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

4,073 篇 会议
53 篇 期刊文献
18 册 图书
1 篇 学位论文

馆藏范围

4,145 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,373 篇 工学
- 1,632 篇 计算机科学与技术...
- 1,017 篇 信息与通信工程
- 853 篇 软件工程
- 788 篇 电气工程
- 231 篇 光学工程
- 113 篇 生物工程
- 102 篇 电子科学与技术（可...
- 77 篇 控制科学与工程
- 76 篇 生物医学工程（可授...
- 72 篇 仪器科学与技术
- 56 篇 机械工程
- 36 篇 化学工程与技术
- 33 篇 网络空间安全
- 29 篇 测绘科学与技术
- 25 篇 动力工程及工程热...
- 25 篇 安全科学与工程
- 17 篇 轻工技术与工程
1,078 篇 理学
- 838 篇 物理学
- 247 篇 数学
- 120 篇 生物学
- 67 篇 统计学（可授理学、...
- 39 篇 系统科学
- 36 篇 化学
1,031 篇 医学
- 1,017 篇 临床医学
- 32 篇 基础医学(可授医学...
- 26 篇 药学(可授医学、理...
152 篇 管理学
- 81 篇 图书情报与档案管...
- 79 篇 管理科学与工程(可...
- 18 篇 工商管理
30 篇 法学
- 28 篇 社会学
11 篇 军事学
9 篇 文学
8 篇 农学
7 篇 教育学
5 篇 经济学

主题

510 篇 image coding
401 篇 image processing
352 篇 visual communica...
311 篇 visualization
235 篇 feature extracti...
229 篇 image segmentati...
173 篇 image compressio...
158 篇 image reconstruc...
146 篇 video coding
133 篇 training
130 篇 cameras
124 篇 humans
118 篇 image color anal...
114 篇 image quality
112 篇 signal processin...
109 篇 image enhancemen...
102 篇 image edge detec...
101 篇 deep learning
95 篇 image retrieval
93 篇 motion estimatio...

机构

36 篇 shanghai jiao to...
29 篇 institute of ima...
24 篇 school of electr...
20 篇 university of sc...
18 篇 shanghai jiao to...
16 篇 shanghai jiao to...
16 篇 tianjin univ sch...
16 篇 beijing universi...
11 篇 university of el...
11 篇 cas key laborato...
11 篇 tsinghua univ de...
10 篇 univ sci & techn...
10 篇 peking univ inst...
10 篇 institute of ima...
9 篇 zhejiang univers...
9 篇 tsinghua univ de...
9 篇 school of electr...
9 篇 xidian univ sch ...
9 篇 shanghai jiao to...
8 篇 school of remote...

作者

34 篇 zhai guangtao
26 篇 sumei li
25 篇 song li
22 篇 li sumei
21 篇 guangtao zhai
18 篇 li li
18 篇 li song
18 篇 min xiongkuo
16 篇 dong liu
16 篇 yang xiaokang
16 篇 shan liu
15 篇 chen zhibo
15 篇 andré kaup
13 篇 xie rong
13 篇 xiongkuo min
13 篇 gao wen
11 篇 heming sun
11 篇 zhibo chen
11 篇 zhenzhong chen
11 篇 gao zhiyong

语言

4,069 篇 英文
49 篇 土耳其文
27 篇 中文
17 篇 其他

检索条件"任意字段=Conference on Visual Communications and Image Processing 2001"

共 4145 条记录，以下是711-720 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：

Point Clouds Registration Algorithm Based on Spatial Structure Similarity of visual Keypoints 16th

Point Clouds Registration Algorithm Based on Spatial Structu...

引用

16th International conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Gao, Yingshuo Ma, Jie Wu, Bingli Zhang, Tianxiang Yang, Yu National Key Laboratory of Multispectral Information Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811912528

In autonomous driving technology, vehicles use LiDAR to accomplish various tasks. Issues are that the point cloud collected by the LiDAR is too sparse and some objects are mutilated, which is a great challenge for tasks such as detection and recognition. It is necessary to merge multiple frames of point clouds with registration algorithm to make data complete and dense. Previous point cloud registration methods run slowly and have low accuracy when dealing with autopilot field point clouds. It is difficult to find the correspondence of different point clouds. While humans can easily find the exact corresponding through the visual information received by eyes, as it contains color and texture. The visual images acquired by optical cameras can simulate the human eyes well. Therefore, we propose to use visual image assisted point cloud registration. Firstly, we extract the keypoints of visual images, then we obtain their depth by depth completion algorithm and back-project them into 3D space as 3D keypoints. Finally, we screen 3D keypoint pairs by spatial structure similarity to estimate the rigid transformation parameters. After experiments, it is proved that our method achieves substantial improvement in both speed and accuracy. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Optical radar

来源：评论

学校读者我要写书评

暂无评论

Unsupervised image-to-image Translation with Patch Pyramid Dual Contrastive Learning for Cross Domain Object Detection

Unsupervised Image-to-Image Translation with Patch Pyramid D...

引用

2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Meng, Yunlong Zhao, Lifan Xu, Lin Ai Institute Shanghai Em-Data Technology Co. Ltd. China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Unsupervised image-to-image (UI2I) translation methods aim to learn a mapping between different visual domains with well-preserved content and consistent structure. It has been proven that the generated images are useful for enhancing the performance of computer vision tasks like object detection in a different domain with distribution discrepancies. Current UI2I translation techniques are highly relied on cycle-structure consistency and content/style disentanglement. In-tense focus has been paid on global translation over an en-tire image, instead of the stylistic variations and complex structure among multiple disparate object instances and back-grounds, resulting in unsatisfactory performance gain for the downstream vision tasks. In this work, we propose Patch Pyramid Dual Contrastive Learning Unsupervised image Translation (PPDCLUIT) framework to strengthen the cross domain object detection performance. We present patch pyra-mid dual contrastive learning strategy for coherent associations at each specific location and propose identity loss to further enforce the object instances preservation. We im-plement extensive experiments to demonstrate the efficacy of our PPDCLUIT framework for cross domain object detection and achieve state-of-the-art performance on the benchmarks of Normal-to-Foggy, and Day-to-Night. © 2022 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Proceedings - 2021 5th International conference on Imaging, Signal processing and communications, ICISPC 2021

Proceedings - 2021 5th International Conference on Imaging, ...

引用

5th International conference on Imaging, Signal processing and communications, ICISPC 2021

ISBN: (纸本)9781665424257

The proceedings contain 17 papers. The topics discussed include: detecting falling rocks by estimating excavation points using laser pointer;a real-time target tracking algorithm based on improved kernel correlation filter;constant false alarm OMP algorithm based on sparse ISAR imaging of stationary aircraft target;detection of anal sac disease, Atresia Ani and rectal prolapse for Canis lupus familiaris using image processing and convolutional neural network;investigation of gas/oil/water distribution based on electrical capacitance and microwave tomography;lung nodule classification of CT images based on the deep learning algorithms;the research on the impact of visual image perception in a user interface on typeface design;and joint robust transmit and receive beamforming based on probability constraint.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Hybrid CNN-Tree Based Model for Enhanced image Classification Performance

A Hybrid CNN-Tree Based Model for Enhanced Image Classificat...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Musa Aydın Zeki Kuş Zeliha Kaya Akçelik Fatih Sultan Mehmet Vakıf Üniversitesi Bilgisayar Mühendisliği ˙Istanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Blood cells play an essential role in various bodily functions, such as protection against infections and the body’s defense. The accurate classification of blood cells, generally grouped as red, white, and platelets is important for clinical diagnosis and hematological analysis. However, identifying these cells is a specialized and time-consuming process. Therefore, there is a hot-topic for high-precision automatic blood cell classification methods. Convolutional neural networks (CNNs) are a deep learning model used for visual data analysis and are very powerful in extracting features from data. In this study, we propose a hybrid classification model that combines the feature extraction power of CNNs with the ensemble-based prediction capabilities of Random Forest and XGBoost algorithms. The proposed hybrid model is compared with different methods on the BloodMNIST dataset in terms of classification performance and inference time. The results show that the tree-based methods outperform CNN by up to 8.49 and 11.62 points and achieve up to 82.9 times better inference times than other methods.

关键词： visualization Signal processing algorithms Cells (biology) Signal processing Predictive models Feature extraction Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

Single image Super-Resolution Using ConvNeXt

Single Image Super-Resolution Using ConvNeXt

引用

IEEE visual communications and image processing (VCIP)

作者： Chenghui You Chaoqun Hong Lijuan Liu Xuehan Lin School of Computer and Information Engineering Xiamen University of Technology Xiamen China

ISBN: (纸本)9781665475938

In recent years, a lot of deep convolution neural networks have been successfully applied in single image super-resolution (SISR). Even in the case of using small convolution kernel, those methods still require large number of parameters and computation. To tackle the problem above, we propose a novel framework to extract features more efficiently. Inspired by the idea of deep separable convolution, we improve the standard residual block and propose the inverted bottleneck block (IBNB). The IBNB replaces the small-sized convolution kernel with the large-sized convolution kernel without introducing additional computation. The proposed IBNB proves that large kernel size convolution is available for SISR. Comprehensive experiments demonstrate that our method surpasses most methods by up to 0.10 ~ 0.32dB in quantitative metrics with fewer parameters.

关键词： Measurement Convolution visual communication Fuses Superresolution Neural networks Feature extraction

来源：评论

学校读者我要写书评

暂无评论

AI-Powered Optimization and Integrity of Hydro Power Plants

AI-Powered Optimization and Integrity of Hydro Power Plants

引用

International conference on Computer and communications Technologies (ICCCT)

作者： Sheela A Navneeth S Yokeshwaran M Sanjeevei A Computer Science and Engineering Sri Sai Ram Engineering College

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

This research addresses the critical task of crack detection, essential for maintaining the safety and integrity of various structures. Detecting cracks is vital for ensuring the longevity and reliability of infrastructures such as buildings, bridges, and roads. Traditional methods like manual visual inspections are often ineffective, subjective, and prone to human error. This paper introduces an automated crack detection approach using grayscale image processing techniques to improve accuracy. The methodology involves converting images to grayscale, enhancing contrast, and applying edge detection algorithms along with morphological operations to effectively identify and isolate cracks. The experimental findings demonstrate that this approach not only accurately detects cracks but also minimizes false positives and reduces computational complexity. This research offers a practical automated solution for crack detection and paves the way for future advancements, including real-time monitoring with machine learning for more comprehensive and scalable infrastructure health assessment.

关键词： visualization Accuracy image edge detection Roads Machine learning Gray-scale Solids Real-time systems Safety Morphological operations

来源：评论

学校读者我要写书评

暂无评论

Automatic SPECT image processing for Parkinson’s Disease Early Detection 4th

Automatic SPECT Image Processing for Parkinson’s Disease Ea...

引用

4th International conference on Smart Applications and Data Analysis, SADASC 2022

作者： Boucherouite, Jihad Jilbab, Abdelilah Jbari, Atman E2SN ENSAM Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9783031204890

The absence of an effective cure to Parkinson as a neurodegenerative disease calls for early diagnosis and appropriate therapeutic process to provide patients with better quality of treatment. Therefore, to diagnose Parkinson’s Disease (PD) at its early stage, clinicians rely on visual observation of dopaminergic deficit in both caudate and putamen in the striatum region of the brain. SPECT images (Single Photon Emission Computed Tomography) are among functional neuroimaging scans that can show putamen and caudate, and hence help visualizing Dopamine deficit. In this work, we developed an automatic SPECT image model to classify patients as Healthy Control (HC) or Early PD, starting from Dicom SPECT images from PPMI (Parkinson’s Progression Markers Initiative) database to Machine learning classification. The approach we proposed starts with image processing of SPECT images, then extraction of boundary, radial, Striatal Binding Ratio (SBR) and threshold features, then classification using Support Vector Machine (SVM). To the best of our knowledge, no work in the literature has used this combination of the mentioned features together in the classification model. The use of this combination demonstrates promising results. We used a database of 526 images, with 130 HC and 396 PD. The results of our approach show that the Medium Gaussian SVM has a high performance with an accuracy of 97.3%, sensitivity of 95.3%, and specificity of 98%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Single photon emission computed tomography

来源：评论

学校读者我要写书评

暂无评论

An Efficient Content-aware Downsampling-based Video Compression Framework

An Efficient Content-aware Downsampling-based Video Compress...

引用

IEEE visual communications and image processing (VCIP)

作者： Hao Jiang Li Chen Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665475938

Recently, deep learning-based video compression algorithms have achieved competitive performance in Bjøntegaard delta (BD) rate, especially those adopting super-resolution networks as post-processing modules in downsampling-based video compression (DBC) frameworks. However, limited by the non-differentiable characteristics of traditional codecs, DBC frameworks mainly focus on improving the performance of super-resolution modules while ignoring optimizing downscaling modules. It is crucial to improve video compression performance without introducing additional modifications to the decoder client in practical application scenarios. We propose a context-aware processing network (CPN) compatible with standard codecs with no computational burden introduced to the client, which preserves the critical information and essential structures during downscaling. The proposed CPN works as a precoder cascaded by standard codecs to improve the compression performance on the server before encoding and transmission. Besides, a surrogate codec is employed to simulate the degradation process of the standard codecs and backpropagate the gradient to optimize the CPN. Experimental results show that the proposed method outperforms latest pre-processing networks and achieves considerable performance compared with the latest DBC frameworks.

关键词： Degradation Codecs image coding Quantization (signal) visual communication Superresolution Video compression

来源：评论

学校读者我要写书评

暂无评论

image Enhancement for High-Resolution visual Contents

Image Enhancement for High-Resolution Visual Contents

引用

International conference on Electronics, Information and communications (ICEIC)

作者： Heunseung Lim Jaehee Lee Hyuncheol Kim Heungmin Oh Joonki Paik The Graduate School of Advanced Imaging Science Multimedia & Film Chung-Ang University Seoul Korea 4by4 Incorporated Pixell Lab Seoul Korea The Graduate School of Artificial Intelligence Seoul Korea

This paper proposes an image enhancement method using gamma neural networks and exponential transformation. When acquiring an image, degradation occurs in very many imaging systems, and the quality of the image acquired by surrounding environmental factors decreases due to the combination of deteriorating elements. Alternatively, work that facilitates post-treatment may be performed by artificially deteriorating for post-treatment directly. However, if the information on these additional tasks is not known, there is a problem that the post-processing process is expensive or additional degradation occurs. To solve this problem, this paper uses a neural network that estimates gamma maps through residual learning for images that require post-processing, and finally applies exponential transformations to perform contrast improvement. The contrast improvement method proposed through the experimental results provides an image with less color distortion compared to the existing method.

关键词： Degradation visualization image color analysis Neural networks Imaging Distortion Environmental factors

来源：评论

学校读者我要写书评

暂无评论

Using Convolutional Neural Networks for image Recognition in IoT Applications

Using Convolutional Neural Networks for Image Recognition in...

引用

Pervasive Computational Technologies (ICPCT), International conference on

作者： Durgam Rajababu Sampenga Veerraju K. Suresh Kumar Sonali Gupta V B Thurai Raaj Rahul Joshi Department of EEE School of Engineering SR University Warangal Telangana India Department of Electronics and Communications Engineering Dadi Institute of Engineering and Technology Anakapalli Visakhapatnam Andhra Pradesh MBA Department Panimalar Engineering College Varadarajapuram Poonamallee Chennai J C Bose University of Science and Technology YMCA Faridabad Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of Journalism & Mass Communication SMeH Manav Rachna International Institute of Research and Studies Haryana India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries.

关键词： Training visualization Automation Computational modeling Surveillance Training data Feature extraction Convolutional neural networks Internet of Things 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415页 << < 68 69 70 71 72 73 74 75 76 77 > >>

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：