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

1,832 篇 会议
14 册 图书
12 篇 期刊文献

馆藏范围

1,858 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,367 篇 工学
- 1,083 篇 计算机科学与技术...
- 777 篇 软件工程
- 458 篇 信息与通信工程
- 334 篇 控制科学与工程
- 326 篇 光学工程
- 259 篇 电气工程
- 174 篇 生物工程
- 122 篇 机械工程
- 108 篇 生物医学工程（可授...
- 57 篇 电子科学与技术（可...
- 48 篇 材料科学与工程（可...
- 48 篇 化学工程与技术
- 48 篇 安全科学与工程
- 38 篇 仪器科学与技术
- 37 篇 交通运输工程
736 篇 理学
- 532 篇 物理学
- 378 篇 数学
- 241 篇 化学
- 176 篇 生物学
- 57 篇 统计学（可授理学、...
394 篇 医学
- 380 篇 临床医学
- 190 篇 医学技术（可授医学...
- 186 篇 特种医学
- 61 篇 基础医学(可授医学...
- 45 篇 药学(可授医学、理...
- 27 篇 公共卫生与预防医...
179 篇 管理学
- 101 篇 管理科学与工程(可...
- 80 篇 图书情报与档案管...
- 31 篇 工商管理
28 篇 农学
- 28 篇 作物学
22 篇 法学
17 篇 经济学
8 篇 教育学
5 篇 军事学
4 篇 文学

主题

322 篇 computer vision
231 篇 image processing
110 篇 deep learning
108 篇 image segmentati...
97 篇 feature extracti...
56 篇 object detection
42 篇 cameras
40 篇 convolutional ne...
39 篇 image edge detec...
37 篇 machine learning
36 篇 image recognitio...
34 篇 visualization
33 篇 neural networks
33 篇 image reconstruc...
30 篇 image enhancemen...
30 篇 image color anal...
28 篇 computational mo...
28 篇 shape
28 篇 training
27 篇 image analysis

机构

6 篇 university of ch...
5 篇 comput & inf sci...
5 篇 department of co...
5 篇 nanjing universi...
4 篇 chitkara univers...
4 篇 shenyang institu...
4 篇 image processing...
4 篇 beijing institut...
4 篇 nanjing universi...
4 篇 college of compu...
3 篇 university of sc...
3 篇 shanghai jiao to...
3 篇 dept of electr &...
3 篇 south china univ...
3 篇 inria sophia-ant...
3 篇 school of comput...
3 篇 department of ma...
3 篇 sichuan universi...
3 篇 department of co...
3 篇 sun yat-sen univ...

作者

6 篇 andayani u.
5 篇 wang jian
5 篇 syahputra m.f.
5 篇 ren mingwu
5 篇 mohana
5 篇 siregar b.
5 篇 li hui
4 篇 yang yang
4 篇 wang wei
4 篇 hassan ghassemia...
4 篇 li jun
4 篇 shohreh kasaei
4 篇 e.j. delp
4 篇 wang jun
3 篇 wang lei
3 篇 luo haibo
3 篇 chen chen
3 篇 paul joseph sure...
3 篇 wang qian
3 篇 ying liu

语言

1,477 篇 英文
375 篇 其他
14 篇 中文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1858 条记录，以下是911-920 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：

Research of computer vision based on system learning ability 3rd

Research of computer vision based on system learning ability

引用

3rd international conference on Intelligent, Interactive Systems and Applications, IISA2018

作者： Luo, Jun Zhai, Ruifang Peng, Hui College of Informatics Huazhong Agricultural University Wuhan430070 China

ISBN: (纸本)9783030028039

Although the computer vision course has a strong theoretical and practical characteristics, many colleges or universities are willing to offer this course as compulsory or elective course for students of information specialty. To aim at the training objectives of colleges’ information undergraduates, we introduce the basic requirements of digital image processing in modern information technology field, point out the limitations of traditional textbooks and learning method on training students ability, and then propose system ability oriented mode for learning practices, it is essential to improve the effects of theoretical and practical learning, and meanwhile, it is significant to enhance the professional basis for undergraduates of information related specialty. © Springer Nature Switzerland AG 2019.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

An image processing Method Reducing Road Marking Line Edge Features for Patrol Robots Identifying Road Boundaries 19

An Image Processing Method Reducing Road Marking Line Edge F...

引用

Proceedings of the 2019 3rd international conference on Advances in image processing

作者： Shuangye Chen Zhi Liu Beijing University of Technology Beijing China

ISBN: (纸本)9781450376754

When automatic patrol robots trying to identify road boundaries through edge detection, road marking lines on the road may cause errors. In this regard, this paper proposes a method to process images base on edge detection results, and builds a system based on the method for experiments. The experimental results show that the proposed method can effectively reduce the edge features of road markings lines while preserving the edge features of real boundaries, so that robots can identify real road boundaries.

关键词： image processing computer vision Hough transform Robotics Road recognition Edge detection

来源：评论

学校读者我要写书评

暂无评论

A video conversion technique to image mosaic 3

A video conversion technique to image mosaic

引用

3rd international conference on Intelligent Systems and Advanced Computing Sciences, ISACS 2019

作者： Laraqui, Abderrahmane Azmi, Kamal Laraqui, Mohammed LARGESS Faculty of SJES El-Jadida UCD B.P 356 Morocco SIE Laboratory FP Ouarzazate Ibn Zohr University BP 32/S Morocco

ISBN: (纸本)9781728148137

Videomosaic is a very important research axis in the field of computer vision. In this article, we will attempt to give details about the most important steps in the process of creating a wider view of the field of vision based on a video scene. This algorithm will be applied in the real-Time conversion of videos to mosaic images. © 2019 IEEE.

关键词： Video signal processing

来源：评论

学校读者我要写书评

暂无评论

Skin Diseases Classification Using Local Binary Pattern and Convolutional Neural Network 3

Skin Diseases Classification Using Local Binary Pattern and ...

引用

3rd international conference on Electrical, Telecommunication and computer Engineering (ELTICOM)

作者： Akmalia, Nurul Sihombing, Poltak Suherman Univ Sumatera Utara Fac Comp Sci & Informat Technol Dept Informat Technol Medan Indonesia Univ Sumatera Utara Fac Comp Sci & Informat Technol Dept Comp Sci Medan Indonesia Univ Sumatera Utara Fac Engn Dept Elect Engn Medan Indonesia

ISBN: (纸本)9781728124759

Skin disease is one of the diseases that are often found in tropical countries like Indonesia. Lack of knowledge about the types and prevention of skin diseases results a person suffering from acute skin diseases. computer technology is expected to help detect disease early so that it can minimize the occurrence of more dangerous diseases. This paper proposes a method for introducing the shape, color, and texture of skin diseases in digital images and classifying the results of image analysis based on the type of disease in human skin. The method used is a combination of Local Binary Pattern (LBP) and Convolutional Neural Network (CNN) methods which can later be used as sensors or vision for skin diseases automatically. The results of this study can help in the early identification of skin diseases, helping parties who want to know the image value of skin diseases by using LBP and classifying it based on the type of disease using CNN. This study shows the level of accuracy of combining LBP with CNN is quite high with an average value of 92%. In addition, this research can also be used as reference material for the development of further research in image processing that uses LBP and classification using CNN.

关键词： image Classification Skin Disease Local Binary Pattern Convolutional Neural Network Deep Learning

来源：评论

学校读者我要写书评

暂无评论

Detecting Spam images with Embedded Arabic Text in Twitter 3

Detecting Spam Images with Embedded Arabic Text in Twitter

引用

15th IAPR international conference on Document Analysis and Recognition (ICDAR) / 3rd international Workshop on Arabic and derived Script Analysis and Recognition (ASAR)

作者： Imam, Niddal Vassilakis, Vassilios G. Univ York Dept Comp Sci York N Yorkshire England

ISBN: (纸本)9781728150543

Detecting image-based spam in Online Social Networks (OSNs), such as Facebook and Twitter, is an ongoing problem. Spam is prevalent in all forms of online communication (such as email and the web) However, researchers' and practitioners' attention has increasingly shifted to spam in OSNs, due to the growing number of spammers and the possible negative effects on users. There are different types of spam messages that can be found in OSNs. Spam images, which are images embedded with malicious text, are one of the most difficult types of spam to tackle. processing images overwhelms classifiers and affects detection performance. Consequently, spammers take advantage of this issue to launch more sophisticated attacks, such as evasion attacks. After observing some Arabic trending hashtags and topics in Twitter, a substantial amount of image-based spam was found. Thus, this paper proposes an approach for detecting image-based spam with Arabic text in Twitter through using Deep Learning (DL) techniques. In this paper, an Efficient and Accurate Scene Text Detector (EAST) and Convolutional Recurrent Neural Network (CRNN) models were used for text detection and text recognition. After the text extraction step, a blacklist and whitelist approach was applied for classifying text as either spam or non-spam. The proposed text classification technique is adaptable and robust against some text classification attacks.

关键词： Twitter spam detection text detection/ recognition Online Social Networks image spam computer vision neural network

来源：评论

学校读者我要写书评

暂无评论

Intelligent interactive edge detection method of image 3

Intelligent interactive edge detection method of image

引用

3rd international conference on Big Data Research, ICBDR 2019

作者： Bo, Zhang Jingchang, Pan Gaoyu, Jiang School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China

ISBN: (纸本)9781450372015

Edge detection is a fundamental problem in image processing and computer vision, it plays an important role in the field of computer vision. Edge detection highlights significant changes in the image by identifying points in the digital image where the brightness changes significantly, and filters the secondary information while retaining the key features of the image, thus reducing the amount of data. Based on more than 3,000 historical geographic images of different time nodes, this study proposes a user-oriented interactive edge detection algorithm, establishes a spatially evolved vector model, extracts historical geographic boundary information, and internal location information and realize the digitization of space-time features. The main work of this study is as follows:1. Select the ROI area, specify the historical geographic area that needs to be extracted, filter the interference information, reduce the processing time, and increase the accuracy.2. The edge extraction algorithm based on Sobel operator is improved, and the edge information of historical geographic regionalization is extracted to realize the detailed modification and improvement of the edge information interactive. 3. Extract the location information within the historical geographic regionalization to fill the framewor of historical geographic regionalization, improve the details of the spatial framework, and facilitate accurate spatial location of information objects. © 2019 Association for Computing Machinery.

关键词： Edge detection

来源：评论

学校读者我要写书评

暂无评论

AECNN: Autoencoder with Convolutional Neural Network for Hyperspectral image Classification 1

引用

3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th Indian conference on computer vision, Graphics and image processing, ICVGIP 2018

作者： Patel, Heena Upla, Kishor P. Sardar Vallabhbhai National Institute of Technology SuratGujarat India

ISBN: (数字)9789811513879

ISBN: (纸本)9789811513862

This paper addresses an approach for classification of hyperspectral imagery (HSI). In remote sensing, the HSI sensor acquires hundreds of images with very narrow but continuous spectral width in visible and near-infrared regions of the electromagnetic (EM) spectrum. Due to the nature of data acquisition with contiguous bands, the HS images are very useful in classification and/or the identification of materials present in the captured geographical area. However, the low spatial resolution and large volume of HS images make the classification of those images as a challenging task. In the proposed approach, we use an autoencoder with convolutional neural network (AECNN) for classification of HS image. Pre-processing procedure with autoencoder leads to obtain optimized weights in the initial layer of CNN model. Moreover, features are enhanced in the HS images by utilizing the autoencoder. The CNN is used for efficient extraction of the features and same is also utilised for the classification of HS data. The potential of the proposed approach has been verified by conducting the experiments on three recent datasets. The experimental results are compared with the results obtained in the geoscience and remote sensing society (GRSS) image Fusion Contest-2018 held at IEEE international Geoscience and Remote Sensing Symposium (IGARSS)-2018 and other existing frameworks for HSI classification. The testing accuracy of classification for the proposed approach is better than that of the other existing deep learning based methods. © Springer Nature Singapore Pte Ltd, 2019.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Research on Text Detection and Recognition Based on OCR Recognition Technology

Research on Text Detection and Recognition Based on OCR Reco...

引用

international conference on Information Systems and computer Aided Education (ICISCAE)

作者： Yuming He Software Engineering Shanghai Starriver Bilingual School Shanghai China

ISBN: (数字)9781728183046

ISBN: (纸本)9781728194608

Optical character recognition (OCR) is an important branch in the field of machine vision. It involves pattern recognition, image processing, digital signal processing, artificial intelligence and other disciplines. It is a comprehensive. It has important use value and theoretical significance in high-tech fields such as word information processing, office automation, machine translation and real-time monitoring system. In the 21st century, with the popularity of smart phones with high-definition cameras, OCR has a new pursuit in its development: more and more people pick up their mobile phones to photograph the things and scenes they see and obtain the text information in the pictures. Therefore, the recognition of characters in natural scenes has become a brand-new topic. In the past, text detection and text recognition algorithms were basically based on artificially designed features and traditional image processing methods. These features and algorithms were difficult to design and needed a lot of professional knowledge and experience support, so the accuracy was not high and they were not generalized. In recent years, with the rapid development of deep learning technology, breakthroughs have been made in the fields of computer vision such as image classification, object detection and semantic segmentation. Deep learning algorithm is a data-driven algorithm. The algorithm based on deep learning can automatically discover and learn the hidden feature rules in a large number of data through iterative training, without too much human intervention, so it has better generalization than traditional image processing related algorithms.

关键词： Deep learning image recognition Text recognition Signal processing algorithms Classification algorithms Optical character recognition software Character recognition

来源：评论

学校读者我要写书评

暂无评论

Detection and Classification of Vehicles based on Deep Semantic Features of Faster R-CNN 3

Detection and Classification of Vehicles based on Deep Seman...

引用

3rd international conference on Innovative Computing, ICIC 2019

作者： Hassan, Saleet Ul Khan, Muhammad Zeeshan Draz, Hafiz Umer Ghani Khan, Muhammad Usman Al-Khawarzami Institute of Computer Science University of Engineering and Technology Lahore Lahore Pakistan

ISBN: (纸本)9781728146829

In the current era, automation of vehicle detection and classification is considered as one of the challenging tasks which leads to the safety of roads and Transportation system. With the development of computer vision and image processing techniques, many researchers overcome the obstacles and achieved the aim. However, they lack accuracy and to handle this issue deep learning technology received a lot of attention from researchers. Deep learning-based Convolution Neural Network Specifically Faster Region (CNN) has been very successful in object detection and image classification. In the proposed system we have used the latest techniques of deep learning to achieve state of the art results. Proposed system uses Faster R-CNN for both detection and classification purposes, it not only saves the time but also has superiority over other machine learning techniques in sense of accuracy and robustness. Firstly, Region Proposal Network detects the vehicle then CNN classifies the vehicle by its type. We have achieved 97.3% accuracy on dataset generated by ourselves. Achieving such an accuracy on locally generated dataset shows the novelty. © 2019 IEEE.

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

Automatic Generation Method of Basketball Continuous Pitching Action Based on Multi-Objective Machine vision

Automatic Generation Method of Basketball Continuous Pitchin...

引用

international conference on Information Systems and computer Aided Education (ICISCAE)

作者： Liang Qiaomei Xu Yi Guangdong University of Foreign Studies Guangzhou Guangdong Province China Guangdong Eco-Engineering Polytechnic Guangzhou Guangdong Province China

ISBN: (数字)9781728183046

ISBN: (纸本)9781728194608

In order to improve the effectiveness of basketball continuous pitching motion guidance, the image characteristics of basketball continuous pitching motion are analyzed with image processing method, and an automatic generation method of basketball continuous pitching motion based on multi-objective machine vision is proposed. The method comprises the following steps of: establishing a shape feature point feature matching model of a basketball continuous pitching motion fuzzy image; extracting posture key action points of the basketball continuous pitching motion fuzzy image by combining a spectral feature detection method; carrying out adaptive enhancement processing on the basketball continuous pitching motion fuzzy image by adopting a shape feature segmentation method; and obtaining sub-band pixel feature points of the basketball continuous pitching motion fuzzy image by combining a point tracking matching method and a feature extraction method. The fuzzy image of basketball continuous pitching motion is enhanced to improve the expression ability of posture information of fuzzy noise points in the image. The significant contrast enhancement processing of the fuzzy image of basketball continuous pitching motion is carried out through the point tracking recognition method, and the multi-objective machine vision features of the fuzzy image of basketball continuous pitching motion are extracted to realize automatic generation and accurate recognition of the fuzzy image of basketball continuous pitching motion. The simulation results show that the output peak signal-to-noise ratio automatically generated by using this method for basketball continuous pitching motion is higher, and the image recognition ability is better. Therefore, it is necessary to improve the performance of the system.

关键词： image recognition PSNR Tracking Shape Motion segmentation Object segmentation Feature extraction

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186页 << < 88 89 90 91 92 93 94 95 96 97 > >>

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：