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

20,242 篇 会议
164 篇 期刊文献
80 册 图书
3 篇 学位论文

馆藏范围

20,489 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

12,755 篇 工学
- 8,298 篇 计算机科学与技术...
- 5,001 篇 软件工程
- 4,985 篇 信息与通信工程
- 4,638 篇 电气工程
- 1,447 篇 控制科学与工程
- 1,321 篇 电子科学与技术（可...
- 1,224 篇 光学工程
- 899 篇 生物医学工程（可授...
- 868 篇 机械工程
- 830 篇 仪器科学与技术
- 811 篇 生物工程
- 326 篇 网络空间安全
- 291 篇 交通运输工程
- 284 篇 安全科学与工程
- 278 篇 化学工程与技术
- 247 篇 动力工程及工程热...
- 189 篇 建筑学
- 179 篇 土木工程
4,662 篇 理学
- 2,149 篇 数学
- 2,072 篇 物理学
- 874 篇 生物学
- 668 篇 统计学（可授理学、...
- 522 篇 系统科学
- 330 篇 化学
1,523 篇 医学
- 1,405 篇 临床医学
- 383 篇 基础医学(可授医学...
- 269 篇 药学(可授医学、理...
1,368 篇 管理学
- 824 篇 管理科学与工程(可...
- 615 篇 图书情报与档案管...
- 208 篇 工商管理
204 篇 法学
136 篇 农学
78 篇 经济学
69 篇 军事学
64 篇 教育学
40 篇 文学
24 篇 艺术学
1 篇 哲学
1 篇 历史学

主题

1,120 篇 image segmentati...
1,067 篇 image coding
1,059 篇 signal processin...
1,010 篇 image processing
987 篇 internet of thin...
952 篇 feature extracti...
830 篇 image reconstruc...
631 篇 internet
553 篇 signal processin...
495 篇 signal to noise ...
476 篇 image edge detec...
475 篇 deep learning
456 篇 training
423 篇 image color anal...
400 篇 cameras
397 篇 robustness
395 篇 image enhancemen...
306 篇 image resolution
292 篇 image recognitio...
287 篇 visualization

机构

58 篇 school of inform...
39 篇 university of sc...
37 篇 signal processin...
29 篇 microsoft resear...
27 篇 university of ch...
26 篇 institute of sig...
26 篇 signal processin...
25 篇 center for signa...
24 篇 university of el...
23 篇 school of electr...
21 篇 ieee
21 篇 college of big d...
20 篇 school of cyber ...
20 篇 department of si...
20 篇 school of inform...
20 篇 signal and infor...
20 篇 school of electr...
19 篇 hsch darmstadt d...
19 篇 peng cheng labor...
19 篇 harbin institute...

作者

31 篇 li zhuo
27 篇 zhuo li
25 篇 sridharan sridha
23 篇 yang yang
23 篇 r.m. mersereau
20 篇 jing zhang
20 篇 wei li
20 篇 gabbouj moncef
19 篇 mansouri majdi
18 篇 zhang lei
18 篇 busch christoph
18 篇 m. gabbouj
17 篇 m.j.t. smith
17 篇 bouzrara kais
17 篇 clinton fookes
17 篇 wang wei
17 篇 zhang yan
16 篇 wang lei
16 篇 takahiro ogawa
16 篇 ghassan alregib

语言

19,942 篇 英文
461 篇 其他
75 篇 中文
7 篇 俄文
6 篇 土耳其文
1 篇 西班牙文

检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

共 20489 条记录，以下是731-740 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：

ADVERSARIAL DEFENSE VIA PERTURBATION-DISENTANGLEMENT IN HYPERSPECTRAL image CLASSIFICATION 30

ADVERSARIAL DEFENSE VIA PERTURBATION-DISENTANGLEMENT IN HYPE...

引用

30th ieee international conference on image Processing (ICIP)

作者： Shi, Cheng Liu, Ying Zhao, Minghua You, Zhenzhen Zhao, Ziyuan Xian Univ Technol Sch Comp Sci & Engn Xian Shaanxi Peoples R China ASTAR Inst Infocomm Res I2R Singapore Singapore

ISBN: (纸本)9781728198354

In recent years, deep neural networks (DNNs) have been widely used in hyperspectral image (HSI) classification. However, it has a strong vulnerability to crafted adversarial examples. Therefore, defense against adversarial examples is an urgent problem to be solved. To date, most defense methods are difficult to defend against unknown attacks. In this paper, we propose a perturbation-disentanglement-based adversarial defense method (PD-Defense) to protect HSI classification networks from unknown attacks. In the proposed method, the adversarial examples are decoupled into attack-invariant features and perturbation features, and the defense is conducted on the attack-invariant feature to defend against unknown attacks. Extensive experiments are performed on two benchmark HSI datasets, including PaviaU and HoustonU 2018. The results indicate that the proposed PD-Defense method achieves an excellent defense performance compared to four state-of-the-art defense methods.

关键词： adversarial attack adversarial defense hyperspectral image

来源：评论

学校读者我要写书评

暂无评论

ROTATION XGBOOST based METHOD FOR HYPERSPECTRAL image CLASSIFICATION WITH LIMITED TRAINING SAMPLES 30

ROTATION XGBOOST BASED METHOD FOR HYPERSPECTRAL IMAGE CLASSI...

引用

30th ieee international conference on image Processing (ICIP)

作者： Feng, Wei Gao, Xinting Dauphin, Gabriel Quan, Yinghui Xidian Univ Sch Elect Engn Dept Remote Sensing Sci & Technol Xian 710071 Peoples R China Xian Key Lab Adv Remote Sensing Xian 710071 Peoples R China Xidian Univ Minist Educ Key Lab Collaborat Intelligence Syst Xian 710071 Peoples R China Univ Paris XIII Inst Galilee Lab Informat Proc & Transmiss L2TI F-93430 Paris France

ISBN: (纸本)9781728198354

The classification of hyperspectral image (HSI) has become the focus of the remote sensing field. However, limited training data, which makes the classification task face a major challenge, is inevitable in remote sensing. To eliminate the negative effects of limited labeled samples, an enhanced ensemble method named RoXGBoost, which inherently combines Rotation Forest (RoF) and eXtreme Gradient Boosting (XGBoost) is proposed in this paper. This algorithm could increase the diversity of base classifiers by random feature selection and data transformation. Five ensemble learning methods, Random Forest (RF), AdaBoost, RoF, Rotation Boost and XGBoost, are applied as comparisons. The results on two benchmark datasets, Indian Pines and Pavia University, demonstrate the effectiveness of the RoXGBoost.

关键词： Rotation Forest XGBoost limited training samples hyperspectral image classification ensemble learning

来源：评论

学校读者我要写书评

暂无评论

A Content-based Multi-Scale Network for Single image Super-Resolution 48

A Content-Based Multi-Scale Network for Single Image Super-R...

引用

48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Ji, Jiahuan Zhong, Baojiang Mu, Kai-Kuang Soochow University School of Computer Science and Technology Suzhou China Nanyang Technological University School of Electrical and Electronic Engineering Singapore

ISBN: (纸本)9781728163277

A novel content-based multi-scale network (CMNet) is proposed in this paper for conducting single image super-resolution (SISR). Its core lies in a content-based multi-scale image representation (CMIR), which is motivated by the fact that the contents of real-world images normally have different scales. Thus, it is expected that individual treatments of these contents would yield superior SISR performance. In our CMIR, the difference curvature (DCurv) is first exploited to generate a primal sketch of the input image. Then, a filter bank is designed and used to obtain a set of coefficient matrices, and each matrix reflects the characteristics of the image content at the corresponding scale. based on these coefficient matrices, the CMIR of the input image is formed. To conduct SISR, each scale of CMIR is processed individually in our CMNet, and the produced multi-scale outputs are then integrated to arrive at the final SISR image with a higher image quality. Extensive experiments have demonstrated that our developed CMNet can deliver superior performance compared with a number of state-of-the-art SISR methods. © 2023 ieee.

关键词： content-based deep learning difference curvature image super-resolution multi-scale image representation

来源：评论

学校读者我要写书评

暂无评论

Adaptive Feature Interaction and Enhancement Network for Medical image Segmentation 17

Adaptive Feature Interaction and Enhancement Network for Med...

引用

17th ieee international conference on signal Processing, ICSP 2024

作者： Cai, Ao Shi, Caijuan Chen, Weibin Wu, Qian Wang, Sen Xiao, Yun North China University of Science and Technology Tangshan China Hebei Key Laboratory of Industrial Intelligent Perception Tangshan China The Affiliated Hospital North China University of Science and Technology Tangshan China

ISBN: (纸本)9798350387384

In recent years, numerous CNN-ViT hybrid models have been extensively studied for medical image segmentation. However, most of these models face significant challenges such as large feature discrepancies between the encoder and decoder, poor inter-level feature interaction, and high computational complexity. To address these issues in medical image segmentation, this paper proposes a novel Adaptive Feature Interaction and Enhancement Network (AFIENet) including the Adaptive Space-Channel Attention (ASCA) block, the Graph Convolution-based Feature Enhancement (GCFE) block, and the Lightweight Feature Extraction (LFE) block. ASCA can activate relevant features and filter out redundant information to effectively mitigate feature differences from the encoder and the decoder. GCFE can enhance the semantic and structural information of the original features to effectively alleviate blur issue of segmentation edge. LFE can provide additional information from the original image to improve segmentation performance with low complexity. To validate the performance of AFIENet, extensive experiments have been conducted and the results demonstrate that AFIENet offers excellent medical image segmentation performance and can be widely applied to related tasks. © 2024 ieee.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Research on Speckle Filtering Method for Synthetic Aperture Radar images based on Deep Learning 6

Research on Speckle Filtering Method for Synthetic Aperture ...

引用

6th international conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Fu, Ning Wang, Qiaomei Cai, Fangkai School of Network and Communication Engineering Chengdu Technological University Chengdu China

ISBN: (纸本)9798331541750

In order to optimize the clarity of synthetic aperture radar (SAR) images and enhance the accuracy of target recognition, this paper proposes a SAR image denoising method using deep learning techniques. This method utilizes Retinex theory to estimate and eliminate lighting components through Gaussian kernel function, supplemented by logarithmic transformation and multi-scale weight superposition techniques, thereby improving the contrast of SAR images and preserving their texture features. Furthermore, the processed SAR image is subjected to multi-level decomposition using Laplace pyramid technique, and the details and noise in the image are separated using EXPAND operation and bandpass filtering technology to achieve better noise elimination effect. In addition, this article also introduces Generative Adversarial Networks (GANs), which are trained through the game between the generator and discriminator to understand the complex distribution characteristics of SAR images and achieve adaptive denoising processing. The experimental results show that the proposed method exhibits higher signal-to-noise ratio and lower image entropy in all tests, indicating its excellent performance in noise reduction and information preservation. © 2024 ieee.

关键词： signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Rethinking Early-Fusion Strategies for Improved Multimodal image Segmentation

Rethinking Early-Fusion Strategies for Improved Multimodal I...

引用

2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Shen, Zhengwen Li, Yulian Zhang, Han Weng, Yuchen Wang, Jun School of Information and Control Engineering China University of Mining and Technology Jiangsu Xuzhou221116 China

ISBN: (纸本)9798350368741

RGB and thermal image fusion have great potential to exhibit improved semantic segmentation in low-illumination conditions. Existing methods typically employ a two-branch encoder framework for multimodal feature extraction and design complicated feature fusion strategies to achieve feature extraction and fusion for multimodal semantic segmentation. However, these methods require massive parameter updates and computational effort during the feature extraction and fusion. To address this issue, we propose a novel multimodal fusion network (EFNet) based on an early fusion strategy and a simple but effective feature clustering for training efficient RGB-T semantic segmentation. In addition, we also propose a lightweight and efficient multiscale feature aggregation decoder based on Euclidean distance. We validate the effectiveness of our method on different datasets and outperform previous state-of-the-art methods with lower parameters and computation. © 2025 ieee.

关键词： feature aggregation feature clustering RGB-T semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

Application of Augmented Reality and 3D Reconstruction Technologies in Power Scenarios 17

Application of Augmented Reality and 3D Reconstruction Techn...

引用

17th ieee international conference on signal Processing, ICSP 2024

作者： Xu, Mingkai Li, Cong Liu, Chunming Li, Xin Yang, Guodong Yu, Dong Zhang, Chunjie Jinan Power Supply Company State Grid Shandong Electric Power Company Operation and Maintenance Department Jinan China Institute of Automation Chinese Academy of Sciences Cas Engineering Laboratory for Intelligent Industrial Vision Beijing China Beijing Jiaotong University School of Computer and Information Technology Beijing China

ISBN: (纸本)9798350387384

As the power system continues to develop, the application of augmented reality (AR) and 3D reconstruction technologies in power scenarios is becoming increasingly widespread. This paper proposes the integration of these two technologies for the detection, maintenance, and fault diagnosis of power equipment. Through real-time video segmentation and detection operations, AR technology can overlay key information onto the field view, helping engineers quickly identify equipment status and potential issues. Simultaneously, 3D reconstruction technology can generate precise models of power equipment and environments, providing comprehensive visual references and analytical tools that enhance maintenance efficiency and safety. The combined application of these technologies can significantly reduce the risks and costs associated with manual operations. © 2024 ieee.

关键词： 3D Reconstruction Augmented reality image Segment Objection detection

来源：评论

学校读者我要写书评

暂无评论

Compgen: Synthesis and Generation of Faces From Edgemaps

Compgen: Synthesis and Generation of Faces From Edgemaps

引用

2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Pandian, Ruban Vishnu Gorle, Abhiram Rao Karthick Krishna, M. Seshadri, Nambi David Koilpillai, R. Indian Institute of Technology Madras Chennai India University of California San Diego United States

ISBN: (纸本)9798350368741

We address the problem of synthesis and generation of faces from edgemaps, motivated by extreme low bit-rate facial compression and the need for robust source-channel coding over noisy channels. Three approaches for image reconstruction are proposed. In the first, a deep learning-based encoder-decoder creates a latent space representation of the original image. An Edgemap-to-Latent Mapper (ELM) network maps the input edgemap to this latent space, with the final image reconstructed using a pre-trained compressive decoder. The second approach retrains the compressive decoder to reconstruct images from the ELM network's output. The third approach jointly trains the ELM network and decoder, enabling direct reconstruction from the edgemap. This end-to-end framework achieves reasonable reconstruction fidelity. We also examine the impact of additive channel noise on edgemap transmission under low SNR conditions, demonstrating that even with significant noise, a DNN-based joint denoiser and edgemap decoder can reconstruct images. At extremely low SNRs, where edgemaps are highly corrupted, the network also exhibits generative capabilities, producing plausible images. © 2025 ieee.

关键词： coding with side information edgemap generation image compression residual network

来源：评论

学校读者我要写书评

暂无评论

Augmented Reality for Advanced Home Applications 5

Augmented Reality for Advanced Home Applications

引用

5th international conference on Smart systems and Inventive technology, ICSSIT 2023

作者： Pronika Mudgal, Devesh Yati, Gaurav Manav Rachna International Institute of Research and Studies Faculty of Engineering and Technology Haryana Faridabad India

ISBN: (纸本)9781665474672

Augmented Reality is a revolutionary technology that is already being used in various technologies like Video Games, Simulations and internet of Things (IoT). The use of Augmented Reality can also be done to take control of devices and appliances, specifically Smart Devices or ones equipped with internet of Things Technologies and Tools. This paper explores the possibility of using Augmented Reality in conjunction to internet of Things and presents an implementation of it on a Prototype with a Light Bulb using various AR tools, Cloud and IOT technologies like Arduino Uno Board. AR technology will bring various benefits including increased security via the required security target image for the light bulb to work and they are explored in this paper along with the limitations of using Augmented Reality (AR) with further improvements and suggestions. The possibility of combining Augmented Reality (AR) or Mixed Reality (MR) with internet of Things (IoT) to create better solutions for control devices, where home automation is explored and also further improvements and limitations are suggested. © 2023 ieee.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Noise Reduction Techniques in Electronic signal Processing 9

Deep Learning-Based Noise Reduction Techniques in Electronic...

引用

9th international conference on Science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Johari, Lalit Ramya, P. Suganya, M. Praveena, Hirald Dwaraka Lathamageshwari, P.S. Rajendiran, M. Iftm University Department of Computer Applications Uttar Pradesh Moradabad244102 India Bannari Amman Institute of Technology Department of Electronics And Communication Engineering Tamil Nadu Sathyamangalam638401 India Annapoorana Engineering College Department of Electronics And Communication Engineering Tamil Nadu Salem636308 India Department of Electronics And Communication Engineering Andhra Pradesh Tirupati517102 India Tamil Nadu Kavaraipettai601206 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

This paper explores the operation of deep literacy- grounded noise reduction ways in electronic signal processing. Noise hindrance poses a significant challenge in colorful signal processing operations, impacting the delicacy and trustability of data analysis and interpretation. Traditional noise reduction styles frequently calculate on heuristic approaches or signal processing algorithms, which may have limited effectiveness, especially in complex and dynamic surroundings. In discrepancy, deep literacy algorithms, similar as convolutional neural networks(CNNs) and intermittent neural networks(RNNs), offer promising results for noise reduction by learning complex patterns and connections directly from data. This paper determines the state- of- the- art deep literacy- grounded noise reduction ways, including denoising autoencoders, generative inimical networks(GANs), and intermittent neural network- grounded models. Case studies and experimental results demonstrate the effectiveness and performance of these ways in colorful signal processing tasks, similar as audio denoising, image restoration, and biomedical signal processing. The integration of deep literacy- grounded noise reduction styles into electronic signal processing systems shows significant eventuality for perfecting signal quality and enhancing the delicacy of data analysis in different operations. © 2024 ieee.

关键词： Recurrent 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 << < 70 71 72 73 74 75 76 77 78 79 > >>

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：