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

1,620 篇 会议
14 册 图书
12 篇 期刊文献

馆藏范围

1,646 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,160 篇 工学
- 888 篇 计算机科学与技术...
- 587 篇 软件工程
- 313 篇 光学工程
- 270 篇 信息与通信工程
- 258 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
543 篇 理学
- 340 篇 物理学
- 194 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

307 篇 computer vision
228 篇 image processing
102 篇 image segmentati...
92 篇 feature extracti...
70 篇 deep learning
50 篇 object detection
42 篇 cameras
39 篇 image edge detec...
35 篇 convolutional ne...
34 篇 visualization
34 篇 image recognitio...
32 篇 machine learning
32 篇 image reconstruc...
31 篇 neural networks
30 篇 image color anal...
28 篇 computational mo...
28 篇 shape
28 篇 training
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

1,615 篇 英文
25 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 1646 条记录，以下是321-330 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：

computer vision System for Railway Track Crack Detection using Deep Learning Neural Network 3

Computer Vision System for Railway Track Crack Detection usi...

引用

3rd international conference on Signal processing and Communication (ICPSC)

作者： Thendral, R. Ranjeeth, A. IFET Coll Engn Dept Comp Sci & Engn Gangarampalaiyam 605108 Tamil Nadu India

ISBN: (纸本)9781665428644

For better inspections and security, we need an efficient railway track crack detection system. In this research, we present a computer vision-based technique to detect the railway track cracks automatically. This system uses images captured by a rolling camera attached just below a self-moving vehicle in the railway department. The source images considered are the cracked and crack-free images. The first step is pre-processing scheme and then apply Gabor transform. In this paper, first order statistical features are extracted from the Gabor magnitude image. These extracted features are given as input to the deep learning neural network for differentiate the cracked track image from the non-cracked track image. Accuracy of the proposed algorithm on the procured images is 94.9 % and an overall error rate of 1.5%.

关键词： crack detection deep learning neural network railroad tracks gabor transform

来源：评论

学校读者我要写书评

暂无评论

A Review of Deep Learning-Based Object Detection Current and Future Perspectives 3rd

A Review of Deep Learning-Based Object Detection Current and...

引用

3rd international conference on Sustainable Expert Systems, ICSES 2022

作者： Sirisha, Museboyina Sudha, S.V. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravathi India

ISBN: (纸本)9789811978739

Object detection is a method to detect and localize the objects present in images and videos and stands as one of the challenging fields of computer vision. Object detection plays a crucial role in multiple real-time applications like video surveillance, autonomous driving, medical image processing, etc. Object detection key challenges such as detecting small objects and addressing class imbalance are addressed with various deep learning detection models. This review article classifies the object detection models into three main categories—Two-stage, One-stage and Transformer based detectors discussing the recent advanced developments in object detection listing some of the most important works in each category. Benchmark datasets used for object detection task with different metrics used for evaluating the performance of object detectors are listed. Performance comparison among various object detectors is plotted showing how advanced detectors achieve better accuracy compared to the existing example detectors. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Traffic Surveillance System and Criminal Detection Using image processing and Deep Learning 3

Traffic Surveillance System and Criminal Detection Using Ima...

引用

3rd international conference on Innovations in Science and Technology for Sustainable Development, ICISTSD 2022

作者： Anoop, Aditya Harikrishnan, G. Nair, Keerthi Sangeetha, B.S. Praseedalekshmi, V. Tasneem Salam, H. College of Engineering Perumon Department of Electronics and Communication Kollam Kerala India

ISBN: (纸本)9781665499361

The unpredictability and blockage of current transportation frameworks frequently produce traffic circumstances that endanger the security of the individuals and it can cause several problems in our life. To identify traffic offenders, we propose a system that will intelligently use image processing and deep learning to penalize them by recognizing their license plate. This will identify criminals, case of accident and initiates an emergency call on detecting it. A GPS-enabled traffic surveillance camera can detect the location of an accident and send a message or call the traffic control room with the accident's location. Retinanet, an object detection technique is used here for detecting vehicles which violates the traffic rule and its number plate will be localized by using image processing operation. The number plate information extracted by Optical Character Recognition and the car's image are further processed by CNN for identifying criminals and alerts are given to the authorities. The accidents can be identified using deep learning and computer vision followed by delivering the signal to Arduino. The dislocated parts of object were detected from the images and fix it as an accident by CNN (Convolutional Neural Network) for certain threshold. When accident happens, alert is given for an emergency vehicle. This framework is made by fundamental GSM module. The system provides a robust and efficient method to detect red light offenders and criminals and provides an immediate emergency calling facility. © 2022 IEEE.

关键词： Optical character recognition

来源：评论

学校读者我要写书评

暂无评论

Research on deep learning algorithms for point cloud upsampling 3

Research on deep learning algorithms for point cloud upsampl...

引用

3rd international conference on Digital Signal and computer Communications, DSCC 2023

作者： Li, Jin Gong, Yan Li, Jiao Yue, Rui Li, Ji Department of Computer and Information Security Guilin University of Electronic Technology China Department of Computer and Artificial Intelligence Changzhou University China Science Island Branch of Graduate School University of Science and Technology of China China School of Nanotechnology and Nanobionics University of Science and Technology of China China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

The integration of 3D vision technology has profoundly impacted various aspects of our lives, with point clouds emerging as the predominant geometric representation in the field due to their simplicity, ease of processing, and low acquisition costs. However, limitations in 3D scanning hardware often result in sparse, noisy, and unevenly distributed point clouds, which impede the effectiveness of subsequent 3D vision tasks. To obtain high-quality and precise point clouds, researchers have introduced upsampling algorithms that enhance point cloud representativeness through *** recent years, deep learning has supplanted traditional algorithms that rely on a priori data, owing to its robust learning representation capabilities and data-driven nature. Consequently, deep learning algorithms for point cloud upsampling have become a highly valuable research direction within the 3D vision domain. This paper delves into recent advancements in deep learning algorithms for point cloud upsampling by first defining the problem and analyzing the technical challenges from six perspectives. It then provides an overview of the relevant benchmarks in point cloud upsampling and traces their developmental *** paper further examines the technical issues in the three critical steps (point cloud local feature extraction, feature space expansion, and coordinate reconstruction) of the overall deep learning algorithm for point cloud upsampling. It categorizes the prevalent technical solutions, discussing their merits and drawbacks, and offers optimization strategies. Lastly, the paper presents a forward-looking perspective on potential research directions in this field from four angles. © 2023 SPIE.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing NLP: Multimodal Integration for Enhanced image-to-Text Extraction

Revolutionizing NLP: Multimodal Integration for Enhanced Ima...

引用

Computational Modelling, Simulation and Optimization (ICCMSO), international conference on

作者： Aparna Chiguru Rajchandar K School of CS & AI SR University Warangal India

ISBN: (数字)9798350361391

ISBN: (纸本)9798350361407

This paper tackles the mixing of picture and written material modalities to enhance natural language processing (NLP), specifically that specializes in picture-to-textual content extraction Our approach combines advanced computer vision techniques and NLP, introducing a multimodal fusion shape that uses Convolutional Neural Networks (CNNs) for image processing and Transformer-primarily based totally models for language statistics. pleasant-grained photograph feature extraction and a move-modal attention mechanism are employed to enhance interpretability and the version's capability to partner visible functions with text. Our approach demonstrates superior performance through benchmark evaluations on specialized datasets, highlighting its effectiveness in achieving enhanced accuracy and efficiency. Beyond theoretical contributions, the research has practical applications in automated image captioning, multimedia content indexing, and accessibility solutions for the visually impaired. This study underscores the synergistic benefits of combining computer vision and NLP techniques, providing a comprehensive understanding of multimodal integration's potential to advance image-to-text extraction in NLP tasks.

关键词： computer vision Attention mechanisms Computational modeling Benchmark testing Feature extraction Natural language processing Multisensory integration

来源：评论

学校读者我要写书评

暂无评论

Rethinking RAFT for Efficient Optical Flow

Rethinking RAFT for Efficient Optical Flow

引用

Iranian Machine vision and image processing (MVIP)

作者： Navid Eslami Farnoosh Arefi Amir M. Mansourian Shohreh Kasaei Department of Computer Engineering Sharif University of Technology Tehran Iran

Despite significant progress in deep learning-based optical flow methods, accurately estimating large displacements and repetitive patterns remains a challenge. The limitations of local features and similarity search patterns used in these algorithms contribute to this issue. Additionally, some existing methods suffer from slow runtime and excessive graphic memory consumption. To address these problems, this paper proposes a novel approach based on the RAFT framework. The proposed Attention-based Feature Localization (AFL) approach incorporates the attention mechanism to handle global feature extraction and address repetitive patterns. It introduces an operator for matching pixels with corresponding counterparts in the second frame and assigning accurate flow values. Furthermore, an Amorphous Lookup Operator (ALO) is proposed to enhance convergence speed and improve RAFT’s ability to handle large displacements by reducing data redundancy in its search operator and expanding the search space for similarity extraction. The proposed method, Efficient RAFT (Ef-RAFT), achieves significant improvements of 10% on the Sintel dataset and 5% on the KITTI dataset over RAFT. Remarkably, these enhancements are attained with a modest 33% reduction in speed and a mere 13% increase in memory usage. The code is available at: https://***/n3slami/Ef-RAFT

关键词：

来源：评论

学校读者我要写书评

暂无评论

Spectral-Spatial Anomaly Detection in Hyperspectral imagery Based on Dual-Domain Autoencoders

Spectral-Spatial Anomaly Detection in Hyperspectral Imagery ...

引用

Iranian Machine vision and image processing (MVIP)

作者： Mohamad Ebrahim Aghili Hassan Ghassemian Maryam Imani Arani Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Hyperspectral anomaly detection is crucial for applications like aerial surveillance in remote sensing images. However, robust identification of anomalous pixels remains challenging. A novel spectral-spatial anomaly detection technique called Dual-Domain Autoencoders (DDA) is proposed to address these challenges. First, Nonnegative Matrix Factorization (NMF) is applied to decompose the hyperspectral data into anomaly and background components. Refinement of the designation is then done using intersection masking. Next, a spectral autoencoder is trained on identified background signature pixels and used to reconstruct the image. The reconstruction error highlights spectral anomalies. Furthermore, a spatial autoencoder is trained on principal component patches from likely background areas. Fused reconstruction error from the spectral and spatial autoencoders is finally used to give enhanced anomaly detection. Experiments demonstrate higher AUC for DDA over individual autoencoders and benchmark methods. The integration of matrix factorization and dual-domain, fused autoencoders thus provides superior anomaly identification. Spatial modeling further constrains the background, enabling accurate flagging of unusual local hyperspectral patterns. This study provides the effectiveness of employing autoencoders trained on intelligently sampled hyperspectral pixel signatures and spatial features for improved spectral-spatial anomaly detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Review of visual SLAM 3

Review of visual SLAM

引用

3rd international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Gong, XiePing Song, LiZhong Yin, Yang Naval University of Engineering China

ISBN: (纸本)9781510657281

Simultaneous localization and mapping (SLAM) based on vision is a research hot spot in the field of robotics. This paper first introduces the classic framework of visual SLAM, and analyzes the role of each module in the entire framework. Moreover, several classic visual SLAM algorithms are introduced, the principle of each algorithm is explained, the corresponding optimization scheme of each algorithm is given, and the advantages and disadvantages of monocular visual SLAM, stereo visual SLAM, and RGB-D visual SLAM are summarized. Additionally, the development of visual SLAM is prospected in this paper. © 2022 SPIE.

关键词： Stereo image processing

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 3

Integrating Region Proposals with Recurrent Neural Networks ...

引用

3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

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

ISBN: (纸本)9798331523893

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. © 2025 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Concurrent Spatial Color Information processing for Video-Based Vehicle Detection Applications 3rd

Concurrent Spatial Color Information Processing for Video-Ba...

引用

3rd EAI international conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2020

作者： Manipriya, S. Mala, C. Mathew, Samson Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India Department of Civil Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India

The increase in the number of vehicles requires an efficient and well-organized solution for traffic management and regulation. Among several traffic management systems of Intelligent Transportation Systems (ITS), video-based system has gained wide popularity that uses computer vision techniques. One of the significant processes of computer vision technology is to detect vehicle from traffic videos. Effective image processing-based computer vision techniques are available to detect vehicles. The accuracy of the detection primarily depends on the section the video being processed for detection, which is completely neglected by the existing image processing technique. Hence, in this chapter, a Concurrent Spatial Color Information processing System (CSCIPS) is proposed that uses a novel detection framework known as Virtual Multi-Loop Crates (VMLC). The proposed framework VMLC uses all the spatial color information for image processing technique concurrently without loss of any information. The performance of the proposed CSCIPS system with VMLC is analyzed with background subtraction-based image processing techniques in benchmark traffic videos. The results show better performance in processing time, storage space, and accuracy for vehicle detection when compared to other existing frameworks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

关键词： Vehicles

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 << < 29 30 31 32 33 34 35 36 37 38 > >>

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：