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71-80 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 Development of DenseNet Transfer Learning Model for image Classification 4

Design and Development of DenseNet Transfer Learning Model f...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Attri, Vikas Kapoor, Ankush Singh, Simranjeet Sharma, Payal University School of Computing Rayat Bahra University Mohali India University Institute of Computing Chandigarh University Mohali India

ISBN: (纸本)9798350387490

Integrating deep learning with computer vision technologies and analysing their application efficiency is the main emphasis of this research. Building hierarchical neural networks, a key component of deep learning, allows for end-to-end feature learning and semantic image processing, marking a significant advancement in the field. When training deep learning algorithms, the successes in computer vision give a solid foundation. Combining these two areas has produced cutting-edge computer vision systems that outperform the state-of-the-art in object detection and machine vision image classification. this research uses a combination of everyday image classification situations to examine the deep neural network models' better performance, draw attention to their shortcomings in interpretability and generalizability, and suggest ways to enhance them in the future. In general, advancements in computer vision will be propelled by the expanding use of deep learning and its efficient integration with large amounts of visual data. this trend will pave the way for intelligent machine vision systems. After briefly introducing deep learning, this paper proposed the most recent model used for deep learning picture classification that generates an accuracy of 96.3 %. Lastly, the accuracy of our deep learning model compared with other models used in the literature for the most challenging datasets, CIFAR-10, imageNet, and CIFAR-100. © 2024 IEEE.

关键词： Machine vision

来源：评论

学校读者我要写书评

暂无评论

A Moving Object Detection Technology for Intelligent Police Based on ViBe Algorithm 4

A Moving Object Detection Technology for Intelligent Police ...

引用

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

作者： Dang, Longfei Yang, Miao Yi, Kai Si, Binghe Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province LuZhou China Sichuan Police College Department of Computer Science and Technology LuZhou China Sichuan Police College Department of Criminal Technology LuZhou China

ISBN: (纸本)9798350326444

the detection of moving targets in the field of public security and policing involves processes such as image processing, background modeling, and object segmentation to identify objects of interest from videos or images, such as people, vehicles, and objects. the current location and size of the target image are determined, preparing for subsequent pedestrian and vehicle recognition. this article uses MATLAB software to detect moving objects using the ViBe algorithm, providing a theoretical basis for the intelligence of police technology. © 2023 IEEE.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

VithAR: vision Transformers for Human Action recognition in Videos 4

ViTHAR: Vision Transformers for Human Action Recognition in ...

引用

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

作者： Divya Rani, R. Prabhakar, C.J. Kuvempu University Department of Computer Science Shimoga India

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375466

Human action recognition is a popular research topic that has drawn the interest of numerous researchers due to its potential applications in the fields of video surveillance, healthcare, human-computer interaction, and sports analysis. But there are a lot of challenges associated with the action recognition, some of these difficulties include differentiation of similar actions, solving occlusion issues, background clutter, and adapting to changes in viewing angle. Recently, vision Transformers (ViT) have demonstrated exceptional performance in action recognition, which represents long video sequences captured across spatial and temporal indexes in a video. In this paper, a method for human action recognition using vision Transformer models referred as VithAR is proposed to enhance the accuracy and efficiency of action recognition in video sequences. Unlike traditional convolutional neural networks, ViTs take advantage of self-attention mechanisms to better capture spatial and temporal relationships. To demonstrate the efficacy of the proposed method, we conducted experimentation on two benchmark datasets, UCF-101 and HMDB-51 datasets, and compared the proposed VithAR method with various state-of-the-art HAR methods. the experimental findings reveal that the proposed VithAR method performed exceptionally well compared to other state-of-the-art HAR methods. the proposed VithAR method achieves the highest recognition accuracy of 96.53% and 79.45% for UCF-101 and HMDB-51 datasets respectively, using the vision Transformer model variant ViT-Base. the experimental findings acquired from the proposed VithAR method show that it performed effectively in dealing with all of the challenges that are commonly encountered in action videos. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

An improved image segmentation model of FCN based on residual network 4

An improved image segmentation model of FCN based on residua...

引用

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

作者： Du, Wenchao Yang, Haotian Toe, Teoh Teik School of Computer Science JiangSu University Zhenjiang China NanyangTechnological University NTU Business AI Lab Singapore

ISBN: (纸本)9798350326444

In this paper, the fcn model with Resnet as backbone is used for training and testing on voc2007 dataset. Twenty categories were trained, tested, and trained for around of about 10,000 pictures. there cognition and segmentation ofimages are realized. the overall recognition rate reaches 92.6%. the overall IoU reaches 68.3%, which is higher than the 5.6% of FCN-8s in theoriginal paper. We have adjusted the network structure of Resnet and improved the algorithm to achieve the accuracy of identification and segmentation with less time cost. the weight of thisalgorithm after training can be applied to specificoccasions of image recognition or object detection. © 2023 IEEE.

关键词： image recognition

来源：评论

学校读者我要写书评

暂无评论

Neurological Disorder Rooted-Impaired Handwriting to Text Using Neural Network image processing for Special Education Applications 16

Neurological Disorder Rooted-Impaired Handwriting to Text Us...

引用

16th international conference on computer and Automation Engineering (ICCAE)

作者： Sebastian, Christian Josefh M. Regala, Mark William J. Paglinawan, Charmaine C. Mapua Univ Sch Elect Elect & Comp Engn Manila StIntramuros Manila Philippines

ISBN: (纸本)9798350370058;9798350370164

Dysgraphia, a neurological condition affecting writing abilities, poses challenges for Special Education teachers in the Philippines due to time constraints. To address this, an OCR system specialized for impaired handwriting is proposed, automating dysgraphic analysis. Using a Pi Camera, LCD, and Raspberry Pi, the system aims to aid SPED teachers and parents. the training dataset comprises 10,000 characters from SPED centers nationwide. Testing includes Mild, Moderate, and Severe Dysgraphia categories with 5 images each, compared to Google Cloud vision and Microsoft Azure computer vision. the system achieved 67.79% accuracy for individual letters and 52.36% for words.

关键词： Dysgraphia Special Education (SPED) Optical Character recognition (OCR) Raspberry PI V4 PI-Camera LCD Touchscreen

来源：评论

学校读者我要写书评

暂无评论

Toward Objective Variety Testing Score Based on computer vision and Unsupervised Machine Learning: Application to Apple Shape 4

Toward Objective Variety Testing Score Based on Computer Vis...

引用

4th international conference on image processing and vision Engineering, IMPROVE 2024

作者： Zine-El-Abidine, Mouad Dutagaci, Helin Rasti, Pejman Aranzana, Maria Jose Dujak, Christian Rousseau, David Université d'Angers LARIS UMR INRAe IRHS France Eskisehir Osmangazi University Department of Electrical-Electronics Engineering Eskisehir Turkey Angers France Centre de Recerca en Agrigenómica: Cerdanyola Del Valles Catalunya Spain

ISBN: (纸本)9789897586934

While precision agriculture or plant phenotyping are very actively moving toward numerical protocols for objective and fast automated measurements, plant variety testing is still very largely guided by manual practices based on visual scoring. Indeed, variety testing is regulated by definite protocols based on visual observation of sketches provided in official catalogs. In this article, we investigated the possibility to shortcut the human visual inspection of these sketches and base the scoring of plant varieties on computer vision similarity of the official sketches with the plants to be inspected. A generic protocol for such a computer vision based approach is proposed and illustrated on apple shape classification. the proposed unsupervised algorithm is demonstrated to be of high value by comparison with classical supervised and self supervised machine and deep learning if some rescaling of the sketches is performed. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

A Survey of Character recognition in Tibetan Historical Documents 4

A Survey of Character Recognition in Tibetan Historical Docu...

引用

4th international conference on computer Graphics, image, and Virtualization, ICCGIV 2024

作者： Gongqu, Zhuome Cairang, Jia Duojie, Cairang Jiayang, Dongzhou Luo, Peng The College of Computer Qinghai Normal University Qinghai Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Qinghai Xining810008 China

ISBN: (纸本)9781510683242

Text recognition for Tibetan historical document images is the automated process of extracting and identifying text from these photos utilizing image processing, computer vision, and natural language processing techniques. It is generally understood to be a method of extracting text from visual data. In order to create editable, searchable, and analytical text forms that readers, researchers, and academics may study, preserve, and pass on, it attempts to analyze, identify, and change the text found in traditional Tibetan historical document images. Recent years have seen significant advancements in the effectiveness and performance of Tibetan historical document image and text identification thanks to the quick development of artificial intelligence technology. However, issues with low image quality, non-standard formatting, and notable font style inconsistencies persist in the field of image recognition of Tibetan historical document texts. As a result, the accuracy and universality of current recognizers are low. First, this article gives a general overview of the basic knowledge of Tibetan historical documents, which are the research objects in this field. this helps to better summarize the work of predecessors and support future *** methods of integrating deep learning and conventional methods for Tibetan historical document image text recognition were then arranged, categorized, summarized, and introduced around the four subtasks of dataset construction, text detection, text recognition, and layout analysis. this served as the basis for sorting through the pertinent statistics and assessment indicators and summarizing the state and advancement of the research. Lastly, future technical development patterns are projected based on the primary issues and problems in this subject that require immediate attention. © 2024 SPIE.

关键词： History

来源：评论

学校读者我要写书评

暂无评论

Research on Grab Algorithm Based on Deep learning 4

Research on Grab Algorithm Based on Deep learning

引用

4th international conference on computers, Information processing and Advanced Education, CIPAE 2023

作者： Xue, Xinyi Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350342710

In the development of artificial intelligence, the use of object grabbing is the direction of future development, and the grabbing method based on data processing and computer vision mode is the focus of research. the data analysis of convolution network and neural network in the mode of detection algorithm can not only effectively improve the low recognition rate, but also further improve the efficiency of object grabbing recognition algorithm. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Research on Weakly Supervised Target Localization in Sonar imagery Based on GradCAM 4

Research on Weakly Supervised Target Localization in Sonar I...

引用

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

作者： Wei, Guangze Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Xiao Xiong, Minglei College of Artificial Intelligence Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China College of Engineering Peking University Beijing China Robot Technology Co. Ltd. Beijing China

ISBN: (纸本)9781510682528

In the unique marine environment, sonar, as a crucial tool for navigation and ranging using sound waves, plays a key role in the recognition and localization of targets in sonar imagery, which is critical to the performance of underwater equipment. In practical applications, fully supervised object detection methods are commonly used to accurately determine the categories and locations of targets in sonar images. However, these methods require laborious individual annotation of target positions and categories. Addressing this challenge, this paper proposes a weakly supervised target localization method for sonar imagery based on Grad-CAM technology. Initially, the method employs the Balanced Ensemble Transfer Learning (BETL) algorithm for target classification in sonar images, utilizing deep transfer learning. Subsequently, the GradCAM technique is applied to the task of sonar image target classification, using the generated heat maps for target localization. this aims to enhance the accuracy and efficiency of sonar image recognition and localization while reducing the burden of annotation work. © 2024 SPIE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Advancing Healthcare Diagnostics with a Hybrid AI Model 4

Advancing Healthcare Diagnostics with a Hybrid AI Model

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Jihane, Benbrik Salma, Rattal Kamal, Ghoumid Miloud, Ar-Reyouchi El Ensao Mohammed i University Dept. Electronics Informatics and Telecommunications Oujda Morocco Abdelmalek Essaâdi University Dept. Telecommunication and Computer Science Tétouan Morocco

ISBN: (纸本)9798331523923

Artificial intelligence (AI) has significantly impacted the field of healthcare by advancing medical diagnostics and decision-making processes. this paper presents a unified framework that integrates Convolutional Multi-Layer Perceptrons (Conv MLP), Convolutional Neural Networks (ConvNet), and vision Transformers (ViTs) to address two critical tasks: sentiment analysis of medical reports and medical image recognition. By leveraging the distinct strengths of each architecture, the proposed hybrid model improves performance, effectively balancing computational efficiency, localized feature extraction, and global dependency modeling. the findings demonstrate that the model reduces task-specific errors and enhances convergence rates, achieving superior results in sentiment and image recognition tasks. this study advances the field of medical AI by overcoming the limitations of single-architecture models and establishing the feasibility of hybrid systems for robust, multi-modal applications. © 2025 IEEE.

关键词： Multilayer neural networks

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 << < 4 5 6 7 8 9 10 11 12 13 > >>

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：