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

3,802 篇 会议
43 篇 期刊文献
34 册 图书

馆藏范围

3,879 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,252 篇 工学
- 1,473 篇 计算机科学与技术...
- 1,066 篇 信息与通信工程
- 852 篇 软件工程
- 601 篇 电气工程
- 240 篇 光学工程
- 117 篇 生物工程
- 106 篇 电子科学与技术（可...
- 95 篇 控制科学与工程
- 90 篇 机械工程
- 87 篇 仪器科学与技术
- 78 篇 生物医学工程（可授...
- 37 篇 化学工程与技术
- 34 篇 网络空间安全
- 24 篇 动力工程及工程热...
- 24 篇 安全科学与工程
- 17 篇 土木工程
- 16 篇 交通运输工程
974 篇 医学
- 963 篇 临床医学
- 39 篇 基础医学(可授医学...
- 31 篇 药学(可授医学、理...
960 篇 理学
- 726 篇 物理学
- 298 篇 数学
- 123 篇 生物学
- 79 篇 统计学（可授理学、...
- 36 篇 化学
- 31 篇 系统科学
161 篇 管理学
- 91 篇 管理科学与工程(可...
- 77 篇 图书情报与档案管...
- 21 篇 工商管理
35 篇 法学
- 31 篇 社会学
12 篇 军事学
11 篇 文学
9 篇 经济学
7 篇 农学
6 篇 教育学

主题

482 篇 image coding
371 篇 image processing
351 篇 visual communica...
296 篇 visualization
230 篇 feature extracti...
210 篇 image segmentati...
165 篇 image compressio...
146 篇 image reconstruc...
141 篇 video coding
140 篇 training
119 篇 image color anal...
113 篇 cameras
108 篇 image enhancemen...
108 篇 deep learning
106 篇 image quality
95 篇 humans
93 篇 image edge detec...
85 篇 three-dimensiona...
82 篇 decoding
80 篇 motion estimatio...

机构

36 篇 shanghai jiao to...
30 篇 institute of ima...
24 篇 school of electr...
21 篇 shanghai jiao to...
20 篇 university of sc...
16 篇 shanghai jiao to...
16 篇 tianjin univ sch...
16 篇 beijing universi...
12 篇 tsinghua univ de...
11 篇 university of el...
11 篇 cas key laborato...
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
17 篇 min xiongkuo
16 篇 dong liu
16 篇 shan liu
15 篇 andré kaup
15 篇 yang xiaokang
14 篇 chen zhibo
13 篇 xie rong
13 篇 xiongkuo min
12 篇 gao wen
11 篇 heming sun
11 篇 zhibo chen
11 篇 zhenzhong chen
11 篇 gao zhiyong

语言

3,807 篇 英文
51 篇 土耳其文
39 篇 中文
13 篇 其他

检索条件"任意字段=Conference on Visual Communications and Image Processing 96"

共 3879 条记录，以下是21-30 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：

Recursive Neural Network: Small Target Detection in Remote Sensing images 17th

Recursive Neural Network: Small Target Detection in Remote S...

引用

17th International conference on Intelligent Robotics and Applications

作者： Yu, Changlin Zhang, Juchao Sun, Zhongyu Pang, Zaixiang Xu, Changxian Sun, Zhongbo Changchun Univ Technol Dept Control Engn Changchun 130012 Peoples R China Acad Sci Guangzhou Inst Geog Guangdong Open Lab Geospatial Informat Technol & Guangzhou 510070 Guangdong Peoples R China

ISBN: (纸本)9789819607945;9789819607952

In this paper, a novel remote sensing image object detection algorithm is proposed by combining constraint energy minimization (CEM) and noise tolerance zeroing neural network (NTZNN). This algorithm combines traditional image processing methods with recursive neural network (RNN) and proposes a multi scene usable NTZNN-CEM object detection model for hyperspectral and RGB remote sensing images. Finally, through numerical simulation experiments and remote sensing image object detection experiments, it has been proven that the NTZNN-CEM algorithm has advantages such as fast detection speed and strong robustness, providing a new visual approach for improving the advanced perception of robots in complex environments.

关键词： Recursive neural network Remote sensing image Object detection

来源：评论

学校读者我要写书评

暂无评论

SteDM: Efficient image Steganography with Diffusion Models 24th

SteDM: Efficient Image Steganography with Diffusion Models

引用

24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Wang, Changguang Shi, Haoyi Li, Qingru Zhao, Dongmei Wang, Fangwei Key Laboratory of Network and Information Security of Hebei Province Hebei Normal University Shijiazhuang050024 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China

ISBN: (纸本)9789819615506

Existing deep learning-based image steganography schemes mostly neglect the latent space of images. These schemes merely adopt simple concatenation of image feature vectors, resulting in a low utilization rate of features, low steganographic image quality, and poor image robustness. This paper introduces the latent diffusion model into an image steganography scheme, namely SteDM. The SteDM firstly uses an encoder to transform the cover image and the secret image into the latent space, then employing a cross-attention mechanism to fuse them during the inverse diffusion process. Then we use a decoder to obtain a steganographic image containing secret image features. During the extraction process, the latent space-based diffusion model is similarly employed. Training loss is defined as a joint optimization of the autoencoder and diffusion model during the training process. Experimental results demonstrate that the SteDM outperforms existing steganography schemes in some aspects such as visual effects, security, and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Banana Variety Classification and Grading at Bunch Level 17th

Banana Variety Classification and Grading at Bunch Level

引用

17th International conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Guru, D.S. Saritha, N. Mahesha, M.C. Department of Studies in Computer Science University of Mysore Karnataka Manasagangotri Mysuru570006 India

ISBN: (纸本)9789819606917

Sorting bananas based on their visual features helps meet consumer requirements in various markets and food processing industries. Many banana varieties look similar in terms of their size and shape, making differentiation challenging and leading to misclassification without detailed visual data. Our pro-posed system focusses on distinguishing these visually similar varieties, especially the closely sized Elakki-bale and Rasbale. We employ transfer learning techniques for banana variety classification and grading to achieve high accuracy. Our bunch-level banana image dataset addresses the gap in existing dataset that are smaller segments mainly hand-level or finger-level, serving the large-scale food processing industry needs. This study also comprises a system for grading bananas into low, medium, and high quality. In our proposed work, during classification, deep features were extracted and various classifiers were tested. The study revealed that the Linear SVM classifier works better for Integrated Model. For Convolutional Model MobileNet features lead in accuracy and most time-efficient in terms of training. EfficientNetB7 gives high accuracy but requiring the longest training duration. In this study, a hierarchical approach was employed for variety classification and grading. The study revealed that the accuracy of our proposed hierarchical classification approach using Convolutional Models is 96.85% which is higher than the conventional single shot multi-class classification approach (94.87%). Our proposed hierarchical approach, focused on bunch-level classification, is compared against state-of-the-art single shot multi-class classification, which primarily targets hand or finger-level classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Banana Grading Banana Variety Classification image processing Techniques

来源：评论

学校读者我要写书评

暂无评论

image Captioning with Multiple Perspectives—A visual Context-Based Approach 5th

Image Captioning with Multiple Perspectives—A Visual Contex...

引用

5th International conference on Computing and Network communications, CoCoNet 2023

作者： Ashwin, G. Chaitanya, V. Kishan Rohith Nair, Priyanka C. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru560035 India

ISBN: (纸本)9789819747108

This study tackles the difficult issues of image captioning while negotiating the complexity of visual data processing. The complexity of visual data and the associated processing requirements make image captioning a daunting task. This research describes an effective method for image captioning that makes advantage of attentional mechanisms. Several models, including Transformer, VGG16, VGG19, Inception, RNN decoders, and Bahdanau’s attention mechanism, are contrasted and analyzed to demonstrate the benefits of integrating attention processes. By allowing the model to focus on the appropriate visual component, attention improves the accuracy of the generated captions. Transformer models, in particular, outperform other methods by capturing complicated relationships and delivering accurate output, and they have the highest BLEU Score of 70. The findings emphasize the significance of attention mechanisms and the relevance of selecting a suitable model architecture to maximize picture captioning performance. The variety of potential applications emphasizes the benefits and potential impact of image captioning in a broad range of scenarios. Several use cases are found beneficial, including supporting the visually impaired, improving product descriptions in e-commerce, assisting medical diagnosis, improving image search ability, and allowing effective communication and comprehension of visual information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Proceedings EC-VIP-MC 2003 - 4th EURASIP conference Focused on Video / image processing and Multimedia communications

Proceedings EC-VIP-MC 2003 - 4th EURASIP Conference Focused ...

引用

4th EURASIP conference Focused on Video / image processing and Multimedia communications, EC-VIP-MC 2003

ISBN: (纸本)9531840547

The proceedings contain 131 papers. The topics discussed include: multimedia in Croatian digital video broadcasting;signal and image data processing in ultrasonic imaging;progressive image coding using regional color correlation;edge-preserving regularization of disparity and motion fields;an edge-preserving high-resolution image reconstruction;a new method for image coding in computer vision;efficient post-processing for block-based compressed video;detecting lesions in a mammogram;a methodology for evaluating the operational effectiveness of facial recognition systems;gallery image effects on facial recognition systems;on edge detection in MRI using the wavelet transform and unsupervised neural networks;improved illumination independent moving object detection in real world video sequences;and virtual image : keyframe or visual icon?.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Proceedings EC-VIP-MC 2003 - 4th EURASIP conference Focused on Video / image processing and Multimedia communications

Proceedings EC-VIP-MC 2003 - 4th EURASIP Conference Focused ...

引用

4th EURASIP conference Focused on Video / image processing and Multimedia communications, EC-VIP-MC 2003

ISBN: (纸本)9531840547

关键词：

来源：评论

学校读者我要写书评

暂无评论

image and Video Compression Using Generative Sparse Representation with Fidelity Controls 17th

Image and Video Compression Using Generative Sparse Represe...

引用

17th Asian conference on Computer Vision, ACCV 2024

作者： Zhou, Lebin Wang, Wei Jiang, Wei Futurewei Technologies Inc. San JoseCA95131 United States

ISBN: (纸本)9789819626434

We propose a framework for learned image and video compression using the generative sparse visual representation (SVR) guided by fidelity-preserving controls. By embedding inputs into a discrete latent space spanned by learned visual codebooks, SVR-based compression transmits integer codeword indices, which is efficient and cross-platform robust. However, high-quality (HQ) reconstruction in the decoder relies on intermediate feature inputs from the encoder via direct connections. Due to the prohibitively high transmission costs, previous SVR-based compression methods remove such feature links, resulting in largely degraded reconstruction quality. In this work, we treat the intermediate features as fidelity-preserving control signals that guide the conditioned generative reconstruction in the decoder. Instead of discarding or directly transferring such signals, we draw them from a low-quality (LQ) fidelity-preserving alternative input that is sent to the decoder with very low bitrate. These control signals provide complementary fidelity cues to improve reconstruction, and their quality is determined by the compression rate of the LQ alternative, which can be tuned to trade off bitrate, fidelity and perceptual quality. Our framework can be conveniently used for both learned image compression (LIC) and learned video compression (LVC). Since SVR is robust against input perturbations, a large portion of codeword indices between adjacent frames can be the same. By only transferring different indices, SVR-based LIC and LVC can share a similar processing pipeline. Experiments over standard image and video compression benchmarks demonstrate the effectiveness of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Joint Dehazing and Low-light image Enhancement with Complete Atmospheric Model 16

Joint Dehazing and Low-light Image Enhancement with Complete...

引用

16th International conference on Signal processing Systems, ICSPS 2024

作者： Wu, Yong Xia, Kexiang Ge, Liyong Ye, Jiongkai Zhejiang Institute of Communications Hangzhou311112 China The School of Automation Hangzhou Dianzi University Hangzhou310000 China

ISBN: (纸本)9781510689251

Current image dehazing algorithms often encounter issues of contrast reduction and color distortion in the shadow regions of images. To address this challenge, this paper proposes a comprehensive atmospheric model that simultaneously considers haze and low-light conditions. This model incorporates a global darkness factor as a disturbance variable alongside the atmospheric scattering model, enabling adaptive luminance balancing without the need to estimate the global atmospheric light or the global darkness factor. Building on the principle of object color rendition, This paper justifies the use of the dark channel prior for haze removal and extends the bright channel prior for low-light image enhancement. Furthermore, we explore the mapping relationship between normal and inverted images based on the complete atmospheric model. By solving for the transmission rates of the global atmospheric light and global darkness factor, we propose an image enhancement algorithm capable of concurrently processing haze and low-light images. The proposed algorithm effectively mitigates issues of contrast reduction and color distortion. Experimental results demonstrate the efficacy of our approach, showing that the visual outcomes of simultaneous haze removal and low-light enhancement surpass those achieved through single-task haze removal. © 2025 SPIE.

关键词： Demulsification

来源：评论

学校读者我要写书评

暂无评论

Underwater Salient Object Detection Based on Swintransformer 5th

Underwater Salient Object Detection Based on Swintransformer

引用

5th China Intelligent Robotics Academic conference

作者： Jiao, Zhan Wang, Ruizi Fu, Bo Zhang, Tianzhuang Zhang, Ning Liaoning Vocat Coll Light Ind Dalian 116100 Peoples R China Liaoning Normal Univ Sch Comp Sci & Artificial Intelligence Dalian 116081 Peoples R China Northeast Normal Univ Training Ctr Minist Educ Studying Overseas Changchun Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Underwater Salient Object Detection (USOD) has emerged as a specialized division of Salient Object Detection (SOD) to meet the increasing demand for underwater exploration. However, two major challenges hinder its development. First, current USOD datasets are limited in size and diversity, making them insufficient for robust model training and evaluation. Second, most existing deep learning-based USOD approaches rely on convolutional encoder-decoder architectures, which improve inference over traditional methods but still suffer from limited receptive fields and high computational costs. To address these challenges, we introduce a new USOD method based on the Swin transformer, which incorporates a multimodal feature fusion module to increase detection accuracy. Our model uses RGB images and depth maps to capture both semantic information and visual details. The multimodal feature fusion module dynamically adjusts feature importance through attention mechanisms, enhancing the ability of the model to extract meaningful information. By using a Swin transformer model with pretrained weights as the encoder backbone, the proposed method mitigates data scarcity issues and captures both the global context and long-range dependencies. The experimental results demonstrate that our approach achieves significant improvements in detecting salient objects in underwater environments.

关键词： Underwater image Salient object detection Swin transformer

来源：评论

学校读者我要写书评

暂无评论

ATTIQA: Generalizable image Quality Feature Extractor Using Attribute-Aware Pretraining 17th

ATTIQA: Generalizable Image Quality Feature Extractor Using ...

引用

17th Asian conference on Computer Vision, ACCV 2024

作者： Kwon, Daekyu Kim, Dongyoung Ki, Sehwan Jo, Younghyun Lee, Hyong-Euk Kim, Seon Joo Yonsei University Seoul Korea Republic of Samsung Advanced Institue of Technology Suwon-si Korea Republic of

ISBN: (纸本)9789819609109

In no-reference image quality assessment (NR-IQA), the challenge of limited dataset sizes hampers the development of robust and generalizable models. Conventional methods address this issue by utilizing large datasets to extract rich representations for IQA. Also, some approaches propose vision language models (VLM) based IQA, but the domain gap between generic VLM and IQA constrains their scalability. In this work, we propose a novel pretraining framework that constructs a generalizable representation for IQA by selectively extracting quality-related knowledge from VLM and leveraging the scalability of large datasets. Specifically, we select optimal text prompts for five representative image quality attributes and use VLM to generate pseudo-labels. Numerous attribute-aware pseudo-labels can be generated with large image datasets, allowing our IQA model to learn rich representations about image quality. Our approach achieves state-of-the-art performance on multiple IQA datasets and exhibits remarkable generalization capabilities. Leveraging these strengths, we propose several applications, such as evaluating image generation models and training image enhancement models, demonstrating our model’s real-world applicability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： visual languages

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388页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：