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

12,548 篇 会议
192 篇 期刊文献
161 册 图书

馆藏范围

12,901 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

6,491 篇 工学
- 4,273 篇 计算机科学与技术...
- 2,180 篇 软件工程
- 1,872 篇 电气工程
- 1,277 篇 信息与通信工程
- 825 篇 控制科学与工程
- 652 篇 光学工程
- 462 篇 生物医学工程（可授...
- 440 篇 电子科学与技术（可...
- 435 篇 机械工程
- 425 篇 仪器科学与技术
- 316 篇 生物工程
- 220 篇 测绘科学与技术
- 152 篇 材料科学与工程（可...
- 142 篇 航空宇航科学与技...
- 133 篇 交通运输工程
- 99 篇 安全科学与工程
- 86 篇 网络空间安全
3,125 篇 理学
- 2,083 篇 物理学
- 888 篇 数学
- 359 篇 生物学
- 246 篇 系统科学
- 224 篇 统计学（可授理学、...
- 109 篇 化学
1,429 篇 医学
- 1,268 篇 临床医学
- 192 篇 特种医学
- 158 篇 基础医学(可授医学...
- 99 篇 药学(可授医学、理...
691 篇 管理学
- 422 篇 管理科学与工程(可...
- 291 篇 图书情报与档案管...
- 84 篇 工商管理
58 篇 法学
57 篇 农学
29 篇 教育学
27 篇 文学
25 篇 经济学
17 篇 军事学
16 篇 艺术学
1 篇 哲学

主题

2,645 篇 image processing
1,386 篇 image segmentati...
1,143 篇 signal processin...
609 篇 cameras
594 篇 feature extracti...
584 篇 image edge detec...
521 篇 signal processin...
472 篇 image reconstruc...
461 篇 real-time system...
460 篇 image coding
390 篇 algorithm design...
334 篇 image color anal...
308 篇 deep learning
306 篇 image enhancemen...
306 篇 pixel
300 篇 real time system...
293 篇 object detection
290 篇 computer archite...
283 篇 neural networks
282 篇 hardware

机构

22 篇 signal and image...
19 篇 samara national ...
14 篇 school of electr...
11 篇 samara national ...
11 篇 univ texas san a...
11 篇 department of el...
11 篇 school of electr...
10 篇 center for signa...
9 篇 information syst...
9 篇 institute of ima...
9 篇 tongji universit...
8 篇 university of to...
8 篇 purdue univ sch ...
8 篇 university of ch...
8 篇 electrical compu...
8 篇 image processing...
8 篇 information syst...
8 篇 samara national ...
7 篇 school of electr...
7 篇 department of ma...

作者

19 篇 anon
13 篇 de-shuang huang
13 篇 jagdish chand ba...
11 篇 p. pirsch
10 篇 jie yang
10 篇 panetta karen
10 篇 j.m. mendel
10 篇 r. chellappa
10 篇 a. ortega
9 篇 c.-c.j. kuo
8 篇 frank hannig
8 篇 bovik alan c.
8 篇 hannig frank
8 篇 mahalanobis a
8 篇 ilias maglogiann...
8 篇 carli m.
7 篇 miller jwv
7 篇 busch christoph
7 篇 semenishchev e.
7 篇 zelensky a.

语言

12,604 篇 英文
211 篇 其他
74 篇 中文
20 篇 土耳其文
3 篇 葡萄牙文
2 篇 西班牙文
2 篇 俄文
1 篇 法文

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

共 12901 条记录，以下是331-340 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：

Real-time image Edge Detection System Design and algorithms for Artificial Intelligence FPGAs 2

Real-time Image Edge Detection System Design and Algorithms ...

引用

2nd IEEE International conference on Artificial Intelligence of Things and Crowdsensing, AIoTCs 2022

作者： Ye, Caizeng Shandong Institute of Commerce and Technology Jinan250103 China

ISBN: (纸本)9798350334104

Extraction of image edges is the basis of image processing class algorithms, for image edge extraction is of great significance. Nowadays, video image data is greatly increases the amount of image data and the processing difficulty. FPGA technology stands out for its parallel processing method and pipeline structure, which enables high-speed processing of large amounts of image data, and its high processing speed ensures real-time image data processing. The application of FPGA technology in the field of image data processing will be unstoppable. The main objective of this paper is to investigate the design and algorithms of a real-time image edge detection system using artificial intelligence and FPGAs. Median filtering and edge detection algorithms in video acquisition and pre-processing systems are an important part of complex algorithms in the field of target recognition and tracking, and the quality of image pre-processing plays an important role in the high accuracy of subsequent algorithms. This paper presents an in-depth study and analysis of the hardware implementation of the HD video capture and pre-processing system, combining the characteristics of FPGA parallel high-speed and pipeline structure, and completes the system design and verification on the hardware platform, fully proving the feasibility and correctness of the system. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

来源：评论

学校读者我要写书评

暂无评论

A Novel image Captioning Approach Using CNN and MLP 1

引用

5th International conference on Data and Information Sciences, ICDIS 2023

作者： Sharma, Swati Tomar, Vivek Yadav, Neha Aggarwal, Mukul KIET Group of Institutions Ghaziabad India Dehradun India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

Computer vision and natural language processing researchers have dedicated significant time and energy to the problem of automatically creating image descriptions. In this work, we propose an artificially intelligent picture captioner built on a hybrid architecture of convolutional neural networks (CNNs) and multilayer perceptron (MLPs). This system will take photographs as input and generate captions based on those images using algorithms from convolutional neural networks (CNN), multilayer perception (MLP), recurrent neural networks (RNN). To generate a natural language caption, CNN first extracts feature from the input image, and then the MLP analyses these features. The proposed model is trained on a dataset of photos with captions by optimizing the parameters of a convolutional neural network (CNN) and a multilayer perceptron (MLP) via a hybrid of supervised learning and reinforcement learning. Our results demonstrate that the suggested model may produce captions that are on par with the best methods currently available in terms of accuracy and variety. The artificially intelligent picture captioner has potential uses in many areas, such as social networking, e-commerce, and image retrieval systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhanced Text-to-image Synthesis Using Generative Adversarial Networks and Skip-Thought Vectors for Improved Visual and Semantic Accuracy

Enhanced Text-to-Image Synthesis Using Generative Adversaria...

引用

2024 International conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Monisha, D. Keerthana, D.R. Manoj Kumar, M.V. Prashanth, B.S. Sneha, H.R. Puneeth, B.H. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

Creating realistic images from textual descriptions is a major challenge in computer vision and natural language processing. Traditional methods often fail to accurately translate text into visual representations, resulting in images that lack detail and context. This study addresses this issue by combining Generative Adversarial Networks (GANs) with Skip-Thought *** proposed approach involves three key stages. First, textual descriptions are embedded using Skip-Thought Vectors, which utilize a Recurrent Neural Network (RNN) encoder with Gated Recurrent Unit (GRU) activation to capture semantic nuances. Next, low-resolution images are generated using GANs, providing a basic visual structure. Finally, these images are refined to high resolution to ensure they are detailed and contextually *** method was validated using the Caltech-UCSD Birds 200 (CUB-200) dataset, showing notable improvements in both visual quality and semantic accuracy over previous methods. This approach enables the generation of detailed and contextually relevant images from text, with potential applications in design, storytelling, e-commerce, and *** automating the generation of high-quality images from text, this research enhances user experience and opens new possibilities for creative and practical uses, marking a significant advancement in the integration of computer vision and natural language processing. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Downhole image Reconstruction in Oil and Gas Drilling through Low Rank Sparse Models 3

Enhancing Downhole Image Reconstruction in Oil and Gas Drill...

引用

3rd International conference on Digital Society and Intelligent systems, DSInS 2023

作者： Zhou, Yixi Li, Lin Xue, Chaoyang Ji, Kexin School of Electronic Engineering Xi'an Shiyou University Xi'an710065 China

ISBN: (纸本)9798350331387

para>Effective subsurface monitoring is vital in oil drilling. This study emphasizes the need for an algorithm to streamline video transmission from underground to the wellsite, overcoming bandwidth and interference challenges for high-quality transmission. It employs low-rank and sparse models to enhance image quality. Comparing three image processing algorithms, the Structured Low Dimensional Representation (SLDR) algorithm stands out. The research aims to improve subsurface image accuracy, ensuring underground pipeline system safety and integrity. © 2023 IEEE.

关键词： image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Brain image Denoising and Segmentation Using Advanced Mixed Thresholding and Edge Preservation Analysis Technique

Brain Image Denoising and Segmentation Using Advanced Mixed ...

引用

2024 International conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Symbiosis Institute of Business Management Nagpur India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the realm of medical imaging, particularly brain imaging, the clarity and accuracy of images are paramount for effective diagnosis and treatment planning. However, the inherent presence of noise in imaging modalities such as MRI often degrades the quality of these crucial images, complicating the segmentation process and potentially leading to inaccurate diagnoses. Addressing this challenge, our research introduces an innovative approach that synergizes advanced mixed thresholding with edge preservation analysis to significantly enhance brain image denoising and segmentation. This technique leverages a novel mixed thresholding method that dynamically adjusts to the specific noise characteristics of brain images, effectively reducing noise while retaining critical image details. Concurrently, our edge preservation analysis ensures that essential structural information is maintained, preventing the loss of vital features during the denoising process. The combination of these methods provides a robust solution to the persistent issue of noise in brain imaging, promising to improve the accuracy of segmentation and, by extension, the reliability of medical diagnoses derived from these images. By integrating advanced algorithms and mathematical models, our approach not only outperforms existing denoising and segmentation techniques but also establishes a new benchmark for image processing in medical diagnostics. © 2024 IEEE.

关键词： image denoising

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection in Load Forecasting for Electric Vehicles Using image processing Techniques 24

Anomaly Detection in Load Forecasting for Electric Vehicles ...

引用

24th EEEIC International conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power systems Europe, EEEIC/I and CPS Europe 2024

作者： Marandi, Saba Moradzadeh, Arash Moayyed, Hamed Assi, Chadi Ghafouri, Mohsen Vale, Zita Concordia University Ciise Department Montreal Canada Qatar University Electrical Engineering Department Doha Qatar Polytechnic of Porto GECAD-LASI Department Porto Portugal

ISBN: (纸本)9798350355185

The incorporation of electric vehicles (EVs) into the power grid presents significant challenges, particularly in meeting the electricity requirements of EVs through effective distribution strategies, efficient energy allocation, and optimal charging station placement. Accurate forecasting of load demand is critical for both the operational and planning aspects of energy systems, particularly due to the influence of EV travel behavior. This study introduces a hybrid approach to EV load forecasting using deep learning models to tackle this challenge. The proposed CLSTM model integrates convolutional neural network (CNN) and long short-term memory (LSTM) algorithms. Furthermore, the study introduces and applies an anomaly detection model based on image processing. The forecasting process initially uses clean data and then advances to a scenario involving a simulated cyber-attack by manipulating certain input data, which is referred to as a false data injection attack. After analyzing the data using various evaluation metrics and confirming anomalies, the image processing model identifies these anomalies through data visualization. In conclusion, a thorough evaluation of the results under both clean and compromised forecasting conditions unequivocally demonstrates that the CLSTM model not only surpasses the performance of traditional models but also exhibits greater robustness and accuracy. This superior performance highlights the CLSTM model's potential as a more reliable and effective solution for forecasting in scenarios impacted by data integrity issues. © 2024 IEEE.

关键词： Vehicle-to-grid

来源：评论

学校读者我要写书评

暂无评论

UAV image object detection based on improved Yolov8 4

UAV image object detection based on improved Yolov8

引用

4th International conference on image processing and Intelligent Control, IPIC 2024

作者： Zhang, Wei Wang, Jiaxin Wang, Dan Kong, Xiangwei Institute of Measurement and Testing Technology Chinese Flight Test Establishment Xi'an710089 China

ISBN: (纸本)9781510682313

In a variety of UAV application scenarios, such as agricultural monitoring, urban safety and traffic management, high-precision image object detection is crucial for real-time analysis and decision-making. Traditional object detection algorithms are often ineffective in dealing with small objects in complex environments. To this end, this paper proposes an improved YOLOv8 model that integrates a NAM (Normalization-based Attention Module) attention mechanism and a Bidirectional Feature Pyramid Network (BiFPN) specifically to optimize the object detection performance of UAV images. The NAM module enhances the model's spatial attention capability by utilizing a normalization process that This enables the model to adaptively emphasize important feature regions in the image, which significantly improves the recognition accuracy of small-scale objects without adding too much computational burden. BiFPN further optimizes the flow of information between different scales, which improves the efficiency of feature utilization and the overall performance of detection. Extensive experiments on the VisDrone2019 dataset show that the improved YOLOv8 model improves the mAP metrics by 11.3% compared to the original model, and performs especially well in scenes with complex backgrounds and the presence of multi-scale objects. This study not only validates the potential application of NAM in UAV vision tasks, but also provides a new technical path for the implementation of deep learning models in real monitoring systems. © 2024 SPIE.

关键词： image analysis

来源：评论

学校读者我要写书评

暂无评论

International conference on Integrated Intelligence and Communication systems, ICIICS 2023

International Conference on Integrated Intelligence and Comm...

引用

2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

ISBN: (纸本)9798350315455

The proceedings contain 286 papers. The topics discussed include: utilizing machine learning to analyze and implement task scheduling algorithms in cloud;real time fire detection and alert triggering system;a dynamic model for blended learning of university English based on K-Means clustering algorithm;real time eye-tracking mouse control using recurrent neural network;collaborative virtual storage technology based on mobile cloud computing;enhanced intrusion detection system using Pearson correlation based long short-term memory;construction of group algorithm model under green innovation capability technology;numerical simulation and analysis of integrated mobile medical building based on finite element method;the implementation and optimization of high-performance image processing algorithms based on artificial intelligence;and research on photographic image classification based on multi-model fusion and data augmentation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Wavelet based Convolutional Autoencoder for Medical image Compression

Wavelet based Convolutional Autoencoder for Medical Image Co...

引用

2023 International conference on Intelligent systems for Communication, IoT and Security, ICISCoIS 2023

作者： Venugopal, Muthalaguraja Palanisamy, Kalavathi Department of Computer Science and Applications Gandhigram Tamil Nadu India

ISBN: (纸本)9798350335835

Deep learning approaches have seen tremendous progress lately, particularly in image processing. In image compression, the deep autoencoders are predominantly showing promising results. Autoencoders have demonstrated exceptional performance in image compression tasks when compared to conventional lossy algorithms used for compression, in both the quantitative and qualitative aspects, despite the fact that they are lossy image compression approaches. This paper, puts forth a wavelet-based autoencoder-style compression approach for medical image compression. The proposed architecture is composed of four modules namely Pre-processing, Encoder, Decoder and Enhancement module. In order to achieve remarkable compression performance on the medical images, this method utilizes the advantage of wavelet decomposition technique to extract finer details from the low and high frequency components of the medical image. The experimental findings suggest that our approach achieves notable image compression performance. It outperforms the established JPEG in terms of the objective and subjective metrics PSNR and SSIM, and it also yields results that are comparable to those of JPEG2000 and BPG. © 2023 IEEE.

关键词： image compression

来源：评论

学校读者我要写书评

暂无评论

Plant Leaf Disease Identification Method Using Computer Vision and Machine Learning algorithms

Plant Leaf Disease Identification Method Using Computer Visi...

引用

2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Sohan, Ismail Hossain Rahman, Saidur Chhabra, Gunjan Kaushik, Keshav Haque, Rezaul Dept. of Cse Dhaka Bangladesh Graphic Era Hill University Dept. of Cse Dehradun India University of Petroleum and Energy Studies School of Computer Science Dehradun India East West University Dept. of Cse Dhaka Bangladesh

ISBN: (纸本)9798350315455

This paper proposes an image processing-based method for detecting plant diseases. The system's three key phases are acquisition, pre-processing, and disease identification. Pictures of plant leaves are taken during the image acquisition stage using a digital camera. The image preprocessing stage involves enhancing and pre-processing the collected images to eliminate noise and extraneous data. The pre-processed photos are finally sorted into healthy or unhealthy, utilizing machine learning techniques in the illness identification step. This paper will represent how we can detect plant leaf diseases using a specific software system. It will also find the percentage of accuracy and error of disease detection based on the number of detected images. The leaf disease finding procedure information will also be explained here step by step. According to experimental data, the proposed approach has a high level of accuracy in detecting plant illnesses. Farmers can utilize the system as a valuable tool to detect plant illnesses early, minimizing yield losses and enhancing crop management techniques. © 2023 IEEE.

关键词： Disease detection Feature extraction image classification image processing Machine 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 << < 30 31 32 33 34 35 36 37 38 39 > >>

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：