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

8,212 篇 会议
158 篇 期刊文献
36 册 图书

馆藏范围

8,405 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

5,774 篇 工学
- 5,035 篇 计算机科学与技术...
- 3,598 篇 软件工程
- 1,534 篇 光学工程
- 865 篇 信息与通信工程
- 662 篇 电气工程
- 641 篇 控制科学与工程
- 501 篇 机械工程
- 449 篇 生物医学工程（可授...
- 372 篇 电子科学与技术（可...
- 349 篇 生物工程
- 237 篇 仪器科学与技术
- 119 篇 化学工程与技术
- 101 篇 建筑学
- 92 篇 土木工程
- 71 篇 安全科学与工程
- 57 篇 材料科学与工程（可...
- 51 篇 交通运输工程
3,196 篇 理学
- 1,979 篇 物理学
- 1,901 篇 数学
- 578 篇 统计学（可授理学、...
- 408 篇 生物学
- 126 篇 化学
- 57 篇 系统科学
486 篇 管理学
- 327 篇 图书情报与档案管...
- 175 篇 管理科学与工程(可...
- 55 篇 工商管理
424 篇 医学
- 407 篇 临床医学
- 105 篇 基础医学(可授医学...
- 79 篇 药学(可授医学、理...
54 篇 艺术学
- 53 篇 设计学（可授艺术学...
53 篇 农学
45 篇 法学
27 篇 教育学
18 篇 经济学
11 篇 军事学
5 篇 文学

主题

1,328 篇 image processing
1,100 篇 computer vision
894 篇 image segmentati...
663 篇 pattern recognit...
538 篇 image reconstruc...
515 篇 image analysis
501 篇 cameras
452 篇 layout
375 篇 shape
366 篇 computer science
317 篇 feature extracti...
268 篇 face recognition
264 篇 image recognitio...
260 篇 robustness
243 篇 humans
202 篇 pixel
200 篇 image edge detec...
192 篇 object recogniti...
189 篇 object detection
188 篇 pattern recognit...

机构

23 篇 department of co...
20 篇 microsoft resear...
17 篇 center for autom...
16 篇 the robotics ins...
15 篇 national laborat...
15 篇 institute of ima...
15 篇 institute of ima...
15 篇 department of co...
15 篇 institute of com...
14 篇 department of co...
14 篇 tsinghua univers...
14 篇 school of comput...
14 篇 nec research ins...
14 篇 school of comput...
13 篇 robotics institu...
13 篇 institute for ro...
12 篇 computer science...
11 篇 carnegie mellon ...
11 篇 swiss fed inst t...
11 篇 inria sophia-ant...

作者

31 篇 anon
27 篇 huang thomas s.
25 篇 jain anil k.
24 篇 s.k. nayar
22 篇 nayar shree k.
22 篇 haralick robert ...
19 篇 timofte radu
18 篇 shum heung-yeung
18 篇 aggarwal j.k.
17 篇 zhang lei
17 篇 hancock edwin r.
16 篇 van gool luc
15 篇 g. healey
14 篇 davis larry s.
14 篇 rosenfeld azriel
14 篇 t. kanade
14 篇 r. szeliski
14 篇 ahuja narendra
13 篇 k. ikeuchi
13 篇 chellappa rama

语言

8,192 篇 英文
126 篇 其他
87 篇 中文
3 篇 土耳其文
1 篇 西班牙文
1 篇 葡萄牙文

检索条件"任意字段=Proceedings - IEEE Computer Society Conference on Pattern Recognition and Image Processing."

共 8406 条记录，以下是311-320 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：

Application of artificial intelligence image recognition technology in power system 3

Application of artificial intelligence image recognition tec...

引用

3rd International conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Du, Xiaoyue Gu, Guangheng Yang, Fan Yunnan Power Grid Materials Co. Ltd. Kunming650220 China

ISBN: (纸本)9781510657281

Under the vigorous promotion of global energy Internet, China's AC/DC power grid construction has been developed rapidly and both artificial intelligence technology and machine learning technology have been developed by leaps and bounds. computer-based image pattern recognition technology is an important part of computer application technology. With the rapid development of digital image processing.technology and pattern recognition technology, image pattern recognition technology has been more comprehensively applied in various aspects such as industry, agriculture, national defense, scientific research and medicine. In this paper, we introduce the image detection technology and image detection algorithm for substation inspection robots and acquire image data files through image acquisition terminal sensors to realize the categorization of feature amount. So as to realize the annotation of pre-set features based on the training model. An optimized classifier and optimized image features are used to build an efficient and accurate image classification model. Artificial intelligence image recognition technology for power equipment image recognition is important to further improve the automation and intelligence level of substations. © 2022 SPIE.

关键词： image recognition

来源：评论

学校读者我要写书评

暂无评论

proceedings - 2022 ieee/CVF conference on computer Vision and pattern recognition, CVPR 2022

Proceedings - 2022 IEEE/CVF Conference on Computer Vision an...

引用

2022 ieee/CVF conference on computer Vision and pattern recognition, CVPR 2022

ISBN: (纸本)9781665469463

The proceedings contain 2072 papers. The topics discussed include: clipped hyperbolic classifiers are super-hyperbolic classifiers;efficient deep embedded subspace clustering;noise is also useful: negative correlation-steered latent contrastive learning;active learning for open-set annotation;understanding and increasing efficiency of Frank-Wolfe adversarial training;robust optimization as data augmentation for large-scale graphs;a re-balancing strategy for class-imbalanced classification based on instance difficulty;the devil is in the margin: margin-based label smoothing for network calibration;towards better plasticity-stability trade-off in incremental learning: a simple linear connector;learning Bayesian sparse networks with full experience replay for continual learning;a variational Bayesian method for similarity learning in non-rigid image registration;learning to learn by jointly optimizing neural architecture and weights;learning to prompt for continual learning;multi-frame self-supervised depth with transformers;and rethinking Bayesian deep learning methods for semi-supervised volumetric medical image segmentation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Ear pattern Based Person recognition 9

Ear Pattern Based Person Recognition

引用

9th ieee International Women in Engineering (WIE) conference on Electrical and computer Engineering, WIECON-ECE 2023

作者： Vijaya, J. Tiwari, Abhay Raj, Shubham Singh, Zorawar DSAI IIIT Naya Raipur Naya Raipur India IIIT Naya Raipur Dept of ECE Naya Raipur India IIIT Naya Raipur Dept of CSE Naya Raipur India

ISBN: (纸本)9798350319651

The use of ear images for automatic identification is an intriguing area of research in the field of biometrics. Similar to other identifying biometrics such as the visage, iris, and fingerprint, the ears contain numerous distinguishing characteristics. As many individuals wear masks, the majority of the scientific community is aware of the negative aspects of the current COVID-19 outbreak. The human ear is the finest material for accurate diagnosis because it does not require the participation of the individual we are attempting to detect, and its structure has not changed significantly over time. Even with a mask, the ear is visible, so locating it is simple. In the human body identification process, ear bio-metrics can be added to other biometric systems to provide identification signals when information from other systems cannot be trusted or is unavailable. This paper describes a deep convolutional neural network architecture with six layers for auditory recognition. The biometrics field has a novel tool for recognizing ear patterns. It is used extensively in image processing. pattern recognition, etc., and has a high theoretical research value and commercial application potential. This project employs the most advanced techniques presently in use to recognize the same people's ear patterns in order to address the problem of inconsistency and to explore the human ear pattern quickly. - Bio-metric modules designed in accordance with industry standards. Utilizing the AMI Ear dataset, evaluate the efficacy of the proposed system in a controlled environment. This method can be used in conjunction with qualitative analysis to identify unique individuals within a large population. © 2023 ieee.

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series

UnCRtainTS: Uncertainty Quantification for Cloud Removal in ...

引用

2023 ieee/CVF conference on computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Ebel, Patrick Fare Garnot, Vivien Sainte Schmitt, Michael Wegner, Jan Dirk Xiang Zhu, Xiao Technical University of Munich Germany University of Zurich Switzerland University of the Bundeswehr Munich Germany

ISBN: (纸本)9798350302493

Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface. Although modern deep learning methods can implicitly learn to ignore such occlusions, explicit cloud removal as pre-processing.enables manual interpretation and allows training models when only few annotations are available. Cloud removal is challenging due to the wide range of occlusion scenarios - from scenes partially visible through haze, to completely opaque cloud coverage. Furthermore, integrating reconstructed images in downstream applications would greatly benefit from trustworthy quality assessment. In this paper, we introduce UnCRtainTS, a method for multi-temporal cloud removal combining a novel attention-based architecture, and a formulation for multivariate uncertainty prediction. These two components combined set a new state-of-the-art performance in terms of image reconstruction on two public cloud removal datasets. Additionally, we show how the well-calibrated predicted uncertainties enable a precise control of the reconstruction quality. © 2023 ieee.

关键词： image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Automatic Skin Lesion Segmentation Based on Higher-Order Spatial Interaction Model 1

Automatic Skin Lesion Segmentation Based on Higher-Order Spa...

引用

1st ieee International conference on Medical Artificial Intelligence, MedAI 2023

作者： Wu, Renkai Liang, Pengchen Huang, Xuan Yang, Ziyuan Shi, Liu Gu, Yuandong Zhu, Haiqin Chang, Qing Shanghai University Shanghai China Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Co. Ltd. Shanghai China

ISBN: (纸本)9798350358780

Dermoscopic image segmentation is a key step in computer-aided diagnosis of skin lesions. The current medical image segmentation model mainstream uses standard convolution and Transformers as the most important components of the model. However, standard convolution is unable to handle the problems of remote information interaction and long distance spatial dependence. Transformers is a hindrance to its application when dealing with medical clinical data, the insufficient amount of data and the large memory and time required for computation are hindering factors. Recently, HorNet, a high-order spatial interaction model that performs well in natural scenarios, has drawn our attention because the high-order interaction model has the common advantages of both convolution and Transformers. In this paper, we propose a new system that uses the HorNet model, which performs well in natural scenes, for medical image segmentation (skin lesion segmentation). To the best of our knowledge, we are the first to use a higher-order interaction model for medical image segmentation. We validate our system on three public datasets and do external validation on our own clinical dataset. The experimental results show that our system outperforms other current medical image segmentation models in several metrics. We think this is due to the higher spatial interaction capability and larger perceptual domain of gnconv in HorNet, which can fully and comprehensively capture the information of the overall medical image, which is crucial for medical image processing. © 2023 ieee.

关键词： computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Brain Controlled Communication system using Machine Learning techniques of computer Vision 5

Brain Controlled Communication system using Machine Learning...

引用

5th ieee International conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Sabharwal, Munish Sultania, Shantam Jindal, Harsh Galgotias University Greater Noida India Wissen Technology Banglore India Chandigarh University Dept. Of Computer Science And Engineering Mohali India

ISBN: (纸本)9798350330861

Nowadays computers have become a necessity for all computers have made a great leap for us and with the help of that we are able to move to a golden age of Artificial Intelligence. Artificial Intelligence or A.I has huge advantages and potentials for making the lives easy and more accessible. However, we as a society have to keep on innovating and developing new innovations and products to help different aspects of the community. The proposed system uses a brain-controlled interface, eye tracking along with a camera and screen for communication and training. The system requires input in the form of an image captured with the help of a camera. After the image is taken as an input, it will be used to perform eye-tracking and eye-blink detection. Both of these actions will be performed by employing a machine learning algorithm. The algorithm will process a morse code entered by the user, and then will enable the speech functionality using built-in speakers. Thus, rendering the system speak out whatever the user wanted to convey. The proposed system follows a coordinated approach for eye pattern detection and mapping. It detects the gazing spot of the eyes, and calculates the midpoint of the eye gaze. As a result, it predicts the eye blink pattern. This eye pattern recognition system is proposed to help out people with disabilities who find it difficult to communicate. © 2023 ieee.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

Target EEG recognition based on CNN and bidirectional GRU hybrid model

Target EEG recognition based on CNN and bidirectional GRU hy...

引用

2022 International conference on image, Signal processing. and pattern recognition, ISPP 2022

作者： Zhao, Haichen College of Computer and Communication Lanzhou University of Technology Lanzhou Gansu730050 China

ISBN: (纸本)9781510654846

To improve the recognition accuracy of target EEG signals, a classification model based on the combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. CNN is used to extract the frequency domain and space domain features of EEG signals, which is connected to bidirectional GRU after the fully connected layer to continue mining the deep timing information of the data, and finally the softmax layer is used to classify the EEG data into target and non-target signals. The model obtained an average classification accuracy of 95.88% on the UC San Diego Rapid Serial Visual Presentation (RSVP) EEG target detection dataset, outperforming the comparison method. It is shown that the proposed method can effectively extract the feature information of the target EEG signal and improve the EEG signal classification accuracy. © 2022 SPIE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approaches for Feature Extraction in Big Data Analytics 10

Deep Learning Approaches for Feature Extraction in Big Data ...

引用

10th ieee Uttar Pradesh Section International conference on Electrical, Electronics and computer Engineering, UPCON 2023

作者： Dhaygude, Amol Dattatray Varma, Raj A. Yerpude, Poonam Swarnkar, Suman Kumar Kumar Jindal, Rupesh Rabbi, Fazle University of Washington Department of Data Science Seattle United States Maharashtra Pune India Indian Institute of Technology Dept of Computer Science & Engineering Bhilai India Shri Shankaracharya Institute of Professional Management and Technology Dept of Computer Science and Engineering Raipur India National Institute of Design Dept of Computer Science & Engineering Ahmedabad India Australian Computer Society Australia

ISBN: (纸本)9798350382471

In the context of big data analytics, this study examines the use of algorithms based on deep learning for feature extraction. Traditional methods usually have trouble sifting through the complexity and volume of data to find the important elements. We investigate the application of auto encoders, transformer-based models, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) to address this problem. Our comprehensive review of existing literature compares these deep learning techniques with traditional methods and highlights their adaptability to large-scale datasets. The efficacy and precision of these methodologies are demonstrated by empirical investigations conducted on authentic datasets across a range of disciplines, including but not limited to time-series analysis, picture identification, and natural language processing. Despite challenges like computational requirements and model interpretability, our findings indicate that deep learning-based feature extraction holds significant promise for enhancing big data analytics, leading to valuable insights and discoveries in various fields. © 2023 ieee.

关键词： Autoencoders Big Data Analytics CNN) Deep Learning Efficiency Feature Extraction image recognition Natural Language processing RNNs Scalability Time-Series Analysis Real-World Datasets Transformer Models

来源：评论

学校读者我要写书评

暂无评论

RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

引用

2024 ieee International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Yichong Liu, Xiaojian Cheng, Fan Pan, Xiaoyong Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions. © 2024 ieee.

关键词： Binding sites

来源：评论

学校读者我要写书评

暂无评论

Joint Global and Local Hierarchical Priors for Learned image Compression

Joint Global and Local Hierarchical Priors for Learned Image...

引用

2022 ieee/CVF conference on computer Vision and pattern recognition, CVPR 2022

作者： Kim, Jun-Hyuk Heo, Byeongho Lee, Jong-Seok School of Integrated Technology Yonsei University Korea Republic of Naver Ai Lab

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, learned image compression methods have out-performed traditional hand-crafted ones including BPG. One of the keys to this success is learned entropy models that estimate the probability distribution of the quantized latent representation. Like other vision tasks, most recent learned entropy models are based on convolutional neural networks (CNNs). However, CNNs have a limitation in modeling long-range dependencies due to their nature of local connectivity, which can be a significant bottleneck in image compression where reducing spatial redundancy is a key point. To overcome this issue, we propose a novel entropy model called Information Transformer (Informer) that exploits both global and local information in a content-dependent manner using an attention mechanism. Our experiments show that Informer improves rate-distortion performance over the state-of-the-art methods on the Kodak and Tecnick datasets without the quadratic computational complexity problem. Our source code is available at https://***/naver-ai/informer. © 2022 ieee.

关键词： image coding Computational modeling Redundancy Rate-distortion Transformers Entropy Probability distribution

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 << < 28 29 30 31 32 33 34 35 36 37 > >>

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：