检索结果-内蒙古大学图书馆

ieee Global conference on Consumer Electronics (GCCE)

作者： Stellar Choi YouMin Jung MinSeo Kim Liang Li HyunSook Lee Woong Choi Dept of Software Application Virtual Reality Kangnam University Yongin Republic of Korea Dept ICT Engineering Virtual Reality Kangnam University Yongin Republic of Korea Dept of ICT Engineering Software Engineering Kangnam University Yongin Republic of Korea College of Information Science and Engineering Ritsumeikan University Kusatsu Japan Dept of AI SW Convergence Research Institute Kangnam University Yongin Republic of Korea College of ICT Construction & Welfare Convergence Kangnam University Yongin Republic of Korea

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Previous research has faced challenges such as the complexity of image outlines and the representation of light when using CartoonGAN. To address this, this paper proposes a Blend Pixel Algorithm(aka BRA) to enhance the output image quality of CartoonGAN. Instead of directly modifying the existing model, this method applies post-processing color correction techniques to transform images into the desired style. In this process, RGB images are converted to HSV images, and appropriate correction values are applied to adjust the colors. Additionally, while preserving the style of CartoonGAN, the corrected images are blended with the original images to ensure that the colors are not significantly different from the original. Experimental results confirm the effectiveness of this method in improving image similarity compared to the existing model. Therefore, this research can play an important role in enhancing the performance of CartoonGAN in the field of visual content generation.

关键词： image quality visualization image transformation Accuracy image color analysis Transforms Dogs Complexity theory Consumer electronics

来源：评论

学校读者我要写书评

暂无评论

Using Convolutional Neural Networks for image Recognition in IoT Applications

Using Convolutional Neural Networks for Image Recognition in...

引用

Pervasive Computational Technologies (ICPCT), International conference on

作者： Durgam Rajababu Sampenga Veerraju K. Suresh Kumar Sonali Gupta V B Thurai Raaj Rahul Joshi Department of EEE School of Engineering SR University Warangal Telangana India Department of Electronics and Communications Engineering Dadi Institute of Engineering and Technology Anakapalli Visakhapatnam Andhra Pradesh MBA Department Panimalar Engineering College Varadarajapuram Poonamallee Chennai J C Bose University of Science and Technology YMCA Faridabad Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of Journalism & Mass Communication SMeH Manav Rachna International Institute of Research and Studies Haryana India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries.

关键词： Training visualization Automation Computational modeling Surveillance Training data Feature extraction Convolutional neural networks Internet of Things image classification

来源：评论

学校读者我要写书评

暂无评论

USTC-TD: A Test Dataset and Benchmark for image and Video Coding in 2020s

arXiv

引用

arXiv 2024年

作者： Li, Zhuoyuan Liao, Junqi Tang, Chuanbo Zhang, Haotian Li, Yuqi Bian, Yifan Sheng, Xihua Feng, Xinmin Li, Yao Gao, Changsheng Li, Li Liu, Dong Wu, Feng MOE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei230027 China

image/video coding has been a remarkable research area for both academia and industry for many years. Testing datasets, especially high-quality image/video datasets are desirable for the justified evaluation of coding-related research, practical applications, and standardization activities. We put forward a test dataset namely USTC-TD, which has been successfully adopted in the practical end-to-end image/video coding challenge of the ieee International conference on visual communications and lmage processing (VCIP) in 2022 and 2023. USTC-TD contains 40 images at 4K spatial resolution and 10 video sequences at 1080p spatial resolution, featuring various content due to the diverse environmental factors (e.g. scene type, texture, motion, view) and the designed imaging factors (e.g. illumination, lens, shadow). We quantitatively evaluate USTC-TD on different image/video features (spatial, temporal, color, lightness), and compare it with the previous image/video test datasets, which verifies its excellent compensation for the shortcomings of existing datasets. We also evaluate both classic standardized and recently learned image/video coding schemes on USTC-TD using objective quality metrics (PSNR, MS-SSIM, VMAF) and subjective quality metric (MOS), providing an extensive benchmark for these evaluated schemes. Based on the characteristics and specific design of the proposed test dataset, we analyze the benchmark performance and shed light on the future research and development of image/video coding. All the data are released online: https://***/USTC-TD . Copyright © 2024, The Authors. All rights reserved.

关键词： image coding

来源：评论

学校读者我要写书评

暂无评论

Research on image Feature Extraction and Matching Algorithms for Simultaneous Localization and Mapping 3

Research on Image Feature Extraction and Matching Algorithms...

引用

3rd ieee International conference on communications, Information System and Computer Engineering, CISCE 2021

作者： Li, Chong Li, Weizhi Wang, Zitong Wan, Yan School of Information and Communication Engineering Beijing University of Posts and Telecommunication Beijing China School of Economics and Management Beijing University of Posts and Telecommunication Beijing China

ISBN: (纸本)9780738112152

Feature point extraction and matching are the key modules in the front-end of visual simultaneous localization and mapping (SLAM), which determines the accuracy of locating and map building robots. To obtain a good performance, the image extraction algorithm being applied should have high efficiency and robustness to the changes of the surrounding environment. In this paper, three widely used feature extraction algorithms, namely Scale-invariant feature transform (SIFT), speed up robust features (SURF) and oriented FAST, and rotated BRIEF (ORB), are studied. The basic concepts and details of the three algorithms are introduced. Then the execution time of the three algorithms is compared to evaluate their efficiency. Meanwhile, the key points matching rate of the three algorithms are compared under various transformations and distortions. The experiment results show that ORB has higher efficiency than SIFT and SURF. However, SIFT has better robustness to image rotation, illumination change, scaling transformation, Gaussian noise and salt-and-pepper noise. In addition, the performance of the ORB-SLAM system is tested on different datasets. It has great ability in camera trajectory estimation and map building and therefore is suitable for deploying in industry. © 2021 ieee.

关键词： Salt and pepper noise

来源：评论

学校读者我要写书评

暂无评论

Low-Power Approximate Computing-based VLSI Architecture for Biomedical Signal processing

Low-Power Approximate Computing-based VLSI Architecture for ...

引用

2025 International conference on visual Analytics and Data visualization, ICVADV 2025

作者： Sathiya, A. Prabhavathy, B. Balasupramani, A. Amutha, A. Kavitha, L. Saravanakumar, R. Department of Electronics and Communication Engineering Karur Thalavapalayam639113 India E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamil Nadu Nagapattinam611002 India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai600124 India Dhanalakshmi Srinivasan College of Engineering Department of Electrical and Electronics Engineering Coimbatore641105 India Thalavapalayam639113 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331521394

The analysis of ECG, EEG and EMG signals requires biomedical signal processing which needs low-power high-performance VLSI architectures to achieve real-time processing. This paper introduces an optimized VLSI architecture which enables biomedical signal processing through energy-efficient digital filtering along with feature extraction as well as pipeline-based clock gating. The proposed architecture combines a distributed arithmetic FIR filter without multipliers to reduce complexity and it integrates a low-power feature extraction system that detects ECG R-peaks and EEG spectral energy and EMG muscle activity effectively. The system uses dynamic clock gating together with power gating to cut down both dynamic power usage and leakage power. The experimental findings show that the developed VLSI architecture both reduces power usage through low hardware complexity while keeping high accuracy levels for biomedical signal processing. The system delivers its performance measurement through power dissipation and area efficiency along with processing speed which enables its use in wearable and implantable medical devices. © 2025 ieee.

关键词： Medical image processing

来源：评论

学校读者我要写书评

暂无评论

MAS-EGNN: A Multi-Agent EGNN-based Spectrum Allocation Optimization Framework for V2X communications

MAS-EGNN: A Multi-Agent EGNN-based Spectrum Allocation Optim...

引用

ieee International conference on Signal and image processing (ICSIP)

作者： Yingxue He Li Peng School of Internet of Things Engineering Jiangnan University Wuxi Jiangsu

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

With the development of V2X technology, efficient spectrum resource management is critical to ensure the reliability and overall system performance of vehicle-to-vehicle communications. Traditional spectrum allocation methods often do not take into account inter-vehicle interference. In this paper, we introduce an innovative approach to eliminate interference in vehicle-to-vehicle communication, the MAS-EGNN framework. Initially, an Equivariant Graph Neural Networks (EGNN) is utilized to dynamically update the graph representation through node and edge conditions to effectively capture the relationships and dependencies between vehicles. Subsequently, multi-intelligence reinforcement learning techniques allow multiple intelligences to interact simultaneously within the environment, with each independently adapting to changes in the surrounding environment to optimize overall network performance. The effectiveness of the approach in improving communication quality and system throughput is verified through the simulation of V2X communication scenarios and the implementation of corresponding optimization strategies. The experimental results show that the method significantly reduces interference and optimizes V2X spectrum allocation compared with the traditional spectrum allocation strategy.

关键词： Adaptation models Heuristic algorithms System performance Vehicular ad hoc networks Interference Reinforcement learning Throughput

来源：评论

学校读者我要写书评

暂无评论

Creating an image Recognition Model to Optimize Technological Processes

Creating an Image Recognition Model to Optimize Technologica...

引用

ieee Region 8 International conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Anton Parfenov Denis Parfenov Research Institute of Digital Intelligent Technologies Orenburg State University Orenburg Russia

ISBN: (数字)9798331532024

ISBN: (纸本)9798331532031

This research developed an innovative approach to analyze data obtained from images, which allows programming of computer numerical control (CNC) machines based on specialized data. This method aims to automate the key manufacturing process, which is an important task in modern manufacturing environments. The collected data and analysis results demonstrated significant benefits in simplifying and speeding up the process of creating keys for CNC machines. Using machine learning and intelligent algorithms to interpret visual data can significantly improve production efficiency and accuracy. Thanks to this, manufacturing enterprises can reduce the time for preparing and reconfiguring equipment, minimizing the human factor and errors, which ultimately reduces costs and improves the quality of the final product. Thus, the introduction of image analysis techniques into automated data processing processes opens up new opportunities for growth and innovation in the industry, allowing companies to remain competitive in a rapidly changing technological landscape. The findings of the study highlight the importance of integrating modern technologies into manufacturing processes and provide the basis for further research in this area.

关键词： Computer vision visualization Technological innovation image recognition Accuracy Manufacturing processes Machine learning algorithms Distortion Manufacturing Computer numerical control

来源：评论

学校读者我要写书评

暂无评论

Fast UAV- image-based Person Re-Identification at the Edge

Fast UAV- Image-based Person Re-Identification at the Edge

引用

Contemporary Computing and communications (InC4), ieee International conference on

作者： Huy Hoang Nguyen Thanh Trung Duong School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Person re-identification (re-id) has been widely used in various applications, such as security surveillance, the retail sector, and the military. Although companies that manufacture security surveillance equipment, such as Hikvision, Huawei, and NEC, have developed and deployed person re-ied systems in real life, this research topic still has several opening challenges, such as high memory usage and computation cost, processes at the edge and re-identification on aerial images. This problem is crucial for UAV-based person re-id applications in the military and security surveillance. This study proposes a deep person re-id model that can balance the tradeoff between accuracy and inference cost when implemented on the edge device and images captured from UAV. Although the mAP score of our proposed model is 2.48 % lower than that of the SOTA model, experimental results verify that our model-based inference cost is much better than the SOTA-model-based result. Indeed, our approach's RAM usage and processing time are 157 % and 528 % better than those of the Sota model, respectively. This result also points out that our proposed model meets the real-time requirement on the Jetson-Xavier-based edge device.

关键词： Training Costs Accuracy Military computing image edge detection Surveillance Computational modeling

来源：评论

学校读者我要写书评

暂无评论

UNVEILING OF HOWimage RESTORATION CONTRIBUTES TO UNDERWATER OBJECT DETECTION 8

UNVEILING OF HOWIMAGE RESTORATION CONTRIBUTES TO UNDERWATER ...

引用

8th ieee International conference on Consumer Electronics-Taiwan (ICCE-TW) - Driving World to 5G and Beyond with Consumer Technology

作者： Peng, Wen-Yi Peng, Yan-Tsung Lien, Wei-Cheng Chen, Chu-Song Natl Chengchi Univ Dept Comp Sci Taipei Taiwan Hyson Technol Inc Brecksville OH USA Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781665433280

Most underwater image restoration works indicate that restoring images can improve their visual quality and performance of computer vision tasks that use the restored data. This paper aims to investigate how image restoration contributes to underwater object detection. We found that utilizing image restoration through data augmentation rather than using it as a pre-processing step can boost underwater object detection accuracy (up to a 1.9% gain in mAP) on URPC2019 dataset.

关键词： visualization Computer vision conferences Object detection image restoration Task analysis

来源：评论

学校读者我要写书评

暂无评论

Real-Time Dynamic image Stitching Using GNN-Based Feature Matching 11

Real-Time Dynamic Image Stitching Using GNN-Based Feature Ma...

引用

11th International conference on Mechatronics and Robotics Engineering, ICMRE 2025

作者： Suh, Young Hoon Kim, Eun Ho Choi, Yeong Gwang Jeon, JaeWook Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon16419 Korea Republic of

ISBN: (纸本)9798331509293

Especially in static situations, image stitching has been routinely used to seamlessly merge many photographs into a panorama. Using it in dynamic and real-time environments, however, presents a number of difficulties, such as managing shifting objects, changing surroundings, and processing limitations. An image stitching look at that works well in dynamic real-time situations is presented in this paper. The method makes use of GNN-based feature matching, dynamic object removal, and real-time synthesis techniques. Comparative studies suggest that, in comparison to traditional methods like SIFT and ORB, the suggested approach meets real-time processing requirements while achieving greater registration precision, lower homography RMSE, and consistent panoramic outputs. The findings imply that the algorithm might be used in fields where precision and efficiency are required in dynamic circumstances, like parking management systems, sports broadcasting, and autonomous driving. © 2025 ieee.

关键词： Photointerpretation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：