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

1,616 篇 会议
14 册 图书
12 篇 期刊文献

馆藏范围

1,642 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,157 篇 工学
- 885 篇 计算机科学与技术...
- 585 篇 软件工程
- 312 篇 光学工程
- 269 篇 信息与通信工程
- 257 篇 电气工程
- 174 篇 生物工程
- 146 篇 控制科学与工程
- 119 篇 机械工程
- 107 篇 生物医学工程（可授...
- 56 篇 电子科学与技术（可...
- 48 篇 材料科学与工程（可...
- 48 篇 化学工程与技术
- 47 篇 安全科学与工程
- 37 篇 仪器科学与技术
- 35 篇 交通运输工程
- 23 篇 建筑学
- 23 篇 网络空间安全
542 篇 理学
- 340 篇 物理学
- 193 篇 数学
- 175 篇 生物学
- 57 篇 化学
- 57 篇 统计学（可授理学、...
206 篇 医学
- 195 篇 临床医学
- 60 篇 基础医学(可授医学...
- 45 篇 药学(可授医学、理...
- 23 篇 公共卫生与预防医...
172 篇 管理学
- 96 篇 管理科学与工程(可...
- 78 篇 图书情报与档案管...
- 31 篇 工商管理
28 篇 农学
- 28 篇 作物学
19 篇 法学
17 篇 经济学
7 篇 教育学
5 篇 军事学
1 篇 文学

主题

306 篇 computer vision
228 篇 image processing
102 篇 image segmentati...
91 篇 feature extracti...
69 篇 deep learning
50 篇 object detection
42 篇 cameras
39 篇 image edge detec...
34 篇 visualization
34 篇 image recognitio...
33 篇 convolutional ne...
32 篇 machine learning
32 篇 image reconstruc...
31 篇 neural networks
29 篇 image color anal...
28 篇 shape
28 篇 training
27 篇 computational mo...
26 篇 image analysis
25 篇 face recognition

机构

6 篇 university of ch...
5 篇 comput & inf sci...
5 篇 department of co...
4 篇 chitkara univers...
4 篇 image processing...
4 篇 beijing institut...
4 篇 college of compu...
3 篇 shanghai jiao to...
3 篇 dept of electr &...
3 篇 south china univ...
3 篇 inria sophia-ant...
3 篇 school of comput...
3 篇 department of ma...
3 篇 shenyang institu...
3 篇 department of co...
3 篇 school of electr...
3 篇 school of comput...
3 篇 college of compu...
3 篇 dept of control ...
3 篇 department of in...

作者

6 篇 andayani u.
5 篇 syahputra m.f.
5 篇 mohana
5 篇 siregar b.
4 篇 yang yang
4 篇 hassan ghassemia...
4 篇 li hui
4 篇 shohreh kasaei
4 篇 e.j. delp
3 篇 luo haibo
3 篇 paul joseph sure...
3 篇 wang wei
3 篇 wang qian
3 篇 ying liu
3 篇 maryam imani
3 篇 saurav sumeet
3 篇 weickert joachim
3 篇 muchtar m.a.
3 篇 xin wang
3 篇 aggarwal j.k.

语言

1,613 篇 英文
23 篇 其他
9 篇 中文
1 篇 德文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1642 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：

3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th Indian conference on computer vision, Graphics and image processing, ICVGIP 2018

3rd Workshop on Computer Vision Applications, WCVA 2018, hel...

引用

3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th Indian conference on computer vision, Graphics and image processing, ICVGIP 2018

ISBN: (纸本)9789811513862

The proceedings contain 10 papers. The special focus in this conference is on computer vision Applications. The topics include: AECNN: Autoencoder with Convolutional Neural Network for Hyperspectral image Classification;optic Disc Segmentation in Fundus images Using Anatomical Atlases with Nonrigid Registration;bird Species Classification Using Transfer Learning with Multistage Training;a Deep Learning Paradigm for Automated Face Attendance;Robust Detection of Iris Region Using an Adapted SSD Framework;dynamic image Networks for Human Fall Detection in 360-degree Videos;image Segmentation and Geometric Feature Based Approach for Fast Video Summarization of Surveillance Videos;supervised Hashing for Retrieval of Multimodal Biometric Data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

3rd international Workshop on computer vision for Analysis of Underwater imagery, CVAUI 2018, 7th international Workshop on Computational Forensics, IWCF 2018, and international Workshop on Multimedia Information processing for Personality and Social Networks Analysis, MIPPSNA 2018 held at 24th international conference on Pattern Recognition, ICPR 2018

3rd International Workshop on Computer Vision for Analysis o...

引用

3rd international Workshop on computer vision for Analysis of Underwater imagery, CVAUI 2018, 7th international Workshop on Computational Forensics, IWCF 2018, and international Workshop on Multimedia Information processing for Personality and Social Networks Analysis, MIPPSNA 2018 held at 24th international conference on Pattern Recognition, ICPR 2018

ISBN: (纸本)9783030057916

The proceedings contain 16 papers. The special focus in this conference is on computer vision for Analysis of Underwater imagery. The topics include: Categorization of Document image Tampering Techniques and How to Identify Them;overview of the Multimedia Information processing for Personality & Social Networks Analysis Contest;handwritten Texts for Personality Identification Using Convolutional Neural Networks;recognition of Apparent Personality Traits from Text and Handwritten images;multimodal Database of Emotional Speech, Video and Gestures;from Text to Speech: A Multimodal Cross-Domain Approach for Deception Detection;marine Snow Removal Using a Fully Convolutional 3D Neural Network Combined with an Adaptive Median Filter;strategies for Tackling the Class Imbalance Problem in Marine image Classification;an Online Platform for Underwater image Quality Evaluation;tracking Sponge Size and Behaviour with Fixed Underwater Observatories;enhancement of Low-Lighting Underwater images Using Dark Channel Prior and Fast Guided Filters;Underwater-GAN: Underwater image Restoration via Conditional Generative Adversarial Network;single image Plankton 3D Reconstruction from Extended Depth of Field Shadowgraph;a Novel Method for Race Determination of Human Skulls.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Integrating Region Proposals with Recurrent Neural Networks for image Paragraph Captioning

Integrating Region Proposals with Recurrent Neural Networks ...

引用

international Symposium on Advanced Computing and Communication (ISACC)

作者： Yogita Ambure Riya Hulule Sakshi Paste Samriddhi Datir Sharmila Kharat Dept. of Electronics and Telecommunication MIT Academy of Engineering Pune Maharashtra India Dept. of Computer Engineering MIT Academy of Engineering Pune Maharashtra India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Capturing rich and coherent paragraphs describing images, this represents an abstract "image paragraph captioning," a challenging task. The area marks a timely intersection of computer vision and natural language processing. In this work, we present an improved framework for the generative creation of paragraph captions for images. This research gives the brief of the method that exploits Region Proposal Networks (RPNs) and Convolutional Neural Networks (CNN), encoder and multilevel decoder using RNN, ensuring a more effective paragraph caption generation *** evaluated our method on the Stanford image Paragraph dataset and presented its ability to generate semantically rich and coherent paragraphs. Experimental results demonstrate that the presented model achieves competitive performance, while improving significantly caption diversity and coherence compared to other state-of-the-art approaches.

关键词： Deep learning Visualization computer vision Recurrent neural networks Translation Natural language processing Decoding Convolutional neural networks Proposals Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Advancing Deep Learning on Edge Devices: Fine-Tuning and Deployment of YOLOv7 Model for Efficient Object Detection in AI Based computer vision Applications

Advancing Deep Learning on Edge Devices: Fine-Tuning and Dep...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sudhanshu Shekhar Sathwik T S Mayank Pritwani Mohana Ramakanth Kumar P Sreelakshmi K Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India Computer Science and Engineering RV College of Engineering® Bengaluru India Electronics and Teleommunication Engineering RV College of Engineering® Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This paper investigates the optimization and deployment of YOLOv7 deep learning model on NVIDIA Jetson Nano, an AI-focused edge computing platform for object detection in various computer vision applications. The work leverages TensorRT and quantization techniques for model acceleration for good detection accuracy. Further it examines performance metrics such as speed, accuracy, and resource utilization for image dataset. The model is trained using 80 different classes of objects and demonstrates the use of 6 classes. The average detection accuracy obtained 92.35% and the average processing time is 117.8ms. This work supports AI by demonstrating the feasibility of running deep learning models on edge devices and provides insight into the challenges and opportunities of optimizing AI models for energy-efficient, real-time operations on edge devices for various computer vision applications.

关键词： Deep learning Performance evaluation computer vision Accuracy Computational modeling image edge detection Object detection Real-time systems Nanoscale devices Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Improving Safety on the Internet: A New Multimodal Framework for Hateful Meme Classification

Improving Safety on the Internet: A New Multimodal Framework...

引用

Disruptive Technologies (ICDT), international conference on

作者： Anusha Duggal Anjali Singh Devesh Garg Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In the fast-changing social media world, a significant mode of communication carrying complex concepts and feelings has materialized: the manufacture of memes. It welcomes bad memes such as hatred, stereotypes, and misinformation, but many memes also have positive values for discussion. This paper directly addresses the challenge of detecting hateful memes. It leverages the “Facebook hateful meme dataset”, Kaggle, 2020 by Facebook AI, a hand-curated dataset, for conducting innovative model training to detect and mitigate the spread of hateful content. Our dataset analyzes multimodal representations of hate speech in memes, integrating both text and images for classification. The results are encouraging with the implementation of advanced neural network architectures, such as convolutional networks for image annotation and recurrent networks called text-data modelling. The implications of this paper extend beyond detection. This is a significant step in combating the spread of hateful content and ensuring a safer online space. Ultimately, this work benefits the larger natural language processing and computer vision community, making an argument necessary for interdisciplinary computational treatment of the intricacies of online hate.

关键词： Deep learning computer vision Recurrent neural networks Social networking (online) Computational modeling Hate speech computer architecture Natural language processing Convolutional neural networks Context modeling

来源：评论

学校读者我要写书评

暂无评论

Comparing the Pre-Trained Models with LSTM Combination to Improve image Captioning Technology

Comparing the Pre-Trained Models with LSTM Combination to Im...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： L D Mukil Tripty Singh Mansi Sharma Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Graphic Era University India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

image captioning, integrating computer vision and natural language processing, has become one of the critical areas of work for better accessibility and advancing technology. In this study, the performance of pre-trained convolutional neural networks-DenseNet, VGG-16, ResNet, and Xception-has been evaluated when coupled with LSTM units for generating captions on Flickr8k. These models have been evaluated on the basis of BLEU and ROUGE scores. The best scores were produced by ResNet-LSTM, which indicates the model's stronger ability to produce captions that are accurate and contextual. VGG-16 with LSTM ranked second; it was almost on par. Xception with LSTM gave results that were moderate. DenseNet with LSTM had the worst score, indicating that the feature extraction mechanism of this model is weak. The following results provide evidence for the practical use of ResNet-LSTM to facilitate access: from assistive technology for visually impaired users to automatically generated content. The comparative study would further indicate which one has more merits or drawbacks compared with others and thereby enable a more informed selection of the models suitable for each particular application.

关键词： Measurement Adaptation models Visualization computer vision Accuracy Computational modeling Feature extraction Convolutional neural networks Long short term memory Context modeling

来源：评论

学校读者我要写书评

暂无评论

Investigating Salient Object Detection Methods Tailored for Edge Computing Infrastructure 3rd

Investigating Salient Object Detection Methods Tailored for...

引用

3rd international conference on Computing, Communication and Learning, CoCoLe 2024

作者： Sebastian, Elsa Martin, John Paul Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam686635 India

ISBN: (纸本)9783031790409

Salient object detection aims in identifying the most significant parts of a scene as viewed by the human eye and plays a vital role in a variety of applications, including object detection, video compression, and augmented reality. Saliency detection has emerged as a fundamental preprocessing step in many computer vision and image processing tasks. Implementing saliency detection in edge computing platforms is challenging, however it promises enhanced real-time responsiveness, reduced bandwidth usage, and improved privacy. This paper examines the most advanced techniques for adapting saliency detection techniques to edge computing and discusses encountered challenges. The paper also gives insights into the evaluation metrics that can be utilized for performance assessment, investigates primary application domain and proposes potential directions for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Mod-ViT: A vision Transformer-Based Framework for Breast Cancer Detection from Multiple Imaging Modalities

Mod-ViT: A Vision Transformer-Based Framework for Breast Can...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Iqra Nissar Shahzad Alam Sarfaraz Masood Dept. of Computer Engineering Jamia Millia Islamia New Delhi India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

The evaluation of breast cancer plays a crucial role in its diagnosis through various imaging modalities. Although Convolutional Neural Network-based deep learning (DL) methods have significantly advanced this process, they are hindered by high computational demands and restricted spatial encoding capabilities. In contrast, vision Transformer-based DL offers a compelling alternative due to its superior ability to encode spatial information efficiently. This study presents a breast cancer detection framework using a modified vision transformer (Mod- ViT) architecture across four publicly available datasets; mammogram (InBreast and MIAS) and ultrasound (BUSI and Mendeley). The workflow involves three key stages: data collection, image preprocessing, and classification. Mod- ViT utilizes transfer learning by modifying the pretrained vision transformer with additional layers, including flattening, batch normalization, dense, dropout, and a fully connected classification layer. The proposed Mod- ViT model was evaluated on each dataset, yielding superior performance metrics compared to VGG-16, ResNet-50, and VGG-19. On the InBreast dataset, Mod-ViT achieved 98.74% accuracy, 96.54% precision, 97.62% recall, and 98.25% F1-score, highlighting its ability to capture global mammogram features effectively. Similarly, on MIAS, BUSI, and Mendeley datasets, Mod- ViT consistently outperformed other models with accuracies of 99.92 %, 98.61 %, and 99.31 %, respectively. These results demonstrate Mod-ViT's robustness in classifying benign, malignant, and normal cases across diverse imaging modalities. By employing advanced attention mechanisms and effectively processing spatial and contextual information, Mod- ViT has shown its capability to address the complexities of breast cancer detection. This work highlights the promise of transformer-based models in enhancing diagnostic precision and reliability in medical imaging.

关键词： computer vision Ultrasonic imaging Accuracy Transfer learning Transformers Breast cancer Mammography Robustness Encoding image preprocessing

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Automated image Captioning: A CNN and Transformer Model Analysis

Deep Learning for Automated Image Captioning: A CNN and Tran...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Kathula Saarika Vasaraju Varsha P.Sri Harsha Anjum Nabi Sheikh Dept. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Automatic image captioning is a multidisciplinary effort that combines machine learning, natural language processing, and computer vision in creating meaningful and context-rich captions for images. Within a short time, this research problem has garnered a lot of attention and new techniques are continuously being proposed. However, there are many dismaying factors as well. For instance, the current models still have a long way to go in attaining human accuracy. This paper seeks to conduct a systematic review of the recent trends and developments in the field of image captioning techniques and models with a focus on 2023 and 2024 by systematically analyzing the 13 publications located in Springer and IEEEXplore libraries. According to our research, Convolutional Neural Networks allow the identification of image content at an early stage, and Recurrent Neural Networks or Long Short-Term Memory networks usually appear at the text generation stage. The most common image datasets are MS COCO and Flickr8k. Almost all studies utilized the BLEU scores (1, 2, 3, and 4) mechanism. It is alleviated that CNN with LSTM models tends to perform better when compared to RNN-modeled networks. Attention mechanisms and Encoder-Decoder architecture are aggressively becoming the two major solutions to strengthening image captioning. This review will help and provide direction to those who are interested in practicing research that aims to promote the techniques and processes of automatic image captioning.

关键词： Deep learning Analytical models Recurrent neural networks Transformers Market research Natural language processing Convolutional neural networks Particle swarm optimization Long short term memory Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool for Non-Invasive Medical Diagnostics

AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool fo...

引用

international Symposium on Advanced Computing and Communication (ISACC)

作者： Aniruddha Ghosh Sayan Snigdha Pal Sumedha Das Spandan Bhattacharya Arijit Bhattacharya Nilanjana De Sudipta Sahana Computer Science and Engineering(Artificial Intelligence and Machine Learning) Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata West Bengal India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Thermography is an innovative technique that creates a heatmap of different portions of the human body by using infrared (IR) radiation where color scales vary for various temperature regions. This work's scope aims to extend thermal imaging applications with machine learning algorithms in conjunction with Azure API services to analyze thermal images and return a preliminary diagnosis. The project consists of generating heatmaps for better visualization results, analyzing them through image processing techniques, and feeding the information to Azure’s Custom vision API to detect anomalies and classify health conditions and finally producing a proper report. This research allows individuals and healthcare professionals to have a quick, non-invasive diagnostic tool that empowers health monitoring and preventive care. It combines the capabilities of contemporary technologies with uses directed toward health practice, leading to inimical patient outcomes and true practices.

关键词： Visualization Machine learning algorithms image color analysis Infrared heating Infrared imaging Thermal analysis Medical diagnosis Predictive analytics Intelligent systems Monitoring

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165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：