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

78,569 篇 会议
789 篇 期刊文献
322 册 图书
2 篇 学位论文

馆藏范围

79,682 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

49,680 篇 工学
- 28,981 篇 计算机科学与技术...
- 23,030 篇 电气工程
- 16,808 篇 软件工程
- 16,450 篇 信息与通信工程
- 5,183 篇 电子科学与技术（可...
- 4,803 篇 光学工程
- 4,516 篇 控制科学与工程
- 3,408 篇 生物医学工程（可授...
- 3,091 篇 机械工程
- 2,813 篇 仪器科学与技术
- 2,032 篇 生物工程
- 697 篇 化学工程与技术
- 558 篇 材料科学与工程（可...
- 515 篇 动力工程及工程热...
- 509 篇 航空宇航科学与技...
- 486 篇 交通运输工程
- 477 篇 安全科学与工程
- 464 篇 网络空间安全
19,815 篇 理学
- 12,145 篇 物理学
- 7,852 篇 数学
- 2,309 篇 生物学
- 2,206 篇 统计学（可授理学、...
- 1,567 篇 系统科学
- 850 篇 化学
10,878 篇 医学
- 10,403 篇 临床医学
- 1,069 篇 基础医学(可授医学...
- 1,030 篇 特种医学
- 694 篇 药学(可授医学、理...
3,215 篇 管理学
- 1,991 篇 管理科学与工程(可...
- 1,350 篇 图书情报与档案管...
389 篇 农学
350 篇 法学
286 篇 军事学
177 篇 经济学
117 篇 文学
115 篇 教育学
67 篇 艺术学

主题

7,219 篇 image processing
6,646 篇 signal processin...
5,255 篇 image coding
5,017 篇 image segmentati...
4,063 篇 image reconstruc...
3,853 篇 signal processin...
3,197 篇 feature extracti...
2,239 篇 image analysis
1,995 篇 digital signal p...
1,708 篇 robustness
1,691 篇 image edge detec...
1,681 篇 cameras
1,647 篇 training
1,607 篇 deep learning
1,588 篇 speech processin...
1,462 篇 pixel
1,422 篇 image resolution
1,374 篇 image enhancemen...
1,332 篇 image color anal...
1,187 篇 visualization

机构

53 篇 university of sc...
49 篇 univ chinese aca...
41 篇 department of si...
40 篇 institute for in...
40 篇 shanghai jiao to...
39 篇 tsinghua univers...
38 篇 school of electr...
38 篇 shanghai jiao to...
36 篇 national institu...
36 篇 school of comput...
34 篇 princeton univ d...
34 篇 beijing inst tec...
33 篇 ibm thomas j. wa...
33 篇 shenzhen interna...
30 篇 institute of inf...
30 篇 beijing key labo...
28 篇 zhejiang univers...
27 篇 nanyang technol ...
27 篇 centre for multi...
27 篇 astar inst infoc...

作者

51 篇 zhang lei
46 篇 wiegand thomas
40 篇 yang bin
33 篇 jie yang
32 篇 jing zhang
32 篇 kaup andre
31 篇 m. vetterli
29 篇 vetterli martin
29 篇 m. gabbouj
29 篇 li li
27 篇 guangtao zhai
27 篇 li xin
27 篇 wang jing
26 篇 karam lina j.
25 篇 jian wang
24 篇 zhang tao
24 篇 m.j.t. smith
24 篇 li yang
24 篇 jun wang
23 篇 kuo c. -c. jay

语言

78,295 篇 英文
580 篇 其他
503 篇 土耳其文
288 篇 中文
9 篇 俄文
5 篇 西班牙文
2 篇 法文
1 篇 葡萄牙文

检索条件"任意字段=IEEE International Conference on Signal and Image Processing Applications"

共 79682 条记录，以下是161-170 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：

Learning to Follow Infrared Prior Repersentation for image Dehazing

Learning to Follow Infrared Prior Repersentation for Image D...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Du, Yiquan Yang, Ming Huo, Haiyang Shi, Jiaqi Ding, Jiangang Pei, Lili School of Information Engineering Chang'an University Shaanxi Xi'an710064 China School of Data Science and Artificial Intelligence Chang'an University Shaanxi Xi'an710064 China

ISBN: (纸本)9798350368741

The infrared image can distinguish the targets from the background based on radiation differences, providing more significant target visibility under dense haze. Fusion of visible haze images with infrared prior representations can generate high-quality fused images for high-level tasks. Consequently, we propose a novel dual-modal fusion network structure that makes full use of infrared prior representations for dehazing. Specifically, we emphasize a Multi-modal Feature Extraction Network (MMFE) to extract deep multi-scale features. Meanwhile, we introduce a Multi-scale Feature Extraction module (MSFE), integrating an Efficient Dual Attenion block (EDAB) to efficiently explore more spatial and marginal information. Additionally, we propose a new feature fusion strategy, which calculates feature fusion weights based on an adaptive multi-head self-attention. Therefore, IPRDehazeNet achieves better dehazing results through dual-modal fusion. Experimental results indicate that IPRDehazeNet outperforms various advanced methods. © 2025 ieee.

关键词： image dehazing Multi-modal Multi-scale Visible-Infrared fusion

来源：评论

学校读者我要写书评

暂无评论

ARM: nnU-Net with Arena Mechanism for Medical image Segmentation

ARM: nnU-Net with Arena Mechanism for Medical Image Segmenta...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Luo, Haoran Guan, Cong Shao, Tengfei Li, Shenglei Tomoji, Kishi Osamu, Yoshie Graduate School of CSE Waseda University Shinjuku-ku Tokyo Japan Graduate School of IPS Waseda University Wakamatsu Ward Fukuoka Kitakyushu Japan

ISBN: (纸本)9798350368741

The success of nnU-Net proves the significance of the rationality of workflow architecture and configuration settings in improving segmentation accuracy. However, since that, most efforts to improve U-Net have continued to address CNN inner limitations caused by architecture. These methods encountered challenges such as limited generalization, difficulty in managing data with varying distribution patterns. To tackle these issues, We designed a standardized processing workflow specifically tailored for the convolutional layers of U-Net: Arena Mechanism (ARM), inspired by game theory, which encompasses three stages: Recruit, Train and Fight. We augment the convolutional layers of U-Net with two additional processing branches (Recruit), serving as "challengers." These challengers are first optimized within a Hybrid Adaptive Weighting module to refine the feature representation of each internal channel (Train). During forward propagation, we design a cooperative loss and reward function to determine the optimal confidence distribution and fusion strategy for branch outputs (Fight). This standardized mechanism is designed to further nnU-Net's core principle of "Automatic Adaptation." Experiments have demonstrated that this approach achieves state-of-the-art (SOTA) performance on both the ACDC, BraTS21 and KiTS datasets. © 2025 ieee.

关键词： CNN Game Theory Medical image Segmentation nnU-Net U-Net

来源：评论

学校读者我要写书评

暂无评论

image-assisted Label Connective Completion for Vessel Segmentation with Insufficient Annotations

Image-assisted Label Connective Completion for Vessel Segmen...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zheng, Xiaoqi Yang, Baoyao Fang, Xiuwen Yao, Wenfang Ye, Mang School of Computer Science and Technology Guangdong University of Technology China School of Computer Science Wuhan University China School of Nursing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798350368741

Automatic and accurate vessel segmentation is crucial for disease diagnosis. Deep learning methods are widely used, but their promising results rely on accurately annotated data. Due to complex vessel morphology and low-contrast image, accurate vessel delineation poses a practical challenge, resulting in insufficient annotations, which is a prominent form of noisy labels. This paper proposes an image-assisted Label Connective Completion method, which enhances label's vessel information by images under the supervision of connectivity to address insufficient annotation issue. Specifically, we develop an image-guided Vessel Enhancement module, which transmits structural information extracted from images based on label navigation to label space, promoting completion of missing annotated parts in original labels. In addition, a branch completion-connectivity loss is designed and introduced as an auxiliary supervision to prevent vessel branch disconnection during label completion. Experimental results on DRIVE, CHASE_DB1 and DCA1 datasets demonstrate that our method outperforms existing noisy labels learning methods. © 2025 ieee.

关键词： Insufficient annotation Label completion Vessel segmentation

来源：评论

学校读者我要写书评

暂无评论

DRSFANet: Dual-Path CNN with Residual and Frequency Attention for image Denoising

DRSFANet: Dual-Path CNN with Residual and Frequency Attentio...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Kumar, Manish Maji, Suman Kumar Saha, Anirban CSIR Fourth Paradigm Institute Bangalore Karnataka 560037 India C.S.E. Department IIT Patna Bihta Bihar Patna801103 India C.S.E. Department Indian Institute of Technology Patna Bihta Bihar Patna801103 India

ISBN: (纸本)9798350368741

image noise, often resulting from disturbances during image acquisition or imperfections in the imaging device, notably degrades the quality of digital images. The challenge of removing this noise has been addressed through various techniques, from traditional filtering and prior-based methods to more recent deep learning approaches. In this paper, we introduce DRSFANet, an advanced dual-path convolutional neural network (CNN) specifically designed to tackle both synthetic Additive White Gaussian Noise (AWGN) and real-world noise. DRSFANet incorporates several innovative components: a residual feature extraction module (FEB) equipped with dilated convolutional layers to enhance the receptive field and mitigate gradient vanishing issues, and novel attention modules-Frequency-Plane Attention Block (FPAB) and Residual Attention Block (RAB)-which improve feature extraction in both frequency and spatial domains. Furthermore, the model features a downsampling (DS) block that effectively consolidates essential features prior to their integration into subsequent network stages. Comprehensive experimental evaluations reveal that DRSFANet outperforms several state-of-the-art denoising methods, demonstrating superior performance in both synthetic and real datasets through rigorous quantitative and qualitative analysis. © 2025 ieee.

关键词： Additive White Gaussian Noise (AWGN) Convolutional Neural Networks (CNN) image Denoising Real Noise Synthetic

来源：评论

学校读者我要写书评

暂无评论

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical image Segmentation

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Ra...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zarch, Niloufar Eghbali Bagher-Ebadian, Hassan Alhanai, Tuka Ghassemi, Mohammad M. Computer Science Department Michigan State University East Lansing United States Department of Radiation Oncology Henry Ford Health Detroit United States Division of Engineering New York University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350368741

Vision Transformers (ViTs) have shown promise in medical image semantic segmentation (MISS) by capturing long-range correlations. However, ViTs often struggle to model local spatial information effectively, which is essential for accurately segmenting fine anatomical details, particularly when applied to small datasets without extensive pre-training. We introduce Gabor and Laplacian of Gaussian Convolutional Swin Network (GLoG-CSUnet), a novel architecture enhancing Transformer-based models by incorporating learnable radiomic features. This approach integrates dynamically adaptive Gabor and Laplacian of Gaussian (LoG) filters to capture texture, edge, and boundary information, enhancing the feature representation processed by the Transformer model. Our method uniquely combines the long-range dependency modeling of Transformers with the texture analysis capabilities of Gabor and LoG features. Evaluated on the Synapse multi-organ and ACDC cardiac segmentation datasets, GLoG-CSUnet demonstrates significant improvements over state-of-the-art models, achieving a 1.14% increase in Dice score for Synapse and 0.99% for ACDC, with minimal computational overhead (only 15 and 30 additional parameters, respectively). GLoG-CSUnet's flexible design allows integration with various base models, offering a promising approach for incorporating radiomics-inspired feature extraction in Transformer architectures for medical image analysis. The code implementation is available on GitHub at: https://***/HAAIL/GLoGCSUnet. © 2025 ieee.

关键词： Gabor Filter Laplacian of Gaussian Medical image Segmentation Radiomics Vision Transformer

来源：评论

学校读者我要写书评

暂无评论

Cross-Modality Fusion Mamba for All-in-One Extreme Weather-Degraded image Restoration

Cross-Modality Fusion Mamba for All-in-One Extreme Weather-D...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Ding, Jiangang Shan, Yihui Pei, Lili Du, Yiquan Zhao, Yuanlin Li, Wei School of Information Engineering Chang'an University Shaanxi Xi'an710064 China School of Data Science and Artificial Intelligence Chang'an University Shaanxi Xi'an710064 China

ISBN: (纸本)9798350368741

A major obstacle for high-level tasks is the unpredictable image degradation. While several architectures proposed to address this, they fail under extreme degradation. Therefore, we introduce a novel cross-modality pipeline called AIRMamba, designed to holistically and robustly restore images degraded due to extreme weather. Specifically, we devise a strategy that utilizes infrared images to create compact high-frequency priors for the restoration process. Meanwhile, we leverage long-range modeling capability of Mamba to achieve both feature extraction and interaction. We emphasize extracting low-frequency representations from the ground truth and achieving this task through a regression-based approach. Consequently, AIRMamba can achieve reliable restoration through large-gap cross-domain guidance. To facilitate this task, we have constructed a cross-modality restoration benchmark, named WeatherInfrared. Our pipeline is simple, robust, and outperforms several state-of-the-art methods in benchmark evaluations. © 2025 ieee.

关键词： Cross-modality Extreme weather Frequency guidance image restoration Mamba

来源：评论

学校读者我要写书评

暂无评论

High-Fidelity Stereoscopic image Rain Removal with Texture Integrity and Disparity Consistency

High-Fidelity Stereoscopic Image Rain Removal with Texture I...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wei, Yanyan Zhang, Zhao Zhao, Zhong-Qiu Zhao, Yang Hong, Richang Yang, Yi Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei China The College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350368741

This paper tackles the challenge of stereoscopic image rain removal by focusing on enhancing texture integrity and disparity consistency. Existing stereoscopic rain removal techniques often fall short due to 1) disruptions in texture coherence caused by complex rain streaks, and 2) inaccuracies in disparity estimation from inadequate feature fusion. To overcome these limitations, we introduce the StereoIRR method, which incorporates: 1) a Long-range and Cross-view Interaction (LCI) framework that preserves texture integrity by mitigating rain's adverse effects on stereoscopic features, and 2) a Dual-view Mutual Attention mechanism that ensures disparity consistency by generating precise mutual attention maps for cross-view feature fusion. Our approach not only maintains the integrity of stereoscopic textures but also significantly reduces errors in disparity estimation. Extensive experiments demonstrate that StereoIRR consistently outperforms state-of-the-art monocular and stereoscopic methods on multiple benchmark datasets. © 2025 ieee.

关键词： Dual-view mutual attention image deraining StereoIRR Stereoscopic and multiview processing

来源：评论

学校读者我要写书评

暂无评论

Retrieval-Augmented Multilingual Citation Generation

Retrieval-Augmented Multilingual Citation Generation

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Liang, Xun Niu, Simin Zhang, Sensen Li, Zhiyu Zhang, Xuan Wu, Bo Xiong, Feiyu Tang, Bo Wang, Hanyu Song, Shichao Wang, Mengwei Yang, Jiawei School of Information Renmin University of China Beijing China Institute for Advanced Algorithms Research Shanghai China Guanghua School of Management Peking University Beijing China Xiangjiang Laboratory Central South University Hunan China

ISBN: (纸本)9798350368741

Retrieval-augmented citation generation (RACG) helps users trust the large language model output by retrieving evidence from reliable sources. However, most current RACG research focuses on single-language tasks, particularly in English, and overlooks the need for cross-lingual evidence retrieval and utilization in real-world applications. To address this issue, we introduce a plug-and-play Retrieval-Augmented Multilingual Citation Generation method (RAMCG) which uses a multilingual retriever to identify relevant evidence from a multilingual knowledge base. The evidence is then combined with the query and processed by a multilingual citation generator. The result is citations that are both accurate and comprehensive. Experiments show that RAMCG outperforms baseline methods in multilingual citation generation and is well-suited for practical use. © 2025 ieee.

关键词： applications of Generative AI Citation Generation Retrieval-Augmented Generation

来源：评论

学校读者我要写书评

暂无评论

Parametric Object Coding in IVAS: Efficient Coding of Multiple Audio Objects at Low Bit Rates

Parametric Object Coding in IVAS: Efficient Coding of Multip...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Eichenseer, Andrea Korse, Srikanth Fuchs, Guillaume Multrus, Markus Fraunhofer IIS Erlangen Germany International Audio Laboratories Erlangen Erlangen Germany

ISBN: (纸本)9798350368741

The recently standardized 3GPP codec for Immersive Voice and Audio Services (IVAS) includes a parametric mode for efficiently coding multiple audio objects at low bit rates. In this mode, parametric side information is obtained from both the object metadata and the input audio objects. The side information comprises directional information, indices of two dominant objects, and the power ratio between these two dominant objects. It is transmitted to the decoder along with a stereo downmix. In IVAS, parametric object coding allows for transmitting three or four arbitrarily placed objects at bit rates of 24.4 or 32 kbit/s and faithfully reconstructing the spatial image of the original audio scene. Subjective listening tests confirm that IVAS provides a comparable immersive experience at lower bit rate and complexity compared to coding the audio objects independently using Enhanced Voice Services (EVS). © 2025 ieee.

关键词： Audio coding communication codec Immersive Voice and Audio Services (IVAS) object-based audio parametric coding

来源：评论

学校读者我要写书评

暂无评论

A Small-footprint Acoustic Echo Cancellation Solution for Mobile Full-Duplex Speech Interactions

A Small-footprint Acoustic Echo Cancellation Solution for Mo...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Jiang, Yiheng Tian, Biao Speech Lab Alibaba Group Beijing China

ISBN: (纸本)9798350368741

In full-duplex speech interaction systems, effective Acoustic Echo Cancellation (AEC) is crucial for recovering echo-contaminated speech. This paper presents a neural network-based AEC solution to address challenges in mobile scenarios with varying hardware, nonlinear distortions and long latency. We first incorporate diverse data augmentation strategies to enhance the model's robustness across various environments. Moreover, progressive learning is employed to incrementally improve AEC effectiveness, resulting in a considerable improvement in speech quality. To further optimize AEC's downstream applications, we introduce a novel post-processing strategy employing tailored parameters designed specifically for tasks such as Voice Activity Detection (VAD) and Automatic Speech Recognition (ASR), thus enhancing their overall efficacy. Finally, our method employs a small-footprint model with streaming inference, enabling seamless deployment on mobile devices. Empirical results demonstrate effectiveness of the proposed method in Echo Return Loss Enhancement and Perceptual Evaluation of Speech Quality, alongside significant improvements in both VAD and ASR results. © 2025 ieee.

关键词： acoustic echo cancellation data augmentation full-duplex interaction post-processing progressive learning

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500页 << < 13 14 15 16 17 18 19 20 21 22 > >>

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：