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

4,745 篇 会议
55 册 图书
12 篇 期刊文献

馆藏范围

4,812 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,154 篇 工学
- 2,814 篇 计算机科学与技术...
- 2,228 篇 软件工程
- 1,265 篇 信息与通信工程
- 761 篇 控制科学与工程
- 691 篇 生物工程
- 666 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...
- 300 篇 网络空间安全
- 182 篇 安全科学与工程
- 164 篇 机械工程
- 160 篇 仪器科学与技术
- 159 篇 交通运输工程
- 153 篇 动力工程及工程热...
- 153 篇 化学工程与技术
- 143 篇 石油与天然气工程
1,643 篇 理学
- 709 篇 生物学
- 636 篇 物理学
- 605 篇 数学
- 242 篇 化学
- 180 篇 统计学（可授理学、...
828 篇 管理学
- 464 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
- 363 篇 临床医学
- 246 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

321 篇 image processing
312 篇 deep learning
230 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
115 篇 machine learning
96 篇 computer applica...
83 篇 neural networks
83 篇 computational mo...
79 篇 visualization
78 篇 object detection
71 篇 natural language...
69 篇 semantics
68 篇 artificial intel...

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...
12 篇 beijing institut...
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 sun yat-sen univ...
9 篇 school of comput...
9 篇 vivekananda glob...
9 篇 college of compu...
8 篇 harbin engineeri...
8 篇 school of comput...
8 篇 maharishi school...
8 篇 gla university d...

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 bin wang
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 bing li
6 篇 li wei
5 篇 gill kanwarparta...
5 篇 zhiguo cao
5 篇 nagaraj p.
5 篇 singh kamred udh...

语言

4,761 篇 英文
50 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 4812 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：

Application Research of Attention Mechanism in Remote Sensing image Classification 5

Application Research of Attention Mechanism in Remote Sensin...

引用

5th international conference on Applied Machine Learning, ICAML 2023

作者： Feng, Hui Li, Yabin Yu, Qiaochan Zhang, Mingrui China Waterborne Transport Research Institute Beijing China Qingdao Shipping Development Research Institute Qingdao China

ISBN: (纸本)9798350341416

The image classification as the key technology in the remote sensing system is mainly based on the image with the characteristics of the electromagnetic wave radiation information of the ground object to distinguish and identify the category of the ground object. The computer classification of remote sensing images is to achieve the purpose of automatic classification and recognition of ground objects through numerical processing of pixels. A high-precision remote sensing image classification method is a prerequisite for various applications. Compared with manual calibration of images, which is time-consuming and laborious, it is necessary to use machine learning methods to automatically process images. © 2023 ieee.

关键词： image classification

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

Time Does Tell: Self-Supervised Time-Tuning of Dense image Representations

Time Does Tell: Self-Supervised Time-Tuning of Dense Image R...

引用

ieee/CVF international conference on computer Vision (ICCV)

作者： Salehi, Mohammadreza Gavves, Efstratios Snoek, Cees G. M. Asano, Yuki M. Univ Amsterdam QUVA Lab Amsterdam Netherlands

ISBN: (纸本)9798350307184

Spatially dense self-supervised learning is a rapidly growing problem domain with promising applications for unsupervised segmentation and pretraining for dense downstream tasks. Despite the abundance of temporal data in the form of videos, this information-rich source has been largely overlooked. Our paper aims to address this gap by proposing a novel approach that incorporates temporal consistency in dense self-supervised learning. While methods designed solely for images face difficulties in achieving even the same performance on videos, our method improves not only the representation quality for videos - but also images. Our approach, which we call time-tuning, starts from image-pretrained models and fine-tunes them with a novel self-supervised temporal-alignment clustering loss on unlabeled videos. This effectively facilitates the transfer of highlevel information from videos to image representations. Time-tuning improves the state- of-the-art by 8-10% for unsupervised semantic segmentation on videos and matches it for images. We believe this method paves the way for further self-supervised scaling by leveraging the abundant availability of videos. The implementation can be found here : https://***/SMSD75/Timetuning

关键词：

来源：评论

学校读者我要写书评

暂无评论

Potato Disease Degree Recognition Model Based on image processing and Contrast Learning 5

Potato Disease Degree Recognition Model Based on Image Proce...

引用

5th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Jia, Liang Changchun University of Science and Technology ChangChun130022 China

ISBN: (纸本)9798350357950

With the development of computer vision technology and machine learning, some achievements have been made in identifying potato diseases by computer models. Potato disease degree recognition is essentially an image classification task. However, unlike the usual plant disease classification, the potato disease image studied in this paper has a complex background under natural conditions, so it is more difficult to identify accurately. In this paper, The image recognition rate of the improved model reaches 97.53%, which is higher than before the improvement. The research of this paper plays a positive role in plant disease control. © 2023 ieee.

关键词： Artificial intelligence convolutional neural networks Deep learning image processing potato disease

来源：评论

学校读者我要写书评

暂无评论

Breaking Temporal Consistency: Generating Video Universal Adversarial Perturbations Using image Models

Breaking Temporal Consistency: Generating Video Universal Ad...

引用

ieee/CVF international conference on computer Vision (ICCV)

作者： Kim, Hee-Seon Son, Minji Kim, Minbeom Kwon, Myung-Joon Kim, Changick Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (纸本)9798350307184

As video analysis using deep learning models becomes more widespread, the vulnerability of such models to adversarial attacks is becoming a pressing concern. In particular, Universal Adversarial Perturbation (UAP) poses a significant threat, as a single perturbation can mislead deep learning models on entire datasets. We propose a novel video UAP using image data and image model. This enables us to take advantage of the rich image data and image model-based studies available for video applications. However, there is a challenge that image models are limited in their ability to analyze the temporal aspects of videos, which is crucial for a successful video attack. To address this challenge, we introduce the Breaking Temporal Consistancy (BTC) method, which is the first attempt to incorporate temporal information into video attacks using image models. We aim to generate adversarial videos that have opposite patterns to the original. Specifically, BTC-UAP minimizes the feature similarity between neighboring frames in videos. Our approach is simple but effective at attacking unseen video models. Additionally, it is applicable to videos of varying lengths and invariant to temporal shifts. Our approach surpasses existing methods in terms of effectiveness on various datasets, including imageNet, UCF-101, and Kinetics-400.

关键词：

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

A Cloud-Edge-Terminal Collaborative System for image-Based Crowd Counting

A Cloud-Edge-Terminal Collaborative System for Image-Based C...

引用

2023 ieee international conference on Metaverse Computing, Networking and applications, MetaCom 2023

作者： Mo, Zijie Xia, Shuaifan Shen, Shuze Du, Siyuan Liu, Qingwen Tongji University Dept. of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350333336

Crowd counting is of great importance for urban management and disaster relief. However, monitoring the whole situation of crowd density in a wide area such as a city will involve processing a great number of images or videos, which requires intensive computation. Transmission of images and videos also requires large bandwidth of network, which might not be satisfied under some circumstances. This work proposed a cloud-edge-terminal collaborative system to shift the computing load to edge devices and enlarge the monitor range via visual sensors installed on portable devices like unmanned aerial vehicles (UAVs). Also, we proposed a light-weight crowd counting neural network to satisfy the resource constraint scenarios. Experiments on realistic and challenging datasets and comparisons with contemporary approaches show that our method achieves competitive performance with less computation and storage costs. © 2023 ieee.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Exposure on Edge Detection and image Segmentation with Instinctive Classification of Brain Tumor Using Artificial Intelligence Methods

Exposure on Edge Detection and Image Segmentation with Insti...

引用

2023 international conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Hariharasudhan, S. Kumaran, U. Suresh Kumar, K. Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350348910

This research article proposes an exposure on edge detection on images followed by image segmentation and proceeds with the automatic classification of brain tumors by applying artificial intelligence functionality. The renowned medical image processing application consents to deliver the anticipated outcome in medical imaging technology. In medicine, image processing is computerized and divided into minuscule units called pixels. Following this, specific development has been decided to produce the desired outcome mage processing involves practical experiments with medical images with self-motivated study and analysis. MRI (Magnetic Resonance image) noise removal is seen in medical images that are noteworthy and important for various image processing operations. The strategy suggested in this research study combines sophisticated bilateral filtering with the artificial intelligence method of neural network applications for pre-and post-processing procedures. The GLSZM method uses artificial intelligence techniques to classify linked images of the human brain, segment the image, and remove noise. The results will be shown as a segmented image with the classification of brain images using the automatic neural network method. © 2023 ieee.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Prototypical Mixing and Retrieval-based Refinement for Label Noise-resistant image Retrieval

Prototypical Mixing and Retrieval-based Refinement for Label...

引用

ieee/CVF international conference on computer Vision (ICCV)

作者： Yang, Xinlong Wang, Haixin Sun, Jinan Zhang, Shikun Chen, Chong Hua, Xian-Sheng Luo, Xiao Peking Univ Beijing Peoples R China Terminus Grp Beijing Peoples R China Univ Calif Los Angeles Los Angeles CA 90095 USA

ISBN: (纸本)9798350307184

Label noise is pervasive in real-world applications, which influences the optimization of neural network models. This paper investigates a realistic but understudied problem of image retrieval under label noise, which could lead to severe overfitting or memorization of noisy samples during optimization. Moreover, identifying noisy samples correctly is still a challenging problem for retrieval models. In this paper, we propose a novel approach called Prototypical Mixing and Retrieval-based Refinement (TITAN) for label noise- resistant image retrieval, which corrects label noise and mitigates the effects of the memorization simultaneously. Specifically, we first characterize numerous prototypes with Gaussian distributions in the hidden space, which would direct the Mixing procedure in providing synthesized samples. These samples are fed into a similarity learning framework with varying emphasis based on the prototypical structure to learn semantics with reduced overfitting. In addition, we retrieve comparable samples for each prototype from simple to complex, which refine noisy samples in an accurate and class-balanced manner. Comprehensive experiments on five benchmark datasets demonstrate the superiority of our proposed TITAN compared with various competing baselines.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Rain Removal for Single image based on DenseNet 11

Rain Removal for Single Image based on DenseNet

引用

11th ieee international conference on computer Science and Network Technology, ICCSNT 2023

作者： Li, Qiming Qiu, Luoying Wang, Likai Shanghai Maritime University Department of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350311594

Under rainy conditions, computer vision systems will be disturbed by rain, resulting in degraded image quality. So, it is important to remove rain streaks from images and preserve image information meanwhile. For removing rain from a single image, the existing methods, whether based on prior knowledge or neural network, are either excessive or incomplete. In order to effectively remove rain streaks from a single image, the guided filtering is proposed for image pre-processing, the DenseNet is used as the basic network, and the dense blocks with different structures are used to build rain removal network. The experimental results show that, the proposed method can effectively extract the rain streaks residual mapping, remove the rain streaks in the rain image, and better preserve and restore the original background of the image. © 2023 ieee.

关键词： Rain

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482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：