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

4,746 篇 会议
12 篇 期刊文献

馆藏范围

4,758 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,155 篇 工学
- 2,813 篇 计算机科学与技术...
- 2,227 篇 软件工程
- 1,264 篇 信息与通信工程
- 759 篇 控制科学与工程
- 691 篇 生物工程
- 668 篇 光学工程
- 530 篇 电气工程
- 454 篇 生物医学工程（可授...
- 419 篇 电子科学与技术（可...
- 299 篇 网络空间安全
- 182 篇 安全科学与工程
- 163 篇 机械工程
- 160 篇 仪器科学与技术
- 159 篇 交通运输工程
- 152 篇 动力工程及工程热...
- 152 篇 化学工程与技术
- 142 篇 石油与天然气工程
1,643 篇 理学
- 709 篇 生物学
- 637 篇 物理学
- 604 篇 数学
- 242 篇 化学
- 180 篇 统计学（可授理学、...
827 篇 管理学
- 463 篇 图书情报与档案管...
- 443 篇 管理科学与工程(可...
- 119 篇 工商管理
434 篇 医学
- 364 篇 临床医学
- 247 篇 基础医学(可授医学...
- 214 篇 药学(可授医学、理...
- 106 篇 公共卫生与预防医...
125 篇 法学
- 109 篇 社会学
95 篇 农学
45 篇 经济学
44 篇 文学
33 篇 教育学
12 篇 军事学

主题

320 篇 image processing
312 篇 deep learning
231 篇 image segmentati...
191 篇 signal processin...
172 篇 image classifica...
167 篇 image enhancemen...
162 篇 computer vision
140 篇 image recognitio...
138 篇 convolutional ne...
136 篇 feature extracti...
125 篇 training
108 篇 machine learning
83 篇 neural networks
83 篇 computational mo...
79 篇 visualization
78 篇 object detection
71 篇 natural language...
69 篇 semantics
66 篇 accuracy
65 篇 acoustics

机构

24 篇 chitkara univers...
17 篇 maharishi school...
15 篇 lovely professio...
15 篇 tianjin universi...
13 篇 university of ch...
12 篇 the islamic univ...
11 篇 school of comput...
11 篇 centre of interd...
10 篇 peng cheng labor...
10 篇 graphic era hill...
9 篇 national univers...
9 篇 school of comput...
9 篇 vivekananda glob...
9 篇 college of compu...
8 篇 school of comput...
8 篇 maharishi school...
8 篇 gla university d...
8 篇 college of infor...
8 篇 school of comput...
7 篇 college of compu...

作者

13 篇 chauhan rahul
11 篇 yang yang
10 篇 anand vatsala
8 篇 kukreja vinay
8 篇 gupta rupesh
7 篇 luo xiaonan
7 篇 zhao yang
6 篇 pei jihong
6 篇 kumar ankit
6 篇 choudhury tanupr...
6 篇 zhu ce
6 篇 ce zhu
6 篇 kumar gaurav
6 篇 li wei
6 篇 yu li
5 篇 gill kanwarparta...
5 篇 zhiguo cao
5 篇 nagaraj p.
5 篇 singh kamred udh...
5 篇 li qiang

语言

4,706 篇 英文
51 篇 其他
17 篇 中文
1 篇 土耳其文

检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4758 条记录，以下是361-370 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：

Analysis of Impact of image Restoration and Segmentation on Classification Model 7

Analysis of Impact of Image Restoration and Segmentation on ...

引用

2023 7th international conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Vispute, Sushma Rahul Rajeswari, K. Nema, Aryan Jagtap, Arya Kulkarni, Mrugendra Mohite, Pranav PCCOE Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

A widely studied problem in computer science is the restoration, segmentation, and classification of images, which involves image processing, computer vision, and machine learning techniques. Deep learning has made significant contributions to this field, bringing machine learning closer to artificial intelligence. Deep learning has a broad range of applications, including in surveillance, healthcare, medicine, and sports. Convolutional neural networks (CNN), a combination of artificial neural networks (ANN) and deep learning techniques, have made incredible advancements in deep learning. This survey compares methods for restoring noisy images, such as wiener filter, wavelet method, and wiener filtering with BM3D technique, using Gaussian blurring and noising methods. The survey also examines the RGB colour model and YCbCr colour model for image segmentation. image classification is studied using CNN, where the survey discusses various parameters of convolutional neural networks, including activation functions and pooling methods. © 2023 ieee.

关键词： computer vision convolutional neural network face detection face recognition image classification image restoration noise reduction segmentation

来源：评论

学校读者我要写书评

暂无评论

image Preheating: Enables Fast Startup of Large-Scale Parallel Containerized Jobs 6

Image Preheating: Enables Fast Startup of Large-Scale Parall...

引用

6th international conference on Software Engineering and computer Science, CSECS 2023

作者： Yeting Gaohongbo Tangbo Yangke Chengdu Aircraft Design & Research Institute Department of Information Technology Chengdu China

ISBN: (纸本)9798350306378

Containers and Kubernetes are becoming the normative way to deploy services in the data center. This rich virtualization ecosystem simplifies application development and management, resolves complex dependencies between applications and execution environments, and improves the efficiency of application deployment. Unfortunately, the startup and deployment delays of containerized jobs significantly limit the efficiency of job execution. In particular, the larger the container image, the longer the image download time, which seriously affects the service processing capability of the system. In this paper, we propose the image preheating technique to accelerate the startup of large-scale distributed parallel containerized jobs. According to the different working scenarios, image preheating is divided into layer preheating and image preheating. First, layer preheating is applied in the scenario where a single machine pulls a single image to start a single container, and we explore the parallel nature of multiple threads in pulling different layers of images and propose a more efficient heuristic algorithm to accelerate parallel pulling of images. Second, image preheating is applied in the scenario where the entire parallel job is deployed and started on the cluster. As soon as one task image of the job is downloaded, the preheating is completed and the entire job starts. In addition, we propose a very practical approach called manual image preheating to provide a better experience for users executing the job. Simulated and real experiments show that the method we designed can improve the download speed of images by 23.54% and the start of jobs in the cluster by 3.73%. © 2023 ieee.

关键词： Preheating

来源：评论

学校读者我要写书评

暂无评论

A Super-Resolution Attention Feedback Network

A Super-Resolution Attention Feedback Network

引用

2023 ieee international conference on Sensors, Electronics and computer Engineering, ICSECE 2023

作者： Wang, Jiahui Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9798350313734

Since the Internet and social media grow rapidly in recent years. image processing is playing an increasingly important role in daily life. A key task for image processing is super-resolution. In a super-resolution task, an image of a high-resolution rate would be built on the basis of a low-resolution picture. Super-resolution technologies could help recover damaged images and recognize the detail of the images. As neutral networks develop quickly, many industries have used deep learning to improve their efficiency. Since neutral networks have a good performance in image processing, they quickly become the dominant methods in super-resolution tasks. Unlike traditional methods, deep learning could work better and more effectively. Based on the super-resolution feedback network (SFBEN), a channel attention mechanism has been added to the feedback block of the network. It is all known that attention mechanisms and feedback are two important ways for a human to deal with an image. This paper combines these mechanisms in the network. The new channel attentional layer uses a convolutional layer to generate a weight matrix and then adjust the input based on the matrix. The application of this channel attentional block reduces the training loss of the whole network and increases the peak signal-to-noise ratio. © 2023 ieee.

关键词： image processing

来源：评论

学校读者我要写书评

暂无评论

A Preprocessing Method for Fragment Perforation image Detection 2

A Preprocessing Method for Fragment Perforation Image Detect...

引用

2nd international conference on image processing, computer Vision and Machine Learning, ICICML 2023

作者： Lu, Guangzhao Sun, Yong Huang, Dan Zhao, Jinqing China Research and Development Academy of Machinery Equipment Beijing China

ISBN: (纸本)9798350331417

The perforation data left by the fragments generated by the explosion of ammunition warheads on the surrounding metal plates can reflect the dispersion characteristics of the fragments, which is a commonly used ammunition power testing method. Using image detection based methods to analyze metal plate perforation images can quickly and accurately detect the number and size of fragment perforation. Therefore, image quality is crucial for the accuracy of the analysis results. This study proposes an image preprocessing method for detecting metal plate perforation images, and compares and analyzes the effects of different filtering methods, morphological processing methods, and perforation edge detection methods on the image preprocessing results, The results show that using median filtering, open operation morphological processing methods, and edge detection methods based on Gaussian fitting models can obtain preprocessed images that better reflect the true bullet hole information. © 2023 ieee.

关键词： image preprocessing metal plate perforation

来源：评论

学校读者我要写书评

暂无评论

Mesh2Tex: Generating Mesh Textures from image Queries

Mesh2Tex: Generating Mesh Textures from Image Queries

引用

ieee/CVF international conference on computer Vision (ICCV)

作者： Bokhovkin, Alexey Tulsiani, Shubham Dai, Angela Tech Univ Munich Munich Germany Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350307184

Remarkable advances have been achieved recently in learning neural representations that characterize object geometry, while generating textured objects suitable for downstream applications and 3D rendering remains at an early stage. In particular, reconstructing textured geometry from images of real objects is a significant challenge - reconstructed geometry is often inexact, making realistic texturing a significant challenge. We present Mesh2Tex, which learns a realistic object texture manifold from uncorrelated collections of 3D object geometry and photorealistic RGB images, by leveraging a hybrid mesh-neural-field texture representation. Our texture representation enables compact encoding of high-resolution textures as a neural field in the barycentric coordinate system of the mesh faces. The learned texture manifold enables effective navigation to generate an object texture for a given 3D object geometry that matches to an input RGB image, which maintains robustness even under challenging real-world scenarios where the mesh geometry approximates an inexact match to the underlying geometry in the RGB image. Mesh2Tex can effectively generate realistic object textures for an object mesh to match real images observations towards digitization of real environments, significantly improving over previous state of the art. Project page: ***/mesh2tex/

关键词：

来源：评论

学校读者我要写书评

暂无评论

Plain Source Code Obfuscation as an Effective Attack Method on IoT Malware image Classification 47

Plain Source Code Obfuscation as an Effective Attack Method ...

引用

47th ieee-computer-Society Annual international conference on computers, Software, and applications (COMPSAC)

作者： Sato, Hayato Immure, Hiroshi Ishida, Shigemi Nakamura, Yoshitaka Future Univ Hakodate Sch Syst Informat Sci Hakodate Japan Kyoto Tachibana Univ Fac Engn Kyoto Japan

ISBN: (纸本)9798350326970

IoT malware is rapidly increasing due to variants easily generated from publicly available source codes. Malware image classification capable of fast and accurate malware identification attracts attention. Since the classification by imaging is affected by malware binary changes, a binary modification without behavioral changes can be a potential attacking method to the classification by imaging. There are concerns that by combining the publicly available malware source code with readily available source code obfuscation tools, it is possible to construct an effective attack that bypasses image classifiers relatively simply. In this study, we show the effectiveness of the attack by source code obfuscation and the possibility of defense against the attack. The contribution of this research is twofold. 1) We showed that Obfuscator-LLVM (oLLVM) code obfuscation could be used as an attack method on malware image classification. The obfuscated malware binaries made by oLLVM were misclassified by VGG16-based image classifier for all the attacked malware families including Mirai, Lightaidra, and Bashlite. 2) We showed that classifier training with obfuscated samples could address this attack method. We confirmed that the malware image classifier trained with obfuscated malware binaries made by oLLVM could classify with an accuracy of 100% the malware family with obfuscation as the obfuscated original malware family.

关键词： Malware image Classification Code Obfuscation IoT Malware

来源：评论

学校读者我要写书评

暂无评论

HIERARCHICAL CONDITIONAL SEMI-PAIRED image-TO-image TRANSLATION FOR MULTI-TASK image DEFECT CORRECTION ON SHOPPING WEBSITES 30

HIERARCHICAL CONDITIONAL SEMI-PAIRED IMAGE-TO-IMAGE TRANSLAT...

引用

30th ieee international conference on image processing (ICIP)

作者： Li, Moyan Fu, Jinmiao Xu, Shaoyuan Liu, Huidong Liu, Jia Wang, Bryan Univ Michigan Ann Arbor MI 48109 USA Amazon Seattle WA USA

ISBN: (纸本)9781728198354

On shopping websites, product images of low quality negatively affect customer experience. Although there are plenty of work in detecting images with different defects, few efforts have been dedicated to correct those defects at scale. A major challenge is that there are thousands of product types and each has specific defects, therefore building defect specific models is unscalable. In this paper, we propose a unified image-to-image (I2I) translation model to correct multiple defects across different product types. Our model leverages an attention mechanism which hierarchically incorporates high-level defect groups and specific defect types to guide the network to focus on defect-related image regions. Evaluated on eight public datasets, our model reduces the Frechet Inception Distance (FID) by 24.6% in average compared with MoNCE, the state-of-the-art I2I method. Another practical challenge on shopping websites is the lack of high quality paired images. We extend our model to be semi-paired by leveraging both paired and unpaired data. Tested on a shopping website dataset to correct three image defects, our model reduces (FID) by 63.2% in average compared with WS-I2I, the state-of-the art semi-paired I2I method.

关键词： image to image translation computer Vision image defect auto-correction

来源：评论

学校读者我要写书评

暂无评论

Development of computerized 3D image Parametric Analysis System Based on BIM 1

Development of Computerized 3D Image Parametric Analysis Sys...

引用

1st international conference on computer Graphics and image processing, CGIP 2023

作者： Zhang, Aiguo Xu, Haiyan Nantong Institute of Technology Jiangsu Nantong China

ISBN: (纸本)9798350333183

Traditional computer 3D modelling techniques require a lot of manpower, material and time, and do not provide multiple information at the same time. BIM technology is a kind of software based on model development, and the 3D image parametric analysis system designed based on this technology can visualise design, reduce the miscommunication of information between designers and viewers, convert the data processing required in complex structured design into various kinds of graphical images that can be presented visually;check the spatial conflicts of components;simulate the construction progress, collaborate with multi-professionals;informatise project management, make it efficient, and obtain visual results quickly. It can check the spatial conflicts of components, simulate construction progress and collaborate with multi-disciplinary cooperation, inform the project management and make it efficient, and obtain visual results quickly to meet the needs of users. It also enables three-dimensional modelling functions, giving it certain rules and specifications, among other features. In addition the system uses a variety of information, which is of great value in engineering applications. © 2023 ieee.

关键词： Project management

来源：评论

学校读者我要写书评

暂无评论

Improved ECA-DenseNet Framework for Brain MRI image Classification 31

Improved ECA-DenseNet Framework for Brain MRI Image Classifi...

引用

31st ieee conference on Signal processing and Communications applications (SIU)

作者： Aydin, Halise Nur Yildiz, Oktay Gazi Univ Bilgisayar Muhendisligi Bolumu Ankara Turkiye

ISBN: (纸本)9798350343557

Early diagnosis is very important in brain tumors. Although Magnetic Resonance (MRI) is widely used for brain tumor detection, it is difficult to detect the tumor manually. Therefore, computer-aided diagnosis systems have been frequently utilized in recent years. In this study, an Efficient Channel Attention-Dense Convolutional Network (ECA-DenseNet) framework is proposed to detect tumors in patients based on brain MRI images. While detecting the tumor, it is tried to determine which type of tumor is present in the patient. In the developed ECA-DenseNet structure, an ECA block has been added to the dense blocks. The ECA block aimed to discard unimportant information and thus reduce the computation time. The improved DenseNet model has been tested on an open-source dataset. The improved model is compared with DenseNet-121, DenseNet-169, DenseNet-201, and DenseNet-264. The experimental results show the improving model has better classification performance than the others. The accuracy of the proposed model was 95.07%.

关键词： DenseNet Efficient Channel Attention Classification SENet Brain MRId

来源：评论

学校读者我要写书评

暂无评论

Research on Multi-Labels image Classification Based on Self-Supervised Model

Research on Multi-Labels Image Classification Based on Self-...

引用

2023 international conference on image processing and computer Vision, IPCV 2023

作者： Xu, Xuetian Guangdong Justice Police Vocational College Department of Information Administration Guang Zhou China

ISBN: (纸本)9781665473941

At present, image classification model has become an essential component for detection system. However, existing identification models are primarily concentrated on the convolutional neural network or utilize transformer model to capture the features of training images, which can obtain acceptable accuracy for single objectives and is hard to dispose multiple objectives with unsatisfied optimization results. In this work, we utilize the self-supervised mechanism to dispose the optimization process for traditional convolutional neural network. Initially, we construct the contrastive learning module to generate the amount of objectives label for the training data, which is utilized to reduce the extraction loss for multiple tasks. Subsequently, the convolutional neural network is established to classify the objectives in the images. From our extensive experimental results and compared analysis with existing classification models, we can directly observe that our proposed model can achieve the multiple images classification with more than 85% identification accuracy with reasonable responding costs. © 2023 ieee.

关键词： image classification

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476页 << < 33 34 35 36 37 38 39 40 41 42 > >>

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：