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检索条件"任意字段=3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023"

共 459 条记录，以下是51-60 订阅

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Semantic Segmentation of Point Cloud Based on Graph Neural Network 3

Semantic Segmentation of Point Cloud Based on Graph Neural N...

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3rd international conference on computer vision and pattern analysis, iccpa 2023

作者： Duan, Shumin Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781510667563

As a key technology in the field of computer vision, point cloud semantic segmentation has been widely used in intelligent life and has become a hot spot and a difficult area of research in 3D vision. Current research on the semantic segmentation of 3D point clouds is mainly focused on the field of deep learning, however, there are two problems with such models: insufficient local feature representation and long model training period. To this end, we propose a point cloud semantic segmentation model based on geometric segmentation and graph neural network to improve the performance of semantic segmentation and model training efficiency. In our method, Firstly geometric segmentation is used for sparse and feature representation of the original point cloud. A directed graph is then constructed to partition the point cloud into "superpoints" and "superedges" with specific feature descriptors. followed by a spatial transformer network (STN) as the backbone of embedded network to encode local features from these superpoints. Finally, the graph neural network is employed for the classification of architectural components such as walls, ceilings, floors, and beams. Experiments were conducted on the Stanford University Large Scale 3D Indoor Spatial Dataset (S3DIS) dataset. Compared with the SPG network that also takes the entire large scene as input, the proposed algorithm improves the overall accuracy (OA), mean accuracy (mAcc), and per-class intersection over union (mIoU) by 1.0%, 2.9%, and 3. 0%. © 2023 SPIE.

关键词： Semantic Segmentation

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Third international conference on computer vision and pattern analysis, iccpa 2023

Third International Conference on Computer Vision and Patter...

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3rd international conference on computer vision and pattern analysis, iccpa 2023

ISBN: (纸本)9781510667563

The proceedings contain 140 papers. The topics discussed include: digital multi-scale visual planning model of spatial-geographical landscape pattern of smart parks;visual question answering model based on fusing global-local feature;image processing of the special sensor microwave/imager based on passive microwave remote sensing;image processing of the special sensor microwave/imager based on passive microwave remote sensing;graptolite image classification based on feature transfer and mixup data enhancement;an image classification method based on few-shot learning;fine-grained image recognition based on multi-branch and multi-scale learning;research on road extraction model of remote sensing image based on the fused convolutional module and attention mechanism;unsupervised aircraft detection in SAR images with image-level domain adaption from optical images;the role of echocardiography segmentation evaluation metrics in clinical diagnosis;and machine vision-based measurement of air compressor crankshaft journal dimensions.

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Traffic Police Command Gesture Recognition Technology Based on Machine vision and Two-Stream Spatio-Temporal Attention Graph Convolutional Network 3

Traffic Police Command Gesture Recognition Technology Based ...

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3rd international conference on computer vision and pattern analysis, iccpa 2023

作者： Li, Yuan College of Computer Science and Technology Xi’an University of Science and Technology Xi’an China

ISBN: (纸本)9781510667563

For the requirement of automatic recognition of traffic police gestures in complex backgrounds based on vision sensors for driverless cars, We propose a method for traffic police gesture action recognition based on two-stream spatio-temporal attention graph convolutional network (2s-AGCN) with two different dimensional skeletal data. Firstly, detect the commanding traffic policeman in the video, extract the 2D and 3D skeletal data with the pose estimation algorithm to reduce the influence of complex background and joint overlap on action recognition, then, build the spatio-temporal graph model;After that, we construct a 2s-AGCN network, input 2D and 3D skeletal sequences into the network to learn the spatio-temporal features of gesture actions. Finally, a fusion of the two-stream information is done and then output the final traffic police gesture category. 2s-AGCN uses Non-Local and TopK at the spatial level to focus on all nodes directly, selecting the strongest K neighbors of interaction strength;Temporal attention is used to focus on the frames that have higher contribution. The ablation study is done on the dataset CTPGD, and the results show that the method significantly improves the recognition accuracy of traffic police command gesture actions, especially those with overlapping skeleton points. © 2023 SPIE.

关键词： Law enforcement

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A Lightweight Network for Crack Detection with Lightweight Feature Extraction and Multi-scale Feature Fusion 3

A Lightweight Network for Crack Detection with Lightweight F...

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3rd international conference on computer vision and pattern analysis, iccpa 2023

作者： Xin, Chen Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing China

ISBN: (纸本)9781510667563

Automated pavement crack detection is of great significance to the efficiency of road maintenance. Benefit from the development of convolutional neural networks(CNNs), automatic crack detection has been gradually developed. While the convolutional neural network improves the accuracy of crack detection, the calculation amount and parameter amount of the model are greatly enhanced. This limits the application of crack detection methods on some mobile devices. In order to solve this problem, We propose a lightweight network which uses a lightweight feature extraction module combined with an attention mechanism to extract features from crack images. We use the multi-scale feature fusion module to achieve the fusion of different scale crack features. Through these modules, We built a network with nearly 0.94M parameters and only 6G FLOPs while achieving comparable crack detection performance. Extensive experiments on DeepCrack dataset show that the proposed network is superior to other comparison networks. © 2023 SPIE.

关键词： Crack detection

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Noise Reduction Method for Seismic Data Based on Subspace Projection and Fast Fourier Convolution 3

Noise Reduction Method for Seismic Data Based on Subspace Pr...

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3rd international conference on computer vision and pattern analysis, iccpa 2023

作者： Chen, Gongwen Su, Xuesong Yao, Qingyu Li, Siyuan School of Computer Science and Engineering Tianjin University of Technology TianJin China Technology Inspection Center of Shengli Oilfield SINOPEC Shandong Dongying China

ISBN: (纸本)9781510667563

The collected seismic data are highly contaminated due to random noise such as ambient noise, high and low frequency noise and instrument self-noise, which in turn affects the subsequent processing process. With the success of deep learning in image noise reduction, the application of deep learning to noise reduction of seismic data has also obtained better results than traditional noise processing methods. In this paper, we propose a noise reduction method for seismic data based on subspace projection and fast Fourier convolution (FFC). The method uses fast Fourier convolution to divide the channel into two branches, local and global, after downsampling, to update the feature map from two directions, and generates subspace vectors to separate seismic signal and noise according to the seismic data features to construct a seismic data noise reduction network. Experimental results show that the proposed method outperforms traditional seismic data noise reduction methods, such as wavelet transform, Fourier transform noise reduction, and classical deep learning methods FFDNet and NBNet, in terms of signal-to-noise ratio (SNR) measures. © 2023 SPIE.

关键词： Convolution

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Design and Implementation of Intelligent Soccer-picking Robot 3

Design and Implementation of Intelligent Soccer-picking Robo...

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3rd international conference on computer vision and pattern analysis, iccpa 2023

作者： Xiang, ShiHao Dong, Chen Hu, ZhanJie Li, HaoZhan Jiang, Yu Zhang, TianYu TianJin University of Technology School of Computer Science and Engineering TianJin China TianJin University of Technology School of Materials Science and Engineering TianJin China TianJin University of Technology College of Mechanical Engineering TianJin China

ISBN: (纸本)9781510667563

To address the time-consuming and laborious task of picking up soccers, an intelligent soccer-picking robot is designed and implemented, which consists of three main parts: mechanical, electronic, and software design. The robot employs a four-wheel drive and hydraulic shock absorber structure to ensure fast and stable operation. The main body structure of the robot is built with aluminum alloy and PLA materials to reduce weight effectively. The Kendryte K210 chip is the main control chip to reduce power consumption and cost. A monocular camera is used to construct a map, and the YOLOX object detection model is used to detect various objects on the soccer field. The artificial potential field algorithm is improved to achieve optimal path planning for the robot. The robot is tested 80 times on a real soccer field, with a successful pickup rate of 92.5%, meeting the actual needs of soccer-picking and filling the gap in the soccer-picking robot market. © 2023 SPIE.

关键词： Object detection

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A UAV Detection Framework Based on Dual Stereo vision System for Low-Altitude Urban Background 3

A UAV Detection Framework Based on Dual Stereo Vision System...

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3rd international conference on computer vision and pattern analysis, iccpa 2023

作者： Zhu, Renwei Chen, Jun Yang, Zhao Li, Na School of Transportation Southeast University Nanjing China College of General Aviation and Flight Nanjing University of Aeronautics and Astronautics Nanjing China College of Civil Aviation Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781510667563

This paper establishes a dual stereo vision system and uses Zhang's calibration method to calculate the homography matrix and various parameters of the binocular camera to achieve system correction. The effect of different processing methods on the UAV image in a low-altitude urban background is analyzed. A multistage filter fusion algorithm combined with various filtering and segmentation methods is designed to realize the background suppression of the low-altitude urban background UAV image and drone target extraction. An improved image processing method is used to suppress the background of a single frame image of a video sequence. The image information other than the UAV is regarded as noise or background. The noise and background are subtracted to finally get the UAV image. This research provides a framework for UAV detection in low-altitude airspace, which can support urban airspace management. © 2023 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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Collision Detection of Sensorless Cooperative Robots Based on Long Short Term Memory Network 3

Collision Detection of Sensorless Cooperative Robots Based o...

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3rd international conference on computer vision and pattern analysis, iccpa 2023

作者： Jin, Zhenjun Sun, Yingjie Li, Jiaxing Tian, Yingliang Shao, Shuai College of Computer Science and Engineering Changchun University of Technology China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781510667563

In man-machine cooperative environment, in order to ensure the safety of the cooperative robot and people, it is required that the cooperative robot can detect the collision quickly and accurately. Since the use of external sensors not only increases the complexity of cooperative robots, but also increases the cost, this paper proposes a collision detection method for cooperative robots without external sensors based on long and short term memory network (LSTM). Firstly, the dynamic trajectory is calculated by using the observation matrix of the dynamic model, and then the operating data of the cooperative robot is collected through the trajectory, and the dynamic parameters of the cooperative robot are obtained by using the least square method. Finally, the LSTM network model is designed to compensate the errors generated in the identification process of the dynamic parameters, and the results before and after the compensation are compared by experiments. The experimental results show that the recognition accuracy of the dynamic model can be effectively improved by the compensated results of the network model, and the collision detection accuracy of the cooperative robot can be effectively improved by comparing the actual torque with the theoretical torque calculated by the compensated dynamic model. © 2023 SPIE.

关键词： Dynamic models

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Automated Depression Diagnosis Based on Facial Expression and Deep Dual-Stream Convolutional Neural Networks 3

Automated Depression Diagnosis Based on Facial Expression an...

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3rd international conference on computer vision and pattern analysis, iccpa 2023

作者： Chen, Yangyang Chen, Yingyu Li, Mi Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781510667563

Depression is a common mental illness characterized by symptoms such as low mood, pessimism, and insomnia. In this study, we developed a deep Dual-Stream CNN to automatically diagnose and classify depression in expression video sequences. The network has two branches that extract static features and dynamic features from static and dynamic expressions, respectively, which are then fused for depression classification. The experiments were performed on the AVEC2014 database, and the results showed that the Dual-Stream model significantly improved the classification performance of depression, achieving an accuracy of 69.08% in depression categorization. © 2023 SPIE.

关键词： Convolutional neural networks

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Face anti-spoofing based on ASFF and LBP self-supervision branch 3

Face anti-spoofing based on ASFF and LBP self-supervision br...

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3rd international conference on computer vision and pattern analysis, iccpa 2023

作者： Ren, Junjie Zhang, Yi Zhang, Ruisheng Luo, Jianfeng Whale Cloud Technology Co. Ltd Nanjing210000 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China

ISBN: (纸本)9781510667563

Since the tremendous advancement of face recognition technology, how to detect real human faces from images has gradually captured the attention of researchers. To address the problem that most face anti-spoofing approaches ignore context information and local information about faces, this paper proposes a face anti-spoofing algorithm that fuses multi-scale features and texture features of images. On the one hand, we extract multi-scale features using adaptively spatial feature fusion (ASFF) and polarized self-attention (PSA) mechanism. On the other hand, local binary patterns (LBP) is used to form an auxiliary self-supervision branch to extract image texture difference information. As a final step, end-to-end live detection is achieved by balancing the difference between two different loss functions based on homoscedastic uncertainty. The EER of the presented method is 0.81% and 1.26% on CASIA-FASD and MSU-MFSD, respectively, and the ACER is only 1.64% and 2.07%±1.05% on the second and third protocols of Oulu_NPU, which indicates the validity of the method. © 2023 SPIE.

关键词： Local binary pattern

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