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

2,984 篇 会议
263 册 图书
58 篇 期刊文献
1 篇 学位论文

馆藏范围

3,306 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,991 篇 工学
- 1,556 篇 计算机科学与技术...
- 772 篇 软件工程
- 499 篇 电气工程
- 404 篇 光学工程
- 288 篇 信息与通信工程
- 263 篇 控制科学与工程
- 171 篇 机械工程
- 134 篇 生物工程
- 120 篇 生物医学工程（可授...
- 118 篇 电子科学与技术（可...
- 93 篇 仪器科学与技术
- 65 篇 材料科学与工程（可...
- 52 篇 安全科学与工程
- 45 篇 化学工程与技术
- 45 篇 交通运输工程
- 23 篇 建筑学
- 23 篇 土木工程
1,040 篇 理学
- 830 篇 物理学
- 233 篇 数学
- 146 篇 生物学
- 62 篇 统计学（可授理学、...
- 42 篇 化学
- 22 篇 系统科学
429 篇 医学
- 416 篇 临床医学
- 44 篇 基础医学(可授医学...
- 30 篇 药学(可授医学、理...
233 篇 管理学
- 163 篇 管理科学与工程(可...
- 76 篇 图书情报与档案管...
- 24 篇 工商管理
34 篇 农学
- 31 篇 作物学
21 篇 法学
12 篇 经济学
9 篇 教育学
6 篇 军事学
6 篇 艺术学
5 篇 文学

主题

589 篇 machine vision
559 篇 image processing
534 篇 computer vision
245 篇 machine learning
205 篇 image segmentati...
198 篇 feature extracti...
194 篇 artificial intel...
159 篇 image processing...
150 篇 pattern recognit...
144 篇 deep learning
133 篇 cameras
92 篇 training
91 篇 computational mo...
85 篇 visualization
84 篇 object detection
84 篇 image recognitio...
82 篇 image analysis
82 篇 inspection
79 篇 information syst...
79 篇 image edge detec...

机构

10 篇 center for machi...
9 篇 tongji universit...
7 篇 chongqing univer...
7 篇 hefei university...
7 篇 south china norm...
7 篇 university of ox...
7 篇 harbin institute...
7 篇 univ minnesota c...
6 篇 department of co...
6 篇 moscow state uni...
6 篇 university of no...
6 篇 dipartimento di ...
6 篇 dipartimento di ...
6 篇 graz university ...
6 篇 computational sc...
6 篇 don state tech u...
6 篇 beijing institut...
6 篇 center for machi...
6 篇 dipartimento di ...
6 篇 university of fr...

作者

18 篇 de-shuang huang
13 篇 waltz fm
10 篇 ngoc thanh nguye...
10 篇 abdenour hadid
9 篇 stan sclaroff
9 篇 batchelor bg
9 篇 voronin v.
8 篇 semenishchev e.
7 篇 andrea vedaldi
7 篇 meriaudeau f
7 篇 alberto del bimb...
6 篇 haijun zhang
6 篇 miller jwv
6 篇 ikenaga takeshi
6 篇 tianyong hao
6 篇 jan-michael frah...
6 篇 zelensky a.
6 篇 thomas brox
6 篇 beyerer juergen
6 篇 hugo jair escala...

语言

3,192 篇 英文
78 篇 其他
37 篇 中文
7 篇 土耳其文
1 篇 俄文

检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

共 3306 条记录，以下是761-770 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：

CHASE: Robust visual Tracking via Cell-Level Differentiable Neural Architecture Search 32

CHASE: Robust Visual Tracking via Cell-Level Differentiable ...

引用

32nd British machine vision conference, BMVC 2021

作者： Marvasti-Zadeh, Seyed Mojtaba Khaghani, Javad Cheng, Li Ghanei-Yakhdan, Hossein Kasaei, Shohreh Vision and Learning Lab University of Alberta Edmonton Canada Digital Image & Video Processing Lab Yazd University Yazd Iran Image Processing Lab Sharif University of Technology Tehran Iran

A strong visual object tracker nowadays relies on its well-crafted modules, which typically consist of manually-designed network architectures to deliver high-quality tracking results. Not surprisingly, the manual design process becomes a particularly challenging barrier, as it demands sufficient prior experience, enormous effort, intuition, and perhaps some good luck. Meanwhile, neural architecture search has gaining grounds in practical applications as a promising method in tackling the issue of automated search of feasible network structures. In this work, we propose a novel cell-level differentiable architecture search mechanism with early stopping to automate the network design of the tracking module, aiming to adapt backbone features to the objective of Siamese tracking networks during offline training. Besides, the proposed early stopping strategy avoids over-fitting and performance collapse problems leading to generalization improvement. The proposed approach is simple, efficient, and with no need to stack a series of modules to construct a network. Our approach is easy to be incorporated into existing trackers, which is empirically validated using different differentiable architecture search-based methods and tracking objectives. Extensive experimental evaluations demonstrate the superior performance of our approach over five commonly-used benchmarks. © 2021. The copyright of this document resides with its authors.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Python-Based Optical Character Recognition (OCR)

Python-Based Optical Character Recognition (OCR)

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Priyanka Kaushik Priyanka Rawat Devyansh Batra P. Vensheeba Delin Saurabh Pratap Singh Rathore S. Kaliappan Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India Dept. of Computer Science and Engineering Chandigarh University Chandigarh India Dept. of Civil Eng. Dhanalakshmi Srinivasan College of Eng. Coimbatore India Dept. of Management & Research ICAPSR New Delhi India Division of Research and Development Lovely Professional University Punjab India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Optical Character Acknowledgment (OCR) stands as a transformative innovation at the crossing point of computer vision and machine learning, encouraging the extraction of printed data from pictures or checked archives. Within the period of advanced change, OCR serves as a foundation for different applications, such as archive digitization, content mining, and cleverly data recovery. The first thing that pops into one’s mind in building vigorous and versatile OCR frameworks for different scenarios is the choice of a successful programming dialect. The Python programming dialect is discussed here, which is renowned for its simplicity, vast libraries, and dynamic community, as an impressive tool to execute OCR arrangements.

关键词： Technological innovation image processing Optical character recognition Pipelines Programming Libraries Data mining Reliability Python Testing

来源：评论

学校读者我要写书评

暂无评论

image CAPTION GENERATOR USING ATTENTION MECHANISM 12

IMAGE CAPTION GENERATOR USING ATTENTION MECHANISM

引用

12th International conference on Computing Communication and Networking Technologies, ICCCNT 2021

作者： Agrawal, Vaishnavi Dhekane, Shariva Tuniya, Neha Vyas, vibha Electronics and Telecommunication Engineering College of Engineering Pune Pune India

ISBN: (纸本)9781728185958

image captioning is used to generate sentences describing the scene captured in the form of images. It identifies objects in the image, performs a few operations, and tries to find the salient features of the image. Once the system identifies this information, it should further generate the most relevant and brief description for the image, which should be both syntactically and semantically correct. With the advancements of Learning techniques, algorithms can generate text in the form of natural sentences that will be able to describe an image in its best form. The natural ability of humans to understand image content and generate descriptive text is a challenging task for a machine to imitate. The applications of image caption generation are extensive and significant. The task involves generating brief captions using various techniques like Natural language processing (NLP), Computer vision (CV), and Deep Learning (DL) techniques. This paper introduces a system that uses an attention mechanism alongside an encoder and a decoder to generate the captions. It uses a pre-trained Convolutional Neural Network (CNN) viz. Inception V3 to extract features of the image and then a Recurrent Neural Network (RNN) viz. GRU to generate a relevant caption. To generate captions, the proposed model uses an attention mechanism that is Bahdanau attention. MSCOCO dataset is used to train the model. The results validate the model's reasonable ability to understand images and generate text. © 2021 IEEE.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Surface Defect Detection of High Precision Cylindrical Metal Parts Based on machine vision 14th

Surface Defect Detection of High Precision Cylindrical Metal...

引用

14th International conference on Intelligent Robotics and applications (ICIRA)

作者： Jiang, YuJie Li, Chen Zhang, Xu Wang, JingWen Liu, ChuZhuang Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Haiphong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9783030890988;9783030890971

The surface quality of high precision cylindrical metal parts is an important index to measure its quality. Most of the existing detection methods still use manual visual inspection. Manual detection is inefficient and difficult to ensure the standard of detection. It is difficult to make an effective judgment for the defects in the critical index, and it is more prone to miss detection and misjudgment. In this paper, the seamless steel pipe used for the shock absorber of bike is taken as the main research object, and machine vision is used for its surface defecting. Combined with the characteristics of arc and high reflection on the surface of steel pipe, an image acquisition and processing system composed of linear light source, linear array camera, encoder and rotation system is proposed. Refer to the national standard GB/T9797-2005, the defects mainly include pit, spalling, pitting, speckle, which is determined by Fourier transform, gradient threshold, and line detection by their four different characteristics. Finally, a complete experimental platform with clamping, blowing, detection, and classification functions is built to test. The experimental results show that the stability, accuracy and detection efficiency of the steel pipe detection system based on machine vision is high, which can meet the needs of daily production detection.

关键词： Cylindrical metal parts machine vision Fourier transform Gradient threshold Line detection Edge detection

来源：评论

学校读者我要写书评

暂无评论

Dangerous Object Detection for visually Impaired People using Computer vision 1

Dangerous Object Detection for Visually Impaired People usin...

引用

1st IEEE International conference on Artificial Intelligence and machine vision, AIMV 2021

作者： Shah, Harsh Shah, Rishi Shah, Shlok Sharma, Paawan Pandit Deendayal Energy University Department of Electrical Engineering Gujarat India Technology Pandit Deendayal Energy University Department of Information and Communication Gujarat India

ISBN: (纸本)9781665442114

In this contemporary world, Artificial Intelligence and machine Learning are one of the leading technologies creating an impact in the world by mimicking human behaviour to solve a particular problem. Hence, these technologies are widely used to aid different obstacles encountered by humans. One such problem widely faced by the mankind is visual impairment. According to World Health Organization, approximately 285 million people suffer with vision impairment. Therefore, applications of machine learning and computer vision can be applied to guide the people with such problems. This paper presents the idea of using object detection to aid the visually impaired people. In this paper, an experiment has been proposed which uses a custom-built image dataset of various dangerous objects. The objects have been categorized into 5 broad categories: Sharp objects, Danger signs, Broken glass, Manhole and Fires. A number of different algorithms have been trained on this custom image dataset containing the menacing objects and their performances have been evaluated. The evaluation indicators for the models are the validation error in terms mean Average Precision (mAP) and the processing time for each model. The models have also been tested in real world scenario by evaluating on a custom video to gauge their performance in terms of accuracy in detection of different objects as well as their ease in deployment by suggesting their frame rate handling capacity. The results are discussed and the most robust and balanced model is suggested at the end of the paper. © 2021 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Investigation of machine Learning Algorithms for Pattern Recognition in image processing

Investigation of Machine Learning Algorithms for Pattern Rec...

引用

International conference on Inventive Research in Computing applications (ICIRCA)

作者： Chennaiah Kate C. Kalpana Arvind Sharma Ajay Singh Yadav Ashok Kumar S. Sandeep Kumar Department of Information Technology St. Peter's Engineering College Hyderabad Telangana India Department of Computer Science and Engineering NPR College of Engineering & Technology Dindigul Tamil Nadu India Department of Electronics and Communication Engineering Government Women Engineering College Ajmer Rajasthan India Department of Mathematics SRM Institute of Science and Technology Ghaziabad Uttar Pradesh India Department of Computer Science BanasthaliVidyapith Rajasthan India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

In order to recognize patterns in images, this study tests the performance of many “machine learning algorithms” and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods (“deep learning, support vector machines, decision trees, and random forests”) and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its “accuracy, precision, recall, and F1 score”, among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Survey on Data Augmentation Techniques

A Survey on Data Augmentation Techniques

引用

International conference on Computing Methodologies and Communication (ICCMC)

作者： K. Nanthini D. Sivabalaselvamani K. Chitra P. Gokul S. KavinKumar S. Kishore Department of Computer Applications Kongu Engineering College Erode Tamil Nadu India

Data augmentation is a technique used to generate new data sets from existing ones and artificially increase the size of a dataset. By providing the model with more training data, it is possible to enhance the performance of the proposed model. Various methods, such as image rotating, cropping, and flipping, and adding noise to audio signals can be used to enhance the data. When the original dataset is limited or unbalanced, the use of data augmentation can result in better generalization and increased performance for unknown data. This also reduces the over-fitting by increasing the number of training data included in the machine learning model. However, it is crucial to select the data augmentation methods that are used to be aware of how these methods will affect the models' performance on actual data. To deal with data scarcity and lack of diversity, computer vision and natural language processing (NLP) models employ data augmentation strategies. Accuracy of machine learning models can be improved further by employing AI/ML approaches like data augmentation. According to an experiment, deep learning model performs better by reducing the training loss and validation loss than a model without augmentation for image classification. This study has briefly discussed about the data augmentation techniques that are used to process the image, text and signal data by increasing the volume and variety of training data and set it as the primary goal for a machine learning model to perform better when it comes to generalization. By employing these techniques, over-fitting can be eliminated and the robustness and accuracy of the model can also be improved. The main objective of this study is to implement data augmentation as a solution for the problem of data scarcity. Data augmentation refers to a group of methods that are used to improve the amount and quality of training datasets so that more effective machine learning models may be constructed to process the data. The ap

关键词： Training Deep learning Solid modeling Computer vision Computational modeling Training data Data models

来源：评论

学校读者我要写书评

暂无评论

TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals

TCCT-Net: Two-Stream Network Architecture for Fast and Effic...

引用

IEEE Computer Society conference on Computer vision and Pattern Recognition Workshops (CVPRW)

作者： Alexander Vedernikov Puneet Kumar Haoyu Chen Tapio Seppänen Xiaobai Li Center for Machine Vision and Signal Analysis University of Oulu Finland State Key Laboratory of Blockchain and Data Security Zhejiang University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need large amounts of input data, computational power, inference time. These constraints challenge embedding systems into devices for real-time use. To address these limitations, we present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain and boost processing speed, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form. Evaluated on the EngageNet dataset, the proposed method outperforms existing baselines, utilizing only two behavioral features (head pose rotations) compared to the 98 used in baseline models. Furthermore, comparative analysis shows TCCT-Net’s architecture offers an order-of-magnitude improvement in inference speed compared to state-of-the-art image-based Recurrent Neural Network (RNN) methods. The code will be released at https://***/vedernikovphoto/TCCT_Net.

关键词： Convolutional codes Accuracy Recurrent neural networks Continuous wavelet transforms Wavelet domain Convolution Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Deep Pre-trained Models for Computer vision applications: Traffic sign recognition 18

Deep Pre-trained Models for Computer Vision Applications: Tr...

引用

18th International Multi-conference on Systems, Signals & Devices (SSD)

作者： Bouaafia, Soulef Messaoud, Seifeddine Maraoui, Amna Ammari, Ahmed Chiheb Khriji, Lazhar Machhout, Mohsen Monastir Univ Elect & Microelect Lab Monastir Tunisia Sultan Qaboos Univ ECE Dept Muscat Oman

ISBN: (纸本)9781665414937

Objects detection and Recognition are an important task for computer vision field and intelligent transportation systems. Generally, these tasks remain challenging for the artificial machines due to the need of pre-learning phase in which the machine acquires an intelligent brain. Some researchers have shown that deep learning tools work well in computer vision, image processing, and pattern recognition. To solve such tasks, this paper focuses on deep Convolutional Neural Network (CNN) and its architectures, such as, VGG16, VGG19, AlexNet, and Resnet50. An overview for the techniques and schemes used for computer vision applications such as Road Sign Recognition will be introduced. Then by customizing the hyperparameters for each pre-trained models, we re-implement these models for the traffic sign recognition application. In the experiments, these pre-trained CNN classifiers are trained and tested with the German Traffic Sign Recognition Benchmark dataset (GTSRB). Experimental results show that the proposed scheme achieved a good performance results in terms of evaluations metrics of traffic signs recognition. A performance comparison analysis between the selected pre-trained models for traffic sign recognition confirmed that the AlexNet model outperforms all other implemented models.

关键词： Deep learning Deep Convolutional Neural Network Pre-trained models Traffic sign recognition

来源：评论

学校读者我要写书评

暂无评论

Fast Coding Mode Prediction for Intra Prediction in VVC SCC

Fast Coding Mode Prediction for Intra Prediction in VVC SCC

引用

IEEE International conference on image processing

作者： Dayong Wang Junyi Yu Xin Lu Frederic Dufaux Hongwei Guo Hui Guo Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des signaux et systèmes School of Engineering Honghe University Mengzi Yunnan China University of Electronic Science and Technology of China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by $34.95 \%$ on average while maintaining almost the same coding efficiency.

关键词： video coding Deep learning image coding Modulation Prediction algorithms Complexity theory Copper

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331页 << < 73 74 75 76 77 78 79 80 81 82 > >>

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：