In the dynamic scene of autonomous vehicles,the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation.To solve this problem,we propose an unsupervised monocular depth estima...In the dynamic scene of autonomous vehicles,the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation.To solve this problem,we propose an unsupervised monocular depth estimation model based on edge enhancement,which is specifically aimed at the depth perception challenge in dynamic scenes.The model consists of two core networks:a deep prediction network and a motion estimation network,both of which adopt an encoder-decoder architecture.The depth prediction network is based on the U-Net structure of ResNet18,which is responsible for generating the depth map of the scene.The motion estimation network is based on the U-Net structure of Flow-Net,focusing on the motion estimation of dynamic targets.In the decoding stage of the motion estimation network,we innovatively introduce an edge-enhanced decoder,which integrates a convolutional block attention module(CBAM)in the decoding process to enhance the recognition ability of the edge features of moving objects.In addition,we also designed a strip convolution module to improve the model’s capture efficiency of discrete moving targets.To further improve the performance of the model,we propose a novel edge regularization method based on the Laplace operator,which effectively accelerates the convergence process of themodel.Experimental results on the KITTI and Cityscapes datasets show that compared with the current advanced dynamic unsupervised monocular model,the proposed model has a significant improvement in depth estimation accuracy and convergence speed.Specifically,the rootmean square error(RMSE)is reduced by 4.8%compared with the DepthMotion algorithm,while the training convergence speed is increased by 36%,which shows the superior performance of the model in the depth estimation task in dynamic scenes.展开更多
In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper prese...In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.展开更多
The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge,particularly regarding the effectiveness of solutions for monocular visual relative po...The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge,particularly regarding the effectiveness of solutions for monocular visual relative pose estimation.This study proposes a novel airborne monocular visual estimation method based on structured scene features to address this challenge.First,a multitask neural network model is established for segmentation,depth estimation,and slope estimation on monocular images.And a monocular image comprehensive three-dimensional information metric is designed,encompassing length,span,flatness,and slope information.Subsequently,structured edge features are leveraged to filter candidate landing regions adaptively.By leveraging the three-dimensional information metric,the optimal landing region is accurately and efficiently identified.Finally,sparse two-dimensional key point is used to parameterize the optimal landing region for the first time and a high-precision relative pose estimation is achieved.Additional measurement information is introduced to provide the autonomous landing guidance information between the aircraft and the optimal landing region.Experimental results obtained from both synthetic and real data demonstrate the effectiveness of the proposed method in monocular pose estimation for autonomous aircraft landing guidance in unknown structured scenes.展开更多
Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical bus...Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.展开更多
Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems,...Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.展开更多
As eye tracking can be used to record moment-to-moment changes of eye movements as people inspect pictures of natural scenes and comprehend information, this paper attempts to use eye-movement technology to investigat...As eye tracking can be used to record moment-to-moment changes of eye movements as people inspect pictures of natural scenes and comprehend information, this paper attempts to use eye-movement technology to investigate how the order of presentation and the characteristics of information affect the semantic mismatch effect in the picture-sentence paradigm. A 3（syntax）×2（semantic relation） factorial design is adopted, with syntax and semantic relations as within-participant variables. The experiment finds that the semantic mismatch is most likely to increase cognitive loads as people have to spend more time, including first-pass time, regression path duration, and total fixation duration. Double negation does not significantly increase the processing difficulty of pictures and information. Experimental results show that people can extract the special syntactic strategy from long-term memory to process pictures and sentences with different semantic relations. It enables readers to comprehend double negation as affirmation. These results demonstrate that the constituent comparison model may not be a general model regarding other languages.展开更多
By releasing the book The Catcher in the Rye,J.D.Salinger received an immediate popularity of his writing career.Hissymbolic use of language has been thoroughly researched but the symbolic scenes which make up Holden&...By releasing the book The Catcher in the Rye,J.D.Salinger received an immediate popularity of his writing career.Hissymbolic use of language has been thoroughly researched but the symbolic scenes which make up Holden's life stage,especiallythe symbolic connotations of ironic resting places in the novel,such as bed,couch and bedroom,has not been paid much attention.It tries to analyse the four scenes: on Holden's history teacher's bed,on the hotel bed with a prostitute,in his sister's bedroom,and on his English teacher's couch,and aims to discover his spiritual chaos as well as adolescent desires in the real world,demon-strating that there is no place for adolescent Holden to rest after he chooses his own stage of scenes in his life.展开更多
Weather is a key factor affecting the control of air traffic.Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air trafficflow management.Curren...Weather is a key factor affecting the control of air traffic.Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air trafficflow management.Current researches mostly use traditional machine learning methods to extract features of weather scenes,and clustering algorithms to divide similar scenes.Inspired by the excellent performance of deep learning in image recognition,this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering(IDCEC),which uses the com-bination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image,retaining useful information to the greatest extent,and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area.Finally,term-inal area of Guangzhou Airport is selected as the research object,the method pro-posed in this article is used to classify historical weather data in similar scenes,and the performance is compared with other state-of-the-art methods.The experi-mental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather;at the same time,compared with the actualflight volume in the Guangz-hou terminal area,IDCEC's recognition results of similar weather scenes are con-sistent with the recognition of experts in thefield.展开更多
Encryption and decryption method of three-dimensional objects uses holograms computer-generated and suggests encoding stage. Information obtained amplitude and phase of a three-dimensional object using mathematically ...Encryption and decryption method of three-dimensional objects uses holograms computer-generated and suggests encoding stage. Information obtained amplitude and phase of a three-dimensional object using mathematically stage transforms overlap stored on a digital computer. Different three-dimensional images restore and develop the system for the expansion of the three-dimensional scenes and camera movement parameters. This article talks about these kinds of digital image processing algorithms as the reconstruction of three-dimensional model of the scene. In the present state, many such algorithms need to be improved in this paper proposing one of the options to improve the accuracy of such reconstruction.展开更多
The interferences,such as the background,eyebrows,eyelashes,eyeglass frames,illumination variations,and specular lens reflection pose challenges for pupil localization in natural scenes.In this paper,we propose a nove...The interferences,such as the background,eyebrows,eyelashes,eyeglass frames,illumination variations,and specular lens reflection pose challenges for pupil localization in natural scenes.In this paper,we propose a novel method comprising improved YOLOv8 and Illumination Adaptive Algorithm(IAA),for fast and accurate pupil localization in natural scenes.We introduced deformable convolution into the backbone of YOLOv8 to enable the model to extract the eye regions more accurately,thus avoiding the interference of background outside the eye on subsequent pupil localization.The IAA can reduce the interference of illumination variations and lens reflection by adjusting automatically the grayscale of the image according to the exposure.Experimental results verified that the improved YOLOv8 exhibited an eye detection accuracy(IOU≥0.5)of 90.2%,while the IAA leads to a 9.15%improvement on 5-pixels error ratio e5 with processing times in the tens of microseconds on GPU.Experimental results on the benchmark database CelebA show that the proposed method for pupil localization achieves an accuracy of 83.05%on e5 and achieves real-time performance of 210 FPS on GPU,outperforming other advanced methods.展开更多
This paper presents a novel system for violent scenes detection,which is based on machine learning to handle visual and audio features.MKL(Multiple Kernel Learning)is applied so that multimodality of videos can be max...This paper presents a novel system for violent scenes detection,which is based on machine learning to handle visual and audio features.MKL(Multiple Kernel Learning)is applied so that multimodality of videos can be maximized.The largest features of our system is that mid-level concepts clustering is proposed and implemented in order to learn mid-level concepts implicitly.By this algorithm,our system does not need manually tagged annotations.The whole system is trained on the dataset from MediaEval 2013 Affect Task and evaluated by its official metric.The obtained results outperformed its best score.展开更多
In European thought and culture,there exists a group of passionate artists who are fascinated by the intention,passion,and richness of artistic expression.They strive to establish connections between different art for...In European thought and culture,there exists a group of passionate artists who are fascinated by the intention,passion,and richness of artistic expression.They strive to establish connections between different art forms.Musicians not only attempt to represent masterpieces through the language of music but also aim to convey subjective experiences of emotions and personal imagination to listeners by adding titles to their musical works.This study examines two pieces,“Scenes of Childhood”and“Children’s Garden”,and analyzes the different approaches employed by the composers in portraying similar content.展开更多
We often see different time and space scenes, different stories and different viewpoints change organically through one lens converge to form a complete film in the movie, and one of a series of the shearing lens elem...We often see different time and space scenes, different stories and different viewpoints change organically through one lens converge to form a complete film in the movie, and one of a series of the shearing lens elements and convergence is the montage technique. Such a method actually also often exists in different scenes in our ancient literature and different scenes and jumping thinking thoughts form a series of montage in the mind, and then it can be expressed through poetry. This case is the same as it in art, different time and spaces, the characters appear in a scene in a picture, and we distinguish them with a series of symbols to convergence in each scene. Therefore, we can say that if the montage was born after his birth of firms in the twentieth century, then it has existed since ancient time, and it has always existed in the fine arts, however, such thinking played an important role in the development of Chinese art, especially in the convergence and performance practices screen scene.展开更多
In the past, many Chinese artists have beenafraid they would be criticized for lagging behindthe world in their conceptions. They are eager todo something unconventional or unorthodox. Theiractions are very similar wi...In the past, many Chinese artists have beenafraid they would be criticized for lagging behindthe world in their conceptions. They are eager todo something unconventional or unorthodox. Theiractions are very similar with the trend Liu展开更多
NPC deputies working on behalf of the people they represent are making a difference back home One of the true signs of spring is the arrival of swallows.But in Beijing,another sign heralds the return of springtime:tho...NPC deputies working on behalf of the people they represent are making a difference back home One of the true signs of spring is the arrival of swallows.But in Beijing,another sign heralds the return of springtime:thousands of deputies come to town to attend a series of two-week-long meetings,where a range of diverse issues are展开更多
Detecting highly-overlapped objects in crowded scenes remains a challenging problem,especially for one-stage detector.In this paper,we extricate YOLOv4 from the dilemma in a crowd by fine-tuning its detection scheme,n...Detecting highly-overlapped objects in crowded scenes remains a challenging problem,especially for one-stage detector.In this paper,we extricate YOLOv4 from the dilemma in a crowd by fine-tuning its detection scheme,named YOLO-CS.Specifically,we give YOLOv4 the power to detect multiple objects in one cell.Center to our method is the carefully designed joint prediction scheme,which is executed through an assignment of bounding boxes and a joint loss.Equipped with the derived joint-object augmentation(DJA),refined regression loss(RL)and Score-NMS(SN),YOLO-CS achieves competitive detection performance on CrowdHuman and CityPersons benchmarks compared with state-of-the-art detectors at the cost of little time.Furthermore,on the widely used general benchmark COCO,YOLOCS still has a good performance,indicating its robustness to various scenes.展开更多
The rapid growth of air traffic has continuously increased the workload of controllers,which has become an important factor restricting sector capacity.If similar traffic scenes can be identified,the historical decisi...The rapid growth of air traffic has continuously increased the workload of controllers,which has become an important factor restricting sector capacity.If similar traffic scenes can be identified,the historical decision-making experience may be used to help controllers decide control strategies quickly.Considering that there are many traffic scenes and it is hard to label them all,in this paper,we propose an active SVM metric learning(ASVM2L)algorithm to measure and identify the similar traffic scenes.First of all,we obtain some traffic scene samples correctly labeled by experienced air traffic controllers.We design an active sampling strategy based on voting difference to choose the most valuable unlabeled samples and label them.Then the metric matrix of all the labeled samples is learned and used to complete the classification of traffic scenes.We verify the effectiveness of ASVM2L on standard data sets,and then use it to measure and classify the traffic scenes on the historical air traffic data set of the Central South Sector of China.The experimental results show that,compared with other existing methods,the proposed method can use the information of traffic scene samples more thoroughly and achieve better classification performance under limited labeled samples.展开更多
基金funded by the Yangtze River Delta Science and Technology Innovation Community Joint Research Project(2023CSJGG1600)the Natural Science Foundation of Anhui Province(2208085MF173)Wuhu“ChiZhu Light”Major Science and Technology Project(2023ZD01,2023ZD03).
文摘In the dynamic scene of autonomous vehicles,the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation.To solve this problem,we propose an unsupervised monocular depth estimation model based on edge enhancement,which is specifically aimed at the depth perception challenge in dynamic scenes.The model consists of two core networks:a deep prediction network and a motion estimation network,both of which adopt an encoder-decoder architecture.The depth prediction network is based on the U-Net structure of ResNet18,which is responsible for generating the depth map of the scene.The motion estimation network is based on the U-Net structure of Flow-Net,focusing on the motion estimation of dynamic targets.In the decoding stage of the motion estimation network,we innovatively introduce an edge-enhanced decoder,which integrates a convolutional block attention module(CBAM)in the decoding process to enhance the recognition ability of the edge features of moving objects.In addition,we also designed a strip convolution module to improve the model’s capture efficiency of discrete moving targets.To further improve the performance of the model,we propose a novel edge regularization method based on the Laplace operator,which effectively accelerates the convergence process of themodel.Experimental results on the KITTI and Cityscapes datasets show that compared with the current advanced dynamic unsupervised monocular model,the proposed model has a significant improvement in depth estimation accuracy and convergence speed.Specifically,the rootmean square error(RMSE)is reduced by 4.8%compared with the DepthMotion algorithm,while the training convergence speed is increased by 36%,which shows the superior performance of the model in the depth estimation task in dynamic scenes.
基金the National Natural Science Foundation of China(No.62063006)to the Guangxi Natural Science Foundation under Grant(Nos.2023GXNSFAA026025,AA24010001)+3 种基金to the Innovation Fund of Chinese Universities Industry-University-Research(ID:2023RY018)to the Special Guangxi Industry and Information Technology Department,Textile and Pharmaceutical Division(ID:2021 No.231)to the Special Research Project of Hechi University(ID:2021GCC028)to the Key Laboratory of AI and Information Processing,Education Department of Guangxi Zhuang Autonomous Region(Hechi University),No.2024GXZDSY009。
文摘In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.
基金co-supported by the Science and Technology Innovation Program of Hunan Province,China(No.2023RC3023)the National Natural Science Foundation of China(No.12272404)。
文摘The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge,particularly regarding the effectiveness of solutions for monocular visual relative pose estimation.This study proposes a novel airborne monocular visual estimation method based on structured scene features to address this challenge.First,a multitask neural network model is established for segmentation,depth estimation,and slope estimation on monocular images.And a monocular image comprehensive three-dimensional information metric is designed,encompassing length,span,flatness,and slope information.Subsequently,structured edge features are leveraged to filter candidate landing regions adaptively.By leveraging the three-dimensional information metric,the optimal landing region is accurately and efficiently identified.Finally,sparse two-dimensional key point is used to parameterize the optimal landing region for the first time and a high-precision relative pose estimation is achieved.Additional measurement information is introduced to provide the autonomous landing guidance information between the aircraft and the optimal landing region.Experimental results obtained from both synthetic and real data demonstrate the effectiveness of the proposed method in monocular pose estimation for autonomous aircraft landing guidance in unknown structured scenes.
基金the National Natural Science Foundation of China(Nos.71731001,61573181,71971114)the Fundamental Research Funds for the Central Universities(No.NS2020045)。
文摘Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.
基金support by the National Natural Science Foundation of China (Grant No. 62005049)Natural Science Foundation of Fujian Province (Grant Nos. 2020J01451, 2022J05113)Education and Scientific Research Program for Young and Middleaged Teachers in Fujian Province (Grant No. JAT210035)。
文摘Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.
基金The National Social Science Foundation of China (No.CBA080236)the Graduate Innovation Project of Jiangsu Province (No.CX08B-016R)
文摘As eye tracking can be used to record moment-to-moment changes of eye movements as people inspect pictures of natural scenes and comprehend information, this paper attempts to use eye-movement technology to investigate how the order of presentation and the characteristics of information affect the semantic mismatch effect in the picture-sentence paradigm. A 3（syntax）×2（semantic relation） factorial design is adopted, with syntax and semantic relations as within-participant variables. The experiment finds that the semantic mismatch is most likely to increase cognitive loads as people have to spend more time, including first-pass time, regression path duration, and total fixation duration. Double negation does not significantly increase the processing difficulty of pictures and information. Experimental results show that people can extract the special syntactic strategy from long-term memory to process pictures and sentences with different semantic relations. It enables readers to comprehend double negation as affirmation. These results demonstrate that the constituent comparison model may not be a general model regarding other languages.
文摘By releasing the book The Catcher in the Rye,J.D.Salinger received an immediate popularity of his writing career.Hissymbolic use of language has been thoroughly researched but the symbolic scenes which make up Holden's life stage,especiallythe symbolic connotations of ironic resting places in the novel,such as bed,couch and bedroom,has not been paid much attention.It tries to analyse the four scenes: on Holden's history teacher's bed,on the hotel bed with a prostitute,in his sister's bedroom,and on his English teacher's couch,and aims to discover his spiritual chaos as well as adolescent desires in the real world,demon-strating that there is no place for adolescent Holden to rest after he chooses his own stage of scenes in his life.
基金supported by the Fundamental Research Funds for the CentralUniversities under Grant NS2020045. Y.L.G received the grant.
文摘Weather is a key factor affecting the control of air traffic.Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air trafficflow management.Current researches mostly use traditional machine learning methods to extract features of weather scenes,and clustering algorithms to divide similar scenes.Inspired by the excellent performance of deep learning in image recognition,this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering(IDCEC),which uses the com-bination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image,retaining useful information to the greatest extent,and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area.Finally,term-inal area of Guangzhou Airport is selected as the research object,the method pro-posed in this article is used to classify historical weather data in similar scenes,and the performance is compared with other state-of-the-art methods.The experi-mental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather;at the same time,compared with the actualflight volume in the Guangz-hou terminal area,IDCEC's recognition results of similar weather scenes are con-sistent with the recognition of experts in thefield.
文摘Encryption and decryption method of three-dimensional objects uses holograms computer-generated and suggests encoding stage. Information obtained amplitude and phase of a three-dimensional object using mathematically stage transforms overlap stored on a digital computer. Different three-dimensional images restore and develop the system for the expansion of the three-dimensional scenes and camera movement parameters. This article talks about these kinds of digital image processing algorithms as the reconstruction of three-dimensional model of the scene. In the present state, many such algorithms need to be improved in this paper proposing one of the options to improve the accuracy of such reconstruction.
基金supported by Guangdong Yangfan Program for Innovative and Entrepreneurial Teams(Grant No.2017YT05G026)the Natural Science Foundation of China(Grant No.51975126)+1 种基金the Key Research and Development Program of Guangdong Province(Grant No.2019B090915001)the Natural Science Foundation of Guangdong Province(Grant No.2022A1515012605).
文摘The interferences,such as the background,eyebrows,eyelashes,eyeglass frames,illumination variations,and specular lens reflection pose challenges for pupil localization in natural scenes.In this paper,we propose a novel method comprising improved YOLOv8 and Illumination Adaptive Algorithm(IAA),for fast and accurate pupil localization in natural scenes.We introduced deformable convolution into the backbone of YOLOv8 to enable the model to extract the eye regions more accurately,thus avoiding the interference of background outside the eye on subsequent pupil localization.The IAA can reduce the interference of illumination variations and lens reflection by adjusting automatically the grayscale of the image according to the exposure.Experimental results verified that the improved YOLOv8 exhibited an eye detection accuracy(IOU≥0.5)of 90.2%,while the IAA leads to a 9.15%improvement on 5-pixels error ratio e5 with processing times in the tens of microseconds on GPU.Experimental results on the benchmark database CelebA show that the proposed method for pupil localization achieves an accuracy of 83.05%on e5 and achieves real-time performance of 210 FPS on GPU,outperforming other advanced methods.
文摘This paper presents a novel system for violent scenes detection,which is based on machine learning to handle visual and audio features.MKL(Multiple Kernel Learning)is applied so that multimodality of videos can be maximized.The largest features of our system is that mid-level concepts clustering is proposed and implemented in order to learn mid-level concepts implicitly.By this algorithm,our system does not need manually tagged annotations.The whole system is trained on the dataset from MediaEval 2013 Affect Task and evaluated by its official metric.The obtained results outperformed its best score.
文摘In European thought and culture,there exists a group of passionate artists who are fascinated by the intention,passion,and richness of artistic expression.They strive to establish connections between different art forms.Musicians not only attempt to represent masterpieces through the language of music but also aim to convey subjective experiences of emotions and personal imagination to listeners by adding titles to their musical works.This study examines two pieces,“Scenes of Childhood”and“Children’s Garden”,and analyzes the different approaches employed by the composers in portraying similar content.
文摘We often see different time and space scenes, different stories and different viewpoints change organically through one lens converge to form a complete film in the movie, and one of a series of the shearing lens elements and convergence is the montage technique. Such a method actually also often exists in different scenes in our ancient literature and different scenes and jumping thinking thoughts form a series of montage in the mind, and then it can be expressed through poetry. This case is the same as it in art, different time and spaces, the characters appear in a scene in a picture, and we distinguish them with a series of symbols to convergence in each scene. Therefore, we can say that if the montage was born after his birth of firms in the twentieth century, then it has existed since ancient time, and it has always existed in the fine arts, however, such thinking played an important role in the development of Chinese art, especially in the convergence and performance practices screen scene.
文摘In the past, many Chinese artists have beenafraid they would be criticized for lagging behindthe world in their conceptions. They are eager todo something unconventional or unorthodox. Theiractions are very similar with the trend Liu
文摘NPC deputies working on behalf of the people they represent are making a difference back home One of the true signs of spring is the arrival of swallows.But in Beijing,another sign heralds the return of springtime:thousands of deputies come to town to attend a series of two-week-long meetings,where a range of diverse issues are
基金the China National Key Research and Development Program(No.2016YFC0802904)National Natural Science Foundation of China(61671470)62nd batch of funded projects of China Postdoctoral Science Foundation(No.2017M623423).
文摘Detecting highly-overlapped objects in crowded scenes remains a challenging problem,especially for one-stage detector.In this paper,we extricate YOLOv4 from the dilemma in a crowd by fine-tuning its detection scheme,named YOLO-CS.Specifically,we give YOLOv4 the power to detect multiple objects in one cell.Center to our method is the carefully designed joint prediction scheme,which is executed through an assignment of bounding boxes and a joint loss.Equipped with the derived joint-object augmentation(DJA),refined regression loss(RL)and Score-NMS(SN),YOLO-CS achieves competitive detection performance on CrowdHuman and CityPersons benchmarks compared with state-of-the-art detectors at the cost of little time.Furthermore,on the widely used general benchmark COCO,YOLOCS still has a good performance,indicating its robustness to various scenes.
基金supported by the National Natural Science Foundation of China(No.61501229)the Fundamental Research Funds for the Central Universities(Nos.2019054,2020045)。
文摘The rapid growth of air traffic has continuously increased the workload of controllers,which has become an important factor restricting sector capacity.If similar traffic scenes can be identified,the historical decision-making experience may be used to help controllers decide control strategies quickly.Considering that there are many traffic scenes and it is hard to label them all,in this paper,we propose an active SVM metric learning(ASVM2L)algorithm to measure and identify the similar traffic scenes.First of all,we obtain some traffic scene samples correctly labeled by experienced air traffic controllers.We design an active sampling strategy based on voting difference to choose the most valuable unlabeled samples and label them.Then the metric matrix of all the labeled samples is learned and used to complete the classification of traffic scenes.We verify the effectiveness of ASVM2L on standard data sets,and then use it to measure and classify the traffic scenes on the historical air traffic data set of the Central South Sector of China.The experimental results show that,compared with other existing methods,the proposed method can use the information of traffic scene samples more thoroughly and achieve better classification performance under limited labeled samples.
