Objective:To evaluate the contribution of poses screen pre-impregnated(PSP) installed at openings and eaves of dwellings in the reduction of malaria transmission in the commune of Aguegues in Benin.Methods:The PSP wer...Objective:To evaluate the contribution of poses screen pre-impregnated(PSP) installed at openings and eaves of dwellings in the reduction of malaria transmission in the commune of Aguegues in Benin.Methods:The PSP were manufactured from preimpregnated Olyset Net.They were installed at windows,eaves and doors of 70 dwellings.320 children aged 6-59 months were treated and 311 children were recruited in the control zone.Variables measured are:plasmodic index(IP),gametoeyte index,parasite density(PD),fever,hemoglobin,anemia. Results:The global IP was 16.62%with PSP and 72.20%without PSP.Gametoeyte index did not differ significantly between the treated zone(27.8) and the control zone(29.1).The total geometric mean of DP was 309 in the treated zone and 600 in the control zone.Hemoglobin level is 8.7 in the control zone and 9.5 in the treated zone.We noted a predominance of anemia in the control zone compared to the treated zone.Conclusions:The PSP have contributed to a significant reduction in morbidity in the commune of Aguegues.展开更多
The death of Muammar Gaddafi marks a new era for Libya.It also poses a huge challenge for Libyan authorities dealing with tribal conflicts.He Wenping, a researcher with the Institute of West-Asian and African Studies ...The death of Muammar Gaddafi marks a new era for Libya.It also poses a huge challenge for Libyan authorities dealing with tribal conflicts.He Wenping, a researcher with the Institute of West-Asian and African Studies at the Chinese Academy of Social Sciences, believes that Libya is in danger of falling into a period of internal strife and tribal conflict.Her thoughts are as follows:展开更多
With a thermal manikin, the effects of dressing poses on clothing thermal insulation are studied. It is found that the thermal insulation of still air layer over human body has not been influenced by the dressing pose...With a thermal manikin, the effects of dressing poses on clothing thermal insulation are studied. It is found that the thermal insulation of still air layer over human body has not been influenced by the dressing poses, but the dressing poses have effects on the thermal insulation of clothing system.展开更多
Lots of progress has been made recently on 2 D human pose tracking with tracking-by-detection approaches. However,several challenges still remain in this area which is due to self-occlusions and the confusion between ...Lots of progress has been made recently on 2 D human pose tracking with tracking-by-detection approaches. However,several challenges still remain in this area which is due to self-occlusions and the confusion between the left and right limbs during tracking. In this work,a head orientation detection step is introduced into the tracking framework to serve as a complementary tool to assist human pose estimation. With the face orientation determined,the system can decide whether the left or right side of the human body is exactly visible and infer the state of the symmetric counterpart. By granting a higher priority for the completely visible side,the system can avoid double counting to a great extent when inferring body poses. The proposed framework is evaluated on the HumanEva dataset. The results show that it largely reduces the occurrence of double counting and distinguishes the left and right sides consistently.展开更多
Aimed at the hydrodynamic response for marine structures slamming into water, based on the mechanism analysis to the slamming process, and by combining 3D N-S equation and k-ε turbulent kinetic equation with structur...Aimed at the hydrodynamic response for marine structures slamming into water, based on the mechanism analysis to the slamming process, and by combining 3D N-S equation and k-ε turbulent kinetic equation with structure fully 6DOF motion equation, a mathematical model for the wind-fluid-solid interaction is established in 3D marine structure slamming wave at free poses and wind-wave-flow complex environments. Compared with the results of physical model test, the numerical results from the slamming wave well correspond with the experimental results. Through the mathematical model, the wave-making issue of 3D marine structure at initial pose falls into water in different complex wind, wave and flow environments is investigated. The research results show that various kinds of natural factors and structure initial poses have different influence on the slamming wave, and there is an obvious rule in this process.展开更多
Facial expression recognition(FER)has numerous applications in computer security,neuroscience,psychology,and engineering.Owing to its non-intrusiveness,it is considered a useful technology for combating crime.However,...Facial expression recognition(FER)has numerous applications in computer security,neuroscience,psychology,and engineering.Owing to its non-intrusiveness,it is considered a useful technology for combating crime.However,FER is plagued with several challenges,the most serious of which is its poor prediction accuracy in severe head poses.The aim of this study,therefore,is to improve the recognition accuracy in severe head poses by proposing a robust 3D head-tracking algorithm based on an ellipsoidal model,advanced ensemble of AdaBoost,and saturated vector machine(SVM).The FER features are tracked from one frame to the next using the ellipsoidal tracking model,and the visible expressive facial key points are extracted using Gabor filters.The ensemble algorithm(Ada-AdaSVM)is then used for feature selection and classification.The proposed technique is evaluated using the Bosphorus,BU-3DFE,MMI,CK^(+),and BP4D-Spontaneous facial expression databases.The overall performance is outstanding.展开更多
Forecasting 3-dimensional skeleton-based human poses from the historical sequence is a classic task,which shows enormous potential in robotics,computer vision,and graphics.Currently,the state-of-theart methods resort ...Forecasting 3-dimensional skeleton-based human poses from the historical sequence is a classic task,which shows enormous potential in robotics,computer vision,and graphics.Currently,the state-of-theart methods resort to graph convolutional networks(GCNs)to access the relationships of human joint pairs to formulate this problem.However,human action involves complex interactions among multiple joints,which presents a higher-order correlation overstepping the pairwise(2-order)connection of GCNs.Moreover,joints are typically activated by the parent joint,rather than driving their parent joints,whereas in existing methods,this specific direction of information transmission is ignored.In this work,we propose a novel hybrid directed hypergraph convolution network(H-DHGCN)to model the high-order relationships of the human skeleton with directionality.Specifically,our H-DHGCN mainly involves 2 core components.One is the static directed hypergraph,which is pre-defined according to the human body structure,to effectively leverage the natural relations of human joints.The second is dynamic directed hypergraph(D-DHG).D-DHG is learnable and can be constructed adaptively,to learn the unique characteristics of the motion sequence.In contrast to the typical GCNs,our method brings a richer and more refined topological representation of skeleton data.On several large-scale benchmarks,experimental results show that the proposed model consistently surpasses the latest techniques.展开更多
Aortic regurgitation(AR)poses distinct challenges in interventional cardiology,necessitating novel approaches for treatment.This editorial examined the evolving landscape of transcatheter aortic valve replacement(TAVR...Aortic regurgitation(AR)poses distinct challenges in interventional cardiology,necessitating novel approaches for treatment.This editorial examined the evolving landscape of transcatheter aortic valve replacement(TAVR)as an alternative therapeutic strategy for AR,particularly in patients deemed high risk for surgery.We explored the anatomical and patho-physiological disparities between AR and aortic stenosis(AS)and elucidates the technical nuances of TAVR procedures in AR pa-tients,emphasizing the need for precise prosthesis positioning and considerations for excessive stroke volume.Additionally,we discussed the safety and efficacy of TAVR compared to SAVR in AR management,drawing insights from recent case series and registry data.Notably,dedicated TAVR devices tailored for AR,such as the J-Valve and JenaValve,demonstrate promising out-comes in reducing residual AR and ensuring procedural success.Conversely,“off-label”TAVR devices,including balloon-ex-pandable and self-expandable platforms,offer feasible alternatives-particularly for large aortic annuli-with favorable device suc-cess rates and low residual AR rates.We highlighted the need for further research,including randomized trials,to delineate the definitive role of TAVR in AR treatment and to address remaining questions regarding device selection and long-term outcomes.In conclusion,TAVR emerges as a viable option for patients with AR,particularly those facing high surgical risks or frailty,with ongoing investigations poised to refine its position in the therapeutic armamentarium.展开更多
Robust non-intrusive eye location plays an important role in vision-based man-mechine interaction. A modified Hausdorff distance based measure to localize the eyes is proposed, which could tolerate various changes in ...Robust non-intrusive eye location plays an important role in vision-based man-mechine interaction. A modified Hausdorff distance based measure to localize the eyes is proposed, which could tolerate various changes in eye pose, shape, and scale. To eliminate the effects of the illumination variations, an 8- neighbour-based transformation of the gray images is proposed. The transformed image is less sensitive to illumination changes while preserves the appearance information of eyes. All the localized candidates of eyes are identified by back-propagation neural networks. Experiments demonstrate that the robust method for eye location is able to localize eyes with different eye sizes, shapes, and poses under different illuminations.展开更多
Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton s...Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused.Moreover,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes shared.Existing Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations efficiently.To solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different views.To make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the graph.Based on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation network.Hourglass-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features efficiently.Experimental results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimation accuracy.展开更多
Self-supervised monocular depth estimation has emerged as a major research focus in recent years,primarily due to the elimination of ground-truth depth dependence.However,the prevailing architectures in this domain su...Self-supervised monocular depth estimation has emerged as a major research focus in recent years,primarily due to the elimination of ground-truth depth dependence.However,the prevailing architectures in this domain suffer from inherent limitations:existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions.These assumptions are often violated in real-world scenarios due to dynamic objects,non-Lambertian reflectance,and unstructured background elements,leading to pervasive artifacts such as depth discontinuities(“holes”),structural collapse,and ambiguous reconstruction.To address these challenges,we propose a novel framework that integrates scene dynamic pose estimation into the conventional self-supervised depth network,enhancing its ability to model complex scene dynamics.Our contributions are threefold:(1)a pixel-wise dynamic pose estimation module that jointly resolves the pose transformations of moving objects and localized scene perturbations;(2)a physically-informed loss function that couples dynamic pose and depth predictions,designed to mitigate depth errors arising from high-speed distant objects and geometrically inconsistent motion profiles;(3)an efficient SE(3)transformation parameterization that streamlines network complexity and temporal pre-processing.Extensive experiments on the KITTI and NYU-V2 benchmarks show that our framework achieves state-of-the-art performance in both quantitative metrics and qualitative visual fidelity,significantly improving the robustness and generalization of monocular depth estimation under dynamic conditions.展开更多
Vision-based relative pose estimation plays a pivotal role in various space missions.Deep learning enhances monocular spacecraft pose estimation,but high computational demands necessitate model simplification for onbo...Vision-based relative pose estimation plays a pivotal role in various space missions.Deep learning enhances monocular spacecraft pose estimation,but high computational demands necessitate model simplification for onboard systems.In this paper,we aim to achieve an optimal balance between accuracy and computational efficiency.We present a Perspective-n-Point(PnP)based method for spacecraft pose estimation,leveraging lightweight neural networks to localize semantic keypoints and reduce computational load.Since the accuracy of keypoint localization is closely related to the heatmap resolution,we devise an efficient upsampling module to increase the resolution of heatmaps with minimal overhead.Furthermore,the heatmaps predicted by the lightweight models tend to show high-level noise.To tackle this issue,we propose a weighting strategy by analyzing the statistical characteristics of predicted semantic keypoints and substantially improve the pose estimation accuracy.The experiments carried out on the SPEED dataset underscore the prospect of our method in engineering applications.We dramatically reduce the model parameters to 0.7 M,merely 2.5%of that required by the top-performing method,and achieve lower pose estimation error and better real-time performance.展开更多
This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation ...This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation accuracy and robustness.We employ a manifold-based optimization approach,leveraging exponential and logarithmic mappings to transform rotation vectors into rotation matrices.The proposed ESKF framework ensures state variables remain near the origin,effectively mitigating singularity issues and enhancing numerical stability.Additionally,due to the small magnitude of state variables,second-order terms can be neglected,simplifying Jacobian matrix computation and improving computational efficiency.Furthermore,we introduce a novel Kalman filter gain computation strategy that dynamically adapts to low-dimensional and high-dimensional observation equations,enabling efficient processing across different sensor modalities.Specifically,for resource-constrained UAV platforms,this method significantly reduces computational cost,making it highly suitable for real-time UAV applications.展开更多
Graph convolutional network(GCN)as an essential tool in human action recognition tasks have achieved excellent performance in previous studies.However,most current skeleton-based action recognition using GCN methods u...Graph convolutional network(GCN)as an essential tool in human action recognition tasks have achieved excellent performance in previous studies.However,most current skeleton-based action recognition using GCN methods use a shared topology,which cannot flexibly adapt to the diverse correlations between joints under different motion features.The video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation algorithms.In this work,we propose a novel graph convolutional learning framework,called PCCTR-GCN,which integrates pose correction and channel topology refinement for skeleton-based human action recognition.Firstly,a pose correction module(PCM)is introduced,which corrects the pose coordinates of the input network to reduce the error in pose feature extraction.Secondly,channel topology refinement graph convolution(CTR-GC)is employed,which can dynamically learn the topology features and aggregate joint features in different channel dimensions so as to enhance the performance of graph convolution networks in feature extraction.Finally,considering that the joint stream and bone stream of skeleton data and their dynamic information are also important for distinguishing different actions,we employ a multi-stream data fusion approach to improve the network’s recognition performance.We evaluate the model using top-1 and top-5 classification accuracy.On the benchmark datasets iMiGUE and Kinetics,the top-1 classification accuracy reaches 55.08%and 36.5%,respectively,while the top-5 classification accuracy reaches 89.98%and 59.2%,respectively.On the NTU dataset,for the two benchmark RGB+Dsettings(X-Sub and X-View),the classification accuracy achieves 89.7%and 95.4%,respectively.展开更多
[Objective]Fish pose estimation(FPE)provides fish physiological information,facilitating health monitoring in aquaculture.It aids decision-making in areas such as fish behavior recognition.When fish are injured or def...[Objective]Fish pose estimation(FPE)provides fish physiological information,facilitating health monitoring in aquaculture.It aids decision-making in areas such as fish behavior recognition.When fish are injured or deficient,they often display abnormal behaviors and noticeable changes in the positioning of their body parts.Moreover,the unpredictable posture and orientation of fish during swimming,combined with the rapid swimming speed of fish,restrict the current scope of research in FPE.In this research,a FPE model named HPFPE is presented to capture the swimming posture of fish and accurately detect their key points.[Methods]On the one hand,this model incorporated the CBAM module into the HRNet framework.The attention module enhanced accuracy without adding computational complexity,while effectively capturing a broader range of contextual information.On the other hand,the model incorporated dilated convolution to increase the receptive field,allowing it to capture more spatial context.[Results and Discussions]Experiments showed that compared with the baseline method,the average precision(AP)of HPFPE based on different backbones and input sizes on the oplegnathus punctatus datasets had increased by 0.62,1.35,1.76,and 1.28 percent point,respectively,while the average recall(AR)had also increased by 0.85,1.50,1.40,and 1.00,respectively.Additionally,HPFPE outperformed other mainstream methods,including DeepPose,CPM,SCNet,and Lite-HRNet.Furthermore,when compared to other methods using the ornamental fish data,HPFPE achieved the highest AP and AR values of 52.96%,and 59.50%,respectively.[Conclusions]The proposed HPFPE can accurately estimate fish posture and assess their swimming patterns,serving as a valuable reference for applications such as fish behavior recognition.展开更多
Pose estimation of spacecraft targets is a key technology for achieving space operation tasks,such as the cleaning of failed satellites and the detection and scanning of non-cooperative targets.This paper reviews the ...Pose estimation of spacecraft targets is a key technology for achieving space operation tasks,such as the cleaning of failed satellites and the detection and scanning of non-cooperative targets.This paper reviews the target pose estimation methods based on image feature extraction and PnP,the target estimation methods based on registration,and the spacecraft target pose estimation methods based on deep learning,and introduces the corresponding research methods.展开更多
Real-time and accurate drogue pose measurement during docking is basic and critical for Autonomous Aerial Refueling(AAR).Vision measurement is the best practicable technique,but its measurement accuracy and robustness...Real-time and accurate drogue pose measurement during docking is basic and critical for Autonomous Aerial Refueling(AAR).Vision measurement is the best practicable technique,but its measurement accuracy and robustness are easily affected by limited computing power of airborne equipment,complex aerial scenes and partial occlusion.To address the above challenges,we propose a novel drogue keypoint detection and pose measurement algorithm based on monocular vision,and realize real-time processing on airborne embedded devices.Firstly,a lightweight network is designed with structural re-parameterization to reduce computational cost and improve inference speed.And a sub-pixel level keypoints prediction head and loss functions are adopted to improve keypoint detection accuracy.Secondly,a closed-form solution of drogue pose is computed based on double spatial circles,followed by a nonlinear refinement based on Levenberg-Marquardt optimization.Both virtual simulation and physical simulation experiments have been used to test the proposed method.In the virtual simulation,the mean pixel error of the proposed method is 0.787 pixels,which is significantly superior to that of other methods.In the physical simulation,the mean relative measurement error is 0.788%,and the mean processing time is 13.65 ms on embedded devices.展开更多
Background:Q uantifying the rich home-c age activities of tree shrews provides a reliable basis for understanding their daily routines and building disease models.However,due to the lack of effective behavioral method...Background:Q uantifying the rich home-c age activities of tree shrews provides a reliable basis for understanding their daily routines and building disease models.However,due to the lack of effective behavioral methods,most efforts on tree shrew behavior are limited to simple measures,resulting in the loss of much behavioral information.Methods:T o address this issue,we present a deep learning(DL)approach to achieve markerless pose estimation and recognize multiple spontaneous behaviors of tree shrews,including drinking,eating,resting,and staying in the dark house,etc.Results:T his high-t hroughput approach can monitor the home-cage activities of 16 tree shrews simultaneously over an extended period.Additionally,we demonstrated an innovative system with reliable apparatus,paradigms,and analysis methods for investigating food grasping behavior.The median duration for each bout of grasping was 0.20 s.Conclusion:T his study provides an efficient tool for quantifying and understand tree shrews'natural behaviors.展开更多
基金supported by the Ministry of Higher Education and Scientific Research of the Government of Benin
文摘Objective:To evaluate the contribution of poses screen pre-impregnated(PSP) installed at openings and eaves of dwellings in the reduction of malaria transmission in the commune of Aguegues in Benin.Methods:The PSP were manufactured from preimpregnated Olyset Net.They were installed at windows,eaves and doors of 70 dwellings.320 children aged 6-59 months were treated and 311 children were recruited in the control zone.Variables measured are:plasmodic index(IP),gametoeyte index,parasite density(PD),fever,hemoglobin,anemia. Results:The global IP was 16.62%with PSP and 72.20%without PSP.Gametoeyte index did not differ significantly between the treated zone(27.8) and the control zone(29.1).The total geometric mean of DP was 309 in the treated zone and 600 in the control zone.Hemoglobin level is 8.7 in the control zone and 9.5 in the treated zone.We noted a predominance of anemia in the control zone compared to the treated zone.Conclusions:The PSP have contributed to a significant reduction in morbidity in the commune of Aguegues.
文摘The death of Muammar Gaddafi marks a new era for Libya.It also poses a huge challenge for Libyan authorities dealing with tribal conflicts.He Wenping, a researcher with the Institute of West-Asian and African Studies at the Chinese Academy of Social Sciences, believes that Libya is in danger of falling into a period of internal strife and tribal conflict.Her thoughts are as follows:
文摘With a thermal manikin, the effects of dressing poses on clothing thermal insulation are studied. It is found that the thermal insulation of still air layer over human body has not been influenced by the dressing poses, but the dressing poses have effects on the thermal insulation of clothing system.
文摘Lots of progress has been made recently on 2 D human pose tracking with tracking-by-detection approaches. However,several challenges still remain in this area which is due to self-occlusions and the confusion between the left and right limbs during tracking. In this work,a head orientation detection step is introduced into the tracking framework to serve as a complementary tool to assist human pose estimation. With the face orientation determined,the system can decide whether the left or right side of the human body is exactly visible and infer the state of the symmetric counterpart. By granting a higher priority for the completely visible side,the system can avoid double counting to a great extent when inferring body poses. The proposed framework is evaluated on the HumanEva dataset. The results show that it largely reduces the occurrence of double counting and distinguishes the left and right sides consistently.
文摘Aimed at the hydrodynamic response for marine structures slamming into water, based on the mechanism analysis to the slamming process, and by combining 3D N-S equation and k-ε turbulent kinetic equation with structure fully 6DOF motion equation, a mathematical model for the wind-fluid-solid interaction is established in 3D marine structure slamming wave at free poses and wind-wave-flow complex environments. Compared with the results of physical model test, the numerical results from the slamming wave well correspond with the experimental results. Through the mathematical model, the wave-making issue of 3D marine structure at initial pose falls into water in different complex wind, wave and flow environments is investigated. The research results show that various kinds of natural factors and structure initial poses have different influence on the slamming wave, and there is an obvious rule in this process.
文摘Facial expression recognition(FER)has numerous applications in computer security,neuroscience,psychology,and engineering.Owing to its non-intrusiveness,it is considered a useful technology for combating crime.However,FER is plagued with several challenges,the most serious of which is its poor prediction accuracy in severe head poses.The aim of this study,therefore,is to improve the recognition accuracy in severe head poses by proposing a robust 3D head-tracking algorithm based on an ellipsoidal model,advanced ensemble of AdaBoost,and saturated vector machine(SVM).The FER features are tracked from one frame to the next using the ellipsoidal tracking model,and the visible expressive facial key points are extracted using Gabor filters.The ensemble algorithm(Ada-AdaSVM)is then used for feature selection and classification.The proposed technique is evaluated using the Bosphorus,BU-3DFE,MMI,CK^(+),and BP4D-Spontaneous facial expression databases.The overall performance is outstanding.
基金supported in part by the National Natural Science Foundation of China(62306141)in part by the Jiangsu Funding Program for Excellent Postdoctoral Talent(2022ZB269)+2 种基金in part by the Natural Science Foundation of Jiangsu Province(BK20220939)in part by the China Postdoctoral Science Foundation(2022M721629)in part by Research Project of University Natural Science Fund of Jiangsu Province(22KJB520002).
文摘Forecasting 3-dimensional skeleton-based human poses from the historical sequence is a classic task,which shows enormous potential in robotics,computer vision,and graphics.Currently,the state-of-theart methods resort to graph convolutional networks(GCNs)to access the relationships of human joint pairs to formulate this problem.However,human action involves complex interactions among multiple joints,which presents a higher-order correlation overstepping the pairwise(2-order)connection of GCNs.Moreover,joints are typically activated by the parent joint,rather than driving their parent joints,whereas in existing methods,this specific direction of information transmission is ignored.In this work,we propose a novel hybrid directed hypergraph convolution network(H-DHGCN)to model the high-order relationships of the human skeleton with directionality.Specifically,our H-DHGCN mainly involves 2 core components.One is the static directed hypergraph,which is pre-defined according to the human body structure,to effectively leverage the natural relations of human joints.The second is dynamic directed hypergraph(D-DHG).D-DHG is learnable and can be constructed adaptively,to learn the unique characteristics of the motion sequence.In contrast to the typical GCNs,our method brings a richer and more refined topological representation of skeleton data.On several large-scale benchmarks,experimental results show that the proposed model consistently surpasses the latest techniques.
文摘Aortic regurgitation(AR)poses distinct challenges in interventional cardiology,necessitating novel approaches for treatment.This editorial examined the evolving landscape of transcatheter aortic valve replacement(TAVR)as an alternative therapeutic strategy for AR,particularly in patients deemed high risk for surgery.We explored the anatomical and patho-physiological disparities between AR and aortic stenosis(AS)and elucidates the technical nuances of TAVR procedures in AR pa-tients,emphasizing the need for precise prosthesis positioning and considerations for excessive stroke volume.Additionally,we discussed the safety and efficacy of TAVR compared to SAVR in AR management,drawing insights from recent case series and registry data.Notably,dedicated TAVR devices tailored for AR,such as the J-Valve and JenaValve,demonstrate promising out-comes in reducing residual AR and ensuring procedural success.Conversely,“off-label”TAVR devices,including balloon-ex-pandable and self-expandable platforms,offer feasible alternatives-particularly for large aortic annuli-with favorable device suc-cess rates and low residual AR rates.We highlighted the need for further research,including randomized trials,to delineate the definitive role of TAVR in AR treatment and to address remaining questions regarding device selection and long-term outcomes.In conclusion,TAVR emerges as a viable option for patients with AR,particularly those facing high surgical risks or frailty,with ongoing investigations poised to refine its position in the therapeutic armamentarium.
文摘Robust non-intrusive eye location plays an important role in vision-based man-mechine interaction. A modified Hausdorff distance based measure to localize the eyes is proposed, which could tolerate various changes in eye pose, shape, and scale. To eliminate the effects of the illumination variations, an 8- neighbour-based transformation of the gray images is proposed. The transformed image is less sensitive to illumination changes while preserves the appearance information of eyes. All the localized candidates of eyes are identified by back-propagation neural networks. Experiments demonstrate that the robust method for eye location is able to localize eyes with different eye sizes, shapes, and poses under different illuminations.
基金supported in part by the National Natural Science Foundation of China under Grants 61973065,U20A20197,61973063.
文摘Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused.Moreover,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes shared.Existing Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations efficiently.To solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different views.To make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the graph.Based on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation network.Hourglass-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features efficiently.Experimental results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimation accuracy.
基金supported in part by the National Natural Science Foundation of China under Grants 62071345。
文摘Self-supervised monocular depth estimation has emerged as a major research focus in recent years,primarily due to the elimination of ground-truth depth dependence.However,the prevailing architectures in this domain suffer from inherent limitations:existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions.These assumptions are often violated in real-world scenarios due to dynamic objects,non-Lambertian reflectance,and unstructured background elements,leading to pervasive artifacts such as depth discontinuities(“holes”),structural collapse,and ambiguous reconstruction.To address these challenges,we propose a novel framework that integrates scene dynamic pose estimation into the conventional self-supervised depth network,enhancing its ability to model complex scene dynamics.Our contributions are threefold:(1)a pixel-wise dynamic pose estimation module that jointly resolves the pose transformations of moving objects and localized scene perturbations;(2)a physically-informed loss function that couples dynamic pose and depth predictions,designed to mitigate depth errors arising from high-speed distant objects and geometrically inconsistent motion profiles;(3)an efficient SE(3)transformation parameterization that streamlines network complexity and temporal pre-processing.Extensive experiments on the KITTI and NYU-V2 benchmarks show that our framework achieves state-of-the-art performance in both quantitative metrics and qualitative visual fidelity,significantly improving the robustness and generalization of monocular depth estimation under dynamic conditions.
基金co-supported by the National Natural Science Foundation of China(Nos.12302252 and 12472189)the Research Program of National University of Defense Technology,China(No.ZK24-31).
文摘Vision-based relative pose estimation plays a pivotal role in various space missions.Deep learning enhances monocular spacecraft pose estimation,but high computational demands necessitate model simplification for onboard systems.In this paper,we aim to achieve an optimal balance between accuracy and computational efficiency.We present a Perspective-n-Point(PnP)based method for spacecraft pose estimation,leveraging lightweight neural networks to localize semantic keypoints and reduce computational load.Since the accuracy of keypoint localization is closely related to the heatmap resolution,we devise an efficient upsampling module to increase the resolution of heatmaps with minimal overhead.Furthermore,the heatmaps predicted by the lightweight models tend to show high-level noise.To tackle this issue,we propose a weighting strategy by analyzing the statistical characteristics of predicted semantic keypoints and substantially improve the pose estimation accuracy.The experiments carried out on the SPEED dataset underscore the prospect of our method in engineering applications.We dramatically reduce the model parameters to 0.7 M,merely 2.5%of that required by the top-performing method,and achieve lower pose estimation error and better real-time performance.
基金National Natural Science Foundation of China(Grant No.62266045)National Science and Technology Major Project of China(No.2022YFE0138600)。
文摘This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation accuracy and robustness.We employ a manifold-based optimization approach,leveraging exponential and logarithmic mappings to transform rotation vectors into rotation matrices.The proposed ESKF framework ensures state variables remain near the origin,effectively mitigating singularity issues and enhancing numerical stability.Additionally,due to the small magnitude of state variables,second-order terms can be neglected,simplifying Jacobian matrix computation and improving computational efficiency.Furthermore,we introduce a novel Kalman filter gain computation strategy that dynamically adapts to low-dimensional and high-dimensional observation equations,enabling efficient processing across different sensor modalities.Specifically,for resource-constrained UAV platforms,this method significantly reduces computational cost,making it highly suitable for real-time UAV applications.
基金The Fundamental Research Funds for the Central Universities provided financial support for this research.
文摘Graph convolutional network(GCN)as an essential tool in human action recognition tasks have achieved excellent performance in previous studies.However,most current skeleton-based action recognition using GCN methods use a shared topology,which cannot flexibly adapt to the diverse correlations between joints under different motion features.The video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation algorithms.In this work,we propose a novel graph convolutional learning framework,called PCCTR-GCN,which integrates pose correction and channel topology refinement for skeleton-based human action recognition.Firstly,a pose correction module(PCM)is introduced,which corrects the pose coordinates of the input network to reduce the error in pose feature extraction.Secondly,channel topology refinement graph convolution(CTR-GC)is employed,which can dynamically learn the topology features and aggregate joint features in different channel dimensions so as to enhance the performance of graph convolution networks in feature extraction.Finally,considering that the joint stream and bone stream of skeleton data and their dynamic information are also important for distinguishing different actions,we employ a multi-stream data fusion approach to improve the network’s recognition performance.We evaluate the model using top-1 and top-5 classification accuracy.On the benchmark datasets iMiGUE and Kinetics,the top-1 classification accuracy reaches 55.08%and 36.5%,respectively,while the top-5 classification accuracy reaches 89.98%and 59.2%,respectively.On the NTU dataset,for the two benchmark RGB+Dsettings(X-Sub and X-View),the classification accuracy achieves 89.7%and 95.4%,respectively.
文摘[Objective]Fish pose estimation(FPE)provides fish physiological information,facilitating health monitoring in aquaculture.It aids decision-making in areas such as fish behavior recognition.When fish are injured or deficient,they often display abnormal behaviors and noticeable changes in the positioning of their body parts.Moreover,the unpredictable posture and orientation of fish during swimming,combined with the rapid swimming speed of fish,restrict the current scope of research in FPE.In this research,a FPE model named HPFPE is presented to capture the swimming posture of fish and accurately detect their key points.[Methods]On the one hand,this model incorporated the CBAM module into the HRNet framework.The attention module enhanced accuracy without adding computational complexity,while effectively capturing a broader range of contextual information.On the other hand,the model incorporated dilated convolution to increase the receptive field,allowing it to capture more spatial context.[Results and Discussions]Experiments showed that compared with the baseline method,the average precision(AP)of HPFPE based on different backbones and input sizes on the oplegnathus punctatus datasets had increased by 0.62,1.35,1.76,and 1.28 percent point,respectively,while the average recall(AR)had also increased by 0.85,1.50,1.40,and 1.00,respectively.Additionally,HPFPE outperformed other mainstream methods,including DeepPose,CPM,SCNet,and Lite-HRNet.Furthermore,when compared to other methods using the ornamental fish data,HPFPE achieved the highest AP and AR values of 52.96%,and 59.50%,respectively.[Conclusions]The proposed HPFPE can accurately estimate fish posture and assess their swimming patterns,serving as a valuable reference for applications such as fish behavior recognition.
文摘Pose estimation of spacecraft targets is a key technology for achieving space operation tasks,such as the cleaning of failed satellites and the detection and scanning of non-cooperative targets.This paper reviews the target pose estimation methods based on image feature extraction and PnP,the target estimation methods based on registration,and the spacecraft target pose estimation methods based on deep learning,and introduces the corresponding research methods.
基金supported by the National Science Fund for Distinguished Young Scholars,China(No.51625501)Aeronautical Science Foundation of China(No.20240046051002)National Natural Science Foundation of China(No.52005028).
文摘Real-time and accurate drogue pose measurement during docking is basic and critical for Autonomous Aerial Refueling(AAR).Vision measurement is the best practicable technique,but its measurement accuracy and robustness are easily affected by limited computing power of airborne equipment,complex aerial scenes and partial occlusion.To address the above challenges,we propose a novel drogue keypoint detection and pose measurement algorithm based on monocular vision,and realize real-time processing on airborne embedded devices.Firstly,a lightweight network is designed with structural re-parameterization to reduce computational cost and improve inference speed.And a sub-pixel level keypoints prediction head and loss functions are adopted to improve keypoint detection accuracy.Secondly,a closed-form solution of drogue pose is computed based on double spatial circles,followed by a nonlinear refinement based on Levenberg-Marquardt optimization.Both virtual simulation and physical simulation experiments have been used to test the proposed method.In the virtual simulation,the mean pixel error of the proposed method is 0.787 pixels,which is significantly superior to that of other methods.In the physical simulation,the mean relative measurement error is 0.788%,and the mean processing time is 13.65 ms on embedded devices.
基金supported by grants from the National Key Research and Development Program of China(2023YFF0724902)the China Postdoctoral Science Foundation(2020?M670027,2023TQ0183)the Local Standards Research of BeiJing Laboratory Tree Shrew(CHYX-2023-DGB001)。
文摘Background:Q uantifying the rich home-c age activities of tree shrews provides a reliable basis for understanding their daily routines and building disease models.However,due to the lack of effective behavioral methods,most efforts on tree shrew behavior are limited to simple measures,resulting in the loss of much behavioral information.Methods:T o address this issue,we present a deep learning(DL)approach to achieve markerless pose estimation and recognize multiple spontaneous behaviors of tree shrews,including drinking,eating,resting,and staying in the dark house,etc.Results:T his high-t hroughput approach can monitor the home-cage activities of 16 tree shrews simultaneously over an extended period.Additionally,we demonstrated an innovative system with reliable apparatus,paradigms,and analysis methods for investigating food grasping behavior.The median duration for each bout of grasping was 0.20 s.Conclusion:T his study provides an efficient tool for quantifying and understand tree shrews'natural behaviors.
