检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

50,480 篇 会议
1,421 册 图书
1,042 篇 期刊文献
1 篇 学位论文

馆藏范围

52,941 篇 电子文献
3 种 纸本馆藏

日期分布

学科分类号

31,809 篇 工学
- 24,802 篇 计算机科学与技术...
- 12,567 篇 软件工程
- 5,155 篇 光学工程
- 4,748 篇 电气工程
- 4,432 篇 信息与通信工程
- 4,257 篇 机械工程
- 3,950 篇 控制科学与工程
- 2,474 篇 生物工程
- 1,729 篇 生物医学工程（可授...
- 1,580 篇 仪器科学与技术
- 1,310 篇 电子科学与技术（可...
- 793 篇 化学工程与技术
- 697 篇 安全科学与工程
- 541 篇 交通运输工程
- 379 篇 建筑学
- 331 篇 土木工程
11,837 篇 理学
- 6,435 篇 物理学
- 5,405 篇 数学
- 2,761 篇 生物学
- 1,911 篇 统计学（可授理学、...
- 797 篇 化学
- 669 篇 系统科学
5,303 篇 医学
- 5,095 篇 临床医学
- 729 篇 基础医学(可授医学...
- 459 篇 药学(可授医学、理...
3,345 篇 管理学
- 1,951 篇 图书情报与档案管...
- 1,533 篇 管理科学与工程(可...
- 479 篇 工商管理
720 篇 艺术学
- 718 篇 设计学（可授艺术学...
428 篇 法学
- 401 篇 社会学
298 篇 农学
197 篇 教育学
163 篇 经济学
63 篇 文学
49 篇 军事学

主题

17,384 篇 computer vision
9,016 篇 pattern recognit...
4,195 篇 training
3,814 篇 feature extracti...
3,134 篇 cameras
2,870 篇 computational mo...
2,790 篇 image segmentati...
2,621 篇 visualization
2,573 篇 shape
2,533 篇 face recognition
2,171 篇 robustness
2,123 篇 computer science
1,972 篇 object detection
1,959 篇 computer archite...
1,878 篇 layout
1,852 篇 object recogniti...
1,802 篇 three-dimensiona...
1,725 篇 neural networks
1,708 篇 humans
1,691 篇 image recognitio...

机构

165 篇 univ chinese aca...
144 篇 tsinghua univers...
136 篇 national laborat...
107 篇 univ sci & techn...
104 篇 zhejiang univers...
100 篇 shanghai jiao to...
95 篇 microsoft resear...
94 篇 university of sc...
85 篇 zhejiang univ pe...
84 篇 shanghai ai lab ...
74 篇 school of comput...
69 篇 computer vision ...
68 篇 peking univ peop...
68 篇 chinese acad sci...
65 篇 chinese univ hon...
63 篇 institute of inf...
62 篇 google res mount...
61 篇 univ oxford oxfo...
59 篇 univ toronto on
57 篇 swiss fed inst t...

作者

91 篇 van gool luc
87 篇 umapada pal
76 篇 zhang lei
64 篇 lee seong-whan
50 篇 vittorio murino
42 篇 yang yi
34 篇 nassir navab
33 篇 li xin
33 篇 jie yang
32 篇 liu yang
31 篇 escalera sergio
31 篇 loy chen change
30 篇 ling haibin
30 篇 h. bischof
29 篇 zhou jie
29 篇 vasconcelos nuno
29 篇 jan-michael frah...
29 篇 hanqing lu
28 篇 blumenstein mich...
27 篇 jia yunde

语言

51,872 篇 英文
835 篇 其他
241 篇 中文
22 篇 土耳其文
5 篇 西班牙文
2 篇 日文
2 篇 葡萄牙文
2 篇 俄文

检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

共 52944 条记录，以下是4431-4440 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Long-Tailed Multi-Label Visual recognition by Collaborative Training on Uniform and Re-balanced Samplings

Long-Tailed Multi-Label Visual Recognition by Collaborative ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Guo, Hao Wang, Song Univ South Carolina Columbia SC 29201 USA

ISBN: (纸本)9781665445092

Long-tailed data distribution is common in many multi-label visual recognition tasks and the direct use of these data for training usually leads to relatively low performance on tail classes. While re-balanced data sampling can improve the performance on tail classes, it may also hurt the performance on head classes in training due to label co-occurrence. In this paper, we propose a new approach to train on both uniform and re-balanced samplings in a collaborative way, resulting in performance improvement on both head and tail classes. More specifically, we design a visual recognition network with two branches: one takes the uniform sampling as input while the other takes the rebalanced sampling as the input. For each branch, we conduct visual recognition using a binary-cross-entropy-based classification loss with learnable logit compensation. We further define a new cross-branch loss to enforce the consistency when the same input image goes through the two branches. We conduct extensive experiments on VOC-LT and COCO-LT datasets. The results show that the proposed method significantly outperforms previous state-of-the-art methods on long-tailed multi-label visual recognition.

关键词： Training Visualization computer vision Head Collaboration Collaborative work pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Programmed Number Plate recognition using Deep Learning 4

Programmed Number Plate Recognition using Deep Learning

引用

4th ieee Global conference for Advancement in Technology, GCAT 2023

作者： Ananthi, M. Fathima, Afrin Ruqayyah M. Srigomathi, S. Abirami, R.K. Computer Science and Business Systems Sri Sairam Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9798350305258

Automatic License Plate recognition (ALPR) systems have become increasingly popular in recent years, as they provide a quick and efficient way to identify vehicles. The PNPR project aims to develop a deep learning-based ALPR system that can accurately detect and recognize license plates in real-time. The system utilizes state-of-the-art computer vision techniques to extract license plate information from CCTV camera feeds. The captured license plate information is then processed and compared to a database of registered plates to determine if the vehicle is authorized or not. The project's primary objective is to develop an accurate and reliable system that can aid law enforcement agencies in identifying stolen, unregistered, or unauthorized vehicles. The proposed PNPR system is designed to be scalable and can be deployed in various settings, including parking lots, toll booths, and border crossings. The results demonstrate that the PNPR system achieves high accuracy and can reliably detect and recognize license plates in real-time. The proposed system can potentially revolutionize how law enforcement agencies handle vehicle identification and monitoring. © 2023 ieee.

关键词： Automatic number plate recognition computer vision Convolutional neural networks Deep learning Image processing License plate recognition Object detection Programmed number plate recognition Security applications Surveillance systems Traffic management Vehicle tracking

来源：评论

学校读者我要写书评

暂无评论

Deep Image Compression with Latent Optimization and Piece-wise Quantization Approximation

Deep Image Compression with Latent Optimization and Piece-wi...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wu, Yuyang Qi, Zhiyang Zheng, Huiming Tao, Lvfang Gao, Wei Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Benefit from its capability of learning high-dimensional compact representation from raw data, the auto-encoders are widely used in various tasks of data compression. In particular, for deep image compression, auto-encoders generally take the responsibility of mapping original images to the latent representation to be coded. In this paper, we propose a new framework for deep image compression by devising a loss function for latent optimization, and adopting the differentiable approximation of quantization. In our experiments, both subjective and objective results can confirm the effectiveness of our contributions.

关键词： computer vision Image coding Quantization (signal) conferences Data compression pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Drowsy Driver Detection System using computer vision and Transfer Learning for Preventing Road Accidents 3

Drowsy Driver Detection System using Computer Vision and Tra...

引用

3rd ieee International conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Bagal, Naisargi Dingreja, Kashish Atterkar, Atharva Bachhav, Prasad Sable, Nilesh P. Yenkikar, Anuradha Alkunte, Ritesh Vishwakarma Institute of Information Technology Department of Computer Science and Engineering - Artificial Intelligence Pune India

ISBN: (纸本)9798350354348

Major traffic accidents are attributed to driver fatigue, according to study on the topic. Driver drowsiness is a state in which the driver of a car is on the verge of falling asleep or losing consciousness. It can be brought on by a number of variables, including biological, physical, psychological, and other factors, all of which can impede and inhibit safe driving. The several methods that can be applied to the implementation of a system for the detection of driver sleepiness will be reviewed in this paper. Improving the ability to identify drowsiness in real time through computer vision is the main objective of all technologies. Thus, our work also pertains to the identification of driver drowsiness, whereby a motorist's tiredness can be ascertained by first recognizing their face, followed by eye tracking. The system compares the extracted eye image with the dataset. The system used the dataset and predicts using transfer learning with our base model as InceptonV3 to identify that it could alert the driver with an alarm if the driver's eyes were closed for a predetermined amount of time, and it could resume monitoring if the driver's eyes were open following the alarm alert. We established a score that grew if the eyes were closed and dropped if they were open. This study aims to reduce traffic accidents by solving the sleepiness detection problem with InceptionV3 with an accuracy of 95.46% compare to 92.11% of ***, driving drowsiness detection - which assesses a driver's degree of fatigue using facial recognition and eye tracking becomes the main focus of our research. © 2024 ieee.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

AdaBoost-SVM Face recognition Algorithm Based on LBP and Gabor Feature Fusion 5

AdaBoost-SVM Face Recognition Algorithm Based on LBP and Gab...

引用

5th International conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Lian, Xiaoqin Liu, Chunquan Gao, Chao Ma, Guochun Gong, Yonggang Li, Jin Beijing Technology and Business University School of Computer and Artificial Intelligence Beijing100048 China China National Light Industry Beijing Technology and Business University Key Laboratory of Industrial Internet and Big Data Beijing100048 China

ISBN: (纸本)9798331506612

In recent years, facial recognition technology has seen rapid advancements and is now extensively utilized in security surveillance and financial transactions. The face recognition process usually includes image preprocessing, feature extraction, and classification. However, it is often difficult for a single feature to fully capture the complex features of a human face, thus affecting the accuracy of recognition. To address this issue, this study presented an AdaBoost-SVM face recognition algorithm that fuses local binary pattern and Gabor features, which can enable comprehensive extraction of facial features from images and enhance recognition accuracy. This method first performs bilateral filtering on the face image to reduce noise and retain edge information. Then, it extracts the local binary pattern and Gabor features and weights the fusion. Finally, it uses the AdaBoost-SVM classifier for identification. Moreover, the algorithm performance was comprehensively evaluated using the ten-fold cross-validation method. The experimental results demonstrated that this method achieves recognition accuracy of 98.75% and 96.25% on ORL and Yale A face databases respectively, and still performs well in complex lighting and expression-changing environments, significantly improving the accuracy of face recognition and its robustness. © 2024 ieee.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Research on Water Conservancy Project Monitoring and Intelligent Identification Method Based on computer vision Technology

Research on Water Conservancy Project Monitoring and Intelli...

引用

2024 International conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Zhu, Jin Yang, Feng Zhejiang Province Water Conservancy and Hydropower Technology Consultation Center Zhejiang Hangzhou310009 China Zhejiang Zhongshui Engineering Technology Co. Ltd. Hangzhou310020 China

ISBN: (纸本)9798350369212

Monitoring and maintenance of water resources projects is essential to ensure project safety and environmental protection. Traditional monitoring methods often rely on manual inspections and sensor data, but these methods suffer from high cost, low efficiency and limited monitoring range. With the rapid development of computer vision technology, its potential for application in water conservancy engineering has gradually emerged. The purpose of this paper is to study the water conservancy project monitoring and intelligent identification methods based on computer vision technology. We first outline the basic concepts of computer vision and its application scenarios in water conservancy engineering, including structural health monitoring, environmental monitoring and disaster management. Then, the specific applications of image processing techniques, machine learning and deep learning in intelligent recognition are described in detail, especially how these techniques can be utilized to achieve an efficient and accurate real-time monitoring system. Through several case studies, this paper demonstrates the effects and advantages of the application of computer vision technology in actual water conservancy projects. Finally, it summarizes the main findings of the current study and looks forward to the future technological development and application prospects, pointing out the direction of further research. The findings of this paper show that computer vision-based monitoring and intelligent identification methods not only improve the accuracy and efficiency of monitoring, but also provide new technical means for the management and maintenance of water conservancy projects. © 2024 ieee.

关键词： Environmental monitoring

来源：评论

学校读者我要写书评

暂无评论

Part-aware Panoptic Segmentation

Part-aware Panoptic Segmentation

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： de Geus, Daan Meletis, Panagiotis Lu, Chenyang Wen, Xiaoxiao Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781665445092

In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly used datasets: Cityscapes and Pascal VOC. Moreover, we present a single metric to evaluate PPS, called Part-aware Panoptic Quality (PartPQ). For this new task, using the metric and annotations, we set multiple baselines by merging results of existing state-of-the-art methods for panoptic segmentation and part segmentation. Finally, we conduct several experiments that evaluate the importance of the different levels of abstraction in this single task.

关键词： Measurement Training Technological innovation computer vision Codes Annotations Merging

来源：评论

学校读者我要写书评

暂无评论

Yolo algorithms to improve the detection of personal protective equipment thought industrial computer vision 30

Yolo algorithms to improve the detection of personal protect...

引用

30th ieee International conference on Electronics, Electrical Engineering and Computing, INTERCON 2023

作者： Velasquez, Ricardo Arias Lara, Jennifer Vanessa Mejia Velasquez, Renato Arias Universidad Tecnológica Del Perú Lima Peru Universidad Nacional de San Agustín de Arequipa Lima Peru

ISBN: (纸本)9798350315578

During 2023, accidents at work are unexpected situations that cause losses in both human and material resources. In Peru there is current legislation that encourages a safe work environment, however, according to 2019 reports from the Ministry of Labor and Employment Promotion, 31.92% of accidents caused this year directly affected the cranial area. The use of personal protective equipment requires finding an optical way to identify the required safety equipment in real time. In response to this problem, the use of artificial vision or computer vision for helmet recognition. The trained model for online personal protective equipment classifier algorithms with computer vision in industrial applications allows to detect helmets, head, safety glasses, industrial vests with precision metrics greater than 88% according to light intensity. Additionally, it is possible to improve the accuracy of the other clusters by training with higher epochs, increasing batch processing from eight to sixteen, and using parallel processing. Despite the fact that the configuration profile for the training process carried out was basic, the database used, due to the varied labeling and number of images, acceptable results were obtained. Also, due to the selection of the YOLOV5m model, transfer learning training contributed to the training time without reducing the accuracy rates. Our findings is the recognition of a model with 5000 pictures with an accuracy of 88% from 400 to 800 lux and limited for the light intensity lower than 400 lux, with 78.63%. With light intensity higher than 800 lux, the recognition time of less than 0.3 seconds/image together with a rate of 98% with a low-cost camera and parallel processing with a Raspberry Pi version 4. © 2023 ieee.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

TAEN: Temporal Aware Embedding Network for Few-Shot Action recognition

TAEN: Temporal Aware Embedding Network for Few-Shot Action R...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ben-Ari, Rami Nacson, Mor Shpigel Azulai, Ophir Barzelay, Udi Rotman, Daniel OriginAI Haifa Israel Technion Haifa Israel IBM Res AI Haifa Israel

ISBN: (纸本)9781665448994

Classification of new class entities requires collecting and annotating hundreds or thousands of samples that is often prohibitively costly. Few-shot learning suggests learning to classify new classes using just a few examples. Only a small number of studies address the challenge of few-shot learning on spatio-temporal patterns such as videos. In this paper, we present the Temporal Aware Embedding Network (TAEN) for few-shot action recognition, that learns to represent actions, in a metric space as a trajectory, conveying both short term semantics and longer term connectivity between action parts. We demonstrate the effectiveness of TAEN on two few shot tasks, video classification and temporal action detection and evaluate our method on the Kinetics-400 and on ActivityNet 1.2 few-shot benchmarks. With training of just a few fully connected layers we reach comparable results to prior art on both few shot video classification and temporal detection tasks, while reaching state-of-the-art in certain scenarios.

关键词： Training computer vision conferences Semantics Benchmark testing Extraterrestrial measurements pattern recognition

来源：评论

学校读者我要写书评

暂无评论

MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data

MVFuseNet: Improving End-to-End Object Detection and Motion ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Laddha, Ankit Gautam, Shivam Palombo, Stefan Pandey, Shreyash Vallespi-Gonzalez, Carlos Aurora Innovat Mountain View CA 94043 USA

ISBN: (纸本)9781665448994

In this work, we propose MVFuseNet, a novel end-to-end method for joint object detection and motion forecasting from a temporal sequence of LiDAR data. Most existing methods operate in a single view by projecting data in either range view (RV) or bird's eye view (BEV). In contrast, we propose a method that effectively utilizes both RV and BEV for spatio-temporal feature learning as part of a temporal fusion network as well as for multi-scale feature learning in the backbone network. Further, we propose a novel sequential fusion approach that effectively utilizes multiple views in the temporal fusion network. We show the benefits of our multi-view approach for the tasks of detection and motion forecasting on two large-scale self-driving data sets, achieving state-of-the-art results. Furthermore, we show that MVFusenet scales well to large operating ranges while maintaining real-time performance.

关键词： computer vision Laser radar conferences Object detection Real-time systems pattern recognition Forecasting

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 440 441 442 443 444 445 446 447 448 449 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：