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

4,504 篇 会议
48 篇 期刊文献
18 册 图书
1 篇 学位论文

馆藏范围

4,571 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,607 篇 工学
- 1,696 篇 计算机科学与技术...
- 1,111 篇 信息与通信工程
- 989 篇 软件工程
- 698 篇 电气工程
- 281 篇 光学工程
- 125 篇 电子科学与技术（可...
- 119 篇 生物工程
- 115 篇 控制科学与工程
- 103 篇 仪器科学与技术
- 97 篇 网络空间安全
- 95 篇 生物医学工程（可授...
- 72 篇 机械工程
- 44 篇 化学工程与技术
- 31 篇 安全科学与工程
- 30 篇 动力工程及工程热...
- 26 篇 轻工技术与工程
1,246 篇 医学
- 1,237 篇 临床医学
- 35 篇 基础医学(可授医学...
- 29 篇 药学(可授医学、理...
1,041 篇 理学
- 745 篇 物理学
- 338 篇 数学
- 128 篇 生物学
- 105 篇 统计学（可授理学、...
- 48 篇 系统科学
- 47 篇 化学
190 篇 管理学
- 103 篇 管理科学与工程(可...
- 98 篇 图书情报与档案管...
- 32 篇 工商管理
77 篇 军事学
- 74 篇 军队指挥学
38 篇 法学
- 30 篇 社会学
10 篇 文学
8 篇 经济学
7 篇 农学
6 篇 教育学

主题

552 篇 image coding
472 篇 image processing
354 篇 visual communica...
318 篇 visualization
276 篇 image segmentati...
260 篇 feature extracti...
181 篇 image compressio...
169 篇 video coding
165 篇 image reconstruc...
158 篇 cameras
153 篇 humans
138 篇 image quality
132 篇 image retrieval
126 篇 training
123 篇 image edge detec...
123 篇 image color anal...
115 篇 image enhancemen...
115 篇 image analysis
113 篇 signal processin...
110 篇 motion estimatio...

机构

36 篇 shanghai jiao to...
29 篇 institute of ima...
28 篇 school of electr...
24 篇 school of electr...
21 篇 university of sc...
18 篇 shanghai jiao to...
17 篇 shanghai jiao to...
16 篇 tianjin univ sch...
16 篇 beijing universi...
14 篇 institute for in...
11 篇 school of comput...
11 篇 university of el...
11 篇 cas key laborato...
11 篇 tsinghua univ de...
10 篇 univ sci & techn...
10 篇 zhejiang univers...
10 篇 peking univ inst...
10 篇 institute of ima...
9 篇 microsoft resear...
9 篇 tsinghua univ de...

作者

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

语言

4,386 篇 英文
128 篇 其他
49 篇 土耳其文
39 篇 中文

检索条件"任意字段=Conference on Visual Communications and Image Processing 2003"

共 4571 条记录，以下是1051-1060 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：

Attention-guided Convolutional Neural Network for Lightweight JPEG Compression Artifacts Removal

Attention-guided Convolutional Neural Network for Lightweigh...

引用

IEEE visual communications and image processing (VCIP)

作者： Gang Zhang Haoquan Wang Yedong Wang Haijie Shen School of Microelectronics Tianjin University Tianjin China Hisense Electric Co. Ltd. Multimedia R&D Center Qingdao China

ISBN: (纸本)9781728173221

JPEG compression artifacts seriously affect the viewing experience. While previous studies mainly focused on the deep convolutional networks for compression artifacts removal, of which the model size and inference speed limit their application prospects. In order to solve the above problems, this paper proposed two methods that can improve the training performance of the compact convolution network without slowing down its inference speed. Firstly, a fully explainable attention loss is designed to guide the network for training, which is calculated by local entropy to accurately locate compression artifacts. Secondly, Fully Expanded Block (FEB) is proposed to replace the convolutional layer in compact network, which can be contracted back to a normal convolutional layer after the training process is completed. Extensive experiments demonstrate that the proposed method outperforms the existing lightweight methods in terms of performance and inference speed.

关键词： Training Performance evaluation image coding visual communication Convolution Transform coding Video compression

来源：评论

学校读者我要写书评

暂无评论

Entropy-based Deep Product Quantization for visual Search and Deep Feature Compression

Entropy-based Deep Product Quantization for Visual Search an...

引用

IEEE visual communications and image processing (VCIP)

作者： Benben Niu Ziwei Wei Yun He Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Beijing China

ISBN: (纸本)9781728173221

With the emergence of various machine-to-machine and machine-to-human tasks with deep learning, the amount of deep feature data is increasing. Deep product quantization is widely applied in deep feature retrieval tasks and has achieved good accuracy. However, it does not focus on the compression target primarily, and its output is a fixed-length quantization index, which is not suitable for subsequent compression. In this paper, we propose an entropy-based deep product quantization algorithm for deep feature compression. Firstly, it introduces entropy into hard and soft quantization strategies, which can adapt to the codebook optimization and codeword determination operations in the training and testing processes respectively. Secondly, the loss functions related to entropy are designed to adjust the distribution of quantization index, so that it can accommodate to the subsequent entropy coding module. Experimental results carried on retrieval tasks show that the proposed method can be generally combined with deep product quantization and its extended schemes, and can achieve a better compression performance under near lossless condition.

关键词： Training visualization Quantization (signal) image coding visual communication Entropy Entropy coding

来源：评论

学校读者我要写书评

暂无评论

Stereo image Super-Resolution Based on Pixel-Wise Knowledge Distillation Strategy

Stereo Image Super-Resolution Based on Pixel-Wise Knowledge ...

引用

IEEE visual communications and image processing (VCIP)

作者： Li Ma Sumei Li School of electrical and information engineering Tianjin University Tianjin China

ISBN: (纸本)9781728173221

In stereo image super-resolution (SR), it is equally important to utilize intra-view and cross-view information. However, most existing methods only focus on the exploration of cross-view information and neglect the full mining of intra-view information, which limits the reconstruction performance of these methods. Since single image SR (SISR) methods are powerful in intra-view information exploitation, we propose to introduce the knowledge distillation strategy to transfer the knowledge of a SISR network (teacher network) to a stereo image SR network (student network). With the help of the teacher network, the student network can easily learn more intra-view information. Specifically, we propose pixel-wise distillation as the implementation method, which not only improves the intra-view information extraction ability of student network, but also ensures the effective learning of cross-view information. Moreover, we propose a lightweight student network named Adaptive Residual Feature Aggregation network (ARFAnet). Its main unit, the ARFA module, can aggregate informative residual features and produce more representative features for image reconstruction. Experimental results demonstrate that our teacher-student network achieves state-of-the-art performance on all benchmark datasets.

关键词： Knowledge engineering Adaptive systems visual communication Aggregates Superresolution Neural networks Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Dictionary Learning-based Reference Picture Resampling in VVC

Dictionary Learning-based Reference Picture Resampling in VV...

引用

IEEE visual communications and image processing (VCIP)

作者： Jens Schneider Christian Rohlfing Institut für Nachrichtentechnik RWTH Aachen University

ISBN: (纸本)9781728173221

Versatile Video Coding (VVC) introduces the con-cept of Reference Picture Resampling (RPR), which allows for a resolution change of the video during decoding, without introducing an additional Intra Random Access Point (IRAP) into the bitstream. When the resolution is increased, an upsampling operation of the reference picture is required in order to apply motion compensated prediction. Conceptually, the upsampling by linear interpolation filters fails to recover frequencies which were lost during downsampling. Yet, the quality of the upsampled reference picture is crucial to the pre-diction performance. In recent years, machine learning based Super-Resolution (SR) has shown to outperform conventional interpolation filters by far in regard to super-resolving a previ-ously downsampled image. In particular, Dictionary Learning-based Super-Resolution (DLSR) was shown to improve the inter-layer prediction in SHVC [1]. Thus, this paper introduces DLSR to the prediction process in RPR. Further, the approach is experimentally evaluated by an implementation based on the VTM-9.3 reference software. The simulation results show a reduction of the instantaneous bitrate of 0.98% on average at the same objective quality in terms of PSNR. Moreover, the peak bitrate reduction is measured to 4.74% for the “Johnny” sequence of the JVET test set.

关键词： Video coding Interpolation image coding Dictionaries visual communication Simulation Bit rate

来源：评论

学校读者我要写书评

暂无评论

360HRL: Hierarchical Reinforcement Learning Based Rate Adaptation for 360-Degree Video Streaming

360HRL: Hierarchical Reinforcement Learning Based Rate Adapt...

引用

IEEE visual communications and image processing (VCIP)

作者： Jun Fu Chen Hou Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728173221

Recently, reinforced adaptive bitrate (ABR) algorithms have achieved remarkable success in tile-based 360-degree video streaming. However, they heavily rely on accurate viewport prediction. To alleviate this issue, we propose a hierarchical reinforcement-learning (RL) based ABR algorithm, dubbed 360HRL. Specifically, 360HRL consists of a top agent and a bottom agent. The former is used to decide whether to download a new segment for continuous playback or re-download an old segment for correcting wrong bitrate decisions caused by inaccurate viewport estimation, and the latter is used to select bitrates for tiles in the chosen segment. In addition, 360HRL adopts a two-stage training methodology. In the first stage, the bottom agent is trained under the environment where the top agent always chooses to download a new segment. In the second stage, the bottom agent is fixed and the top agent is optimized with the help of a heuristic decision rule. Experimental results demonstrate that 360HRL outperforms existing RL-based ABR algorithms across a broad of network conditions and quality of experience (QoE) objectives.

关键词： Training visual communication image processing Bit rate Estimation Reinforcement learning Streaming media

来源：评论

学校读者我要写书评

暂无评论

CNN-based Super Resolution for Video Coding Using Decoded Information

CNN-based Super Resolution for Video Coding Using Decoded In...

引用

IEEE visual communications and image processing (VCIP)

作者： Chaoyi Lin Yue Li Kai Zhang Zhaobin Zhang Li Zhang Multimedia Lab Bytedance Inc.

ISBN: (纸本)9781728173221

Down-sampling followed by an up-sampling is a well-known strategy to compress high-resolution pictures given a limited bandwidth in image as well as video coding. Recently, inspired by the latest advances of image super resolution (SR) technologies using convolutional neural network (CNN), CNN-based SR has been explored for resampling-based coding. However, the side information generated during the compression process is not utilized efficiently by the SR network in prior arts. In this paper, we propose a CNN-based SR method for video coding, where more side information is leveraged as a supplement to reconstruction samples. Specifically, we introduce prediction samples to be the auxiliary information, as it can provide the texture and directional information about the original picture. Considering the different characteristics, we design different networks for the luma and chroma components. When designing the chroma up-sampling CNN, the luma reconstruction is used as the auxiliary information of the chroma network, which exploits the cross-component correlation. Experimental results show that the proposed method achieves 11.07% BD-rate savings in all-intra configuration compared with VTM-11.0. Further experiments validate the effectiveness of using luma information to aid the chroma up-sampling process.

关键词： Video coding image coding image resolution Correlation visual communication Bit rate Bandwidth

来源：评论

学校读者我要写书评

暂无评论

DCTResNet: Transform Domain image Deblocking for Motion Blur images

DCTResNet: Transform Domain Image Deblocking for Motion Blur...

引用

IEEE visual communications and image processing (VCIP)

作者： Paras Maharjan Ning Xu Xuan Xu Yuyan Song Zhu Li University of Missouri Kansas City USA Kwai Inc. CA USA

ISBN: (纸本)9781728173221

Pixel recovery with deep learning has shown to be very effective for a variety of low-level vision tasks like image super-resolution, denoising, and deblurring. Most existing works operate in the spatial domain, and there are few works that exploit the transform domain for image restoration tasks. In this paper, we present a transform domain approach for image deblocking using a deep neural network called DCTResNet. Our application is compressed video motion deblur, where the input video frame has blocking artifacts that make the deblurring task very challenging. Specifically, we use a block-wise Discrete Cosine Transform (DCT) to decompose the image into its low and high-frequency sub-band images and exploit the strong sub-band specific features for more effective deblocking solutions. Since JPEG also uses DCT for image compression, using DCT sub-band images for image deblocking helps to learn the JPEG compression prior to effectively correct the blocking artifacts. Our experimental results show that both PSNR and SSIM for DCTResNet perform more favorably than other state-of-the-art (SOTA) methods, while significantly faster in inference time.

关键词： Deep learning image coding Superresolution Noise reduction Transform coding Transforms image restoration

来源：评论

学校读者我要写书评

暂无评论

Faster and Finer Pose Estimation for Object Pool in a Single RGB image

Faster and Finer Pose Estimation for Object Pool in a Single...

引用

IEEE visual communications and image processing (VCIP)

作者： Lee Aing Wen-Nung Lie Jui-Chiu Chiang National Chung Cheng University (CCU) Chiayi Taiwan Center for Innovative Research on Aging Society (CIRAS) National Chung Cheng University (CCU) Chiayi Taiwan Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI) National Chung Cheng University (CCU) Chiayi Taiwan

ISBN: (纸本)9781728173221

Predicting/estimating the 6DoF pose parameters for multi-instance objects accurately in a fast manner is an important issue in robotic and computer vision. Even though some bottom-up methods have been proposed to be able to estimate multiple instance poses simultaneously, their accuracy cannot be considered as good enough when compared to other state-of-the-art top-down methods. Their processing speed still cannot respond to practical applications. In this paper, we present a faster and finer bottom-up approach of deep convolutional neural network to estimate poses of the object pool even multiple instances of the same object category present high occlusion/overlapping. Several techniques such as prediction of semantic segmentation map, multiple keypoint vector field, and 3D coordinate map, and diagonal graph clustering are proposed and combined to achieve the purpose. Experimental results and ablation studies show that the proposed system can achieve comparable accuracy at a speed of 24.7 frames per second for up to 7 objects by evaluation on the well-known Occlusion LINEMOD dataset.

关键词： Computer vision Three-dimensional displays visual communication Robot kinematics image processing Semantics Pose estimation

来源：评论

学校读者我要写书评

暂无评论

EMERGING THREAT OF DEEP FAKE: HOW TO IDENTIFY AND PREVENT IT 22

EMERGING THREAT OF DEEP FAKE: HOW TO IDENTIFY AND PREVENT IT

引用

Proceedings of the 6th International conference on Future Networks & Distributed Systems

作者： Murooj Amer Taha Wijdan Mahood Khudhair Ahmed Mahmood Khudhur Omar Abdulkareem Mahmood Yousif I. Hammadi Riyam Shihab Ahmed Al-husseinawi Ahmed Aziz Department of Computer Engineering Techniques Bilad Alrafidain University College Iraq Department of Communications Engineering University of Diyala Iraq Department of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of International Business Management Tashkent State University of Economics Uzbekistan

ISBN: (纸本)9781450399050

Although manipulations of visual and aural media have been around for as long as there have been media, the relatively recent introduction of deep fakes has marked a turning point in the creation of fake information. Deep fakes are automated methods that allow the creation of fake information that is becoming increasingly difficult for human observers to see. These procedures are made possible by the most recent technological advancements in artificial intelligence and deep learning. Deep Learning is a powerful method that is now being implemented in a variety of industries, including natural language processing, computer vision, image processing, and machine vision. Deep fakes are created by the use of deep learning algorithms in order to synthesize and modify photos, videos, or sounds of a person, to the point that human people are unable to discern the fake from the real one. In this article, we present a workable description of deep fakes as well as an outline of the technology that lies underneath them. In order to assist people in thinking about the future of deep fakes, we outline the benefits of the deepfake as well as the potential threats. This study presents a complete analysis of deep fake methods and discusses the most effective strategies for preventing counterfeiting.

关键词：

来源：评论

学校读者我要写书评

暂无评论

iWave3D: End-to-end Brain image Compression with Trainable 3-D Wavelet Transform

iWave3D: End-to-end Brain Image Compression with Trainable 3...

引用

IEEE visual communications and image processing (VCIP)

作者： Dongmei Xue Haichuan Ma Li Li Dong Liu Zhiwei Xiong University of Science and Technology of China Hefei China

ISBN: (纸本)9781728173221

With the rapid development of whole brain imaging technology, a large number of brain images have been produced, which puts forward a great demand for efficient brain image compression methods. At present, the most commonly used compression methods are all based on 3-D wavelet transform, such as JP3D. However, traditional 3-D wavelet transforms are designed manually with certain assumptions on the signal, but brain images are not as ideal as assumed. What's more, they are not directly optimized for compression task. In order to solve these problems, we propose a trainable 3-D wavelet transform based on the lifting scheme, in which the predict and update steps are replaced by 3-D convolutional neural networks. Then the proposed transform is embedded into an end-to-end compression scheme called iWave3D, which is trained with a large amount of brain images to directly minimize the rate-distortion loss. Experimental results demonstrate that our method outperforms JP3D significantly by 2.012 dB in terms of average BD-PSNR.

关键词： Wavelet transforms Neuroimaging Brain image coding visual communication Rate-distortion Entropy

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458页 << < 102 103 104 105 106 107 108 109 110 111 > >>

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：