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Orbital parameter characterization and objects cataloging for Earth-Moon collinear libration points 认领 引用
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作者 Chenyuan QIAO Xi LONG +2 位作者 Leping YANG Yanwei ZHU Peng WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期551-566,共16页
Owing to the chaotic and non-integrable nature of three-body dynamics,the conventional Keplerian elements are rendered inadequate for cataloging cislunar space objects.Currently,there has been a conspicuous absence of... Owing to the chaotic and non-integrable nature of three-body dynamics,the conventional Keplerian elements are rendered inadequate for cataloging cislunar space objects.Currently,there has been a conspicuous absence of universally recognized parameters for the characterization and cataloging of such objects,thereby posing an urgent challenge to cislunar space situational awareness.This paper proposes a novel approach to parameterize the orbits of Earth-Moon collinear libration points by leveraging the theoretical frameworks of canonical transformations.First,under the Hamiltonian-form dynamical equations of the libration point,symplectic transformations are employed to extract 3 modes of motion from locally linearized part.A subsequent canonical transformation then decouples the hyperbolic invariant manifold from the center manifold within the nonlinear remainder.Finally,6 characteristic parameters obtained via action-angle variables are established in a bijective correspondence with the state variables,where two parameters characterize the motion of the invariant manifold and four parameters characterize the motion of the central manifold.Furthermore,a distribution map of the Earth-Moon libration point orbits is drawn utilizing Poincare sections,which can be used to describe the distribution of libration point object.Simulation results demonstrate that the proposed parameters are not only applicable to orbit identification and object cataloging but also exhibit remarkable consistency and robustness against variations in observation arc length and observational errors. 展开更多
关键词 Canonical transformation Characteristic parameters Cislunar space Collinear libration points Objects cataloging Orbital calculations Spacecraft
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Accurate Real-Time Measurement of Small and Irregular Road Abandoned Objects Using a Lightweight Vision-Based Framework 认领 引用
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作者 Ying Tang Chuanyi Ma +1 位作者 Feng Guo Wenhao Sun 《Computers, Materials & Continua》 SCIE EI 2026年第8期1535-1557,共23页
Road Abandoned Objects(RAOs)pose significant threats to traffic safety,particularly due to their small size,irregular shapes,and unpredictable distribution in complex road environments.The primary objective of this st... Road Abandoned Objects(RAOs)pose significant threats to traffic safety,particularly due to their small size,irregular shapes,and unpredictable distribution in complex road environments.The primary objective of this study is to develop an accurate and real-time detection framework for RAOs while maintaining low computational cost for practical deployment.To achieve this,we propose RAO-YOLO,a lightweight vision-based detection framework built upon an enhanced YOLO architecture.Specifically,a Mixed Aggregation Network(MANet)is introduced to improve multi-scale feature representation,and a Lightweight Shared Detail-Enhanced Detection(LSDD)head is designed to enhance localization accuracy for small and irregular objects.Furthermore,a Focal-MPDIoU loss function is proposed to address sample imbalance and geometric irregularity during training.Extensive experiments conducted on the RAOD dataset demonstrate that the proposed method achieves superior performance compared to state-of-the-art detectors,achieving a mAP@0.5:0.95 of 56.1%while maintaining real-time inference speed.These results validate the effectiveness of the proposed framework for practical intelligent transportation applications. 展开更多
关键词 Road abandoned objects real-time object detection road safety intelligent transportation systems
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TQU-GraspingObject:3D Common Objects Detection,Recognition,and Localization on Point Cloud for Hand Grasping in Sharing Environments 认领 引用
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作者 Thi-Loan Nguyen Huy-Nam Chu +2 位作者 The-Thanh Hua Trung-Nghia Phung Van-Hung Le 《Computers, Materials & Continua》 SCIE EI 2026年第5期1701-1722,共22页
To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determ... To support the process of grasping objects on a tabletop for the blind or robotic arm,it is necessary to address fundamental computer vision tasks,such as detecting,recognizing,and locating objects in space,and determining the position of the grasping information.These results can then be used to guide the visually impaired or to execute grasping tasks with a robotic arm.In this paper,we collected,annotated,and published the benchmark TQUGraspingObject dataset for testing,validation,and evaluation of deep learning(DL)models for detecting,recognizing,and localizing grasping objects in 2D and 3D space,especially 3D point cloud data.Our dataset is collected in a shared room,with common everyday objects placed on the tabletop in jumbled positions by Intel RealSense D435(IR-D435).This dataset includes more than 63k RGB-D pairs and related data such as normalized 3D object point cloud,3D object point cloud segmented,coordinate system normalizationmatrix,3D object point cloud normalized,and hand pose for grasping each object.At the same time,we also conducted experiments on fourDL networks with the best performance:SSD-MobileNetV3,ResNet50-Transformer,ResNet101-Transformer,and YOLOv12.The results present that YOLOv12 has the most suitable results in detecting and recognizing objects in images.All data,annotations,toolkit,source code,point cloud data,and results are publicly available on our project website:http://gffzz188fe103f8f1460asqu99ouuwwk5x6opf.ffgz.tsg.suse.edu.cn/HuaTThanhIT2327Tqu/datasetv2. 展开更多
关键词 Grasping object of blind/Robot arm TQU-graspingobject benchmark dataset 3D point cloud data deep learning(DL) object detectionecognition intel realsense D435(IR-D435)
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An Unsupervised Online Detection Method for Foreign Objects in Complex Environments 认领 引用
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作者 YANG Xiaoyang YANG Yanzhu DENG Haiping 《Journal of Donghua University(English Edition)》 CAS 2026年第1期140-151,共12页
In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often fa... In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications. 展开更多
关键词 foreign object detection unsupervised learning data augmentation complex environment bogie dataset
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Transorbital craniocerebral injury caused by metallic foreign objects 认领 引用
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作者 Chongqing Yang Hongguang Cui +2 位作者 Xiawei Wang Chenying Yu Yan Long 《World Journal of Emergency Medicine》 SCIE CAS CSCD 2025年第3期277-279,共3页
Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.[1]The clinical prognosis of transorbital craniocerebral... Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.[1]The clinical prognosis of transorbital craniocerebral injury is closely related to the size,shape,speed,nature,and trajectory of the foreign object,as well as the incidence of central nervous system damage and secondary complications.The foreign objects reported to have caused these injuries are categorized into wooden items,metallic items,[2-8]and other materials,which penetrate the intracranial region via fi ve major pathways,including the orbital roof (OR),superior orbital fissure (SOF),inferior orbital fissure(IOF),optic canal (OC),and sphenoid wing.Herein,we present eight cases of transorbital craniocerebral injury caused by an unusual metallic foreign body. 展开更多
关键词 transorbital craniocerebral injury ocular cerebral structures foreign objectas central nervous system damage penetrating head injury foreign objects metallic foreign objects clinical prognosis
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Implementing Convolutional Neural Networks to Detect Dangerous Objects in Video Surveillance Systems 认领 引用
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作者 Carlos Rojas Cristian Bravo +1 位作者 Carlos Enrique Montenegro-Marín Rubén González-Crespo 《Computers, Materials & Continua》 SCIE EI 2025年第12期5489-5507,共19页
The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance ... The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time.While traditional video surveillance relies on human monitoring,this approach suffers from limitations such as fatigue and delayed response times.This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety.Our approach leverages state-of-the-art convolutional neural networks(CNNs),specifically You Only Look Once version 4(YOLOv4)and EfficientDet,for real-time object detection.The system was trained on a comprehensive dataset of over 50,000 images,enhanced through data augmentation techniques to improve robustness across varying lighting conditions and viewing angles.Cloud-based deployment on Amazon Web Services(AWS)ensured scalability and efficient processing.Experimental evaluations demonstrated high performance,with YOLOv4 achieving 92%accuracy and processing images in 0.45 s,while EfficientDet reached 93%accuracy with a slightly longer processing time of 0.55 s per image.Field tests in high-traffic environments such as train stations and shopping malls confirmed the system’s reliability,with a false alarm rate of only 4.5%.The integration of automatic alerts enabled rapid security responses to potential threats.The proposed CNN-based system provides an effective solution for real-time detection of dangerous objects in video surveillance,significantly improving response times and public safety.While YOLOv4 proved more suitable for speed-critical applications,EfficientDet offered marginally better accuracy.Future work will focus on optimizing the system for low-light conditions and further reducing false positives.This research contributes to the advancement of AI-driven surveillance technologies,offering a scalable framework adaptable to various security scenarios. 展开更多
关键词 Automatic detection of objects convolutional neural networks deep learning real-time image processing video surveillance systems automatic alerts
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Semantic segmentation of camouflage objects via fusing reconstructed multispectral and RGB images 认领 引用 被引量：1
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作者 Feng Huang Gonghan Yang +5 位作者 Jing Chen Yixuan Xu Jingze Su Guimin Huang Shu Wang Wenxi Liu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2025年第8期324-337,共14页
Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging du... Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing. 展开更多
关键词 Camouflage object detection Reconstructed multispectral image(MSI) Unmanned aerial vehicle(UAV) Semantic segmentation Remote sensing
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An intelligent detection method for directional bolt hole objects of shield tunnel lining structures 认领 引用
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作者 Yiding Ma Dechun Lu +3 位作者 Fanchao Kong Tao Tian Dongmei Zhang Xiuli Du 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第12期7555-7569,共15页
Most image-based object detection methods employ horizontal bounding boxes(HBBs)to capture objects in tunnel images.However,these bounding boxes often fail to effectively enclose objects oriented in arbitrary directio... Most image-based object detection methods employ horizontal bounding boxes(HBBs)to capture objects in tunnel images.However,these bounding boxes often fail to effectively enclose objects oriented in arbitrary directions,resulting in reduced accuracy and suboptimal detection performance.Moreover,HBBs cannot provide directional information for rotated objects.This study proposes a rotated detection method for identifying apparent defects in shield tunnels.Specifically,the oriented region-convolutional neural network(oriented R-CNN)is utilized to detect rotated objects in tunnel images.To enhance feature extraction,a novel hybrid backbone combining CNN-based networks with Swin Transformers is proposed.A feature fusion strategy is employed to integrate features extracted from both networks.Additionally,a neck network based on the bidirectional-feature pyramid network(Bi-FPN)is designed to combine multi-scale object features.The bolt hole dataset is curated to evaluate the efficacyof the proposed method.In addition,a dedicated pre-processing approach is developed for large-sized images to accommodate the rotated,dense,and small-scale characteristics of objects in tunnel images.Experimental results demonstrate that the proposed method achieves a more than 4%improvement in mAP50-95compared to other rotated detectors and a 6.6%-12.7%improvement over mainstream horizontal detectors.Furthermore,the proposed method outperforms mainstream methods by 6.5%-14.7%in detecting leakage bolt holes,underscoring its significant engineering applicability. 展开更多
关键词 Apparent defects of shield tunnels Rotated object detection Swin transformer Oriented region-convolutional neural network(oriented R-CNN)
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Physics-Informed Graph Learning for Shape Prediction in Robot Manipulate of Deformable Linear Objects 认领 引用
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作者 Meixuan Wang Junliang Wang +2 位作者 Jie Zhang Xinting Liao Guojin Li 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第6期154-165,共12页
Shape prediction of deformable linear objects(DLO)plays critical roles in robotics,medical devices,aerospace,and manufacturing,especially in manipulating objects such as cables,wires,and fibers.Due to the inherent fle... Shape prediction of deformable linear objects(DLO)plays critical roles in robotics,medical devices,aerospace,and manufacturing,especially in manipulating objects such as cables,wires,and fibers.Due to the inherent flexibility of DLO and their complex deformation behaviors,such as bending and torsion,it is challenging to predict their dynamic characteristics accurately.Although the traditional physical modeling method can simulate the complex deformation behavior of DLO,the calculation cost is high and it is difficult to meet the demand of real-time prediction.In addition,the scarcity of data resources also limits the prediction accuracy of existing models.To solve these problems,a method of fiber shape prediction based on a physical information graph neural network(PIGNN)is proposed in this paper.This method cleverly combines the powerful expressive power of graph neural networks with the strict constraints of physical laws.Specifically,we learn the initial deformation model of the fiber through graph neural networks(GNN)to provide a good initial estimate for the model,which helps alleviate the problem of data resource scarcity.During the training process,we incorporate the physical prior knowledge of the dynamic deformation of the fiber optics into the loss function as a constraint,which is then fed back to the network model.This ensures that the shape of the fiber optics gradually approaches the true target shape,effectively solving the complex nonlinear behavior prediction problem of deformable linear objects.Experimental results demonstrate that,compared to traditional methods,the proposed method significantly reduces execution time and prediction error when handling the complex deformations of deformable fibers.This showcases its potential application value and superiority in fiber manipulation. 展开更多
关键词 Deformable linear objects Fiber Physics-informed graph neural network(PIGNN) Shape prediction
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Study on Color Difference of Color Reproduction of 3D Objects 认领 引用
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作者 GU Chong DENG Yi-qiang 《印刷与数字媒体技术研究》 CAS 北大核心 2025年第4期33-38,69,共6页
To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,a... To investigate the applicability of four commonly used color difference formulas(CIELAB,CIE94,CMC(1:1),and CIEDE2000)in the printing field on 3D objects,as well as the impact of four standard light sources(D65,D50,A,and TL84)on 3D color difference evaluations,50 glossy spheres with a diameter of 2cm based on the Sailner J4003D color printing device were created.These spheres were centered around the five recommended colors(gray,red,yellow,green,and blue)by CIE.Color difference was calculated according to the four formulas,and 111 pairs of experimental samples meeting the CIELAB gray scale color difference requirements(1.0-14.0)were selected.Ten observers,aged between 22 and 27 with normal color vision,were participated in this study,using the gray scale method from psychophysical experiments to conduct color difference evaluations under the four light sources,with repeated experiments for each observer.The results indicated that the overall effect of the D65 light source on 3D objects color difference was minimal.In contrast,D50 and A light sources had a significant impact within the small color difference range,while the TL84 light source influenced both large and small color difference considerably.Among the four color difference formulas,CIEDE2000 demonstrated the best predictive performance for color difference in 3D objects,followed by CMC(1:1),CIE94,and CIELAB. 展开更多
关键词 Color difference formula 3D objects Light source Gray scale Normalized residual sum of squares
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Transforming Education with Photogrammetry:Creating Realistic 3D Objects for Augmented Reality Applications 认领 引用
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作者 Kaviyaraj Ravichandran Uma Mohan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期185-208,共24页
Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in ed... Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector. 展开更多
关键词 Augmented reality education immersive learning 3D object creation photogrammetry and StructureFromMotion
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Exploration of the Application of Artificial Intelligence Technology in the Transformation of Old Objects 认领 引用
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作者 Tonghuan Zhang Xinyu Yang +1 位作者 Ying Chen Qiufan Xie 《Journal of Electronic Research and Application》 2025年第2期51-57,共7页
With the rapid development of technology,artificial intelligence(AI)is increasingly being applied in various fields.In today’s context of resource scarcity,pursuit of sustainable development and resource reuse,the tr... With the rapid development of technology,artificial intelligence(AI)is increasingly being applied in various fields.In today’s context of resource scarcity,pursuit of sustainable development and resource reuse,the transformation of old objects is particularly important.This article analyzes the current status of old object transformation and the opportunities brought by the internet to old objects and delves into the application of artificial intelligence in old object transformation.The focus is on five aspects:intelligent identification and classification,intelligent evaluation and prediction,automation integration,intelligent design and optimization,and integration of 3D printing technology.Finally,the process of“redesigning an old furniture,such as a wooden desk,through AI technology”is described,including the recycling,identification,detection,design,transformation,and final user feedback of the old wooden desk.This illustrates the unlimited potential of the“AI+old object transformation”approach,advocates for people to strengthen green environmental protection,and drives sustainable development. 展开更多
关键词 Artificial Intelligence(AI) Old object transformation Environmental protection
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Security and privacy issues of physical objects in the IoT:Challenges and opportunities 认领 引用 被引量：8
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作者 Xuanxia Yao Fadi Farha +3 位作者 Rongyang Li Ismini Psychoul Liming Chen Huansheng Ning 《Digital Communications and Networks》 SCIE EI CSCD 2021年第3期373-384,共12页
In the Internet of Things(IoT),security and privacy issues of physical objects are crucial to the related applications.In order to clarify the complicated security and privacy issues,the life cycle of a physical objec... In the Internet of Things(IoT),security and privacy issues of physical objects are crucial to the related applications.In order to clarify the complicated security and privacy issues,the life cycle of a physical object is divided into three stages of pre-working,in-working,and post-working.On this basis,a physical object-based security architecture for the IoT is put forward.According to the security architecture,security and privacy requirements and related protecting technologies for physical objects in different working stages are analyzed in detail.Considering the development of IoT technologies,potential security and privacy challenges that IoT objects may face in the pervasive computing environment are summarized.At the same time,possible directions for dealing with these challenges are also pointed out. 展开更多
关键词 Security Privacy preserving Physical objects Life cycle Internet of things
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基于SuperMap Objects的农用地定级系统设计与开发 认领 引用 被引量：4
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作者 雷少刚 张绍良 +3 位作者 卞正富 黄继辉 陈津浦 金波 《中国土地科学》 CSSCI 北大核心 2006年第2期28-32,共5页
研究目的:基于GIS进行农用地定级系统的设计与开发,解决现有农用地定级系统中存在的一些问题。研究方法:系统分析法、实证比较分析法。研究结果:指出了现有农用地定级系统中存在的三个问题,提出了系统设计原则、系统的功能设计、数据处... 研究目的:基于GIS进行农用地定级系统的设计与开发,解决现有农用地定级系统中存在的一些问题。研究方法:系统分析法、实证比较分析法。研究结果:指出了现有农用地定级系统中存在的三个问题,提出了系统设计原则、系统的功能设计、数据处理流程设计、数据组织结构设计以及对进一步开发农用地定级系统的建议与展望。研究结论:本系统成功地应用于安徽省宿州市桥区农用地级别的划分,该系统可靠,稳定,适应性较强。 展开更多
关键词 土地管理 农用地定级 SupcrMap Objects 系统设计
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Numerical Investigations on Harbor Oscillations Induced by Falling Objects 认领 引用 被引量：4
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作者 GAO Jun-liang BI Wen-jing +1 位作者 ZHANG Jian ZANG Jun 《China Ocean Engineering》 SCIE EI CSCD 2023年第3期458-470,共13页
In this paper,the open-sourced computational fluid dynamics software,OpenFOAM~?,is used to study the fluctuation phenomenon of the water body inside a horizontally one-dimensional enclosed harbor basin with constant w... In this paper,the open-sourced computational fluid dynamics software,OpenFOAM~?,is used to study the fluctuation phenomenon of the water body inside a horizontally one-dimensional enclosed harbor basin with constant water depth triggered by falling wedges with various horizontal falling positions,initial falling velocities and masses.Based on both Fourier transfo rm analysis and wavelet spectrum analysis for the time series of the free surface elevations inside the harbor basin,it is found for the first time that the wedge falling inside the harbor can directly trigger harbor resonance.The influences of the three factors(including the horizontal falling position,the initial falling velocity,and the mass)on the response amplitudes of the lowest three resonant modes are also investigated.The results show that when the wedge falls on one of the nodal points of a resonant mode,the mode would be remarkably suppressed.Conversely,when the wedge falls on one of the anti-nodal points of a resonant mode,the mode would be evidently triggered.The initial falling velocity of the wedge mainly has a remarkable effect on the response amplitude of the most significant mode,and the latter shows a gradual increase trend with the increase of the former.While for the other two less significant modes,their response amplitudes fluctuate around certain constant values as the initial falling velocity rises.In general,the response amplitudes of all the lowest three modes are shown to gradually increase with the mass of the wedge. 展开更多
关键词 harbor oscillations seiches falling objects resonant mode response amplitude
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Optical encryption of multiple three-dimensional objects based on multiple interferences and single-pixel digital holography 认领 引用 被引量：3
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作者 Ying Wang Qi Liu +1 位作者 Jun Wang Qiong-Hua Wang 《Chinese Physics B》 SCIE EI CAS CSCD 2018年第3期253-259,共7页
We present an optical encryption method of multiple three-dimensional objects based on multiple interferences and single-pixel digital holography. By modifying the Mach-Zehnder interferometer, the interference of the ... We present an optical encryption method of multiple three-dimensional objects based on multiple interferences and single-pixel digital holography. By modifying the Mach-Zehnder interferometer, the interference of the multiple objects beams and the one reference beam is used to simultaneously encrypt multiple objects into a ciphertext. During decryption, each three-dimensional object can be decrypted independently without having to decrypt other objects. Since the single- pixel digital holography based on compressive sensing theory is introduced, the encrypted data of this method is effectively reduced. In addition, recording fewer encrypted data can greatly reduce the bandwidth of network transmission. Moreover, the compressive sensing essentially serves as a secret key that makes an intruder attack invalid, which means that the system is more secure than the conventional encryption method. Simulation results demonstrate the feasibility of the proposed method and show that the system has good security performance. 展开更多
关键词 multiple three-dimensional objects encryption single-pixel digital holography phase-shifting in- terference compressive sensing
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Visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity 认领 引用 被引量：3
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作者 CHEN Yunhai JIANG Nan +2 位作者 CAO Yibing YANG Zhenkai ZHAO Xinke 《Journal of Geographical Sciences》 SCIE CSCD 2021年第7期1059-1081,共23页
Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-... Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains. 展开更多
关键词 COVID-19 spatio-temporal objects multi-granularity case information visualization visual analysis spatial correlation analysis
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Review of heterogeneous material objects modeling in additive manufacturing 认领 引用 被引量：2
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作者 Bin Li Jianzhong Fu +2 位作者 Jiawei Feng Ce Shang Zhiwei Lin 《Visual Computing for Industry,Biomedicine,and Art》 EI 2020年第1期53-70,共18页
This review investigates the recent developments of heterogeneous objects modeling in additive manufacturing(AM),as well as general problems and widespread solutions to the modeling methods of heterogeneous objects.Pr... This review investigates the recent developments of heterogeneous objects modeling in additive manufacturing(AM),as well as general problems and widespread solutions to the modeling methods of heterogeneous objects.Prevalent heterogeneous object representations are generally categorized based on the different expression or data structure employed therein,and the state-of-the-art of process planning procedures for AM is reviewed via different vigorous solutions for part orientation,slicing methods,and path planning strategies.Finally,some evident problems and possible future directions of investigation are discussed. 展开更多
关键词 Review Heterogeneous objects modeling Heterogeneous materials Additive manufacturing
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SADDLE-POINT BASED SEPARATION OF TOUCHED OBJECTS IN 2-D IMAGE 认领 引用 被引量：6
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作者 Chen Ken Larry E. Banta Jiang Gangyi 《Journal of Electronics(China)》 2006年第3期452-456,共5页
In many image analysis and processing problems, discriminating the size and shape of each individual object in an aggregate pile projected in an image is an important practice. It is relatively easy to distinguish the... In many image analysis and processing problems, discriminating the size and shape of each individual object in an aggregate pile projected in an image is an important practice. It is relatively easy to distinguish these features among the objects already separated from each other. The problems will be undoubtedly more complex and of greater challenge if the objects are touched or/and overlapped. This letter presents an algorithm that can be used to separate the touches and overlaps existing in the objects within a 2-D image. The approach is first to convert the gray-scale image to its corresponding binary one and then to the 3-D topographic one using the erosion operations. A template (or mask) is engineered to search the topographic surface for the saddle point, from which the segmenting orientation is determined followed by the desired separating operation. The algorithm is tested on a real image and the running result is adequately satisfying and encouraging. 展开更多
关键词 Image processing Segmentation Objects separation Morphological processing Touch and overlap Aggregates images
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Cast shadow detection for moving objects based on binocular stereo vision 认领 引用 被引量：3
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作者 彭祺 仲思东 屠礼芬 《Journal of Central South University》 SCIE EI CAS 2014年第2期651-658,共8页
A novel cast shadow detection approach was proposed.A stereo vision system was used to capture images instead of traditional single camera.It was based on an assumption that cast shadows were on a special plane.The im... A novel cast shadow detection approach was proposed.A stereo vision system was used to capture images instead of traditional single camera.It was based on an assumption that cast shadows were on a special plane.The image obtained from one camera was inversely projected to the plane and then transformed to the view from another camera.The points on the plane shared the same position between original image and the transformed image.As a result,the cast shadows can be detected.In order to improve the efficiency of cast shadow detection and decrease computational complexity,the obvious object areas in CIELAB color space were removed and the potential shadow areas were obtained.Experimental results demonstrate that the proposed approach can detect cast shadows accurately even under various illuminations. 展开更多
关键词 cast shadow detection stereo vision moving objects flat ground
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