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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

共 18817 条记录，以下是451-460 订阅

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Brightness Attention Mechanism for Few-Shot Action Recognition 3

Brightness Attention Mechanism for Few-Shot Action Recogniti...

引用

3rd international conference on Electronic Information Engineering and computer Science, EIECS 2023

作者： Tian, Zhiyuan Xue, Wanli Zhang, Ying Tianjin University of Technology School of Computer Science and Engineering Tianjin300384 China

ISBN: (纸本)9798350316674

Few-shot action recognition remains a challenging problem in computer vision, particularly in scenarios where the model needs to generalize well from limited examples. In this study, we present a novel concept known as the Brightness Attention Mechanism(BAM), seamlessly integrated into the ResNet50 architecture, to enhance the model's sensitivity to brighter regions in the image. Our approach begins with the use of an image segmentation model that isolates human figures, followed by blending these segmented images with original images to accentuate the subject. We then apply BAM to give more attention to these highlighted regions during the action recognition task. The data from our experiments reveal a marked increase in the accuracy of few-shot action recognition, surpassing both conventional techniques and the latest attention-based methods. Our methodology advances the state of the art for few-shot scenarios across Kinetics, HMDB51, and UCF101. © 2023 IEEE.

关键词： action recognition brightness attention computer vision image fusion

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Variable-Domain Fuzzy based Dual-Loop control for Stewart Platform Dynamic Target Tracking 3

Variable-Domain Fuzzy based Dual-Loop Control for Stewart Pl...

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3rd international conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2024

作者： Min, Shengyang Luo, Jie Zhou, Yudong School of Automation Wuhan University of Technology WuHan China Wuhan Branch Office LiuZhou WuLing Automobile Inoustry Co LTD. WuHan China

ISBN: (纸本)9798331530280

This paper investigates a dynamic target tracking method for the Stewart platform based on monocular vision distance measurement. The innovative approach applies a variable-domain fuzzy PID control algorithm to a mature Stewart platform, achieving efficient dynamic target tracking through a comprehensive dual-loop control system. AMESim and MATLAB co-simulation were used to develop a physical model of the Stewart platform, analyze its operational principles and characteristics, and conduct open-loop experiments to validate the model's reliability. The feasibility and performance of different control algorithms were compared on the established model, and the results were used to guide parameter tuning on the physical system. The findings indicate that this method, compared to traditional PID and fuzzy PID controllers, significantly enhances the Stewart platform's dynamic tracking capability in underwater environments, demonstrating high application value and practical feasibility. © 2024 IEEE.

关键词： Open loop control

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An Elevator Forbidden Target Discovery Method Based on computer vision 3

An Elevator Forbidden Target Discovery Method Based on Compu...

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3rd IEEE international conference on computer Systems, ICCS 2023

作者： Xia, Zhihao Soochow University School of Mechanical and Electrical Engineering Suzhou China

ISBN: (纸本)9798350313666

Intelligent monitoring has been widely used in the field of object detection and pedestrian detection. At the same time, its application in elevator monitoring is gradually increasing. One such application is the discovery of forbidden targets. There are challenges to this task: 1) Lack of datasets;2) The image size that can be handled is small;3) Uncertain angle and spatial position of the object;4) The object has partial occlusion. To improve the prediction effect of the model on such images, this paper proposes a new vision transformer model. The standard vision transformer often uses class tokens or patch tokens to design classification heads. We believe that both tokens contain unique and valuable information. Therefore, in this paper, we propose a convolution-based classification head with dual inputs. Our classification head uses both class token and patch tokens to focus on more features for effectively handle the occlusion. We introduce convolution into transformer to enhance the perception of 2D spatial locations. We made a new dataset to train our model. The results show that our model achieves 97.78% accuracy on the dataset, which is 1.51% better than the standard vision transformer. © 2023 IEEE.

关键词： Elevators

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Proceeding - 2023 IEEE 3rd international Maghreb Meeting of the conference on Sciences and Techniques of Automatic control and computer Engineering, MI-STA 2023

Proceeding - 2023 IEEE 3rd International Maghreb Meeting of ...

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3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic control and computer Engineering, MI-STA 2023

ISBN: (纸本)9798350319897

The proceedings contain 152 papers. The topics discussed include: statistical study of optimizing preparation new SOFC anode;application of business process management in the Libyan international medical university: history and development;the influence of phase-shifted line-of-sight component with correlated-shadows Rician fading over the performance of cell-free massive MIMO;understanding the threat posed by Chinese cyber warfare units;a cloud computing model for Libyan higher education institutions;assessing virtual reality sickness in highly immersive virtual laboratory environments : simulator sickness questionnaire and mitigation strategies;detection of breast tumor using microstrip ring resonator;detection of communication network intruders using artificial neural networks;strongly coupled magnetic resonance based wireless power transfer system for implantable medical devices;a compact patch coupler using cross-slots for 5g applications;classifying leukemia through DNA expression data mining techniques;channel gain in micro-urban environment assisted intelligent reflecting surface;sliding mode control for the satellite with the influence of time delay;and a proposed method for detecting network intrusion using deep learning approach.

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Object Detection Algorithms with Deep Learning: An Overview 3

Object Detection Algorithms with Deep Learning: An Overview

引用

3rd international conference on Electronic Information Engineering and computer Science, EIECS 2023

作者： Tang, Wei Chen, Jinlong Ning, Yi Xu, Kejian Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Guilin University of Electronic Technology School of Continuing Education Guilin China

ISBN: (纸本)9798350316674

Deep learning is a key branch of machine learning that leads the frontier of scientific research and has a significant impact on the field of computer vision. With the development of deep learning, object detection, as a research hotspot in computer vision, has attracted a great number of researchers to conduct in-depth research. This paper introduces single-stage, two-stage and Transformer-based object detection algorithms and its backbone network structures, and provides a detailed comparison of the advantages and shortcomings of these algorithms. Finally, the practical applications of these algorithms in various fields and their performance evaluation on critical datasets are presented. © 2023 IEEE.

关键词： Deep Learning Object Detection Performance Evaluation Transformer

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： Data integrity

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Performance Analysis of LiDAR Data Processing on Multi-Core CPU and GPU Architectures 3

Performance Analysis of LiDAR Data Processing on Multi-Core ...

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3rd international conference on Computing and Machine Intelligence (ICMI)

作者： Alzyout, Mohammad S. Al Nounou, Abd Alrahman Tikkisetty, Yashwanth Naidu Alawneh, Shadi Oakland Univ Elect & Comp Engn Dept Rochester MI 48063 USA

ISBN: (纸本)9798350372977;9798350372984

The projection process of the LiDAR 3D Point Cloud data is one of the crucial steps in computer vision applications. It involves several steps to achieve the finalized accurate results. Many current studies leverage the benefits of using a GPU in the computing capability. This paper presents a comparative study of the implementation and testing of this process on single-core, multi-core CPU and GPU architectures. The computational efficiency of each platform is evaluated through a series of benchmarks, including data extraction, segmentation, and transformation tasks. Our analysis reveals the inherent parallelization benefits of GPUs in handling large-scale point cloud data, while also considering the accessibility of multi-core CPUs. Also, a comparison between the NVIDIA RTX 3070 and NVIDIA RTX 4060 is provided. The RTX 3070 showed roughly a speed up of 8 times over the RTX 4060. In addition, the Multi-core implementation outperforms up to 10 times over the single-core. These results overall show the benefits of using the Multi-core and the GPU accelerating approaches to this application, with the availability for further improvements.

关键词： LiDAR 3D Point Cloud CPU GPU Multi-Core OpenACC CUDA ROI Extraction Segmentation Projection

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DiffST: High-Level Style Feature Transfer Via Diffusion Models 3

DiffST: High-Level Style Feature Transfer Via Diffusion Mode...

引用

3rd international conference on computer Science, Electronic Information Engineering and Intelligent control Technology, CEI 2023

作者： Cheng, Hongche School of Computer Science and Technology Ustc Hefei China

ISBN: (纸本)9798350372052

Image style transfer aims to transfer the style of one image to another to create a new image with a specific artistic style. Existing style transfer algorithms primarily focus on transferring low-level features, such as textures, colors, strokes, etc., while neglecting the transfer of high-level features like semantic elements and object shapes. To address this issue, we propose DiffST, a new style transfer architecture based on pretrained text-to-image diffusion models. Specifically, DiffST can fully extract the feature of the style images to guide the redrawing of the content image, thereby completing the style transfer. Moreover, DiffST introduces a new image redrawing method called two-stage inversion, which effectively preserves the information of the content image and enhances the quality of the generated image. We demonstrate the quality of our transfer using numerous different paintings and compare DiffST with state-of-the-art methods, proving the superiority of DiffST. © 2023 IEEE.

关键词： computer vision

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Cooperative control of slip rate of distributed drive electric vehicles based on modularization and Multi-agent 3

Cooperative control of slip rate of distributed drive electr...

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3rd international conference on Intelligent Mechanical and Human-computer Interaction Technology, IHCIT 2024

作者： Niu, Qiang Wei, Wenjun School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou730070 China

ISBN: (纸本)9781510683105

To solve the drive slip prevention problem of distributed drive electric vehicles under extremely complex road conditions, this paper proposes a multi-agent body distributed control method based on modularized architecture. Firstly, the modular approach is used to reconstruct the whole vehicle structure, consider each hub motor wheel as an independent agent, and establish the slip rate model of each wheel agent. Then, a distributed model predictive control method based on multi-agent is designed to solve the drive anti-slip problem considering multi-constraints and multi-objectives. Finally, the anti-slip performance is verified by simulation under low adhesion and split road conditions. The results show that the distributed control strategy under the modularized architecture can synergistically control the wheels to achieve good anti-slip effect. This provides a theoretical and practical reference for further development of modular vehicle in the future. © 2024 SPIE.

关键词： Predictive control systems

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Driver fatigue detection and early warning 3

Driver fatigue detection and early warning

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3rd international conference on Electronic Information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Wang, Yetong Zhang, Liyan Du, Jiaxin Wang, Xinyu Fang, Liu School of Computer and Communication Engineering Dalian Jiaotong University Dalian China

ISBN: (纸本)9781510680449

In recent years, with the rapid increase in the number of cars in various countries, traffic accidents have also occurred frequently. Among them, traffic accidents caused by driver fatigue driving account for a large proportion, and how to improve the safety of driver driving has become a key issue. Therefore, this article proposes a driver face recognition and fatigue driving warning method based on computer vision. This article first uses the Adaboost face detection algorithm for face detection, and uses the Dlib toolkit to label facial key points. Then, by using these key points, the eye aspect ratio, mouth aspect ratio, and Euclidean distance between the two feature points of the nose are calculated. These three parameters are used as fatigue feature values for fatigue state judgment and corresponding warning experiments are given. Tests have shown that this method can reflect the fatigue status of drivers in real time and has good detection performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Fatigue testing

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