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

4,593 篇 会议
498 册 图书
65 篇 期刊文献

馆藏范围

5,155 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,376 篇 工学
- 2,738 篇 计算机科学与技术...
- 1,816 篇 软件工程
- 722 篇 信息与通信工程
- 721 篇 光学工程
- 661 篇 电气工程
- 415 篇 生物工程
- 359 篇 控制科学与工程
- 348 篇 生物医学工程（可授...
- 234 篇 机械工程
- 223 篇 电子科学与技术（可...
- 125 篇 仪器科学与技术
- 122 篇 化学工程与技术
- 116 篇 安全科学与工程
- 95 篇 交通运输工程
- 90 篇 材料科学与工程（可...
- 68 篇 建筑学
- 58 篇 土木工程
1,680 篇 理学
- 926 篇 物理学
- 721 篇 数学
- 452 篇 生物学
- 203 篇 统计学（可授理学、...
- 119 篇 化学
- 57 篇 系统科学
490 篇 管理学
- 269 篇 图书情报与档案管...
- 241 篇 管理科学与工程(可...
- 79 篇 工商管理
439 篇 医学
- 417 篇 临床医学
- 127 篇 基础医学(可授医学...
- 89 篇 药学(可授医学、理...
60 篇 法学
- 59 篇 社会学
54 篇 农学
35 篇 教育学
31 篇 艺术学
29 篇 经济学
11 篇 文学
11 篇 军事学

主题

811 篇 computer vision
622 篇 pattern recognit...
535 篇 image processing
355 篇 image processing...
347 篇 artificial intel...
336 篇 image segmentati...
269 篇 feature extracti...
202 篇 image recognitio...
171 篇 deep learning
155 篇 computer graphic...
149 篇 computer imaging...
137 篇 face recognition
122 篇 object recogniti...
121 篇 machine learning
117 篇 convolutional ne...
114 篇 image analysis
113 篇 object detection
108 篇 information syst...
105 篇 cameras
105 篇 shape

机构

25 篇 istituto italian...
24 篇 google deepmind ...
24 篇 meta ai barcelon...
23 篇 politecnico di t...
14 篇 national center ...
14 篇 institute of ima...
10 篇 shanghai jiao to...
10 篇 tsinghua univers...
10 篇 university of ch...
10 篇 institute of aut...
9 篇 institute of ima...
9 篇 computer vision ...
8 篇 indian statistic...
8 篇 inria nancy gran...
8 篇 dalian universit...
8 篇 icube université...
8 篇 erasmus mc - uni...
8 篇 tencent jarvis l...
8 篇 university of ba...
7 篇 department of co...

作者

24 篇 cristian canton
24 篇 alessio del bue
24 篇 tatiana tommasi
24 篇 jordi pont-tuset
17 篇 de-shuang huang
14 篇 stefanie speidel
12 篇 wilfried philips
9 篇 paul scheunders
9 篇 h. bischof
9 篇 jie yang
9 篇 stéphane cotin
9 篇 dan popescu
9 篇 mohamed kamel
8 篇 umapada pal
8 篇 aurélio campilho
8 篇 yefeng zheng
8 篇 kok wai wong
8 篇 christian barill...
8 篇 ilias maglogiann...
8 篇 tieniu tan

语言

5,032 篇 英文
103 篇 其他
52 篇 中文

检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

共 5156 条记录，以下是421-430 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：

An Effective Approach for image Denoising Using Wavelet Transform Involving Deep Learning Techniques 4

An Effective Approach for Image Denoising Using Wavelet Tran...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Shukla, Abhay Seethalakshmi, K. Hema, P. Musale, Jitendra Chandrakant Axis Institute of Technology and Management Department of Computer Science and Engineering Uttar Pradesh Kanpur208024 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department Computer Science and Engineering Tamilnadu Chennai62 India R.M.K. College of Engineering and Technology Department of Mathematics Tamil Nadu Thiruvallur601206 India Shri Jagdishprasad Jhabarmal Tibrewala University University in Jhunjhunu Department of Computer Science and Engineering Rajasthan India

ISBN: (纸本)9798350300888

In contemporary years, image denoising has developed as an important investigation topic in the arena of image processing due to the increasing demand for high-quality images. Deep learning approaches have recently demonstrated outstanding results in a variety of image processing applications. In this research, a wavelet transform and deep learning-based method is suggested for image denoising. To successfully remove noise from an image, the suggested method is based on a hybrid network that combines wavelet transform with convolutional neural networks (CNNs). the suggested method is divided into two steps, one based on wavelet transforms and the other on deep learning. the input image is divided into wavelet coefficients at various scales and orientations in the wavelet transform-based stage. the noise is subsequently filtered out of the wavelet coefficients using a thresholding method. A CNN is taught to acquire the plotting among the noisy image and the denoised image during the deep learning-based step. the experimental results show that, in terms of both quantitative metrics and visual quality of the denoised images, the suggested strategy outperforms a number of state-of-the-art image denoising methods. the suggested method is also independent of the prior knowledge of the noise statistics and resilient to various types of noise. Overall, the suggested method successfully tackles image denoising by fusing the compensations of deep learning and wavelet transform. the suggested method could be used for a variety of computer vision applications that call for high-quality image denoising. © 2023 IEEE.

关键词： image denoising

来源：评论

学校读者我要写书评

暂无评论

image Despeckling Using Hybrid Method 1

引用

4th international conference on Communications and Cyber-Physical Engineering, ICCCE 2021

作者： Saritha, M. Rasane, Krupa Jain College of Engineering Visvesvaraya Belagavi Belgaum India

ISBN: (数字)9789811679858

ISBN: (纸本)9789811679841

the Synthetic Aperture Radar (SAR) is a satellite imaging which is severely degraded by noise and appears as granular pattern which affects the overall quality of the image and causes difficulty in interpretation and further processing like segmentation, detection and recognition of the target in the image for computer vision systems and also for manual interpreters. Speckle noise is multiplicative nature and it is complicated to filter it. In this paper a hybrid methods consisting of Frost filter and bayes thresholding are implemented for the removal of Speckle noise and qualitative measures like PSNR and MSE are evaluated. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Research on Network Traffic Feature Map Generation and Evaluation Methods 4

Research on Network Traffic Feature Map Generation and Evalu...

引用

4th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2023

作者： Jiang, Anxing Shi, Kai Wang, Mengshu Zhang, Zeri Tianjin University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798350329490

With the continuous development of machine learning, it has been widely used in the fields of image processing and computer vision. However, it needs to be transformed into images before feeding network traffic data into a deep learning model for training. the existing methods for directly converting network traffic data into images demonstrate unfavorable results. therefore, this paper proposes a method for generating network traffic feature maps. this method first uses information entropy and classical k-nearest neighbors estimation algorithm to calculate the entropy values for discrete and continuous variables respectively, then the entropy values are used to filter network traffic. Finally, feature maps are generated through an encoder. Experiments on the UNSW_NB15 and CIC-IDS2017 datasets show that the proposed method outperforms other methods in terms of metrics such as PSNR and SSIM. © 2023 IEEE.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Boosting CRM Chatbot Solutions with Flash Attention and Probabilistic Inference 4th

Boosting CRM Chatbot Solutions with Flash Attention and Prob...

引用

4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Gunay, Ahmet Can Ozara, Muhammet Furkan Celik, Ahmet Erkan Akbulut, Akhan Next4biz R&D Ctr Pakpen Plaza 40-4 TR-34734 Istanbul Turkiye Istanbul Kultur Univ Dept Comp Engn TR-34536 Istanbul Turkiye

ISBN: (纸本)9783031821493;9783031821509

In an effort to deliver exceptional customer service, organizations are increasingly recognising the critical role that advanced language models (LLMs) play in the integration of CRM systems. this article explores the forefront of chatbot technology, with a particular focus on the essential function they play in contemporary consumer relationship management. the ability of chatbots to provide personalized and contextually pertinent responses to customer inquiries has been significantly enhanced by the rapid development of LLMs, specifically in the areas of sentiment, intent, and context comprehension. Organizations have the opportunity to enhance customer contentment and optimize operations by leveraging the capabilities of these advanced AI systems. In addition, offline capabilities guarantee continuous support, thereby enhancing customer confidence and loyalty in a time when connectivity fluctuations continue to be a challenge. the present study introduces an innovative offline chatbot system that aims to overcome the limitations of traditional cloud-based counterparts. By efficiently integrating data from various sources, such as project documentation and social media, this chatbot is capable of operating independently and delivering assistance to users, even in locations with sporadic internet connectivity. the experimental outcomes illustrate that our newly developed chatbot model outperforms established benchmarks, as evidenced by its 4.1-second inference time, 90.2-point BLEU score, and 9.7-point WER score. these metrics underscore the model's effectiveness, precision, and timeliness in handling user inquiries and producing responses of exceptional quality. the experiments we conduct are designed to validate the effectiveness of our model in improving customer service experiences within CRM systems through comprehensive performance testing.

关键词： Natural Language processing NLP Chatbot Flash Attention Probabilistic Inference Data ingestion

来源：评论

学校读者我要写书评

暂无评论

recognition of Handwritten English Character Using Convolutional Neural Network 4

Recognition of Handwritten English Character Using Convoluti...

引用

4th international conference on Artificial Intelligence and Speech Technology, AIST 2022

作者： Katoch, Sapna Rakhra, Manik Singh, Dalwinder Lovely Professional University Department of Computer Science Phagwara India

ISBN: (纸本)9781665499026

In the domain of computer vision and image processing, one of the most active and difficult study fields is handwritten character recognition. It may be used as a reading tool for bank checks, for identifying characters on forms, and for a slew of other purposes. the optical character recognition of the papers is similar to documents produced by hand by a human. this OCR is put to use to improve the simplification of the process of character translation, which may be obtained from a broad range of file types, such as image and word document files. Researchers have made tremendous progress in HCR by making use of vast amounts of raw data and new breakthroughs in Deep Learning and Machine Learning algorithms. the fundamental purpose of this research paper is to give a solution for several techniques of handwriting recognition. these methods include the usage of touch input through a mobile screen as well as the use of an image file. CNN is used to identify characters in a test dataset in this work. Work on CNNs' capacity to detect characters from a picture dataset and their accuracy of recognition will be examined. Characters are recognized by CNN by comparing and contrasting their shapes and distinguishing characteristics. the dataset A-Z Handwritten was used to test our CNN implementation's handwriting accuracy and model gives the 100% result to recognize the character. © 2022 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Intelligent Monitoring and Management of Trade Logistics Based on Big Data and computer vision Technology 4

Intelligent Monitoring and Management of Trade Logistics Bas...

引用

4th international conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Gao, Zhongpo Kang, Chengwen Wu, Xufeng School of Economics Harbin University of Commerce Harbin150028 China School of Economics and Management Harbin University Harbin150086 China

Technologies such as big data and computer vision are increasingly being applied in trade logistics, and their integration provides strong support for achieving modernization and intelligence in the logistics industry. this article utilizes various sensors, global positioning systems, radio frequency identification and other technologies to obtain real-time information on the location, temperature, and transportation status of goods. Video surveillance and image recognition methods can be used, combined with computer vision technology, to monitor the loading and unloading process of goods and the condition of transportation vehicles in real time. this article studies an intelligent monitoring algorithm that achieves anomaly detection, warning prompts, and ensures the safety and smoothness of transportation. Exploration of transportation path optimization in batch 1 experimental group (big data analysis) saved 3 hours of time and cost by 500 yuan. this article explores the intelligent monitoring and management of trade logistics based on big data and computer vision technology, which can help improve transportation efficiency and reduce costs. © 2024 the Authors.

关键词： Radio frequency identification (RFID)

来源：评论

学校读者我要写书评

暂无评论

Handwritten Text recognition using Deep Learning Algorithms 4

Handwritten Text Recognition using Deep Learning Algorithms

引用

4th international conference on Artificial Intelligence and Speech Technology, AIST 2022

作者： Ansari, Arbaj Kaur, Baljinder Rakhra, Manik Singh, Arun Singh, Dalwinder Lovely Professional University Department of Computer Science and Engineering Phagwara India Lovely Professional University Computer Science and Engineering Phagwara India

ISBN: (纸本)9781665499026

Since a pen is more convenient than a keyboard, most scripts are now produced by hand;this often leads to mistakes due to the illegibility of human handwriting. To combat this issue, handwriting recognition has rapidly emerged as a top research priority. computer vision algorithms involving optical character recognition were previously employed in traditional handwriting recognition systems. It is a challenging undertaking to train an optical character recognition (OCR) system with these constraints in mind. the OCR method has many problems. In this study, we employ Convolutional Neural Networks (CNNs), Long Short-Term Memories (LSTMs) built on Recurrent Neural Network (RNN) architecture, and Connectionist Temporal Classification (CTC) to recognise handwritten text (CTC). To train and evaluate the network, we use the Information Acquisition MNIST dataset, which includes an English language handwriting test. Here, image processing is handled by OpenCV, while word recognition and training are handled by TensorFlow. Python is used throughout the development of this system, with the console serving as the final destination for the output. © 2022 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

PFMRC: A Unified MRC Framework for Chinese Named Entity recognition via Semantic Feature Fusion 4

PFMRC: A Unified MRC Framework for Chinese Named Entity Reco...

引用

4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Deng, Zhenrong Zhang, Jinglin Xiao, Yannan Wei, Shiwei Guilin University of Electronic Technology Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin China Nanning Research Institute Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China

ISBN: (纸本)9798331531225

the landscape of natural language processing (NLP) has been revolutionized by advancements that enable the upstream training of knowledge-rich models for downstream tasks, particularly named entity recognition (NER). Traditional NER approaches, treating it as a sequence labeling problem, often fail to deeply understand the whole text and to capture syntactic nuances of text. To address these issues, we introduce PFMRC, a novel machine reading comprehension (MRC) framework that leverages the strengths of both contextual semantics and syntactic information for enhanced NER performance. PFMRC employs a dual-pronged feature extraction strategy, utilizing RoBERTa and BiGRU to encode text features for contextual semantic information and representing them as node features of the graph structure, and a weighted embedding representation to translate syntactic dependencies into graph-structured edge features. Our model further incorporates Graph Attention Networks (GAT) to harmonize node and edge features, facilitating a nuanced understanding of text. the integration of prior knowledge and the modeling of intricate syntactic relationships through Graph Attention Networks (GAT) not only improves entity recognition accuracy but also enriches the feature representation, leading to more robust NER outcomes. Extensive experiments on three Chinese datasets demonstrate PFMRC's superior performance over state-of-the-art models. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Energy Consumption Optimization of Swin Transformer Based on Local Aggregation and Group-Wise Transformation 4

Energy Consumption Optimization of Swin Transformer Based on...

引用

4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Liu, Yuhong Chen, Shengbo Lei, Zhou Wang, Peng School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9798350326444

the Transformer is a natural language processing model that uses attention mechanisms, originally introduced by Google in 2017. Due to its excellent feature extraction capabilities, researchers in the industry have explored the application of Transformer in computer vision tasks. Following the success of the vision Transformer in 2020, the Swin Transformer was introduced, which outperforms traditional convolutional neural networks on various benchmarks in the vision field. As a result, Swin Transformer has become a popular backbone for practical production applications. While Swin Transformer excels in accuracy, its large parameter size and high energy consumption pose challenges for deployment at the edge. Addressing this issue is crucial for the wide-scale adoption of Swin Transformer. Current research on energy consumption for Transformer-based models is still in its early stages. To reduce model size and energy consumption while preserving accuracy, this study focuses on model compression. Specifically, we propose a combination of group-wise transformation and local aggregation methods from traditional CNN models. By introducing local aggregation and group-wise transformation calculation methods while maintaining the original shifted window attention mechanism of Swin Transformer, the proposed approach effectively reduces the number of model parameters. Our experimental results show that the Swin-Transformer model trained with this method on an RTX-3090 graphics card can save up to 24% of energy, with an accuracy improvement of about 1%. © 2023 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

An integrated CycleGAN-Diffusion approach for realistic image generation 4

An integrated CycleGAN-Diffusion approach for realistic imag...

引用

4th international conference on Signal processing and Machine Learning, CONF-SPML 2024

作者： Liu, Boyan Ji, Jiayi Gu, Liuqing Jiang, Ziheng University of Bristol Bristol United Kingdom Xi’an Jiaotong-Liverpool University Suzhou China Wuhan University Wuhan China Shanghai University Shanghai China

ISBN: (纸本)9781510674721

Cycle-Consistent Adversarial Network (CycleGAN) has been pivotal for image style transfer with the realm of computer vision. However, the quality of CycleGAN’s output often lacks fine-grained details when generating realistic images. therefore, this paper proposes a realistic image generation approach based on an integrated CycleGAN-Diffusion network to achieve higher image quality with a comparative small model. To evaluate the Diffusion model’s ability to produce high-fidelity images under resource constraints and to compare its image quality with datasets processed using CycleGAN, we apply irregular masks with Gaussian noise during the inpainting and restoration phases. For assessing sample fidelity, we utilizes Mean Squared Error (MSE), Inception Score (IS) and Fréchet inception distance (FID) in this paper. through extensive experiments, the proposed network is proved to perform better in generating high fidelity images, helping us to achieve FID scores of 5.98 and IS scores of 8.34 at 160×160 resolutions in the process of restoration and achieve IS scores of 7.69 in the inpainting process, both outperforming previous CycleGAN. © 2024 SPIE.

关键词： Restoration

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 << < 39 40 41 42 43 44 45 46 47 48 > >>

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：