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

15,139 篇 会议
247 篇 期刊文献
227 册 图书
3 篇 学位论文

馆藏范围

15,616 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

8,104 篇 工学
- 5,426 篇 计算机科学与技术...
- 2,798 篇 软件工程
- 2,443 篇 电气工程
- 1,573 篇 信息与通信工程
- 1,011 篇 控制科学与工程
- 834 篇 光学工程
- 572 篇 电子科学与技术（可...
- 555 篇 生物医学工程（可授...
- 550 篇 机械工程
- 511 篇 仪器科学与技术
- 419 篇 生物工程
- 286 篇 测绘科学与技术
- 204 篇 航空宇航科学与技...
- 189 篇 交通运输工程
- 180 篇 材料科学与工程（可...
- 145 篇 安全科学与工程
- 119 篇 化学工程与技术
4,114 篇 理学
- 2,771 篇 物理学
- 1,130 篇 数学
- 473 篇 生物学
- 325 篇 系统科学
- 275 篇 统计学（可授理学、...
- 146 篇 化学
1,618 篇 医学
- 1,437 篇 临床医学
- 215 篇 特种医学
- 203 篇 基础医学(可授医学...
- 134 篇 药学(可授医学、理...
859 篇 管理学
- 515 篇 管理科学与工程(可...
- 379 篇 图书情报与档案管...
- 130 篇 工商管理
78 篇 法学
69 篇 农学
42 篇 经济学
40 篇 教育学
28 篇 文学
25 篇 军事学
21 篇 艺术学
1 篇 哲学

主题

2,700 篇 image processing
1,404 篇 image segmentati...
1,148 篇 signal processin...
793 篇 real-time system...
744 篇 cameras
743 篇 feature extracti...
552 篇 image edge detec...
539 篇 signal processin...
479 篇 image reconstruc...
470 篇 image coding
447 篇 algorithm design...
372 篇 deep learning
368 篇 object detection
345 篇 computer vision
344 篇 hardware
337 篇 image color anal...
335 篇 real time system...
333 篇 clustering algor...
333 篇 accuracy
316 篇 computer archite...

机构

22 篇 signal and image...
19 篇 samara national ...
17 篇 school of electr...
12 篇 information syst...
11 篇 samara national ...
11 篇 univ texas san a...
11 篇 department of el...
11 篇 school of electr...
10 篇 department of el...
10 篇 electrical compu...
9 篇 school of electr...
9 篇 institute of ima...
9 篇 tongji universit...
9 篇 purdue univ sch ...
9 篇 university of ch...
9 篇 center for signa...
8 篇 shanghai jiao to...
8 篇 information syst...
8 篇 tampere univ tec...
8 篇 university of to...

作者

22 篇 anon
13 篇 de-shuang huang
12 篇 jagdish chand ba...
11 篇 jie yang
11 篇 p. pirsch
10 篇 hannig frank
10 篇 panetta karen
10 篇 c.-c.j. kuo
10 篇 j.m. mendel
10 篇 plaza antonio
10 篇 r. chellappa
9 篇 semenishchev e.
9 篇 zelensky a.
9 篇 bovik alan c.
9 篇 wei li
8 篇 frank hannig
8 篇 yves wiaux
8 篇 a.s. willsky
8 篇 mahalanobis a
8 篇 ilias maglogiann...

语言

15,262 篇 英文
254 篇 其他
81 篇 中文
26 篇 土耳其文
4 篇 俄文
3 篇 葡萄牙文
2 篇 法文
1 篇 西班牙文

检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

共 15616 条记录，以下是571-580 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：

Fairness-aware Dimensionality Reduction 31

Fairness-aware Dimensionality Reduction

引用

31st European Signal processing conference, EUSIPCO 2023

作者： Kose, O. Deniz Shen, Yanning Electrical Engineering and Computer Science University of California Irvine Irvine United States

ISBN: (纸本)9789464593600

In this era of digitalization, the massive increase in available data leads to great potential for advancing various domains. However, the available data, such as images, videos, and speech signals, generally lie in high-dimensional space, which calls for efficient dimensionality reduction techniques to process them efficiently. Furthermore, while the fairness of algorithms is essential for their deployment in real-world systems, the effect of dimensionality reduction on fairness is an under-explored research area. Motivated by this, this paper puts forth a fairness-aware dimensionality reduction framework that is capable of properly compressing the data while mitigating bias. Specifically, our design targets at reducing the correlation between the compressed data and sensitive attributes, while projecting the data into a new coordinate system where most of its variation can be described. Experimental results on the CelebA dataset demonstrate that the proposed dimensionality reduction framework can improve group fairness measures for image classification while providing comparable utility to the conventional techniques. © 2023 European Signal processing conference, EUSIPCO. All rights reserved.

关键词： image classification

来源：评论

学校读者我要写书评

暂无评论

Intelligentization of film and television post-production based on complex image processing and scene switching algorithms 4

Intelligentization of film and television post-production ba...

引用

4th International conference on Smart systems and Inventive Technology, ICSSIT 2022

作者： Wang, Jiangtao Wang, Qi College of Art Hebei University Baoding071000 China

ISBN: (纸本)9781665401180

Intelligentization of the film and television post-production based on the complex image processing and scene switching algorithms is studied in the paper. At present, the film and television media is in a period of rapid de velopment, and new media formats are constantly emerging. The emergence of high-definition television HDTV, the rapid development of the network puts forward new requirements for film and television post-production, in order to adapt to these changes, various software manufacturers continue to improve software functions and keep pace with the *** development of computer virtual technology and the application of the design field have caused a fundamental change in the traditional design process. Therefore, the handover procedure caused by any delay should be minimized, which has attracted the attention of many researchers and seeking new solutions. The novel solutions are proposed model improve the complex image processing and scene switching algorithm performance. Through the verification, the performance is guaranteed. © 2022 IEEE

关键词： Digital television

来源：评论

学校读者我要写书评

暂无评论

Innovative Skin Melanoma Diagnosis using Deep Learning and African Gorilla Troops Optimizer 1

Innovative Skin Melanoma Diagnosis using Deep Learning and A...

引用

1st International conference on Software, systems and Information Technology, SSITCON 2024

作者： Parthasarathi, P. Nivedha, S. Balamurugan, M. Yuvaraj, D. Siamala Devi, S. Sangeethaa, S.N. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India Sona College of Technology Dpartment of Computer Science and Engineering Salem India Kongu Engineering College Department of Computer Science and Design Perundurai India Sri Eshwar College of Engineering Kuniamuthur Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350352931

Skin melanoma, a dangerous kind of skin cancer, necessitates accurate identification and diagnosis to improve patient outcomes. The use of deep learning algorithms into medical imaging promises significant advancements in skin disease analysis, particularly melanoma diagnosis. This study looks at the usage of a novel optimisation technique, the African Gorilla Troops Optimizer (AGTO), to enhance deep learning models in the segmentation and analysis of skin images. The AGTO, which is based on African gorilla groups' social and hunting activities, is an effective approach of improving convolutional neural networks (CNNs) for accurate and reliable skin image segmentation. The proposed approach uses AGTO to distinguish melanoma lesions from benign skin disorders, which increases their identification. Extensive research on benchmark skin image datasets has confirmed the AGTO-enhanced deep learning model's durability and performance, emphasising its clinical use in dermatology and the proposed model shows 98 %accuracy. This work not only underlines the need of advanced optimisation techniques in medical image processing, but it also paves the way for more effective diagnostic tools for skin problems. © 2024 IEEE.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Role, Significance, and Impact of Method Noise in Multi-Modal Medical image Fusion 5

Role, Significance, and Impact of Method Noise in Multi-Moda...

引用

5th International conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Bhosekar, Shailesh Singh, Prabhishek Garg, Deepak Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798331522100

Multi modal medical image fusion in, for instance, combining information from computed tomography (CT) scans, positron emission tomography (PET) and magnetic resonance imaging (MRI) into one single dataset, improves the accuracy of diagnostics due to atomistic and functional details of the images. In this paper, the role, importance, and effect of method noise in multi-modal medical image fusion, is considered to assess and improve fusion algorithms. The effectiveness of an algorithm can be determined in terms of its efficacy at preserving salient image content and removing unwanted artifacts while maintaining the former, which is defined by method noise, the residual noise after image processing. In this paper, I review the theoretical basis of method noise, discuss its function in both fusion and denoising processes, and discuss its practical and conceptual implications, showing its importance for quality assessment and algorithm refinement. We apply method noise to the Non-Subsampled Shearlet Transform (NSST) to demonstrate application in multi-modal medical image fusion. Comparative experiments using NSST based fusion with /without method noise demonstrate significantly different visual clarity and quantitative metrics as measured with different metrics. Results show that method noise both maintains image integrity by preventing detail loss and aids in the integration of complementary detail from different modalities. The need for method noise in advancing image fusion techniques is emphasized in this paper, and a framework for future research and application of fusion in diagnostic imaging is presented. © 2025 IEEE.

关键词： Positron emission tomography

来源：评论

学校读者我要写书评

暂无评论

Dualswin-Ynet: A Novel Bimodal Fusion Network for Ship Detection in Remote Sensing images 27th

Dualswin-Ynet: A Novel Bimodal Fusion Network for Ship Dete...

引用

27th International conference on Pattern Recognition, ICPR 2024

作者： Chen, Dongdong Ju, Rusheng Liu, Xiaoyang Liu, Jiyuan Zhang, Jun Qiu, Sihang College of Systems Engineering National University of Defense Technology No. 109 Deya Road Kaifu District Changsha China

ISBN: (纸本)9783031781681

Ship detection in remote sensing images has a wide range of research needs in both military and civilian applications. Traditional object detection algorithms rely solely on optical images as input, which are susceptible to various interferences such as cloud and fog occlusion, scene noise, etc., resulting in unsatisfactory detection accuracy. Based on the YOLO framework, this paper designs a ship detection model that integrates visible light and infrared images through dual-modal fusion, and explores various feature fusion methods. It achieves data-level fusion network SwinFuse-Ynet, feature-level fusion network E-Dualswin-Ynet and decision-level fusion network L-Dualswin-Ynet. Comprehensive validations are conducted on the multispectral satellite image datasets MMShip and vi-ship. The experimental results demonstrate that the three proposed networks effectively complement infrared feature information, reduce interference from complex scene factors, and improve detection accuracy. In particular, E-Dualswin-Ynet improved by introducing SwinTransformer and Wise-IoU can more fully amplify the complementarity between infrared and visible light modalities, obviously better than other single-modal detection methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Knowledge graph construction for power grid systems based on fuzzy matching algorithm 3

Knowledge graph construction for power grid systems based on...

引用

3rd International conference on Advanced algorithms and Signal image processing, AASIP 2023

作者： Haitao, Gui Junyang, Tian Heming, Hou Renming, Li Guilin Power Supply Bureau Guangxi Power Grid Co Ltd Guilin Guangxi541002 China Guangxi Power Grid Power Dispatching and Control Center Nanning Guangxi530023 China

ISBN: (纸本)9781510668522

To facilitate the processing of data pertaining to defects and faults in the power grid regulation system, as well as knowledge graph-based support to help regulators in their decision-making, a knowledge graph construction method employing a fuzzy matching algorithm has been devised, the outcomes are presented in the form of a visual knowledge graph. Initially, a fuzzy matching algorithm is devised to to match the defect with the defect-fault database. Subsequently, the Neo4j database is utilized to construct a knowledge graph that provides guidance on fault locations. Lastly, by conducting a case analysis of the data extracted from a specific region's distribution network, experimental results demonstrate the method's capability to accurately match target data. Moreover, the visual knowledge graph constructed has proven effective in enhancing regulators' decision-making efficiency. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Defects

来源：评论

学校读者我要写书评

暂无评论

AI-Enhanced image Translation for Seamless Communication in Global Tourism

AI-Enhanced Image Translation for Seamless Communication in ...

引用

2024 International conference on Advances in Data Engineering and Intelligent Computing systems, ADICS 2024

作者： Rubasri, S. Shafiur Rahman, N.F. Syed Khalid, J. vignesh Balaji, R. Prasath, R. Krishna, B.V. Kcg College Of Technology Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

An inventive program called Jargon Lens was created to meet the various language requirements of international visitors. AI-enhanced image translation technology, jargon lens empowers travelers to effortlessly overcome language barriers by seamlessly translating text in images to their preferred language. The core of this project is to create a user-friendly mobile application that can recognize and translate text within images accurately and in real-time. This will enable users to easily understand and communicate in different languages by simply using the Smartphone camera, users can scan or upload and translate text in real-time, ensuring a swift and accurate understanding of their surroundings. Beyond personal advantages, Jargon Lens supports constructive encounters that promote understanding and cultural interchange between visitors and local populations. As a technological solution, Jargon Lens aligns with the evolving landscape of global tourism, where diversity and inclusivity are paramount. This technology combine's computer vision such as optical character recognition (OCR). OCR algorithms analyze the image to recognize and extract the textual content and natural language processing (NLP) to perform the translation. In conclusion, Jargon Lens emerges as a transformative tool for tourists, offering a sophisticated yet easy-to-use solution for navigating language barriers. With its focus on real-time multilingual image translation, Jargon Lens stands as a key enabler for enriched and hassle-free travel experiences, bridging communication gaps in a rapidly evolving global tourism ecosystem. © 2024 IEEE.

关键词： Optical character recognition

来源：评论

学校读者我要写书评

暂无评论

An Efficient image to Speech Generation System for visually Impaired Individuals Using Deep Learning Techniques 10

An Efficient Image to Speech Generation System for Visually ...

引用

10th International conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Priyanka, G. Nivaashini, M. Shree Akshaya, B. Subashini, M. Ritish, G. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

As the world is becoming more visually oriented, visually impaired people are struggling to access and understand visual content. This paper aims to represent the model created to assist blind and visually impaired people. This method leverages deep learning techniques to convert images into meaningful audio outputs. It involves capturing input images and processing them using advanced deep-learning algorithms. The extracted features are translated into speech. The resulting auditory output provides an alternative sensory experience for individuals with visual impairments, enabling them to perceive and interpret visual information through sound. This study implements four deep learning models namely CNN-LSTM, Resnet34, YOLOv5 and vision Transformer to process sequential visual data capturing intricate patterns and dependencies within images. By integrating these models in this work, the image to audio converter not only achieves superior accuracy in image recognition but also enhances the richness and depth of the auditory output, creating a nuanced and immersive sensory experience for users. The performance of the proposed models was evaluated using several metrics and it was noted that vision transformer outperformed the remaining models. This seamless integration of deep learning models amplifies the study's effectiveness in translating diverse visual inputs into detailed and coherent auditory output, thereby enhancing accessibility and inclusivity for individuals with visual impairments. © 2024 IEEE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Big Data Analysis in Astronomy by the Lemur Software 6

Big Data Analysis in Astronomy by the Lemur Software

引用

6th IEEE International conference on Information and Telecommunication Technologies and Radio Electronics, UkrMiCo 2023

作者： Khlamov, Sergii Savanevych, Vadym Briukhovetskyi, Olexander Trunova, Tetiana Kharkiv National University of Radio Electronics Department of Media Systems and Technologies Kharkiv Ukraine Kharkiv National University of Radio Electronics Department of Systems Engineering Kharkiv Ukraine Western Radio Technical Surveillance Center State Space Agency of Ukraine Mukachevo Ukraine

ISBN: (纸本)9798350348484

A very quick progress in information technologies as well as the telescope creation technologies produce a big volume of astronomical big data as well as high dimensional information. All such big data are needed to be analyzed and reviewed in time. But in astronomy such big data can be received in both in the real-time/online mode and from the historical archives. In this paper we have presented the several aspects of the analysis process of such big astronomical data. We have reviewed them and provided some global approaches for the big astronomical data analysis. Also, we have presented examples of sources for big data, different astronomical instruments, big telescopes, different data processing methods and algorithms that are used for the big data analysis process in astronomy. The paper describes an applying of the different data processing methods and algorithms adopted to the various types of astronomical information in scope of the Collection Light Technology (CoLiTec) project. During this research the Lemur software was developed for the online processing and analysis of the big volume of the astronomical big data. The especial OnLine Data Analysis System (OLDAS) mode was created for resolving the different tasks during astronomical big data analysis and processing, like preparation, extracting, classification, preprocessing, clustering, data mining, transforming, image processing, etc. © 2023 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Keyword-Based Diverse image Retrieval by Semantics-aware Contrastive Learning and Transformer 23

Keyword-Based Diverse Image Retrieval by Semantics-aware Con...

引用

46th International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR)

作者： Zhao, Minyi Wang, Jinpeng Liao, Dongliang Wang, Yiru Duan, Huanzhong Zhou, Shuigeng Fudan Univ Sch Comp Sci Shanghai Peoples R China Tsinghua Univ Tsinghua Shenzhen Intl Grad Sch Shenzhen Peoples R China Tencent Inc Wechat Grp Guangzhou Peoples R China

ISBN: (纸本)9781450394086

In addition to relevance, diversity is an important yet less studied performance metric of cross-modal image retrieval systems, which is critical to user experience. Existing solutions for diversity-aware image retrieval either explicitly post-process the raw retrieval results from standard retrieval systems or try to learn multi-vector representations of images to represent their diverse semantics. However, neither of them is good enough to balance relevance and diversity. On the one hand, standard retrieval systems are usually biased to common semantics and seldom exploit diversity-aware regularization in training, which makes it difficult to promote diversity by post-processing. On the other hand, multi-vector representation methods are not guaranteed to learn robust multiple projections. As a result, irrelevant images and images of rare or unique semantics may be projected inappropriately, which degrades the relevance and diversity of the results generated by some typical algorithms like top-k To cope with these problems, this paper presents a new method called CoLT that tries to generate much more representative and robust representations for accurately classifying images. Specifically, CoLT first extracts semantics-aware image features by enhancing the preliminary representations of an existing one-toone cross-modal system with semantics-aware contrastive learning. Then, a transformer-based token classifier is developed to subsume all the features into their corresponding categories. Finally, a post-processing algorithm is designed to retrieve images from each category to form the final retrieval result. Extensive experiments on two real-world datasets Div400 and Div150Cred show that CoLT can effectively boost diversity, and outperforms the existing methods as a whole (with a higher F1 score).

关键词： Cross-modal retrieval Keyword-based image retrieval Diversification retrieval Transformer

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 << < 54 55 56 57 58 59 60 61 62 63 > >>

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：