In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In additi...In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In addition,methods for deploying multiple cameras for motion capture of users(e.g.,performers)are widely used in computer graphics.As the need to minimize and optimize the number of cameras grows to reduce costs,various technologies and research approaches focused on Optimal Camera Placement(OCP)are continually being proposed.However,as most existing studies assume homogeneous camera setups,there is a growing demand for studies on heterogeneous camera setups.For instance,technical demands keep emerging in scenarios with minimal camera configurations,especially regarding cost factors,the physical placement of cameras given the spatial structure,and image capture strategies for heterogeneous cameras,such as high-resolution RGB cameras and depth cameras.In this study,we propose a pre-visualization and simulation method for the optimal placement of heterogeneous cameras in XR environments,accounting for both the specifications of heterogeneous cameras(e.g.,field of view)and the physical configuration(e.g.,wall configuration)in real-world spaces.The proposed method performs a visibility analysis of cameras by considering each camera’s field-of-view volume,resolution,and unique characteristics,along with physicalspace constraints.This approach enables the optimal position and rotation of each camera to be recommended,along with the minimum number of cameras required.In the results of our study conducted in heterogeneous camera combinations,the proposed method achieved 81.7%~82.7%coverage of the target visual information using only 2~3 cameras.In contrast,single(or homogeneous)-typed cameras were required to use 11 cameras for 81.6%coverage.Accordingly,we found that camera deployment resources can be reduced with the proposed approaches.展开更多
To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of ...To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of view and operates at both visible and near-infrared wavelengths.Using the principles of light field imaging,the proposed design enables 3D reconstruction of optical surfaces,thus enabling vertical surface height measurements with enhanced accuracy.Using Zemax-based simulations,we evaluate the system’s modulation transfer function,its optical aberrations,and its tolerance to shape variations through Zernike coefficient adjustments.The results demonstrate that this camera can achieve the required spatial resolution while also maintaining high imaging quality and thus offers a promising solution for advanced optical surface defect inspection.展开更多
Background:Studies have shown that heart rate variability(HRV)is a predictor of the prognosis of cardiovascular diseases.Contact heartbeat monitoring equipment is widely used,especially in hospitals,and benefits from ...Background:Studies have shown that heart rate variability(HRV)is a predictor of the prognosis of cardiovascular diseases.Contact heartbeat monitoring equipment is widely used,especially in hospitals,and benefits from the rapidity and accuracy of the detection of physiological health indicators.However,long-term contact with equipment has many adverse effects.The purpose of this study was to improve the accuracy of HRV detection via noncontact equipment,thus enabling HRV to be assessed in various scenarios.Methods:A novel deep learning approach was proposed for measuring heartbeats through camera videos.First,we performed facial segmentation and divided the face into 16 grid cells with different light balance scores.After the trend is filtered by the Hamming window,a transformer-based neural network is used to further filter the signal.Finally,heart rate(HR)and HRV are estimated.Results:We used 1 million synthetic data points for pretraining and a public dataset in combination with a dataset that we constructed for task training.The final results were obtained on a test dataset that we constructed.The accuracy for HR with a low light balance score(0.867-0.983)was greater than that with a high score(0.667-0.750).Our method had higher accuracy in estimating HR than traditional filtering methods(0.167-0.417)and state-of-the-art neural network filtering methods(0.783-0.917)did.The root mean square error of the HRV from the time domain was the lowest,and the correlation index score was the highest for the HRV from the frequency domain estimated by our method compared with those estimated by two neural networks.Conclusions:Light balance,large sample training,and two-stage training can improve the accuracy of HRV estimation.展开更多
LiDAR and camera are two of the most common sensors used in the fields of robot perception,autonomous driving,augmented reality,and virtual reality,where these sensors are widely used to perform various tasks such as ...LiDAR and camera are two of the most common sensors used in the fields of robot perception,autonomous driving,augmented reality,and virtual reality,where these sensors are widely used to perform various tasks such as odometry estimation and 3D reconstruction.Fusing the information from these two sensors can significantly increase the robustness and accuracy of these perception tasks.The extrinsic calibration between cameras and LiDAR is a fundamental prerequisite for multimodal systems.Recently,extensive studies have been conducted on the calibration of extrinsic parameters.Although several calibration methods facilitate sensor fusion,a comprehensive summary for researchers and,especially,non-expert users is lacking.Thus,we present an overview of extrinsic calibration and discuss diverse calibration methods from the perspective of calibration system design.Based on the calibration information sources,this study classifies these methods as target-based or targetless.For each type of calibration method,further classification was performed according to the diverse types of features or constraints used in the calibration process,and their detailed implementations and key characteristics were introduced.Thereafter,calibration-accuracy evaluation methods are presented.Finally,we comprehensively compare the advantages and disadvantages of each calibration method and suggest directions for practical applications and future research.展开更多
Hydrocarbon micro-seepage can cause oxidation reduction reactions and produce altered minerals in surface sediments and soft. The typical altered minerals mapping by their diagnostic spectral features on hyper-spectra...Hydrocarbon micro-seepage can cause oxidation reduction reactions and produce altered minerals in surface sediments and soft. The typical altered minerals mapping by their diagnostic spectral features on hyper-spectral images is an important tool for the petroleum exploration industry. In this study, the airborne hyper-spectral data were used to investigate the altered minerals induced by hydrocarbon micro-seepages by spectral feature fitting （SFF） in the loess coverage area of Xifeng Oflfield. The results re- veal that the distribution region of the altered minerals induced by hydrocarbon micro-seepage is larger than the known oilfield exploration area. The potential hydrocarbon micro-seepage region was also re- vealed by the distribution of altered minerals besides the known hydrocarbon area. A fast index was pro- posed by the absorption depths of clay and carbonate minerals for assessment of hydrocarbon micro- seepage. And it gave much clearer boundaries for the hydrocarbon micro-seepage in the loess coverage area than those by the altered mineral mapping. In addition, some field samples were analyzed by X-ray diffrac- tion （XRD） and atomic absorption spectrophotometer to validate the results. Within the extents of hydro- carbon micro-seepage, there are lower contents of ferric iron and higher contents of carbonate minerals in these samples. Therefore, it is satisfactory to have the airborne hyper-spectral data to outline the extents of hydrocarbon micro-seepage for further hydrocarbon exploration in the loess coverage area.展开更多
High-resolution hyper-spectral image (HHR) provides both detailed structural and spectral information for urban study. However, due to the inherent correlation between spectral bands and within-class variability in th...High-resolution hyper-spectral image (HHR) provides both detailed structural and spectral information for urban study. However, due to the inherent correlation between spectral bands and within-class variability in the data, the data processing of HHR is a challenging work. In this paper, based on spectral mixture analysis theory, a new stack of parts description features were extracted, and then incorporated with a stack of morphology based spatial features. Partially supervised constrained energy minimization (CEM) and unsupervised nonnegative matrix factorization (NMF) were used to extract the part-features. The joint features were then integrated by SVM classifier. The advantages of this method are the representation of physical composition of the urban area by the parts-features and the show of multi-scale structure information by morphology profiles. Experiments with an airborne hyper-spectral data flightline over the Washington DC Mall were performed, and the performance of the proposed algorithm was evaluated in comparison with well-known nonparametric weighted feature extraction (NWFE) and feature selection method. The results shown that the proposed features-joint scheme consistently outperforms the traditional methods, and so can provide an effective option for processing HHR data in urban area.展开更多
Haze is mainly caused by the suspended particulate matters in the air,of which the particulate matters pollution harms leaf vegetables.In this paper,oilseed rapes at four different growing periods were investigated in...Haze is mainly caused by the suspended particulate matters in the air,of which the particulate matters pollution harms leaf vegetables.In this paper,oilseed rapes at four different growing periods were investigated in a simulated particulate pollution environment.In combination of hyper-spectral technology and micro examination,the response of hyper-spectral characteristics of the leaf to particulate matters was investigated in-depth.The hyperspectral,chlorophyll content,net photosynthetic rate and stomatal conductance of leaf were obtained.The deposition and adsorption of particulate matters on the leaf were observed by Environmental Scanning Electron Microscope(ESEM).Normalized difference vegetation index(NDVI),modified red edge normalized(mNDVI705)and modified red edge simple ratio index(mSR705)were selected as characteristic parameters and the range of 510 nm~620 nm as the sensitive band.16 methods were used to establish the physiological information inversion model.The main results were as follows:Under the influence of particulate matters,the spectral reflectance decreased as a whole.With the increase of leaf age,the phenomenon of blue shift aggravated.The amplitude of yellow and blue edge decreased with overall decreasing vegetation indices.The furrows and irregular band protrusions in leaves were favorable for keeping particulate matters.With longer affecting time and more deposition of particle matters on the leaf,the stomatal opening became smaller.After comparing,principal component regression(PCR)+multiple scatter correction(MSC)+second derivative(SD)+Savitzky-Golay smooth(SG),and partial least square(PLS)+multiple scatter correction(MSC)+first derivative(FD)+Savitzky-Golay smooth(SG)were determined the best method to establish the inversion model of chlorophyll content and net photosynthetic rate respectively.This study may bring novel ideas for the diagnosis and analysis of the physiological response of leaf vegetables under particulate matters pollution using hyper-spectral technology.展开更多
A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition （EMD）, from an artificial red tide experiment in the East C...A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition （EMD）, from an artificial red tide experiment in the East China Sea in 2002. A set of characteristic parameters that describe absorbing crest and reflecting crest of the red tide and its recognition methods are put forward based on general pictre data, with which the spectral information of certain non-dominant alga species of a red tide occurrence is analyzed for establishing the foundation to estimate the species. Comparative experiments have proved that the method is effective. Meanwhile, the transitional area between red-tide zone and non-red-tide zone can be detected with the information of thickness of algae influence, with which a red tide can be forecast.展开更多
A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial l...A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.展开更多
Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution...Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution sparse compound-eye camera(CEC)based on dual-end collaborative optimization is proposed,which provides a cost-effective way to break through the trade-off among the field of view,resolution,and imaging speed.In the optical end,a sparse CEC based on liquid lenses is designed,which can realize large-field-of-view imaging in real time,and fast zooming within 5 ms.In the computational end,a disturbed degradation model driven super-resolution network(DDMDSR-Net)is proposed to deal with complex image degradation issues in actual imaging situations,achieving high-robustness and high-fidelity resolution enhancement.Based on the proposed dual-end collaborative optimization framework,the angular resolution of the CEC can be enhanced from 71.6"to 26.0",which provides a solution to realize high-resolution imaging for array camera dispensing with high optical hardware complexity and data transmission bandwidth.Experiments verify the advantages of the CEC based on dual-end collaborative optimization in high-fidelity reconstruction of real scene images,kilometer-level long-distance detection,and dynamic imaging and precise recognition of targets of interest.展开更多
Observatories typically deploy all-sky cameras for monitoring cloud cover and weather conditions.However,many of these cameras lack scientific-grade sensors,r.esulting in limited photometric precision,which makes calc...Observatories typically deploy all-sky cameras for monitoring cloud cover and weather conditions.However,many of these cameras lack scientific-grade sensors,r.esulting in limited photometric precision,which makes calculating the sky area visibility distribution via extinction measurement challenging.To address this issue,we propose the Photometry-Free Sky Area Visibility Estimation(PFSAVE)method.This method uses the standard magnitude of the faintest star observed within a given sky area to estimate visibility.By employing a pertransformation refitting optimization strategy,we achieve a high-precision coordinate transformation model with an accuracy of 0.42 pixels.Using the results of HEALPix segmentation is also introduced to achieve high spatial resolution.Comprehensive analysis based on real allsky images demonstrates that our method exhibits higher accuracy than the extinction-based method.Our method supports both manual and robotic dynamic scheduling,especially under partially cloudy conditions.展开更多
This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,an...This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.展开更多
Blackmagic Camera for iOS 3.2版本可直接从i Phone推流到You Tube、Vimeo和Twitch,无需借助其他硬件或第三方软件,只要选择平台、输入推流密钥,就能以专业质量直播。本次更新还加入了对SRT推流的支持,可以把高质量、低延迟的视频发送...Blackmagic Camera for iOS 3.2版本可直接从i Phone推流到You Tube、Vimeo和Twitch,无需借助其他硬件或第三方软件,只要选择平台、输入推流密钥,就能以专业质量直播。本次更新还加入了对SRT推流的支持,可以把高质量、低延迟的视频发送到Blackmagic Streaming Decoder或其他支持SRT的广电制作系统,非常适合远程制作、新闻采集、活动实况报道。另外,现在用外部存储时会有更清晰的通知信息。当硬盘在录制、待机或上传素材过程中被断开时,警告会立即出现。展开更多
Camera technology advancement and deployment continue to play a vital role in modern life and production,amongst other capabilities,generating relevant visual information.The challenge of optimizing the use and deploy...Camera technology advancement and deployment continue to play a vital role in modern life and production,amongst other capabilities,generating relevant visual information.The challenge of optimizing the use and deployment of camera networks in various applications(e.g.,surveillance,traffic monitoring,and public safety to name but just a few)has attracted considerable attention from both academia and industries.Camera planning is the first step in addressing this challenge.The surveillance objectives and scenes of a camera network dictate the modelling and optimization algorithms for camera planning.However,existing reviews have primarily focused on models or optimization algorithms,with insufficient attention given to surveillance scenes.This review aims to bridge this gap by 1)Classifying surveillance scenes into the urban environment and rural outdoor environment and comparing the surveillance requirements and challenges;2)Summarizing the details of camera coverage optimization in the relevant literature from the perspective of deployment scenes;and 3)Proposing a new surveillance scene-Solar Insecticidal Lamps with the Internet of Things—as a case study to analyze the surveillance requirement and challenges in agricultural outdoor environment.Finally,we state the technical outlook on the physical safety of outdoor electronic devices in agriculture settings and provide insights to draw more attention and effort into this area.展开更多
This paper designs and implements a single-camera 360°panoramic imaging system based on motor-driven fisheye rotation.The system utilizes a stepper motor for precise angular control,enabling the camera to rotate ...This paper designs and implements a single-camera 360°panoramic imaging system based on motor-driven fisheye rotation.The system utilizes a stepper motor for precise angular control,enabling the camera to rotate around its optical center to capture multi-view images,thereby avoiding the parallax and geometric mismatch problems inherent in traditional multi-camera configurations.To address the strong distortion characteristics of fisheye images,an equidistant projection model is adopted for distortion correction.On this basis,a brightness normalization method combining global linear brightness correction and local illumination compensation is proposed to enhance stitching consistency.By establishing a geometry model constrained by camera rotation and integrating cylindrical projection with cosine-weighted blending,the system achieves high-precision panoramic stitching and seamless visual transitions.展开更多
Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned a...Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned aerial vehicles.Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties.Reconstructing the trajectories of points from asynchronous cameras is a significant challenge.It encompasses two coupled sub-problems:Trajectory reconstruction and camera synchronization.Present methods typically address only one of these sub-problems individually.This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras,simultaneously solving both sub-problems.Firstly,we extend the trajectory intersection method to asynchronous cameras to resolve the limitation of traditional triangulation that requires camera synchronization.Secondly,we develop models for camera temporal information and target motion,based on imaging mechanisms and target dynamics characteristics.The parameters are optimized simultaneously to achieve trajectory reconstruction without accurate time parameters.Thirdly,we optimize the camera rotations alongside the camera time information and target motion parameters,using tighter and more continuous constraints on moving points.The reconstruction accuracy is significantly improved,especially when the camera rotations are inaccurate.Finally,the simulated and real-world experimental results demonstrate the feasibility and accuracy of the proposed method.The real-world results indicate that the proposed algorithm achieved a localization error of 112.95 m at an observation distance range of 15-20 km.展开更多
In various imaging applications such as autonomous vehicles and drones,autofocus lenses are indispensable for capturing clear images.However,conventional camera calibration methods typically rely either on processing ...In various imaging applications such as autonomous vehicles and drones,autofocus lenses are indispensable for capturing clear images.However,conventional camera calibration methods typically rely either on processing multiple images at a fixed focal length or on detecting multi-plane markers in a single image and then applying multi-image calibration models.This paper proposes a flexible and accurate calibration approach that extracts subpixel saddle points from a single image containing three non-coplanar calibration boards.To compute accurate homography matrices for the three boards,outliers are removed by eliminating chessboard points that deviated from the fitted grid lines according to their row and column positions.Initial estimates of the intrinsic parameters and the poses of the three planar chessboards are obtained using the three homography matrices in combination with Zhang’s calibration method.During parameter refinement,a multi-objective optimization function is constructed,incorporating three error terms:(1)Reprojection error of the inlier grid points;(2)Mechanism-driven error derived from the relationship between homography matrices and camera parameters;(3)Cross-planar linearity constraint error,which preserves the pre-imaging collinearity of any five points across different planes after projection.For weight selection in the optimization process,confidence intervals of the detected grid points are analyzed by horizontally rotating the reprojection lines to reduce bias introduced by line slope.The optimal weights are determined by minimizing the number of points whose confidence intervals does not intersect the reprojected lines.When multiple candidates yield similar reprojection performance,the parameter set with the smallest reprojection error is selected as the final result.This method efficiently estimates both intrinsic and extrinsic camera parameters.Simulations and real-world experiments validate the high precision and effectiveness of the proposed approach.Our technique is straightforward,practical,and holds significant theoretical and practical value for rapid and reliable camera calibration.展开更多
基金supported by the 2024 Research Fund of University of Ulsan.
文摘In recent years,three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly,enabling remote collaboration among users in extended Reality(XR)environments.In addition,methods for deploying multiple cameras for motion capture of users(e.g.,performers)are widely used in computer graphics.As the need to minimize and optimize the number of cameras grows to reduce costs,various technologies and research approaches focused on Optimal Camera Placement(OCP)are continually being proposed.However,as most existing studies assume homogeneous camera setups,there is a growing demand for studies on heterogeneous camera setups.For instance,technical demands keep emerging in scenarios with minimal camera configurations,especially regarding cost factors,the physical placement of cameras given the spatial structure,and image capture strategies for heterogeneous cameras,such as high-resolution RGB cameras and depth cameras.In this study,we propose a pre-visualization and simulation method for the optimal placement of heterogeneous cameras in XR environments,accounting for both the specifications of heterogeneous cameras(e.g.,field of view)and the physical configuration(e.g.,wall configuration)in real-world spaces.The proposed method performs a visibility analysis of cameras by considering each camera’s field-of-view volume,resolution,and unique characteristics,along with physicalspace constraints.This approach enables the optimal position and rotation of each camera to be recommended,along with the minimum number of cameras required.In the results of our study conducted in heterogeneous camera combinations,the proposed method achieved 81.7%~82.7%coverage of the target visual information using only 2~3 cameras.In contrast,single(or homogeneous)-typed cameras were required to use 11 cameras for 81.6%coverage.Accordingly,we found that camera deployment resources can be reduced with the proposed approaches.
基金supported by the Jilin Science and Technology Development Plan (20240101029JJ) for the following study:synchronized high-speed detection of surface shape and defects in the grinding stage of complex surfaces (KLMSZZ202305)for the high-precision wide dynamic large aperture optical inspection system for fine astronomical observation by the National Major Research Instrument Development Project (62127901)+2 种基金for ultrasmooth manufacturing technology of large diameter complex curved surface by the National Key R&D Program(2022YFB3403405)for research on the key technology of rapid synchronous detection of surface shape and subsurface defects in the grinding stage of large diameter complex surfaces by the International Cooperation Project(2025010157)The Key Laboratory of Optical System Advanced Manufacturing Technology,Chinese Academy of Sciences (2022KLOMT02-04) also supported this study
文摘To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of view and operates at both visible and near-infrared wavelengths.Using the principles of light field imaging,the proposed design enables 3D reconstruction of optical surfaces,thus enabling vertical surface height measurements with enhanced accuracy.Using Zemax-based simulations,we evaluate the system’s modulation transfer function,its optical aberrations,and its tolerance to shape variations through Zernike coefficient adjustments.The results demonstrate that this camera can achieve the required spatial resolution while also maintaining high imaging quality and thus offers a promising solution for advanced optical surface defect inspection.
基金National Natural Science Foundation of China,Grant/Award Number:72204169Department of Science and Technology of Sichuan Province,Grant/Award Number:2021YFS0393。
文摘Background:Studies have shown that heart rate variability(HRV)is a predictor of the prognosis of cardiovascular diseases.Contact heartbeat monitoring equipment is widely used,especially in hospitals,and benefits from the rapidity and accuracy of the detection of physiological health indicators.However,long-term contact with equipment has many adverse effects.The purpose of this study was to improve the accuracy of HRV detection via noncontact equipment,thus enabling HRV to be assessed in various scenarios.Methods:A novel deep learning approach was proposed for measuring heartbeats through camera videos.First,we performed facial segmentation and divided the face into 16 grid cells with different light balance scores.After the trend is filtered by the Hamming window,a transformer-based neural network is used to further filter the signal.Finally,heart rate(HR)and HRV are estimated.Results:We used 1 million synthetic data points for pretraining and a public dataset in combination with a dataset that we constructed for task training.The final results were obtained on a test dataset that we constructed.The accuracy for HR with a low light balance score(0.867-0.983)was greater than that with a high score(0.667-0.750).Our method had higher accuracy in estimating HR than traditional filtering methods(0.167-0.417)and state-of-the-art neural network filtering methods(0.783-0.917)did.The root mean square error of the HRV from the time domain was the lowest,and the correlation index score was the highest for the HRV from the frequency domain estimated by our method compared with those estimated by two neural networks.Conclusions:Light balance,large sample training,and two-stage training can improve the accuracy of HRV estimation.
基金Supported by Beijing Natural Science Foundation(Grant No.L241012)the National Natural Science Foundation of China(Grant No.62572468).
文摘LiDAR and camera are two of the most common sensors used in the fields of robot perception,autonomous driving,augmented reality,and virtual reality,where these sensors are widely used to perform various tasks such as odometry estimation and 3D reconstruction.Fusing the information from these two sensors can significantly increase the robustness and accuracy of these perception tasks.The extrinsic calibration between cameras and LiDAR is a fundamental prerequisite for multimodal systems.Recently,extensive studies have been conducted on the calibration of extrinsic parameters.Although several calibration methods facilitate sensor fusion,a comprehensive summary for researchers and,especially,non-expert users is lacking.Thus,we present an overview of extrinsic calibration and discuss diverse calibration methods from the perspective of calibration system design.Based on the calibration information sources,this study classifies these methods as target-based or targetless.For each type of calibration method,further classification was performed according to the diverse types of features or constraints used in the calibration process,and their detailed implementations and key characteristics were introduced.Thereafter,calibration-accuracy evaluation methods are presented.Finally,we comprehensively compare the advantages and disadvantages of each calibration method and suggest directions for practical applications and future research.
基金supported by the National High Technology Research and Development Program of China(No.2012AA12A308)China Geological Surveys(No.1212011087112)
文摘Hydrocarbon micro-seepage can cause oxidation reduction reactions and produce altered minerals in surface sediments and soft. The typical altered minerals mapping by their diagnostic spectral features on hyper-spectral images is an important tool for the petroleum exploration industry. In this study, the airborne hyper-spectral data were used to investigate the altered minerals induced by hydrocarbon micro-seepages by spectral feature fitting （SFF） in the loess coverage area of Xifeng Oflfield. The results re- veal that the distribution region of the altered minerals induced by hydrocarbon micro-seepage is larger than the known oilfield exploration area. The potential hydrocarbon micro-seepage region was also re- vealed by the distribution of altered minerals besides the known hydrocarbon area. A fast index was pro- posed by the absorption depths of clay and carbonate minerals for assessment of hydrocarbon micro- seepage. And it gave much clearer boundaries for the hydrocarbon micro-seepage in the loess coverage area than those by the altered mineral mapping. In addition, some field samples were analyzed by X-ray diffrac- tion （XRD） and atomic absorption spectrophotometer to validate the results. Within the extents of hydro- carbon micro-seepage, there are lower contents of ferric iron and higher contents of carbonate minerals in these samples. Therefore, it is satisfactory to have the airborne hyper-spectral data to outline the extents of hydrocarbon micro-seepage for further hydrocarbon exploration in the loess coverage area.
基金Supported by the Major State Basic Research Development Program(973Program)of China(No.2009CB723905)the National High TechnologyResearch and Development Program(863Program)of China(No.2009AA12Z114)the National Natural Science Foundation of China(Nos.40930532,40901213,40771139)
文摘High-resolution hyper-spectral image (HHR) provides both detailed structural and spectral information for urban study. However, due to the inherent correlation between spectral bands and within-class variability in the data, the data processing of HHR is a challenging work. In this paper, based on spectral mixture analysis theory, a new stack of parts description features were extracted, and then incorporated with a stack of morphology based spatial features. Partially supervised constrained energy minimization (CEM) and unsupervised nonnegative matrix factorization (NMF) were used to extract the part-features. The joint features were then integrated by SVM classifier. The advantages of this method are the representation of physical composition of the urban area by the parts-features and the show of multi-scale structure information by morphology profiles. Experiments with an airborne hyper-spectral data flightline over the Washington DC Mall were performed, and the performance of the proposed algorithm was evaluated in comparison with well-known nonparametric weighted feature extraction (NWFE) and feature selection method. The results shown that the proposed features-joint scheme consistently outperforms the traditional methods, and so can provide an effective option for processing HHR data in urban area.
基金This work was funded under the auspices of the National Natural Science Foundation for Young Scientists Fund(31801259)the National Natural Science Foundation for Young Scientists Fund(32001418)the Science and Technology Development Project of Jilin Province(20200402015NC).
文摘Haze is mainly caused by the suspended particulate matters in the air,of which the particulate matters pollution harms leaf vegetables.In this paper,oilseed rapes at four different growing periods were investigated in a simulated particulate pollution environment.In combination of hyper-spectral technology and micro examination,the response of hyper-spectral characteristics of the leaf to particulate matters was investigated in-depth.The hyperspectral,chlorophyll content,net photosynthetic rate and stomatal conductance of leaf were obtained.The deposition and adsorption of particulate matters on the leaf were observed by Environmental Scanning Electron Microscope(ESEM).Normalized difference vegetation index(NDVI),modified red edge normalized(mNDVI705)and modified red edge simple ratio index(mSR705)were selected as characteristic parameters and the range of 510 nm~620 nm as the sensitive band.16 methods were used to establish the physiological information inversion model.The main results were as follows:Under the influence of particulate matters,the spectral reflectance decreased as a whole.With the increase of leaf age,the phenomenon of blue shift aggravated.The amplitude of yellow and blue edge decreased with overall decreasing vegetation indices.The furrows and irregular band protrusions in leaves were favorable for keeping particulate matters.With longer affecting time and more deposition of particle matters on the leaf,the stomatal opening became smaller.After comparing,principal component regression(PCR)+multiple scatter correction(MSC)+second derivative(SD)+Savitzky-Golay smooth(SG),and partial least square(PLS)+multiple scatter correction(MSC)+first derivative(FD)+Savitzky-Golay smooth(SG)were determined the best method to establish the inversion model of chlorophyll content and net photosynthetic rate respectively.This study may bring novel ideas for the diagnosis and analysis of the physiological response of leaf vegetables under particulate matters pollution using hyper-spectral technology.
基金Shandong Natural Science Fund (No.Y2007G32)the Doctoral Fund of Qingdao University of Science & Technology (No.0022143).
文摘A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition （EMD）, from an artificial red tide experiment in the East China Sea in 2002. A set of characteristic parameters that describe absorbing crest and reflecting crest of the red tide and its recognition methods are put forward based on general pictre data, with which the spectral information of certain non-dominant alga species of a red tide occurrence is analyzed for establishing the foundation to estimate the species. Comparative experiments have proved that the method is effective. Meanwhile, the transitional area between red-tide zone and non-red-tide zone can be detected with the information of thickness of algae influence, with which a red tide can be forecast.
基金National Key Research and Development Program of China(No.2016YFF0103604)National Natural Science Foundations of China(Nos.61171165,11431015,61571230)+1 种基金National Scientific Equipment Developing Project of China(No.2012YQ050250)Natural Science Foundation of Jiangsu Province,China(No.BK20161500)
文摘A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.
基金financial supports from National Natural Science Foundation of China(Grant Nos.U23A20368 and 62175006)Academic Excellence Foundation of BUAA for PhD Students.
文摘Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution sparse compound-eye camera(CEC)based on dual-end collaborative optimization is proposed,which provides a cost-effective way to break through the trade-off among the field of view,resolution,and imaging speed.In the optical end,a sparse CEC based on liquid lenses is designed,which can realize large-field-of-view imaging in real time,and fast zooming within 5 ms.In the computational end,a disturbed degradation model driven super-resolution network(DDMDSR-Net)is proposed to deal with complex image degradation issues in actual imaging situations,achieving high-robustness and high-fidelity resolution enhancement.Based on the proposed dual-end collaborative optimization framework,the angular resolution of the CEC can be enhanced from 71.6"to 26.0",which provides a solution to realize high-resolution imaging for array camera dispensing with high optical hardware complexity and data transmission bandwidth.Experiments verify the advantages of the CEC based on dual-end collaborative optimization in high-fidelity reconstruction of real scene images,kilometer-level long-distance detection,and dynamic imaging and precise recognition of targets of interest.
基金supported by Natural Science Foundation of Jilin Province(20210101468JC)Chinese Academy of Sciences and Local Government Cooperation Project(2023SYHZ0027,23SH04)National Natural Science Foundation of China(12273063&12203078)。
文摘Observatories typically deploy all-sky cameras for monitoring cloud cover and weather conditions.However,many of these cameras lack scientific-grade sensors,r.esulting in limited photometric precision,which makes calculating the sky area visibility distribution via extinction measurement challenging.To address this issue,we propose the Photometry-Free Sky Area Visibility Estimation(PFSAVE)method.This method uses the standard magnitude of the faintest star observed within a given sky area to estimate visibility.By employing a pertransformation refitting optimization strategy,we achieve a high-precision coordinate transformation model with an accuracy of 0.42 pixels.Using the results of HEALPix segmentation is also introduced to achieve high spatial resolution.Comprehensive analysis based on real allsky images demonstrates that our method exhibits higher accuracy than the extinction-based method.Our method supports both manual and robotic dynamic scheduling,especially under partially cloudy conditions.
基金Supported by the Fundamental Research Funds for the Central Universities(2024300443)the Natural Science Foundation of Jiangsu Province(BK20241224).
文摘This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.
文摘Blackmagic Camera for iOS 3.2版本可直接从i Phone推流到You Tube、Vimeo和Twitch,无需借助其他硬件或第三方软件,只要选择平台、输入推流密钥,就能以专业质量直播。本次更新还加入了对SRT推流的支持,可以把高质量、低延迟的视频发送到Blackmagic Streaming Decoder或其他支持SRT的广电制作系统,非常适合远程制作、新闻采集、活动实况报道。另外,现在用外部存储时会有更清晰的通知信息。当硬盘在录制、待机或上传素材过程中被断开时,警告会立即出现。
基金supported by the National Natural Science Foundation of China(62072248).
文摘Camera technology advancement and deployment continue to play a vital role in modern life and production,amongst other capabilities,generating relevant visual information.The challenge of optimizing the use and deployment of camera networks in various applications(e.g.,surveillance,traffic monitoring,and public safety to name but just a few)has attracted considerable attention from both academia and industries.Camera planning is the first step in addressing this challenge.The surveillance objectives and scenes of a camera network dictate the modelling and optimization algorithms for camera planning.However,existing reviews have primarily focused on models or optimization algorithms,with insufficient attention given to surveillance scenes.This review aims to bridge this gap by 1)Classifying surveillance scenes into the urban environment and rural outdoor environment and comparing the surveillance requirements and challenges;2)Summarizing the details of camera coverage optimization in the relevant literature from the perspective of deployment scenes;and 3)Proposing a new surveillance scene-Solar Insecticidal Lamps with the Internet of Things—as a case study to analyze the surveillance requirement and challenges in agricultural outdoor environment.Finally,we state the technical outlook on the physical safety of outdoor electronic devices in agriculture settings and provide insights to draw more attention and effort into this area.
基金Graduate Innovation Ability Training Program of the Hebei Provincial Department of Education,2025(Project No.:CXZZSS2025095)。
文摘This paper designs and implements a single-camera 360°panoramic imaging system based on motor-driven fisheye rotation.The system utilizes a stepper motor for precise angular control,enabling the camera to rotate around its optical center to capture multi-view images,thereby avoiding the parallax and geometric mismatch problems inherent in traditional multi-camera configurations.To address the strong distortion characteristics of fisheye images,an equidistant projection model is adopted for distortion correction.On this basis,a brightness normalization method combining global linear brightness correction and local illumination compensation is proposed to enhance stitching consistency.By establishing a geometry model constrained by camera rotation and integrating cylindrical projection with cosine-weighted blending,the system achieves high-precision panoramic stitching and seamless visual transitions.
基金supported by the Hunan Provin〓〓cial Natural Science Foundation for Excellent Young Scholars(Grant No.2023JJ20045)the National Natural Science Foundation of China(Grant No.12372189)。
文摘Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned aerial vehicles.Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties.Reconstructing the trajectories of points from asynchronous cameras is a significant challenge.It encompasses two coupled sub-problems:Trajectory reconstruction and camera synchronization.Present methods typically address only one of these sub-problems individually.This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras,simultaneously solving both sub-problems.Firstly,we extend the trajectory intersection method to asynchronous cameras to resolve the limitation of traditional triangulation that requires camera synchronization.Secondly,we develop models for camera temporal information and target motion,based on imaging mechanisms and target dynamics characteristics.The parameters are optimized simultaneously to achieve trajectory reconstruction without accurate time parameters.Thirdly,we optimize the camera rotations alongside the camera time information and target motion parameters,using tighter and more continuous constraints on moving points.The reconstruction accuracy is significantly improved,especially when the camera rotations are inaccurate.Finally,the simulated and real-world experimental results demonstrate the feasibility and accuracy of the proposed method.The real-world results indicate that the proposed algorithm achieved a localization error of 112.95 m at an observation distance range of 15-20 km.
基金supported by the Research on the Reform of Curriculum Assessment Methods for College Mathematics Platform Courses(No.53111104016)。
文摘In various imaging applications such as autonomous vehicles and drones,autofocus lenses are indispensable for capturing clear images.However,conventional camera calibration methods typically rely either on processing multiple images at a fixed focal length or on detecting multi-plane markers in a single image and then applying multi-image calibration models.This paper proposes a flexible and accurate calibration approach that extracts subpixel saddle points from a single image containing three non-coplanar calibration boards.To compute accurate homography matrices for the three boards,outliers are removed by eliminating chessboard points that deviated from the fitted grid lines according to their row and column positions.Initial estimates of the intrinsic parameters and the poses of the three planar chessboards are obtained using the three homography matrices in combination with Zhang’s calibration method.During parameter refinement,a multi-objective optimization function is constructed,incorporating three error terms:(1)Reprojection error of the inlier grid points;(2)Mechanism-driven error derived from the relationship between homography matrices and camera parameters;(3)Cross-planar linearity constraint error,which preserves the pre-imaging collinearity of any five points across different planes after projection.For weight selection in the optimization process,confidence intervals of the detected grid points are analyzed by horizontally rotating the reprojection lines to reduce bias introduced by line slope.The optimal weights are determined by minimizing the number of points whose confidence intervals does not intersect the reprojected lines.When multiple candidates yield similar reprojection performance,the parameter set with the smallest reprojection error is selected as the final result.This method efficiently estimates both intrinsic and extrinsic camera parameters.Simulations and real-world experiments validate the high precision and effectiveness of the proposed approach.Our technique is straightforward,practical,and holds significant theoretical and practical value for rapid and reliable camera calibration.
