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

4,553 篇 会议
67 篇 期刊文献
21 册 图书
2 篇 学位论文

馆藏范围

4,643 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,604 篇 工学
- 1,805 篇 计算机科学与技术...
- 1,158 篇 信息与通信工程
- 1,042 篇 软件工程
- 625 篇 电气工程
- 331 篇 光学工程
- 139 篇 控制科学与工程
- 137 篇 电子科学与技术（可...
- 135 篇 生物工程
- 109 篇 生物医学工程（可授...
- 104 篇 仪器科学与技术
- 84 篇 机械工程
- 44 篇 化学工程与技术
- 32 篇 网络空间安全
- 26 篇 动力工程及工程热...
- 26 篇 安全科学与工程
- 22 篇 建筑学
1,139 篇 医学
- 1,122 篇 临床医学
- 42 篇 基础医学(可授医学...
- 37 篇 药学(可授医学、理...
- 28 篇 特种医学
1,135 篇 理学
- 825 篇 物理学
- 390 篇 数学
- 143 篇 生物学
- 96 篇 统计学（可授理学、...
- 43 篇 化学
- 41 篇 系统科学
224 篇 管理学
- 122 篇 管理科学与工程(可...
- 111 篇 图书情报与档案管...
- 27 篇 工商管理
38 篇 法学
- 30 篇 社会学
12 篇 军事学
10 篇 教育学
9 篇 经济学
9 篇 文学
8 篇 农学
2 篇 艺术学

主题

551 篇 image processing
535 篇 image coding
353 篇 visual communica...
315 篇 visualization
263 篇 feature extracti...
262 篇 image segmentati...
173 篇 image reconstruc...
171 篇 image compressio...
158 篇 cameras
158 篇 humans
152 篇 video coding
139 篇 image color anal...
130 篇 training
129 篇 image analysis
128 篇 image quality
127 篇 image edge detec...
123 篇 image enhancemen...
110 篇 image retrieval
103 篇 motion estimatio...
102 篇 deep learning

机构

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

作者

34 篇 zhai guangtao
26 篇 song li
26 篇 sumei li
22 篇 li sumei
21 篇 guangtao zhai
19 篇 li song
18 篇 li li
18 篇 min xiongkuo
17 篇 andré kaup
17 篇 yang xiaokang
16 篇 dong liu
16 篇 shan liu
14 篇 chen zhibo
14 篇 b. macq
13 篇 xie rong
13 篇 xiongkuo min
12 篇 gao wen
11 篇 m. vetterli
11 篇 heming sun
11 篇 zhibo chen

语言

4,530 篇 英文
82 篇 土耳其文
29 篇 中文
18 篇 其他
1 篇 俄文

检索条件"任意字段=Conference on Visual Communications and Image Processing 2005"

共 4643 条记录，以下是811-820 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：

Enhanced image Deblurring with the Fusion of Generative Adversarial Networks and Transformer Models 1st

Enhanced Image Deblurring with the Fusion of Generative Adve...

引用

1st International conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024

作者： Ranjan, Arti Ravinder, M. New Delhi India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India

ISBN: (纸本)9783031808418

In the domain of image processing, deblurring remains a challenging task, particularly in restoring high-fidelity details from blurred images. This paper introduces a novel approach that synergizes the strengths of Generative Adversarial Networks (GANs) and Transformer models to address this challenge. Our methodology leverages the generative capability of GANs to produce realistic and sharp images, while incorporating the Transformer’s prowess in capturing long-range dependencies and intricate details within images. This adversarial training regime is designed to refine the model’s ability to generate deblurred images that are not only accurate in detail but also high in perceptual quality. Through extensive experiments on various datasets, our model demonstrates superior performance in deblurring, outperforming existing state-of-the-art methods in terms of both objective metrics and subjective visual quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

MRIQA: Subjective Method and Objective Model for Magnetic Resonance image Quality Assessment

MRIQA: Subjective Method and Objective Model for Magnetic Re...

引用

IEEE visual communications and image processing (VCIP)

作者： Qi Chen Fang Liu Huiyu Duan Yao Wang Xiongkuo Min Yan Zhou Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665475938

Magnetic Resonance Imaging (MRI) is widely used for medical diagnosis, staging and follow-up of disease. However, MRI images may have artifacts due to various reasons such as patient movement or machine distortion, which may be unintentionally introduced during the procedure of medical image acquisition, processing, etc. These artifacts may affect the effectiveness of diagnosis or even cause false diagnosis. To solve this problem, we propose a general medical image quality assessment (MIQA) methodology, including subjective MIQA procedures and objective MIQA algorithms. We further apply this methodology to MRI images in this paper due to its widespread use in practical applications. We first establish a magnetic resonance imaging quality assessment (MRIQA) database, which contains 3809 MRI images. Then a subjective image quality assessment experiment is conducted by expert doctors according to the diagnostic value of these images, which split all MRI images into 1285 low quality images and 2524 high quality images. We then conduct a baseline deep learning experiment, and propose an attention based MIQANet model to automatically separate MRI images into high quality and low quality based on their diagnosis value. Our proposed method achieves a great quality assessment accuracy of 96.59%. The constructed MRIQA database and proposed MIQA model will be public available to further promote medical IQA research.

关键词： image quality Deep learning Databases visual communication Magnetic resonance imaging Neural networks Magnetic resonance

来源：评论

学校读者我要写书评

暂无评论

A Weighted Mean Absolute Error Metric for image Quality Assessment

A Weighted Mean Absolute Error Metric for Image Quality Asse...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Hao, Sihan Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728180687

Pixel-wise image quality assessment (IQA) algorithms, such as mean square error (MSE), mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) correlate well with perceptual quality when dealing with images sharing the same distortion type but not well when processing images in different distortion types, which is inconsistent with human visual system (HVS). Although a large number of metrics based on image error has been proposed, there are still difficulties and limitations. To solve this problem, a full reference image quality assessment (FR-IQA) method based on MAE is proposed in this paper. The metric divides the image error (difference between distorted image and reference image) map into smooth region and texture-edge region, calculates their mean values respectively, and then gives them different weights considering the masking effect. The key innovation of this paper is to propose a distortion significance measurement, which is a visual quality coefficient that can effectively indicate the influence of different distortion types on perceptual quality and unify them with HVS. The segmented image error maps are weighted by the distortion significance coefficient. The experimental results on four largest benchmark databases show that the most of the distortions are successfully evaluated and the results are consistent with HVS.

关键词： Error map MAE FR-IQA distortion significance coefficient

来源：评论

学校读者我要写书评

暂无评论

No-Reference Stereoscopic image Quality Assessment Based On visual Attention Mechanism

No-Reference Stereoscopic Image Quality Assessment Based On ...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Li, Sumei Zhao, Ping Chang, Yongli Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728180687

In this paper, we proposed an optimized model based on the visual attention mechanism(VAM) for no-reference stereoscopic image quality assessment (SIQA). A CNN model is designed based on dual attention mechanism (DAM), which includes channel attention mechanism and spatial attention mechanism. The channel attention mechanism can give high weight to the features with large contribution to final quality, and small weight to features with low contribution. The spatial attention mechanism considers the inner region of a feature, and different areas are assigned different weights according to the importance of the region within the feature. In addition, data selection strategy is designed for CNN model. According to VAM, visual saliency is applied to guide data selection, and a certain proportion of saliency patches are employed to fine tune the network. The same operation is performed on the test set, which can remove data redundancy and improve algorithm performance. Experimental results on two public databases show that the proposed model is superior to the state-of-the-art SIQA methods. Cross-database validation shows high generalization ability and high effectiveness of our model.

关键词： stereoscopic image visual attention mechanism dual attention mechanism data selection

来源：评论

学校读者我要写书评

暂无评论

Cleaning Highly Unbalanced Multisource image Dataset for Quality Control in Cervical Precancer Screening 4th

Cleaning Highly Unbalanced Multisource Image Dataset for Qua...

引用

4th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2021

作者： Xue, Zhiyun Guo, Peng Angara, Sandeep Pal, Anabik Jeronimo, Jose Desai, Kanan T. Ajenifuja, Olusegun K. Adepiti, Clement A. Sanjose, Silvia D. Schiffman, Mark Antani, Sameer National Library of Medicine BethesdaMD20894 United States National Cancer Institute RockvilleMD20850 United States Obafemi Awolowo University Ile-Ife Nigeria

ISBN: (纸本)9783031070044

Automated visual evaluation (AVE) of uterine cervix images is a deep learning algorithm that aims to improve cervical pre-cancer screening in low or medium resource regions (LMRR). image quality control is an important pre-step in the development and use of AVE. In our work, we use data retrospectively collected from different sources/providers for analysis. In addition to good images, the datasets include low-quality images, green-filter images, and post Lugol’s iodine images. The latter two are uncommon in VIA (visual inspection with acetic acid) and should be removed along with low-quality images. In this paper, we apply and compare two state-of-the-art deep learning networks to filter out those two types of cervix images after cervix detection. One of the deep learning networks is DeepSAD, a semi-supervised anomaly detection network, while the other is ResNeSt, an improved variant of the ResNet classification network. Specifically, we study and evaluate the algorithms on a highly unbalanced large dataset consisting of four subsets from different geographic regions acquired with different imaging device types. We also examine the cross-dataset performance of the algorithms. Both networks can achieve high performance (accuracy above 97% and F1 score above 94%) on the test set. © Springer Nature Switzerland AG 2022.

关键词： Iodine

来源：评论

学校读者我要写书评

暂无评论

Low Light RAW image Enhancement Using Paired Fast Fourier Convolution and Transformer

Low Light RAW Image Enhancement Using Paired Fast Fourier Co...

引用

IEEE visual communications and image processing (VCIP)

作者： Yichi Zhang Hengyu Liu Dandan Ding Zhan Ma School of Information Science and Technology Hangzhou Normal University Hangzhou China School of Electronic Science and Engineering Nanjing University Nanjing China

ISBN: (纸本)9781665475938

Compared to RGB images non-linearly mapped from RAW data through the image Signal Processor (ISP), RAW data are linear to scene radiance and contain more native information, which is better to be modeled in many vision tasks. This work proposes to enhance low-light images in the RAW domain via a cross-scale framework using paired Fast Fourier Convolution (FFC) and Transformer, driving the network to characterize images effectively. The entire framework has three scales to abstract low-level, mid-level, and high-level representations of input images. We embed paired FFC and Transformer in each scale to attain spatial-spectral information extraction and aggregation. Specifically, by transforming features from the spatial domain into the spectral domain with FFC, pixel correlations can be effectively exploited locally and globally, generating representative features for the input image. Immediately, the Transformer using multi-head self-attention mechanism is applied to aggregate and embed important features. Experimental results demonstrate that our method significantly outperforms state-of-the-art low-light enhancement works in both full reference assessment metrics, including PSNR, MPSNR, and SSIM, and no-reference metrics, such as NIMA. Meanwhile, the perceptual quality of the proposed method is more visually pleasing than that of other methods.

关键词： Measurement Convolution visual communication Transforms Transformers Feature extraction Information retrieval

来源：评论

学校读者我要写书评

暂无评论

Eye Movement Analysis using Virtual Reality in Early Detection of Alzheimer’s Disease

Eye Movement Analysis using Virtual Reality in Early Detecti...

引用

image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Haiwei Zuo King Hann Lim Eswaran Sivaraman Jonathan Then Sien Phang Shenzhen City Polytechnic School of Information and Communications Technology Curtin Malaysia Research Institute Curtin University Malaysia

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Early detection of Alzheimer’s disease (AD) is important at the initial stage to provide prior treatment and control. In traditional diagnostic methods, medical doctors perform manual AD checking by asking patients to repeat several round of text reading and game exercises. The quantification of this manual assessment can be subjective due to the profession experience and it requires a further verification using the brain imaging. In this paper, an automated digital AD assessment method using Virtual Reality (VR) console is proposed to evaluate patient’s condition by tracking their eye movement during text reading. First, the eye movement pattern is captured using VR console during text reading. The recorded eye movement is then converted into frequency domain to study its frequency components during reading process. The simulation of AD symptom is set up by prompting subjects to read their familiar native language and unfamiliar language. As a result, the experiment shows that there are significant differences in eye movement patterns between native and unfamiliar language reading. The group of subjects reading unfamiliar language have random eye movement in less efficient visual processing. This pattern is similar to the patients who suffer in the AD symptom. In addition, the frequency features are extracted to study its correlations with reading comprehension performance in order to detect the early AD condition.

关键词： visualization Correlation Tracking Frequency-domain analysis Virtual reality Manuals Frequency conversion Feature extraction Alzheimer's disease Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Spectral Analysis of Aerial Light Field for Optimization Sampling and Rendering of Unmanned Aerial Vehicle

Spectral Analysis of Aerial Light Field for Optimization Sam...

引用

IEEE visual communications and image processing (VCIP)

作者： Qiuming Liu Yichen Wang Ying Wei Lei Xie Changjian Zhu Ruoxuan Zhou School of Software Engineering Jiangxi Univ. of Sci. and Tech. Jiangxi China School of Comm. and Infor. Engineering Chongqing Univ. of Posts and Telecom. Chongqing China School of Electron. and Inform. Comm. Huazhong Univ. of Sci. and Tech. Wuhan China Nanchang Key lab. of Virtual Digital Factory and Cultural Communications Jiangxi China

ISBN: (纸本)9781665475938

The aerial light field (ALF) can render higher quality images of large-scale 3D scenes. In this paper, we apply the ALF technology to study the image captured and novel view rendering of unmanned aerial vehicle (UAV), which exists some problems, such as large scene and depth of field. First, we design an ALF sampling model using spectral analysis of Fourier theory. Based on the ALF sampling model, the exact expression of ALF spectrum is derived. By the spectral support of ALF, we analyze the influence of pitch angle on light field sampling and its bandwidth. Particularly, the bandwidth of ALF can be applied to determine the minimum sampling rate for UAV. Additionally, we design a reconstruction filter that is related to pitch angle to render novel views of UAV. Finally, our experiments show that our sampling and rendering methods can improve the rendering quality of UAV novel view rendering.

关键词： Analytical models Three-dimensional displays visual communication Bandwidth Rendering (computer graphics) Autonomous aerial vehicles Light fields

来源：评论

学校读者我要写书评

暂无评论

Self-supervised Continual Learning for Object Recognition in image Sequences 28th

Self-supervised Continual Learning for Object Recognition in...

引用

28th International conference on Neural Information processing

作者： Dai, Ruiqi Lefort, Mathieu Armetta, Frederic Guillermin, Mathieu Difiner, Stefan Univ Lyon INSA Lyon Lyon France Univ Claude Bernard Lyon 1 Univ Lyon Lyon France CNRS UMR5205 LIRIS Villeurbanne France Univ Catholique Lyon Lyon France

ISBN: (纸本)9783030923068;9783030923075

The autonomous learning of different objects in images, with a continual and unsupervised context, relies on detecting unknown objects and recognizing known ones based on the learned visual representation. Novelty detection is challenging because of the internal representation drifts of known objects not been seen for a long time. Most existing approaches either perform offline unsupervised learning on a large dataset, or continual supervised learning. Nevertheless, very few existing approaches propose unsupervised continual learning for object recognition. In this paper, we propose a new neural network-based approach for continually learning representations of objects from image sequences, that is able to autonomously detect novel objects and to recognize previously learned ones during training. It is based on a statistical test, performed on internal representations, adapted to counterbalance the concept drift, without storing any image. Experimental results show that our approach outperforms the state of the art on MNIST and Fashion-MNIST datasets. In particular, our approach avoids to over-segment the distribution of clusters, which artificially increases traditional indicators such as clustering accuracy.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Multi-stage Locally and Long-range Correlated Feature Fusion for Learned In-loop Filter in VVC

Multi-stage Locally and Long-range Correlated Feature Fusion...

引用

IEEE visual communications and image processing (VCIP)

作者： Birendra Kathariya Zhu Li Hongtao Wang Geert Van Der Auwera University of Missouri-Kansas City Kansas City USA Qualcomm San Diego USA

ISBN: (纸本)9781665475938

Versatile Video Coding (VVC)/H.266 is currently the state-of-the-art video coding standard with significant improvement in coding efficiency over its predecessor High Efficiency Video Coding (HEVC)/H.26S. Nonetheless, VVC is also block-based video coding technology where decoded pictures contain compression artifacts. In VVC, in-loop filters serve to suppress these compression artifacts. In this paper, convolution neural network (CNN) is utilized to better facilitate the suppression of compression artifacts over VVC. Nonetheless, our approach has uniqueness in obtaining better features by exploiting locally correlated spatial features in the pixel domain as well as long-range correlated spectral features in the discrete cosine transform (DCT) domain. In particular, we utilized CNN-features from DCT transformed input to extract high-frequency components and induce long-range correlation into the spatial CNN-features by employing multi-stage feature fusion. Our experimental result shows that the proposed approach achieves significant coding improvements up to 9.70% on average Bjantegaard Delta (BD)-Bitrate savings under AI configurations for luma (Y) components.

关键词： image coding Correlation visual communication Convolution Neural networks Transforms Feature extraction

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465页 << < 78 79 80 81 82 83 84 85 86 87 > >>

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：