Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variati...Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.展开更多
The GeoEduc3D project aims to provide educational games for smartphones based on Geomatics and use augmented reality techniques in order to make these games more immersive. To improve the immersive and interactive asp...The GeoEduc3D project aims to provide educational games for smartphones based on Geomatics and use augmented reality techniques in order to make these games more immersive. To improve the immersive and interactive aspects of those games, we focused on the exploitation of spatial context in this particular application framework (serious games, augmented reality, smart phones, and multi-users environment). Our work has thus led to the design of a solution dedicated to the management of spatial context in a multi-players environment on and for smartphones. Several contributions have been made: modeling spatial context, proposing a service-oriented architecture to manage this context, defining a Web Service Spatial Context (WSCS) and implementation of a WSCS prototype and a mobile client according to an environment exploiting FourSquare, a geo-social application.展开更多
There is an emerging recognition of the importance of utilizing contextual information in authorization decisions. Controlling access to resources in the field of wireless and mobile networking require the definition ...There is an emerging recognition of the importance of utilizing contextual information in authorization decisions. Controlling access to resources in the field of wireless and mobile networking require the definition of a formal model for access control with supporting spatial context. However, traditional RBAC model does not specify these spatial requirements. In this paper, we extend the existing RBAC model and propose the SC-RBAC model that utilizes spatial and location-based information in security policy definitions. The concept of spatial role is presented, and the role is assigned a logical location domain to specify the spatial boundary. Roles are activated based on the current physical position of the user which obtsined from a specific mobile terminal. We then extend SC-RBAC to deal with hierarchies, modeling permission, user and activation inheritance, and prove that the hierarchical spatial roles are capable of constructing a lattice which is a means for articulate multi-level security policy and more suitable to control the information flow security for safety-critical location-aware information systems. Next, con- strained SC-RBAC allows express various spatial separations of duty constraints, location-based cardinality and temporal constraints for specify fine-grained spatial semantics that are typical in location-aware systems. Finally, we introduce 9 in- variants for the constrained SC-RBAC and its basic security theorem is proven. The constrained SC-RBAC provides the foundation for applications in need of the constrained spatial context aware access control.展开更多
Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. ...Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. Feature quantization reduces the discriminative power of local features and unavoidably causes many false local matches between images, which degrades the retrieval accuracy. To filter those false matches, geometric context among visual words has been popularly explored for the verification of geometric consistency. However, existing studies with global or local geometric verification are either computationally expensive or achieve limited accuracy. To address this issue, in this paper, we focus on partialduplicate Web image retrieval, and propose a scheme to encode the spatial context for visual matching verification. An efficient affine enhancement scheme is proposed to refine the verification results. Experiments on partial-duplicate Web image search, using a database of one million images, demonstrate the effectiveness and efficiency of the proposed approach.Evaluation on a 10-million image database further reveals the scalability of our approach.展开更多
We introduce a new method for visualizing and analyzing information landscapes of ideas and events posted on public web pages through customized web-search engines and keywords.This research integrates GIScience and w...We introduce a new method for visualizing and analyzing information landscapes of ideas and events posted on public web pages through customized web-search engines and keywords.This research integrates GIScience and web-search engines to track and analyze public web pages and their web contents with associated spatial relationships.Web pages searched by clusters of keywords were mapped with real-world coordinates(by geolocating their Internet Protocol addresses).The resulting maps represent web information landscapes consisting of hundreds of populated web pages searched by selected keywords.By creating a Spatial Web Automatic Reasoning and Mapping System prototype,researchers can visualize the spread of web pages associated with specific keywords,concepts,ideas,or news over time and space.These maps may reveal important spatial relationships and spatial context associated with selected keywords.This approach may provide a new research direction for geographers to study the diffusion of human thought and ideas.A better understanding of the spatial and temporal dynamics of the‘collective thinking of human beings’over the Internet may help us understand various innovation diffusion processes,human behaviors,and social movements around the world.展开更多
Structured study of spatial objects and their relationships leads to a better cognition of the geospatial information and creates the concept of context at a higher level of abstraction.This study is aimed at providin...Structured study of spatial objects and their relationships leads to a better cognition of the geospatial information and creates the concept of context at a higher level of abstraction.This study is aimed at providing a comprehensive definition of the context for geospatial objects.A combination of binary qualitative spatial relationships(i.e.direction,distance,and topological relations)among the members of a set of spatial objects will be used accordingly.In addition,by incorporating the general concept of context,obtained from either static data(attributes in a database)or dynamic data(sensors),the compact context of spatial objects will be introduced.Our framework for presentation of the involved knowledge and conception about the objects in context is also explored using ontology and description logic because of powerful conceptualization of relationships,either spatial or non-spatial,integrally.For this purpose,the hierarchies of main structure and object properties are formed at first.The constraint and characteristics of classes,such as subclasses,equivalent classes,cardinality etc.,and object properties,such as being functional,transitive,symmetric,asymmetric,inverse functional,disjoint etc.,are discovered and presented in more detail using web ontology language in description logic mode.The implementation is then performed in the framework of semantic web and extensible markup language syntaxes.The method ultimately facilitates,spatial reasoning by effective querying in a semantic framework taking pellet reasoner and SPARQL(a recursive acronym for SPARQL Protocol and RDF Query Language).展开更多
Animal survival necessitates adaptive behav-iors in volatile environmental contexts.Virtual reality(VR)technology is instrumental to study the neural mechanisms underlying behaviors modulated by environmental con-text...Animal survival necessitates adaptive behav-iors in volatile environmental contexts.Virtual reality(VR)technology is instrumental to study the neural mechanisms underlying behaviors modulated by environmental con-text by simulating the real world with maximized control of contextual elements.Yet current VR tools for rodents have limited flexibility and performance(e.g.,frame rate)for context-dependent cognitive research.Here,we describe a high-performance VR platform with which to study con-textual behaviors immersed in editable virtual contexts.This platform was assembled from modular hardware and custom-written software with flexibility and upgradability.Using this platform,we trained mice to perform context-dependent cognitive tasks with rules ranging from discrim-ination to delayed-sample-to-match while recording from thousands of hippocampal place cells.By precise manipula-tions of context elements,we found that the context recogni-tion was intact with partial context elements,but impaired by exchanges of context elements.Collectively,our work establishes a configurable VR platform with which to investigate context-dependent cognition with large-scale neural recording.展开更多
A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely...A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.展开更多
针对复杂环境下猫眼目标探测易受环境干扰、特征区分度不足等问题,提出一种基于空间上下文的决策级融合猫眼目标检测算法(Decision-level Fusion based on Spatial Context,DFSC)。算法由三个模块组成:在猫眼目标检测模块中,提出基于自...针对复杂环境下猫眼目标探测易受环境干扰、特征区分度不足等问题,提出一种基于空间上下文的决策级融合猫眼目标检测算法(Decision-level Fusion based on Spatial Context,DFSC)。算法由三个模块组成:在猫眼目标检测模块中,提出基于自适应迭代最大类间方差的图像二值化方法,结合迭代前景细化策略和动态收敛机制,在精确提取猫眼目标连通域的同时保留局部细节信息;构建了傅里叶功率谱和归一化加权质心偏移特征描述子,提升猫眼与干扰目标的可区分性;提出基于自适应环境感知的多维特征加权融合方法,实现特征权重的自适应优化。在通用目标检测模块中,将可变形卷积DCNv3引入YOLOv8骨干网络的C2f模块,提升对遮挡目标和小目标的检测性能。在基于空间上下文的决策级融合模块中,通过计算猫眼目标的遮挡率来评估其与环境干扰目标的空间关系,从而有效抑制虚警。在基于自主研发的激光主动探测系统构建的猫眼目标检测数据集上开展实验,结果表明,与现有主流算法相比,召回率由92.2%提升至98.9%,精度由49.0%提升至74.5%,单帧耗时8.3 ms,显著降低了算法在复杂环境下的虚警率。展开更多
基金the Key Research and Development Program of Hainan Province(Grant Nos.ZDYF2023GXJS163,ZDYF2024GXJS014)National Natural Science Foundation of China(NSFC)(Grant Nos.62162022,62162024)+2 种基金the Major Science and Technology Project of Hainan Province(Grant No.ZDKJ2020012)Hainan Provincial Natural Science Foundation of China(Grant No.620MS021)Youth Foundation Project of Hainan Natural Science Foundation(621QN211).
文摘Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.
文摘The GeoEduc3D project aims to provide educational games for smartphones based on Geomatics and use augmented reality techniques in order to make these games more immersive. To improve the immersive and interactive aspects of those games, we focused on the exploitation of spatial context in this particular application framework (serious games, augmented reality, smart phones, and multi-users environment). Our work has thus led to the design of a solution dedicated to the management of spatial context in a multi-players environment on and for smartphones. Several contributions have been made: modeling spatial context, proposing a service-oriented architecture to manage this context, defining a Web Service Spatial Context (WSCS) and implementation of a WSCS prototype and a mobile client according to an environment exploiting FourSquare, a geo-social application.
文摘There is an emerging recognition of the importance of utilizing contextual information in authorization decisions. Controlling access to resources in the field of wireless and mobile networking require the definition of a formal model for access control with supporting spatial context. However, traditional RBAC model does not specify these spatial requirements. In this paper, we extend the existing RBAC model and propose the SC-RBAC model that utilizes spatial and location-based information in security policy definitions. The concept of spatial role is presented, and the role is assigned a logical location domain to specify the spatial boundary. Roles are activated based on the current physical position of the user which obtsined from a specific mobile terminal. We then extend SC-RBAC to deal with hierarchies, modeling permission, user and activation inheritance, and prove that the hierarchical spatial roles are capable of constructing a lattice which is a means for articulate multi-level security policy and more suitable to control the information flow security for safety-critical location-aware information systems. Next, con- strained SC-RBAC allows express various spatial separations of duty constraints, location-based cardinality and temporal constraints for specify fine-grained spatial semantics that are typical in location-aware systems. Finally, we introduce 9 in- variants for the constrained SC-RBAC and its basic security theorem is proven. The constrained SC-RBAC provides the foundation for applications in need of the constrained spatial context aware access control.
基金supported in part to Dr.Wen-Gang Zhou by the Fundamental Research Funds for the Central Universities of China under Grant Nos.WK2100060014 and WK2100060011the Start-Up Funding from the University of Science and Technology of China under Grant No.KY2100000036+6 种基金the Open Project of Beijing Multimedia and Intelligent Software Key Laboratory in Beijing University of Technology,and the sponsor from Intel ICRI MNC projectin part to Dr.Hou-Qiang Li by the National Natural Science Foundation of China(NSFC)under Grant Nos.61325009,61390514,and 61272316in part to Dr.Yijuan Lu by the Army Research Office(ARO)of USA under Grant No.W911NF-12-1-0057the National Science Foundation of USA under Grant No.CRI 1305302in part to Dr.Qi Tian by ARO under Grant No.W911NF-12-1-0057the Faculty Research Award by NEC Laboratories of America,respectivelywas supported in part by NSFC under Grant No.61128007
文摘Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. Feature quantization reduces the discriminative power of local features and unavoidably causes many false local matches between images, which degrades the retrieval accuracy. To filter those false matches, geometric context among visual words has been popularly explored for the verification of geometric consistency. However, existing studies with global or local geometric verification are either computationally expensive or achieve limited accuracy. To address this issue, in this paper, we focus on partialduplicate Web image retrieval, and propose a scheme to encode the spatial context for visual matching verification. An efficient affine enhancement scheme is proposed to refine the verification results. Experiments on partial-duplicate Web image search, using a database of one million images, demonstrate the effectiveness and efficiency of the proposed approach.Evaluation on a 10-million image database further reveals the scalability of our approach.
文摘We introduce a new method for visualizing and analyzing information landscapes of ideas and events posted on public web pages through customized web-search engines and keywords.This research integrates GIScience and web-search engines to track and analyze public web pages and their web contents with associated spatial relationships.Web pages searched by clusters of keywords were mapped with real-world coordinates(by geolocating their Internet Protocol addresses).The resulting maps represent web information landscapes consisting of hundreds of populated web pages searched by selected keywords.By creating a Spatial Web Automatic Reasoning and Mapping System prototype,researchers can visualize the spread of web pages associated with specific keywords,concepts,ideas,or news over time and space.These maps may reveal important spatial relationships and spatial context associated with selected keywords.This approach may provide a new research direction for geographers to study the diffusion of human thought and ideas.A better understanding of the spatial and temporal dynamics of the‘collective thinking of human beings’over the Internet may help us understand various innovation diffusion processes,human behaviors,and social movements around the world.
文摘Structured study of spatial objects and their relationships leads to a better cognition of the geospatial information and creates the concept of context at a higher level of abstraction.This study is aimed at providing a comprehensive definition of the context for geospatial objects.A combination of binary qualitative spatial relationships(i.e.direction,distance,and topological relations)among the members of a set of spatial objects will be used accordingly.In addition,by incorporating the general concept of context,obtained from either static data(attributes in a database)or dynamic data(sensors),the compact context of spatial objects will be introduced.Our framework for presentation of the involved knowledge and conception about the objects in context is also explored using ontology and description logic because of powerful conceptualization of relationships,either spatial or non-spatial,integrally.For this purpose,the hierarchies of main structure and object properties are formed at first.The constraint and characteristics of classes,such as subclasses,equivalent classes,cardinality etc.,and object properties,such as being functional,transitive,symmetric,asymmetric,inverse functional,disjoint etc.,are discovered and presented in more detail using web ontology language in description logic mode.The implementation is then performed in the framework of semantic web and extensible markup language syntaxes.The method ultimately facilitates,spatial reasoning by effective querying in a semantic framework taking pellet reasoner and SPARQL(a recursive acronym for SPARQL Protocol and RDF Query Language).
基金supported by the National Science and Technology Innovation 2030 Major Program(2022ZD0205000)the National Key R&D Program of China,the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB32010105,XDBS01010100)+3 种基金Shanghai Municipal Science and Technology Major Project(2018SHZDZX05)Lingang Lab(LG202104-01-08)the National Natural Science Foundation of China(31771180 and 91732106)an International Collaborative Project of the Shanghai Science and Technology Committee(201978677).
文摘Animal survival necessitates adaptive behav-iors in volatile environmental contexts.Virtual reality(VR)technology is instrumental to study the neural mechanisms underlying behaviors modulated by environmental con-text by simulating the real world with maximized control of contextual elements.Yet current VR tools for rodents have limited flexibility and performance(e.g.,frame rate)for context-dependent cognitive research.Here,we describe a high-performance VR platform with which to study con-textual behaviors immersed in editable virtual contexts.This platform was assembled from modular hardware and custom-written software with flexibility and upgradability.Using this platform,we trained mice to perform context-dependent cognitive tasks with rules ranging from discrim-ination to delayed-sample-to-match while recording from thousands of hippocampal place cells.By precise manipula-tions of context elements,we found that the context recogni-tion was intact with partial context elements,but impaired by exchanges of context elements.Collectively,our work establishes a configurable VR platform with which to investigate context-dependent cognition with large-scale neural recording.
基金Supported by the National Natural Science Foundation of China(60772066)
文摘A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.
文摘针对复杂环境下猫眼目标探测易受环境干扰、特征区分度不足等问题,提出一种基于空间上下文的决策级融合猫眼目标检测算法(Decision-level Fusion based on Spatial Context,DFSC)。算法由三个模块组成:在猫眼目标检测模块中,提出基于自适应迭代最大类间方差的图像二值化方法,结合迭代前景细化策略和动态收敛机制,在精确提取猫眼目标连通域的同时保留局部细节信息;构建了傅里叶功率谱和归一化加权质心偏移特征描述子,提升猫眼与干扰目标的可区分性;提出基于自适应环境感知的多维特征加权融合方法,实现特征权重的自适应优化。在通用目标检测模块中,将可变形卷积DCNv3引入YOLOv8骨干网络的C2f模块,提升对遮挡目标和小目标的检测性能。在基于空间上下文的决策级融合模块中,通过计算猫眼目标的遮挡率来评估其与环境干扰目标的空间关系,从而有效抑制虚警。在基于自主研发的激光主动探测系统构建的猫眼目标检测数据集上开展实验,结果表明,与现有主流算法相比,召回率由92.2%提升至98.9%,精度由49.0%提升至74.5%,单帧耗时8.3 ms,显著降低了算法在复杂环境下的虚警率。
