methods. Extensive experiments on several LLIE datasets and the DARK FACE face detection dataset fully demonstrate our method's advantages, both subjectively and objectively. [ABSTRACT FROM AUTHOR]

3D object detection is a challenging and promising task for autonomous driving and robotics, benefiting significantly from multi-sensor fusion, such as LiDAR and cameras. Conventional methods for sensor fusion rely on a projection matrix to align the features from LiDAR and cameras. However, these methods often suffer from inadequate flexibility and robustness, leading to lower alignment accuracy under complex environmental conditions. Addressing these challenges, in this paper, we propose a novel Bidirectional Attention Fusion module, named BAFusion, which effectively fuses the information from LiDAR and cameras using cross-attention. Unlike the conventional methods, our BAFusion module can adaptively learn the cross-modal attention weights, making the approach more flexible and robust. Moreover, drawing inspiration from advanced attention optimization techniques in 2D vision, we developed the Cross Focused Linear Attention Fusion Layer (CFLAF Layer) and integrated it into our BAFusion pipeline. This layer optimizes the computational complexity of attention mechanisms and facilitates advanced interactions between image and point cloud data, showcasing a novel approach to addressing the challenges of cross-modal attention calculations. We evaluated our method on the KITTI dataset using various baseline networks, such as PointPillars, SECOND, and Part-A2, and demonstrated consistent improvements in 3D object detection performance over these baselines, especially for smaller objects like cyclists and pedestrians. Our approach achieves competitive results on the KITTI benchmark. [ABSTRACT FROM AUTHOR]

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

To simulate a natural lightning strike, the breakdown should occur within 1–3 μs in an aircraft lightning zoning test. However, the breakdown voltage–time characteristic of the combined gap is hard to derive because of the complex breakdown process. In this paper, a series of positive lightning impulse discharge tests in a rod electrode–floating aircraft model–plate combined gap were conducted. The leader propagation processes in both sub-gaps were observed using a high-speed camera. The potential of the floating aircraft model was analyzed. We propose a potential calculation model for the floating aircraft during the leader propagation phase. After fitting test data, an empirical formula for the connected streamer plasma was given. Taking that model into account, the calculation result of the average breakdown time of the combined gap has an error of <5.7%. [ABSTRACT FROM AUTHOR]

In the past, comprehensive information was imperative for image processing, prompting a preference for high-depth cameras. However, in our research, we discovered that the abundance of image details may impede phase retrieval. Consequently, this paper presents an iterative phase retrieval method based on a low bit depth. Through simulations and experiments, this approach has proven effective in evidently enhancing phase retrieval outcomes. Furthermore, the concept of low bit depth holds promise for broader application across diverse domains within the field of image retrieval. [ABSTRACT FROM AUTHOR]

The precise thermal control of aerial cameras is crucial for the acquisition of high-resolution imagery, and an accurate temperature prediction is essential to achieve this. This paper presents a methodology for modifying thermal network models to improve the accuracy of temperature prediction for aerial cameras. Seven types of thermal parameters are extracted from the thermal network model, and a thermally sensitive analysis identifies eleven key parameters to streamline the processing time. Departing from traditional methods that rely on steady-state data, this study conducts transient thermal tests and leverages polynomial fitting to facilitate thorough parameter modification. To ensure data reliability, the Monte-Carlo algorithm is employed to explore the parameter spaces of key parameters, analyzing temperature errors. Subsequently, the Least-Squares method is utilized to obtain optimal estimates of the key parameter values. As a result, the updated model demonstrates significantly improved accuracy in temperature predictions, achieving a reduction in the maximum absolute error between the predicted and experimental results from 22 °C to 4 °C, and a lowering of the relative error from 33.8% to 6.1%. The proposed modification method validates its effectiveness in modeling and enhancing the precision of thermal network models for aerial cameras. [ABSTRACT FROM AUTHOR]

Space cameras play a pivotal role in various fields, such as astronomical exploration. When operating in orbit, these cameras encounter the relative motion between the target and the camera during the exposure, resulting in image motion, which affects the imaging quality. Therefore, it is necessary to compensate for this image motion. This paper investigates the in-orbit image motion compensation (IMC) method for space cameras with a long integration time based on a two-axis pointing platform. Firstly, the mechanical design of the camera is introduced. Secondly, the in-orbit IMC model for the camera is analyzed, and the angular motion needed for compensating for the image motion by the two-axis pointing platform are derived. Factors influencing the compensation accuracy are also analyzed. The effectiveness of the IMC model is verified through simulations. Finally, the in-orbit experimental results indicate that the energy concentration of the target star images obtained exceeded 70%, demonstrating excellent performance in space cameras and effectively enhancing imaging quality using IMC technology. [ABSTRACT FROM AUTHOR]

Although the field of geomatics has seen multiple technological advances in recent years which enabled new applications and simplified the consolidated ones, some tasks remain challenging, inefficient, and time- and cost-consuming. This is the case of accurate tridimensional surveys of narrow spaces. Static laser scanning is an accurate and reliable approach but impractical for extensive tunnel environments; on the other hand, portable laser scanning is time-effective and efficient but not very reliable without ground control constraints. This paper describes the development process of a novel image-based multi-camera system meant to solve this specific problem: delivering accurate, reliable, and efficient results. The development is illustrated from the system conceptualization and initial investigations to the design choices and requirements for accuracy. The resulting working prototype has been put to the test to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a new deep-learning architecture designed to enhance the spatial synchronization between CMOS and event cameras by harnessing their complementary characteristics. While CMOS cameras produce high-quality imagery, they struggle in rapidly changing environments—a limitation that event cameras overcome due to their superior temporal resolution and motion clarity. However, effective integration of these two technologies relies on achieving precise spatial alignment, a challenge unaddressed by current algorithms. Our architecture leverages a dynamic graph convolutional neural network (DGCNN) to process event data directly, improving synchronization accuracy. We found that synchronization precision strongly correlates with the spatial concentration and density of events, with denser distributions yielding better alignment results. Our empirical results demonstrate that areas with denser event clusters enhance calibration accuracy, with calibration errors increasing in more uniformly distributed event scenarios. This research pioneers scene-based synchronization between CMOS and event cameras, paving the way for advancements in mixed-modality visual systems. The implications are significant for applications requiring detailed visual and temporal information, setting new directions for the future of visual perception technologies. [ABSTRACT FROM AUTHOR]

Parallax processing and structure preservation have long been important and challenging tasks in image stitching. In this paper, an image stitching method based on sliding camera to eliminate perspective deformation and asymmetric optical flow to solve parallax is proposed. By maintaining the viewpoint of two input images in the mosaic non-overlapping area and creating a virtual camera by interpolation in the overlapping area, the viewpoint is gradually transformed from one to another so as to complete the smooth transition of the two image viewpoints and reduce perspective deformation. Two coarsely aligned warped images are generated with the help of a global projection plane. After that, the optical flow propagation and gradient descent method are used to quickly calculate the bidirectional asymmetric optical flow between the two warped images, and the optical-flow-based method is used to further align the two warped images to reduce parallax. In the image blending, the softmax function and registration error are used to adjust the width of the blending area, further eliminating ghosting and reducing parallax. Finally, by comparing our method with APAP, AANAP, SPHP, SPW, TFT, and REW, it has been proven that our method can not only effectively solve perspective deformation, but also gives more natural transitions between images. At the same time, our method can robustly reduce local misalignment in various scenarios, with higher structural similarity index. A scoring method combining subjective and objective evaluations of perspective deformation, local alignment and runtime is defined and used to rate all methods, where our method ranks first. [ABSTRACT FROM AUTHOR]

To identify the most visually salient regions in a set of paired RGB and depth maps, in this paper, we propose a multimodal feature fusion supervised RGB-D image saliency detection network, which learns RGB and depth data by two independent streams separately, uses a dual-stream side-supervision module to obtain saliency maps based on RGB and depth features for each layer of the network separately, and then uses a multimodal feature fusion module to fuse the latter 3 layers of RGB and depth high-dimensional information to generate high-level significant prediction results. Experiments on three publicly available datasets show that the proposed network outperforms the current mainstream RGB-D saliency detection models with strong robustness due to the use of a dual-stream side-surveillance module and a multimodal feature fusion module. We use the proposed RGB-D SOD model for background defocusing in realistic scenes and achieve excellent visual results. [ABSTRACT FROM AUTHOR]

Accurate localization in underground coal mining is a challenging technology in coal mine safety production. This paper proposes a low-cost battery-free localization scheme based on depth images, called MineBL. The main idea is to utilize the battery-free low-cost reflective balls as position nodes and realize underground target localization with a series of algorithms. In particular, the paper designs a data enhancement strategy based on small-target reorganization to increase the identification accuracy of tiny position nodes. Moreover, a novel ranging algorithm based on multi-filter cooperative denoising has been proposed, and an optimized weighted centroid location algorithm based on multilateral location errors has been designed to minimize underground localization errors. Many experiments in the indoor laboratories and the underground coal mine laboratories have been conducted, and the experimental results have verified that MineBL has good localization performances, with localization errors less than 30 cm in 95% of cases. Therefore, MineBL has great potential to provide a low-cost and effective solution for precise target localization in complex underground environments. [ABSTRACT FROM AUTHOR]

Planar double-jointed robot is a kind of widely used robot. In the past decades, with the development of science and technology, planar double-jointed robot has a great breakthrough in many aspects, especially in the actual camera and attitude analysis. In recent years, pun-section manipulator has made great progress in technology, but its kinematic characteristics and the human body's ability to move have a great gap. So, in the moving plane, we need to study how to improve its camera ability to find a more stable and effective camera mode, and to a certain extent, to meet its requirements in complex working conditions. Simulating the pose of two joints, we seek a more stable and effective camera mode to improve its moving speed, expand its moving space, and lay a solid foundation for its application. Planar double-jointed robot is the most representative one at present. It includes machinery, material, electronics, computer, sensor, drive, intelligent control, artificial intelligence, and so on. Because it is closer to the human body, more suitable for people's work and work, so it does not need to change its environment, so it is regarded as having a high affinity for people's machinery. Its characteristic is that it has the good adaptability and the flexibility, but the double joint camera's support discontinuity enables it to be able to adapt each kind of complex situation well. It can not only climb the stairs but also in the messy ground smooth through and also in the inconvenient passage through. The aim of this paper is to analyze the unique advantages and irreplaceable characteristics of the planar double-jointed robot based on the bionic and mechanical research results. This paper analyzes the mechanical technology by means of literature review and actual data analysis. The experimental results show that the biggest difference between the two articulated robots and other robots is that they have two human-like joint motions. [ABSTRACT FROM AUTHOR]