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

4,595 篇 会议
498 册 图书
65 篇 期刊文献

馆藏范围

5,157 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,378 篇 工学
- 2,739 篇 计算机科学与技术...
- 1,816 篇 软件工程
- 723 篇 信息与通信工程
- 720 篇 光学工程
- 661 篇 电气工程
- 415 篇 生物工程
- 361 篇 控制科学与工程
- 348 篇 生物医学工程（可授...
- 236 篇 机械工程
- 223 篇 电子科学与技术（可...
- 126 篇 仪器科学与技术
- 124 篇 化学工程与技术
- 117 篇 安全科学与工程
- 96 篇 交通运输工程
- 90 篇 材料科学与工程（可...
- 68 篇 建筑学
- 58 篇 土木工程
1,680 篇 理学
- 926 篇 物理学
- 720 篇 数学
- 452 篇 生物学
- 203 篇 统计学（可授理学、...
- 120 篇 化学
- 57 篇 系统科学
491 篇 管理学
- 269 篇 图书情报与档案管...
- 242 篇 管理科学与工程(可...
- 79 篇 工商管理
439 篇 医学
- 417 篇 临床医学
- 127 篇 基础医学(可授医学...
- 89 篇 药学(可授医学、理...
60 篇 法学
- 59 篇 社会学
54 篇 农学
35 篇 教育学
31 篇 艺术学
29 篇 经济学
11 篇 文学
11 篇 军事学

主题

813 篇 computer vision
624 篇 pattern recognit...
535 篇 image processing
355 篇 image processing...
347 篇 artificial intel...
336 篇 image segmentati...
271 篇 feature extracti...
203 篇 image recognitio...
171 篇 deep learning
155 篇 computer graphic...
149 篇 computer imaging...
137 篇 face recognition
121 篇 machine learning
121 篇 object recogniti...
117 篇 convolutional ne...
115 篇 image analysis
113 篇 object detection
108 篇 information syst...
105 篇 cameras
105 篇 shape

机构

25 篇 istituto italian...
24 篇 google deepmind ...
24 篇 meta ai barcelon...
23 篇 politecnico di t...
14 篇 national center ...
14 篇 institute of ima...
10 篇 shanghai jiao to...
10 篇 tsinghua univers...
10 篇 university of ch...
10 篇 institute of aut...
9 篇 institute of ima...
9 篇 computer vision ...
8 篇 indian statistic...
8 篇 inria nancy gran...
8 篇 dalian universit...
8 篇 icube université...
8 篇 erasmus mc - uni...
8 篇 tencent jarvis l...
8 篇 university of ba...
7 篇 department of co...

作者

24 篇 cristian canton
24 篇 alessio del bue
24 篇 tatiana tommasi
24 篇 jordi pont-tuset
17 篇 de-shuang huang
14 篇 stefanie speidel
12 篇 wilfried philips
9 篇 paul scheunders
9 篇 h. bischof
9 篇 jie yang
9 篇 stéphane cotin
9 篇 dan popescu
9 篇 mohamed kamel
8 篇 umapada pal
8 篇 aurélio campilho
8 篇 yefeng zheng
8 篇 kok wai wong
8 篇 christian barill...
8 篇 ilias maglogiann...
8 篇 tieniu tan

语言

5,033 篇 英文
104 篇 其他
52 篇 中文

检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

共 5158 条记录，以下是21-30 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：

Design and Implementation of an image recognition System Based on the Embedded AI SOC Chip YuLong810A 4

Design and Implementation of an Image Recognition System Bas...

引用

4th international conference on Electronic Information Engineering and computer Science, EIECS 2024

作者： Lei, Zhiguang Qiu, Xin'An Lei, Zhiyong Lei, Zhijun Lanzhou Institute of Physics Lanzhou China Beijing Racobit Electronic Information Technology Co. Ltd. Beijing China China Electronics Technology Group Corporation 58th Research Institute Wuxi China

ISBN: (纸本)9798331531409

To enhance real-time image recognition capabilities, an efficient embedded image recognition system was designed using the YuLong810A chip. By optimizing the hardware platform, image preprocessing, feature extraction algorithms, and classification models, the system achieves high-precision image recognition and classification. Experimental results indicate that the system significantly outperforms traditional methods in various performance metrics, demonstrating exceptional accuracy and stability. this suggests a broad range of applications in the field of intelligent vision. © 2024 IEEE.

关键词： Embedded systems

来源：评论

学校读者我要写书评

暂无评论

Enhanced Feline Facial recognition: Advancing Cat Face Detection with YOLOv8 and TensorRT 4

Enhanced Feline Facial Recognition: Advancing Cat Face Detec...

引用

4th international conference on computer vision and pattern Analysis, ICCPA 2024

作者： Wang, Teng University College Durham University DurhamDH1 3LE United Kingdom

ISBN: (纸本)9781510682528

In the field of computer vision, the ability to accurately detect and recognise animal features in various environments is an area of growing interest and application. this study presents an advanced cat face detection system utilising the You Only Look Once Version 8 (YOLOv8) architecture, enhanced with TensorRT optimisation for real-time processing. the approach involves a comprehensive data augmentation process to improve detection accuracy across diverse cat breeds and environmental conditions. Performance evaluation is based on quantifiable metrics;the optimised model achieves a notable reduction in inference time from 50.1ms to 0.9ms and a decrease in GPU power usage from 77 watts to 63 watts, without compromising accuracy. the accelerated processing speed and reduced power requirements make the system highly suitable for real-time applications, such as pet monitoring systems or behaviour analysis tools, where rapid and accurate detection is paramount. the research highlights the potential of deep learning algorithms in precise animal feature recognition and contributes to the field of computer vision by addressing challenges in small, diverse object detection. © 2024 SPIE.

关键词： Object detection Data modeling Performance modeling Education and training Facial recognition systems Animals computer vision technology Deep learning

来源：评论

学校读者我要写书评

暂无评论

Geometric shape recognition based on FAST feature points and hough transform 4

Geometric shape recognition based on FAST feature points and...

引用

4th international conference on Machine Learning and computer Application, ICMLCA 2023

作者： Kang, Jinchuan Lu, Jun Zhang, Xu Cao, Yuan Chengdu University of Information Technology Chengdu China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

With the rapid development of computer vision and image processing, geometric shape recognition has become a highly regarded research area. this study aims to explore a method that combines the FAST feature point recognition algorithm with the Hough transform for fast and accurate recognition of geometric shapes in images, including lines, circles, and rectangles. the FAST feature point recognition algorithm is known for its speed and robustness in effectively detecting key points in images, while the Hough transform allows mapping these key points into parameter space, enabling us to detect and recognize specific geometric shapes. this research focuses on explaining the principles of the method, presenting experimental results, performance evaluations, and discussing future research directions. © 2024 SPIE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

2024 4th international conference on Multimedia processing, Communication and Information Technology, MPCIT 2024 - Proceedings

2024 4th International Conference on Multimedia Processing, ...

引用

4th international conference on Multimedia processing, Communication and Information Technology, MPCIT 2024

ISBN: (纸本)9798350375466

the proceedings contain 60 papers. the topics discussed include: a light weight CNN algorithm for the detection of various tomato plant diseases;a novel convolutional neural network classifier for real-time traffic sign detection;brain controlled robotic car using mind wave;crop sentry: ai-enhanced protection for agriculture field;deep learning-based modular framework for risk mitigation through crowd counting;FPGA implementation of exudates detection in fundus images through machine learning;license plate recognition using federated learning;machine learning driven exploration and identification of steel surfaces: leveraging advanced computer vision techniques;and unified image mastery : an integrated approach to image enhancement, object detection and classification.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on Detection Algorithm of Hot Work Based on Target recognition and image processing 4

Research on Detection Algorithm of Hot Work Based on Target ...

引用

4th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Zhao, Guinan Li, Lei Liu, Xiaodong Zhao, Ran Tang, Wenjie Gan, Fangji Shandong Hi-Speed Construction Management Group Co. Ltd Shandong Jinan250001 China Technology Co. Ltd Sichuan Chengdu610051 China Sichuan University Sichuan Chengdu610065 China

ISBN: (纸本)9798350359589

To enable real-time monitoring of fire operations within construction sites and to reduce the chance of fires, this paper proposes a detection algorithm that incorporates target recognition and image processing. Firstly, we add an attention mechanism to YOLO V7 to increase the localization accuracy of highlighted areas. Secondly, we use an image processing algorithm to identify the presence of fire in the localized area and recheck the localized highlighted area to improve the detection accuracy. After the verification is complete, this paper proposes a method based on monocular visual ranging to detect the presence of staff around the fire light to further confirm the authenticity of the fire operation and finally complete the detection of the fire moving operation. To verify the reliability and recognition accuracy of the algorithm, experiments were conducted on the dataset collected at the construction site, including ablation experiments on different target recognition networks and the application of different attention mechanisms. In addition to experiments on image segmentation of different highlighted areas located, and finally, extensive verification experiments on monocular visual ranging experiments are carried out. the experimental results show that the algorithm enables an effective detection of fire operations. © 2024 IEEE.

关键词： Attention Mechanism Fire operation image processing monocular vision Object detection network

来源：评论

学校读者我要写书评

暂无评论

Object detection algorithm based on residual deformable convolution pyramid 4

Object detection algorithm based on residual deformable conv...

引用

4th international conference on computer vision and pattern Analysis, ICCPA 2024

作者： Huang, Bin Wang, Sanqian Li, Linjie Fu, Xiang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang*** China School of Software Nanchang Hangkong University Nanchang*** China

ISBN: (纸本)9781510682528

Considering the challenges associated with robots in optoelectronic imaging applications, typically require real-time and accurate recognition and localization of targets, especially in complex environments. Due to the potential challenges of noise, blurring, or dense targets in the images obtained by optoelectronic imaging systems, traditional object detection algorithms may face issues such as semantic information loss and difficulty in detecting small targets. By introducing residual deformable convolution structures, feature representation can be enhanced in optoelectronic imaging systems, and the shape of the convolution kernel can be dynamically adjusted to adapt to the non rigid deformation of the target, thereby better capturing the detailed features of small targets. Experimental results exhibit that RAFPN enhances the mAP of object detection algorithms RetinaNet, Faster R-CNN, and GFL by 1.2%, 0.9%, and 1.1% respectively on the MS COCO dataset, effectively enhancing the performance of object detection algorithms in optoelectronic imaging. © 2024 SPIE.

关键词： Optoelectronic devices

来源：评论

学校读者我要写书评

暂无评论

Research on Intelligent image Reconstruction computer Face recognition System Based on Statistical pattern Classification 4

Research on Intelligent Image Reconstruction Computer Face R...

引用

4th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Li, Qinsheng Jia, Yan Cui, Yunkun College of Physical Education Taishan University Tai'an271000 China Shandong First Medical University Department of General Education Tai'an271000 China

ISBN: (纸本)9798350359589

Multi-directional gradients, fusion and compensation methods are used to generate images that can express the original features, and then a series of processing is done to achieve the purpose of face recognition. Firstly, the gradients in four directions are obtained. Secondly, the gradient is generated by the fusion operation of four gradient values. Finally, the fusion gradient is used to correct the original image, and the complete facial image feature map is obtained. Combined with the characters, the synchronization of voice and voice timing is enhanced to enhance the realism of facial animation. Using the existing speech, text and the speaker's face information, the pre-trained lip discrimination algorithm and video quality discrimination algorithm are presented. It achieves lip animation with high authenticity and synchronizes lip sound simultaneously. Using the Matlab2016 experimental simulation platform, several different types of face sample databases were tested. the experimental results show that this method can achieve better recognition effect under the condition of occlusion. © 2024 IEEE.

关键词： computer system face recognition image reconstruction Intelligent image

来源：评论

学校读者我要写书评

暂无评论

An intrusion monitoring system design based on 3D face recognition 4

An intrusion monitoring system design based on 3D face recog...

引用

4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Li, Xiaomin Li, Xinran Gu, Shuming School of Electronic Information Engineering Zhuhai College of Science and Technology Jinwan Zhuhai519041 China School of Electrical Engineering Hebei University of Technology Tianjin300130 China

ISBN: (纸本)9798350326444

the embedded microprocessor, face recognition sensor, and body sensing radar sensor are used to design an intrusion monitoring system with a 2-layer architecture of 'upper computer + lower computer', which has the main functions of automatic identification of intrusion objects, illegal intrusion alarm, intrusion object identification and statistical analysis of intrusion behavior. the system uses a combination of 'binocular stereo vision + structured light + active infrared fill light' technology to perform face recognition and prevent the intrusion of illegal objects. the test results show that the face recognition technology adopted in this paper can effectively improve the reliability of face recognition and reduce the false recognition rate, and the system is easy to deploy, has good environmental adaptiveness, and is flexible in use. © 2023 IEEE.

关键词： Stereo image processing

来源：评论

学校读者我要写书评

暂无评论

Object Detection Using thermal Imaging 10

Object Detection Using Thermal Imaging

引用

10th IEEE international conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Akshay, S. Neelakandan, Shukoh Nath, Aswin G. Amrita Vishwa Vidyapeetham Sch Comp Dept Comp Sci Mysore Karnataka India Ruhr Univ Bochum Computat Engn Bochum Germany

ISBN: (纸本)9798350385939;9798350385922

Object recognition is a powerful innovation within computer vision. It identifies and tracks objects in images and videos, with applications like facial recognition, vehicle detection, pedestrian counting, self-driving vehicles, and security systems. imageAI, a Python library, enables deep learning and computer vision applications. Single Shot Detectors (SSDs) efficiently detect multiple objects with speed and accuracy. Tools like YOLO, OpenCV, and Pillow enhance object recognition. this project improves image efficiency using IR thermal detection, overcoming low light and fog limitations. thermal cameras enable nighttime detection. By using filters like edge detection, negative image transformations, gray-scale adjustments, and contrast stretching, we enhance IR image details, improving object detection accuracy.

关键词： Object detection computer vision image processing Infrared imaging (IR) thermal imaging YOLOv4

来源：评论

学校读者我要写书评

暂无评论

Research on image recognition based on Reinforcement Learning 4

Research on Image Recognition based on Reinforcement Learnin...

引用

4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Luo, Jiabin Luo, Rongzhen School of Mathematics and Statistics Northeastern University at Qinhuangdao Hebei China University Baoding Campus of North China Electric Power University Department of Mechanical Engineering Hebei China

ISBN: (纸本)9798350326444

Nowadays, the images recognition has become a fundamental component in computer vision and assist to identify the objectives in the complex images. With the development of images processing methods, the image-level objectives recognition accuracy has been greatly enhanced by utilizing the machine learning models or neural networks. However, existing methods are mainly concentrated on the primary features of input images and concentrate on some certain areas, which ignore the environment features and the deep investigation of the image data-set. In this paper, we propose a novel image recognition method to identify the objectives and obtain the policy gradients for decreasing orders. Furthermore, we compare our proposed models with existing traditional machine learning methods to evaluate the performance of recognition accuracy. From our extensive experimental results, we can conclude that our proposed methods can achieve the subjective detection from numerous images data-set with reasonable computation costs. © 2023 IEEE.

关键词： image recognition

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 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：