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

6,811 篇 会议
95 篇 期刊文献
35 册 图书

馆藏范围

6,941 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,695 篇 工学
- 2,613 篇 计算机科学与技术...
- 1,546 篇 信息与通信工程
- 1,367 篇 软件工程
- 937 篇 电气工程
- 471 篇 光学工程
- 345 篇 控制科学与工程
- 244 篇 生物工程
- 223 篇 电子科学与技术（可...
- 205 篇 生物医学工程（可授...
- 174 篇 网络空间安全
- 133 篇 机械工程
- 107 篇 仪器科学与技术
- 94 篇 安全科学与工程
- 89 篇 材料科学与工程（可...
- 84 篇 化学工程与技术
- 84 篇 交通运输工程
- 72 篇 建筑学
- 72 篇 土木工程
- 67 篇 动力工程及工程热...
1,367 篇 理学
- 866 篇 物理学
- 412 篇 数学
- 263 篇 生物学
- 101 篇 统计学（可授理学、...
- 76 篇 化学
1,147 篇 医学
- 1,118 篇 临床医学
- 85 篇 基础医学(可授医学...
495 篇 管理学
- 268 篇 图书情报与档案管...
- 253 篇 管理科学与工程(可...
- 60 篇 工商管理
70 篇 法学
- 65 篇 社会学
29 篇 农学
25 篇 经济学
25 篇 教育学
23 篇 文学
17 篇 军事学
1 篇 艺术学

主题

1,013 篇 visualization
576 篇 image processing
517 篇 image coding
486 篇 feature extracti...
424 篇 deep learning
403 篇 image segmentati...
383 篇 accuracy
364 篇 visual communica...
344 篇 training
249 篇 computational mo...
229 篇 image enhancemen...
226 篇 signal processin...
186 篇 image edge detec...
185 篇 image color anal...
173 篇 image reconstruc...
169 篇 convolutional ne...
161 篇 real-time system...
160 篇 three-dimensiona...
160 篇 semantics
159 篇 object detection

机构

43 篇 shanghai jiao to...
35 篇 institute of ima...
29 篇 shanghai jiao to...
28 篇 university of sc...
28 篇 beijing universi...
26 篇 school of electr...
17 篇 university of el...
16 篇 shanghai jiao to...
16 篇 tianjin univ sch...
15 篇 university of ch...
14 篇 shanghai jiao to...
13 篇 zhejiang univers...
13 篇 peng cheng labor...
13 篇 moscow technical...
13 篇 school of commun...
12 篇 univ sci & techn...
12 篇 tsinghua univ de...
11 篇 school of remote...
11 篇 school of electr...
11 篇 peking universit...

作者

42 篇 zhai guangtao
27 篇 song li
27 篇 sumei li
26 篇 guangtao zhai
25 篇 andré kaup
22 篇 li sumei
20 篇 li li
18 篇 li song
18 篇 min xiongkuo
17 篇 xiongkuo min
17 篇 dong liu
14 篇 chen zhibo
14 篇 xie rong
12 篇 yang yang
12 篇 yang xiaokang
12 篇 shan liu
11 篇 liu dong
11 篇 jürgen seiler
11 篇 heming sun
11 篇 chen chen

语言

6,672 篇 英文
205 篇 其他
117 篇 中文
50 篇 土耳其文

检索条件"任意字段=2024 Conference on Visual Communications and Image Processing"

共 6941 条记录，以下是191-200 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：

CNN-Based Circular Economy in E-Waste Management with Blockchain and IoT Integration

CNN-Based Circular Economy in E-Waste Management with Blockc...

引用

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

作者： Santhuja, P. Suresh, A. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

Increasing electronic waste (e-waste) is a serious environmental and financial problem that calls for creative ideas for effective resource recovery and recycling. This paper proposes an IoT- and Blockchain-integrated e-waste management system based on Convolutional Neural Networks (CNNs) to boost automated waste classification and, thus, circular economy practices. While Blockchain guarantees open and safe tracking of waste transportation, IoT sensors and RFID tags allow real-time monitoring of e-waste. Training on a collection of 50,000 e-waste images, a CNN-based image classification model attained an accuracy of 96.2% in classifying components into recyclables, reusables, and hazardous items. Using automated decision-making, the system showed a 28% reduction in processing time and a 32% gain in sorting efficiency over conventional approaches. Blockchain incorporation enhanced traceability through 100% secure transaction records, lowering fraud and illegal disposal. Experimental data show that this method improves 23% resource recovery rates by promoting sustainable e-waste management. The proposed architecture minimizes environmental effects by encouraging ethical e-waste disposal and a closed-loop recycling system. These results show how Blockchain-secured, AI-driven IoT devices might help to advance circular economic ideas for world e-waste management. © 2025 IEEE.

关键词： Electronic Waste

来源：评论

学校读者我要写书评

暂无评论

10th China Health Information processing conference, CHIP 2024

10th China Health Information Processing Conference, CHIP 20...

引用

10th China Health Information processing conference, CHIP 2024

ISBN: (纸本)9789819637546

The proceedings contain 32 papers. The special focus in this conference is on China Health Information processing. The topics include: Predicting Patients’ Physician Selection Behavior Based on Multimodal Data: A Comparison of Feature Engineering and End-to-End Approaches;Automatic Pancreatitis CT image Segmentation Model Based on UNet and SE Module;utilizing Retrieval-Augmented Generation for Open-Domain Question Answering in Healthcare;self-supervised Learning Driven Doctor Recommendation Model: Combining Communication Ability and Professional Competence;prompt-Aware Large Language Model for Sleep Stages Classification;a Method for the Classification Public Health Questions Based on Model Ensemble and Voting Mechanism;A 3D MRI Brain image Segmentation and Reconstruction System Based on Augmented Reality Technology;Analysis of Hospitalization Data Before, During and After the COVID-19 Epidemic Using Short Time Series Clustering;look, Imitate and Refine: A Hierarchical Multimodel Retrieval Augmented Vision-Language Model for Radiology Report Generation;CTGLM: A Vision-Language Model for Automated Chinese Chest CT Report Generation;Pattern Matching of Positive and Negative DNA Sequences with General Gaps and One-off Constraints;survey of Suicidal Tendency Recognition Based on Social Media;global Trends in the Application of Extended Reality Technology to Autism Research: Based on the Web of Bibliometric Analysis of Science (1999.1.1–2023.3.4);a Review of Machine Learning-Based Assessment of Depression.

关键词：

来源：评论

学校读者我要写书评

暂无评论

10th China Health Information processing conference, CHIP 2024

10th China Health Information Processing Conference, CHIP 20...

引用

10th China Health Information processing conference, CHIP 2024

ISBN: (纸本)9789819637515

关键词：

来源：评论

学校读者我要写书评

暂无评论

10th China Health Information processing conference, CHIP 2024

10th China Health Information Processing Conference, CHIP 20...

引用

10th China Health Information processing conference, CHIP 2024

ISBN: (纸本)9789819642977

关键词：

来源：评论

学校读者我要写书评

暂无评论

Absolute visual Localization for Aerial Vehicles Based on Deep Learning image Matching 24

Absolute Visual Localization for Aerial Vehicles Based on De...

引用

Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Yi Yang Linfeng Xu Boya Wang Yibin Han Dong Ye Harbin Institute of Technology Harbin Heilongjiang China

ISBN: (纸本)9798400713880

The range of applications for aerial vehicles is expanding rapidly, encompassing various fields such as transportation, security, rescue, and mapping. The foundation for the execution of these tasks is accurate localization. The prevailing localization method for aerial vehicles is currently reliant on the Global Navigation Satellite System (GNSS). However, this method is vulnerable to disruptions caused by occlusion or interference, particularly in environments such as mountainous regions, forests, and urban canyons. Inertial navigation systems and visual odometers are commonly employed in GNSS-denied environments; however, their long-term accuracy is poor due to error accumulation. Absolute visual localization, based on image matching, exhibits superior long-term accuracy and does not require external devices, making it a suitable alternative when GNSS is unavailable. Additionally, deep learning has demonstrated remarkable potential in image matching, offering higher accuracy compared to traditional methods. The proposed methodology utilizes a deep learning-based image matching technique, employing the LoFTR model to match features on rectified aerial images and segmented satellite maps. Addressing the sensitivity of the conventional regional perspective transformation to nonlinear image differences, a central perspective transformation is proposed to calculate the precise position of the aerial vehicle on the map. The experimental results, obtained from flights at an altitude of 500 m, demonstrate that the proposed method, with a spatial resolution of 1.1 m for the aerial images and 2.4 m for the map blocks, exhibits a mean absolute error of 17.39 m. This outcome signifies a 54.55% reduction in error when contrasted with the conventional approach on the same dataset, attaining a level of accuracy comparable to that of general GNSS localization. Furthermore, the proposed method exhibits robustness to seasonal alterations in ground scene, providing an accurate and

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Improving Accuracy of image Clustering Using Convolutional Neural Network and Learning from Confusion

Improving Accuracy of Image Clustering Using Convolutional N...

引用

28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Mokter, Md Farhad Oh, Jung Hwan Department of Computer Science and Engineering University of North Texas DentonTX United States

ISBN: (纸本)9783031859328

image clustering is a very useful but difficult task in machine learning and computer vision. Recently, convolutional neural network (CNN) is taking a major role in image clustering in which it is used to extract features to be used for an objective function of similarity or dissimilarity measures. There are two problems in the image clustering methods using CNN. First, it is still challenging to estimate a correct number of clusters initially, which decides the quality of the clustering result mostly. Second, a clustering algorithm frequently clusters some images with semantically different contents, into a same cluster (due to their similar color distributions) and generates incorrect clustering result. In this paper we propose a framework for image clustering with CNN and learning from confusion, which can address these problems. The experiments show that the proposed framework increases image clustering effectiveness significantly. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Targeted image Manipulation 1st

Targeted Image Manipulation

引用

1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Sanjay Sen, S. Patel, Sameer Dushyant Bedict, A. Dilip, Golda Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031823824

This paper addresses the problem of customization of an image at an object-level, where the aim is to enable users to customize images containing objects unseen during training without explicit supervision. Our approach utilizes a self-supervised method coupled with a semantic embedding space to facilitate object-level manipulation without the need for paired data. Through extensive experiments on various datasets, we demonstrate the effectiveness of our method in accurately customizing images with unseen objects while preserving visual coherence. Our results showcase the potential of targeted image manipulation in diverse applications such as content creation and image editing. In conclusion, this work presents a promising avenue for enabling users to seamlessly customize images containing unseen objects, opening up new possibilities for creative expression and content manipulation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： content manipulation novel image customization object-level customization self-supervised method semantic embedding space

来源：评论

学校读者我要写书评

暂无评论

Lung Cancer Diagnosis Using image Enhancement and Machine Learning Methods 6th

Lung Cancer Diagnosis Using Image Enhancement and Machine Le...

引用

6th International conference on Computational Intelligence in communications and Business Analytics, CICBA 2024

作者： Khan, Takreem Fatima Zubair, Swaleha Department of Computer Science Aligarh Muslim University Aligarh India

ISBN: (纸本)9783031813412

Lung cancer is one of the most prevalent cancers in the world and a leading cause of mortality. It causes due to the uncontrollable proliferation of malignant cells in the lungs. The likelihood of curing lung cancer can be increased by early detection and treatment of the disease in its early stages. As a result of this, practitioners find computer assisted diagnosis helpful in accurately identifying the cancerous cells. The integration of machine learning with image processing techniques has been heavily incorporated into numerous computer-aided methods, and they have proven to be extremely effective. The aim of this research is to outline a clear procedure for detecting nodules using image enhancement techniques on computed tomography images that were obtained from the LIDC IDRI dataset, and the use of machine learning algorithm to categorize those nodules into two classes: benign and malignant. Our suggested model stands out from other proposals since it focuses on early detection of cancer nodule and XGBoost outperforms other machine learning algorithm in classifying identified nodules with an accuracy of 91%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Detecting Emotions of MEMEs Using a Hybrid Approach of Deep Learning

Detecting Emotions of MEMEs Using a Hybrid Approach of Deep ...

引用

3rd International conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Patel, Monali Diwanji, Hiteishi L.J University Gujarat Ahmedabad India L.D Engineering College Gujarat Ahmedabad India

ISBN: (纸本)9783031862892

Memes have evolved into a powerful tool for social interaction on platforms like Twitter, Instagram, Facebook, Pinterest, where they communicate complex emotions through a blend of images, text, and emojis. In this research, we propose a hybrid deep learning model that not only processes the textual components but also integrates the expressive use of emojis to better capture the nuanced emotions embedded in memes. While traditional sentiment analysis approaches often fall short in understanding these intricate emotional cues, our deep learning approach aims to overcome these challenges. By leveraging multimodal features—the textual content alongside visual cues like emojis. We provide a more holistic method for detecting and classifying emotions in memes, enhancing the accuracy of sentiment detection. Our research also distinguishes itself by focusing specifically on harmful, offensive, and trolling content, using hybrid deep learning models that integrate both Natural Language processing (NLP) techniques and image recognition. This approach not only enables the system to detect sentiment but also classifies different types of toxic behavior (like trolling) that are prevalent in meme culture. The dataset used for experimentation contains a range of memes annotated with these emotional and behavioral labels. In sum, this study contributes to the existing literature by presenting an advanced, context-aware method for meme classification, emphasizing the importance of both text and visual elements. The experiments conducted showcase the effectiveness of our model in accurately detecting complex emotional expressions, particularly in memes designed to provoke or offend. This research pushes the boundaries of meme analysis, helping to mitigate online toxicity while providing new tools for sentiment analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： context-aware method Deep learning emotion detection Multimodal features Natural Language processing

来源：评论

学校读者我要写书评

暂无评论

A 3D MRI Brain image Segmentation and Reconstruction System Based on Augmented Reality Technology 10th

A 3D MRI Brain Image Segmentation and Reconstruction System ...

引用

10th China Health Information processing conference, CHIP 2024

作者： Zhao, Wang Lu, Peixin Hu, LianTing Lu, Long School of Management Engineering Henan University of Engineering Zhengzhou China School of Safety Science and Emergency Management Wuhan University of Technology Wuhan China Guangdong Provincial People’s Hospital Guangzhou China School of Information Management Wuhan University Wuhan China The Center for Healthcare Big Data Research The Big Data Institute Wuhan University Wuhan China Institute of Pediatrics Guangzhou Women and Children’s Medical Center Guangzhou Medical University Guangzhou China School of Public Health Wuhan University Wuhan China

ISBN: (纸本)9789819637546

Background With the rapid development of information technology and the digitization of medical devices, various diseases require the use of medical imaging equipment for diagnosis. At present, various medical imaging diagnostic equipment such as CT and nuclear magnetic resonance can provide two-dimensional planar images of diseases. Doctors urgently need to accurately determine the spatial location, size, geometry, and spatial relationship with the surrounding tissue. Therefore, it is very important to use computer technology to segment 3D MRI images, determine the location of lesions, and then perform 3D reconstruction. Method At present, automatic recognition and marking of brain images are displayed in two dimensions. Therefore, it is necessary to use 3D visualization technology for reconstruction. In addition, it can be combined with virtual and real, and some additional information is superimposed on the brain image for integrated display. In addition, a combination of virtual and real needs to be superimposed, and some additional information is superimposed on the brain image for integrated display. The research focus of this paper includes two main parts: disease segmentation and 3D reconstruction visualization. Firstly, the disease segmentation method based on 3D MRI brain image files was designed, and then the feature extraction and 3D reconstruction functions were designed. Thereby forming a complete process of disease region segmentation and three-dimensional reconstruction. Results This study is based on a three-dimensional MRI brain image segmentation algorithm. The algorithm is advanced in technology, high in accuracy, and can effectively identify the location of the disease. Then, this study used the Unity tool to implement a three-dimensional reconstruction and visual display program for brain image disease segmentation. Therefore, the doctor can quickly and intuitively grasp the spatial information inside the brain and the information of the lesion

关键词： image segmentation

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 << < 16 17 18 19 20 21 22 23 24 25 > >>

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：