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

78 篇 会议
8 册 图书
3 篇 期刊文献

馆藏范围

89 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

57 篇 工学
- 50 篇 计算机科学与技术...
- 26 篇 软件工程
- 10 篇 生物工程
- 8 篇 信息与通信工程
- 7 篇 电气工程
- 7 篇 控制科学与工程
- 4 篇 仪器科学与技术
- 4 篇 生物医学工程（可授...
- 3 篇 光学工程
- 3 篇 电子科学与技术（可...
- 3 篇 石油与天然气工程
- 3 篇 交通运输工程
- 2 篇 建筑学
- 2 篇 土木工程
- 2 篇 安全科学与工程
- 1 篇 材料科学与工程（可...
- 1 篇 动力工程及工程热...
- 1 篇 水利工程
- 1 篇 化学工程与技术
22 篇 理学
- 12 篇 生物学
- 6 篇 数学
- 4 篇 物理学
- 3 篇 化学
- 1 篇 地质学
- 1 篇 统计学（可授理学、...
11 篇 管理学
- 7 篇 图书情报与档案管...
- 6 篇 管理科学与工程(可...
- 2 篇 工商管理
3 篇 医学
- 3 篇 临床医学
1 篇 教育学
- 1 篇 教育学
1 篇 军事学

主题

18 篇 pattern recognit...
4 篇 neural networks
4 篇 artificial intel...
3 篇 support vector m...
3 篇 face recognition
3 篇 data mining and ...
3 篇 computer applica...
3 篇 feature extracti...
3 篇 machine learning
3 篇 information retr...
3 篇 computer vision
3 篇 shape
2 篇 iris recognition
2 篇 active vision
2 篇 decision support...
2 篇 pattern classifi...
2 篇 statistics
2 篇 image segmentati...
2 篇 local binary pat...
2 篇 computer communi...

机构

2 篇 department of co...
2 篇 national laborat...
2 篇 college of compu...
2 篇 department of el...
2 篇 democritus unive...
2 篇 department of co...
2 篇 department of el...
2 篇 university of pi...
1 篇 college of compu...
1 篇 laboratoire ura ...
1 篇 cybersecurity ma...
1 篇 vietnam national...
1 篇 trinity coll dub...
1 篇 katholieke univ ...
1 篇 icme university ...
1 篇 lee kong chian f...
1 篇 computer enginee...
1 篇 hasso plattner i...
1 篇 centre for patte...
1 篇 univ texas san a...

作者

2 篇 sandeep kumar
2 篇 ignacio rojas
2 篇 munish kumar
2 篇 phung dinh
2 篇 monali gulhane
2 篇 tran truyen
2 篇 venkatesh svetha
2 篇 nazri a. zamani
2 篇 gonzalo joya
2 篇 ilias maglogiann...
2 篇 lazaros iliadis
1 篇 pereira luis fel...
1 篇 jiang xiaoyi
1 篇 nitin
1 篇 turki turki
1 篇 wang lei
1 篇 alizadeh aylar z...
1 篇 akter nasrin
1 篇 meysam alavi
1 篇 pan shuai

语言

88 篇 英文
1 篇 中文

检索条件"任意字段=12th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2023"

共 89 条记录，以下是41-50 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：

pattern recognition applications and methods: 4th international conference, icpram 2015 Lisbon, Portugal, January 10–12, 2015 revised selected papers 4th

Pattern recognition applications and methods: 4th internatio...

引用

4th international conference on pattern recognition applications and methods, icpram 2015

作者： Fred, Ana De Marsico, Maria Figueiredo, Mário Instituto de Telecomunicações Instituto Superior Técnico University of Lisbon Lisbon Portugal Sapienza Università di Roma Rome Italy

来源：评论

学校读者我要写书评

暂无评论

Towards Better Ocular recognition for Secure Real-World applications 17

Towards Better Ocular Recognition for Secure Real-World Appl...

引用

17th IEEE international conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom) / 12th IEEE international conference on Big Data Science and Engineering (IEEE BigDataSE)

作者： Parizi, Reza M. Dehghantanha, Ali Choo, Kim Kwang Raymond Kennesaw State Univ Dept Software Engn & Game Dev Marietta GA 30060 USA Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX USA

ISBN: (纸本)9781538643877

In the last decade, biometric has become one of the most common uses for verification and authentication of someone's personal identity. Biometrics are defined as the measurement and analysis of unique physical or behavioral characteristics. Fingerprint is undoubtedly the most well-known biometric, but in the recent years ocular recognition has taken a huge jump forward. Ocular recognition (OR) is one of the most reliable personal identification methods in biometrics that can have flexible applications as opposed to the iris recognition. the basic breakdown of the eye is the iris, cornea, retina, pupil, sclera, and conjunctiva. the iris is where eye is actually colored;this is determined by the amount and type of pigment in one's iris. Iris recognition is an accurate form of ocular recognition. It works by using biometric systems to apply pattern recognition to a person's eye since no two people have the same patterns in their eye. Ocular recognition has been used as an identification method on both Apple and Android devices without much popularity. the reason for this is mainly related to the issues with the underlying algorithm used that affects the accuracy and security measures. Many different algorithms have been proposed and tested;some are more accurate than others. Our goal is to investigate different technologies currently used to help combine the best parts of both (Iris and Retina) in order to propose a better system for future implementation. With the proper design, ocular recognition could be a great tool for user authentication in emerging Internet of things (IoT) and mobile markets.

关键词： Ocular recognition Iris recognition Biometrics authentication Secure applications Mobile security Digital verification

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence applications and Innovations: 12th IFIP WG 12.5 international conference and Workshops, AIAI 2016, thessaloniki, Greece

引用

2018年

作者： Lazaros S Iliadis Ilias G Maglogiannis

ISBN: (纸本)9783319831688

this book constitutes the refereed proceedings of the 12th IFIP WG 12.5 international conference on Artificial Intelligence applications and Innovations, AIAI 2016, and three parallel workshops, held in thessaloniki, Greece, in September 2016. the workshops are the third Workshop on New methods and Tools for Big Data, MT4BD 2016, the 5th Mining Humanistic Data Workshop, MHDW 2016, and the First Workshop on 5G - Putting Intelligence to the Network Edge, 5G-PINE 2016. the 30 revised full papers and 8 short papers presented at the main conference were carefully reviewed and selected from 65 submissions. the 17 revised full papers and 7 short papers presented at the 3 parallel workshops were selected from 33 submissions. the papers cover a broad range of topics such as artificial neural networks, classification, clustering, control systems - robotics, data mining, engineering application of AI, environmental applications of AI, feature reduction, filtering, financial-economics modeling, fuzzy logic, genetic algorithms, hybrid systems, image and video processing, medical AI applications, multi-agent systems, ontology, optimization, pattern recognition, support vector machines, text mining, and Web-social media data AI modeling.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Better Ocular recognition for Secure Real-World applications

Towards Better Ocular Recognition for Secure Real-World Appl...

引用

IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Reza M. Parizi Ali Dehghantanha Kim-Kwang Raymond Choo Kennesaw State University Kennesaw GA US The University of Sheffield Sheffield Sheffield GB University of Texas at San Antonio San Antonio TX US

In the last decade, biometric has become one of the most common uses for verification and authentication of someone's personal identity. Biometrics are defined as the measurement and analysis of unique physical or behavioral characteristics. Fingerprint is undoubtedly the most well-known biometric, but in the recent years ocular recognition has taken a huge jump forward. Ocular recognition (OR) is one of the most reliable personal identification methods in biometrics that can have flexible applications as opposed to the iris recognition. the basic breakdown of the eye is the iris, cornea, retina, pupil, sclera, and conjunctiva. the iris is where eye is actually colored; this is determined by the amount and type of pigment in one's iris. Iris recognition is an accurate form of ocular recognition. It works by using biometric systems to apply pattern recognition to a person's eye since no two people have the same patterns in their eye. Ocular recognition has been used as an identification method on both Apple and Android devices without much popularity. the reason for this is mainly related to the issues with the underlying algorithm used that affects the accuracy and security measures. Many different algorithms have been proposed and tested; some are more accurate than others. Our goal is to investigate different technologies currently used to help combine the best parts of both (Iris and Retina) in order to propose a better system for future implementation. With the proper design, ocular recognition could be a great tool for user authentication in emerging Internet of things (IoT) and mobile markets.

关键词： Iris recognition Retina Authentication Fingerprint recognition Hardware Software

来源：评论

学校读者我要写书评

暂无评论

On Efficient Computation of Tensor Subspace Kernels for Multi-dimensional Data 12

On Efficient Computation of Tensor Subspace Kernels for Mult...

引用

12th international Joint conference on Computer Vision, Imaging and Computer Graphics theory and applications (VISIGRAPP)

作者： Cyganek, Boguslaw Wozniak, Michal AGH Univ Sci & Technol Al Mickiewicza 30 PL-30059 Krakow Poland Wroclaw Univ Sci & Technol Wroclaw Poland

ISBN: (纸本)9789897582264

In pattern classification problems kernel based methods and multi-dimensional methods have shown many advantages. However, since the well-known kernel functions are defined over one-dimensional vector spaces, it is not straightforward to join these two domains. Nevertheless, there are attempts to develop kernel functions which can directly operate with multi-dimensional patterns, such as the recently proposed kernels operating on the Grassmannian manifolds. these are based on the concept of the principal angles between the orthogonal spaces rather than simple distances between vectors. An example is the chordal kernel operating on the subspaces obtained after tensor unfolding. However, a real problem with these methods are their high computational demands. In this paper we address the problem of efficient implementation of the chordal kernel for operation with tensors in classification tasks of real computer vision problems. the paper extends our previous works in this field. the proposed method was tested in the problems of object recognition in computer vision. the experiments show good accuracy and accelerated performance.

关键词： Reproducing Kernel Hilbert Spaces Chordal Kernel Multi-dimensional patterns Tensors Grassmannian Manifolds pattern Classification

来源：评论

学校读者我要写书评

暂无评论

Gender recognition Using Local Block Difference pattern 12th

Gender Recognition Using Local Block Difference Pattern

引用

12th international conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP)

作者： Lai, Chih-Chin Wu, Chih-Hung Pan, Shing-Tai Lee, Shie-Jue Lin, Bor-Haur Natl Univ Kaohsiung Dept Elect Engn Kaohsiung 81148 Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung 81148 Taiwan Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 80424 Taiwan

ISBN: (纸本)9783319502120;9783319502113

Determining the gender of a person in a given image or video by a machine is a challenging problem. It has been attracting research attention due to its many potential real-life applications. As the human face provides important visual information for gender perception, a large number of studies have investigated gender recognition from face perception. In this paper, we present a method which uses Local Block Difference pattern for feature extraction to identify the gender from the face images. the recognition is performed by using a support vector machine, which had been shown to be superior to traditional pattern classifiers in the gender recognition problem. Experimental results on the FERET database are provided to demonstrate the proposed approach is an effective method, compared to other similar methods.

关键词： Gender recognition Local Block Difference pattern Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

Pose-Based Temporal-Spatial Network (PTSN) for Gait recognition with Carrying and Clothing Variations 12th

Pose-Based Temporal-Spatial Network (PTSN) for Gait Recognit...

引用

12th Chinese conference on Biometric recognition, CCBR 2017

作者： Liao, Rijun Cao, Chunshui Garcia, Edel B. Yu, Shiqi Huang, Yongzhen College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China 7ma A 21406 PlayaHavana Cuba

ISBN: (纸本)9783319699226

One of the most attractive biometric techniques is gait recognition, since its potential for human identification at a distance. But gait recognition is still challenging in real applications due to the effect of many variations on the appearance and shape. Usually, appearance-based methods need to compute gait energy image (GEI) which is extracted from the human silhouettes. GEI is an image that is obtained by averaging the silhouettes and as result the temporal information is removed. the body joints are invariant to changing clothing and carrying conditions. We propose a novel pose-based gait recognition approach that is more robust to the clothing and carrying variations. At the same time, a pose-based temporal-spatial network (PTSN) is proposed to extract the temporal-spatial features, which effectively improve the performance of gait recognition. Experiments evaluated on the challenging CASIA B dataset, show that our method achieves state-of-the-art performance in both carrying and clothing conditions. © 2017, Springer international Publishing AG.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence applications and Innovations 1st ed. 2016

引用

丛书名： IFIP Advances in Information and Communication Technology

2016年

作者： Lazaros Iliadis Ilias Maglogiannis

ISBN: (数字)9783319449449

ISBN: (纸本)9783319449432

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Learning Framework to Improve Unsupervised Gene Network Inference 1

引用

12th international conference on Machine Learning and Data Mining (MLDM)

作者： Turki, Turki Bassett, William Wang, Jason T. L. King Abdulaziz Univ Dept Comp Sci POB 80221 Jeddah 21589 Saudi Arabia New Jersey Inst Technol Bioinformat Program Newark NJ 07102 USA Dept Comp Sci Newark NJ 07102 USA

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

Network inference through link prediction is an important data mining problem that finds many applications in computational social science and biomedicine. For example, by predicting links, i.e., regulatory relationships, between genes to infer gene regulatory networks (GRNs), computational biologists gain a better understanding of the functional elements and regulatory circuits in cells. Unsupervised methods have been widely used to infer GRNs;however, these methods often create missing and spurious links. In this paper, we propose a learning framework to improve the unsupervised methods. Given a network constructed by an unsupervised method, the proposed framework employs a graph sparsification technique for network sampling and principal component analysis for feature selection to obtain better quality training data, which guides three classifiers to predict and clean the links of the given network. the three classifiers include neural networks, random forests and support vector machines. Experimental results on several datasets demonstrate the good performance of the proposed learning framework and the classifiers used in the framework.

关键词： Feature selection Graph mining Network analysis applications in biology and medicine

来源：评论

学校读者我要写书评

暂无评论

12th IFIP WG 12.5 international conference and Workshops on Artificial Intelligence applications and Innovations, AIAI 2016

12th IFIP WG 12.5 International Conference and Workshops on ...

引用

12th IFIP WG 12.5 international conference and Workshops on Artificial Intelligence applications and Innovations, AIAI 2016

ISBN: (纸本)9783319449432

the proceedings contain 62 papers. the special focus in this conference is on Medical Artificial Intelligence Modeling, Classification - pattern recognition, Ontology-Web and Social Media AI Modeling, Environmental AI Modeling, Machine Learning-Learning, Optimization-SVM, Agents-Robotics-Control, Artificial Neural Network Modeling and New methods and Tools for Big Data wokshop. the topics include: A cumulative training approach to schistosomiasis vector density prediction;mobile and evolving tool to predict colorectal cancer survivability;machine learning preprocessing method for suicide prediction;increasing diversity in random forests using naive bayes;identifying asperity patterns via machine learning algorithms;combining prototype selection with local boosting;modeling economic open data;web image indexing using wice and a learning-free language model;an intelligent internet search assistant based on the random neural network;deep neural networks for web page information extraction;environmental impact on predicting olive fruit fly population using trap measurements;a hybrid soft computing approach producing robust forest fire risk indices;applying artificial neural networks to short-term pm2.5 forecasting modeling;convolutive audio source separation using robust ica and reduced likelihood ratio jump;use of flight simulators in analyzing pilot behavior;active learning algorithms for multi-label data;design of an advanced smart forum for tesys e-learning platform;efficient support vector machine classification using prototype selection and generation;genetic search of pickup and delivery problem solutions for self-driving taxi routing and introducing chemotaxis to a mobile robot.

关键词：

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9页 << < 1 2 3 4 5 6 7 8 9 > >>

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：