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

92 篇 会议
38 篇 期刊文献

馆藏范围

130 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

82 篇 工学
- 52 篇 计算机科学与技术...
- 44 篇 软件工程
- 22 篇 信息与通信工程
- 19 篇 生物工程
- 10 篇 控制科学与工程
- 8 篇 机械工程
- 7 篇 生物医学工程（可授...
- 4 篇 光学工程
- 4 篇 电子科学与技术（可...
- 3 篇 电气工程
- 3 篇 航空宇航科学与技...
- 2 篇 力学（可授工学、理...
- 2 篇 仪器科学与技术
- 2 篇 材料科学与工程（可...
- 2 篇 建筑学
- 2 篇 土木工程
- 2 篇 化学工程与技术
- 2 篇 交通运输工程
46 篇 理学
- 24 篇 数学
- 18 篇 生物学
- 16 篇 物理学
- 10 篇 统计学（可授理学、...
- 3 篇 系统科学
- 2 篇 化学
35 篇 管理学
- 21 篇 图书情报与档案管...
- 14 篇 管理科学与工程(可...
- 2 篇 工商管理
8 篇 法学
- 8 篇 社会学
2 篇 医学
1 篇 经济学
- 1 篇 应用经济学
1 篇 教育学
- 1 篇 教育学
1 篇 文学

主题

10 篇 feature extracti...
6 篇 training
5 篇 image segmentati...
4 篇 task analysis
4 篇 visual communica...
4 篇 visualization
4 篇 semantics
4 篇 data models
3 篇 image matching
3 篇 netconf
3 篇 motion planning
3 篇 three-dimensiona...
3 篇 speech
3 篇 neural networks
3 篇 convolution
3 篇 vectors
3 篇 pattern recognit...
3 篇 behavioral resea...
3 篇 databases
3 篇 image processing

机构

37 篇 pattern recognit...
24 篇 pattern recognit...
10 篇 pattern recognit...
4 篇 huawei beijing r...
4 篇 pattern recognit...
4 篇 beijing academy ...
4 篇 national lab of ...
4 篇 pattern recognit...
3 篇 university of sc...
3 篇 college of life ...
3 篇 shanghai jiao to...
3 篇 department of st...
3 篇 pattern recognit...
3 篇 institute for pa...
2 篇 department of el...
2 篇 shenzhen key lab...
2 篇 nec labs china
2 篇 guangdong provin...
2 篇 lanzhou universi...
2 篇 institute of the...

作者

20 篇 ma zhanyu
19 篇 zhanyu ma
19 篇 jun guo
16 篇 guo jun
15 篇 zhang chuang
11 篇 honggang zhang
10 篇 wu ming
8 篇 bin zhang
7 篇 xie jiyang
7 篇 guohui li
6 篇 chuang zhang
6 篇 jiyang xie
5 篇 yan li
5 篇 xuesong gao
5 篇 xu weiran
5 篇 zhiqing lin
4 篇 cai chao
4 篇 ding mingyue
3 篇 hong yu
3 篇 xinyu wu

语言

126 篇 英文
2 篇 其他
2 篇 中文

检索条件"机构=Lab. of Pattern Recognition and Intelligent System"

共 130 条记录，以下是31-40 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：

A NEW COMBINATION FEATURE OF S-LBP AND MAIN COLOR DESCRIPTOR FOR VIDEO FRAME RETRIEVAL

A NEW COMBINATION FEATURE OF S-LBP AND MAIN COLOR DESCRIPTOR...

引用

2010 2nd IEEE International Conference on Network Infrasturcture & Digital Content(2010年第二届IEEE网络基础设施与数字内容国际会议 IC-NIDC 2010)

作者： Lixia Liu Xinxin Wan Honggang Zhang Aiping Feng Jun Guo Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications B Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications

In this paper, we draw Local Binary pattern (LBP) and main color descriptor into content-based video indexing and retrieval. LBP is a powerful texture descriptor for its tolerance against illumination changes and its computational simplicity. Main color descriptor is used for representing main color distribution of the image. We propose a simplified LBP (S-LBP) operator as the texture descriptor. In most cases, texture distributions that ignore the actual positions or spatial arrangement in the image are not sufficient for representing an image. Therefore, we use zigzag based Run-length coding principle to produce S-LBP patterns histogram. Then we apply the combination of S-LBP feature and main color descriptor to represent an image in video retrieval. The experiment that to retrieval which video a given frame comes from demonstrates the combination method is efficient and effective.

关键词： S-LBP Main color Key frames Image and Video retrieval

来源：评论

学校读者我要写书评

暂无评论

Cross-Layer Feature based Multi-Granularity Visual Classification

Cross-Layer Feature based Multi-Granularity Visual Classific...

引用

IEEE Visual Communications and Image Processing (VCIP)

作者： Junhan Chen Dongliang Chang Jiyang Xie Ruoyi Du Zhanyu Ma Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665475938

In contrast to traditional fine-grained visual clas-sification, multi-granularity visual classification is no longer limited to identifying the different sub-classes belonging to the same super-class (e.g., bird species, cars, and aircraft models). Instead, it gives a sequence of lab.ls from coarse to fine (e.g., Passeriformes → Corvidae → Fish Crow), which is more convenient in practice. The key to solving this task is how to use the relationships between the different levels of lab.ls to learn feature representations that contain different levels of granularity. Interestingly, the feature pyramid structure naturally implies different granularity of feature representation, with the shallow layers representing coarse-grained features and the deep layers representing fine-grained features. Therefore, in this paper, we exploit this property of the feature pyramid structure to decouple features and obtain feature representations corre-sponding to different granularities. Specifically, we use shallow features for coarse-grained classification and deep features for fine-grained classification. In addition, to enable fine-grained features to enhance the coarse-grained classification, we propose a feature reinforcement module based on the feature pyramid structure, where deep features are first upsampled and then combined with shallow features to make decisions. Experimental results on three widely used fine-grained image classification datasets such as CUB-200-2011, Stanford Cars, and FGVC-Aircraft validate the method's effectiveness. Code availab.e at https://***/PRIS-CV/CGVC.

关键词： Visualization Cross layer design Codes Visual communication Image processing Fish Birds

来源：评论

学校读者我要写书评

暂无评论

IMPROVEMENT OF XACML ACCESS CONTROL MECHANISM BASED ON NETCONF SUBTREE FILTERING RPC

IMPROVEMENT OF XACML ACCESS CONTROL MECHANISM BASED ON NETCO...

引用

2010 2nd IEEE International Conference on Network Infrasturcture & Digital Content(2010年第二届IEEE网络基础设施与数字内容国际会议 IC-NIDC 2010)

作者： Xuesong Gao Jinjin Wang Guohui Li Bin Zhang Yan Li Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications B Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications

The Network Configuration Protocol (NETCONF) is a new network Management Protocol which becomes more and more widely used in network management area. To make NETCONF much safer, we extend the eXtensible Access Control Markup Language(XACML) access control mechanism and implement it on our NETCONF network management system—BUPT-NEP. We use subtree filtering expression to represent resource instead of xpath expression, which makes the new mechanism suitable for access control on NETCONF subtree filtering rpc

关键词： XACML NETCONF subtree filtering rpc

来源：评论

学校读者我要写书评

暂无评论

Image Caption via Visual Attention Switch on DenseNet

Image Caption via Visual Attention Switch on DenseNet

引用

IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Yanlong Hao Jiyang Xie Zhiqing Lin Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China

We introduce a novel approach that is used to convert images into the corresponding language descriptions. This method follows the most popular encoder-decoder architecture. The encoder uses the recently proposed densely convolutional neural network (DenseNet) to extract the feature maps. Meanwhile, the decoder uses the long short time memory (LSTM) to parse the feature maps to descriptions. We predict the next word of descriptions by taking the effective combination of feature maps with word embedding of current input word by “visual attention switch”. Finally, we compare the performance of the proposed model with other baseline models and achieve good results.

关键词： Feature extraction Decoding Visualization Switches Training Dictionaries Data models

来源：评论

学校读者我要写书评

暂无评论

Cycled merging registration of point clouds for 3D human body modeling

Cycled merging registration of point clouds for 3D human bod...

引用

International Workshop on Sensing, Processing and Learning for intelligent Machines (SPLINE)

作者： Yanjie Chen Yuhong Li Feng Qi Zhanyu Ma Honggang Zhang Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781467389181

In this paper, we present a cycled merging registration method based on Iterative Closest Point (ICP). We capture the point clouds by a static Kinect with the object rotating on a turntable. Different views of scan are combined by ICP and then a globally consistent human model is obtained. Our method simplifies the process of successively registration, which is usually used to solve multi-views registration from a single cycle. The main contribution of this paper is to propose a pairwise-to-global registration method, which aligns several sub-integrate views in a merging order. Our method is consistent with some cycled registration constraints which are suitable for non-rigid registration. After all point clouds are merged, the surface of the model can be estimated by Moving Least Square (MLS). A model of a part of non-rigid human body is constructed in our experiments.

关键词： Three-dimensional displays Iterative closest point algorithm Solid modeling Image reconstruction Clothing Merging Biological system modeling

来源：评论

学校读者我要写书评

暂无评论

Supervoxels for human and clothes in RGB-D image

Supervoxels for human and clothes in RGB-D image

引用

International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic systems Technology (Wireless VITAE)

作者： Feng Qi Honggang Zhang Bo Zhang Zhanyu Ma Na Liu Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China

In order to reconstruct human body and remodel clothes in virtual try on and size-fitting scenario, we preprocess the input point cloud into supervoxels through over-segmentation. Over-segmentation of an image into superpixels is a widely used preprocessing approach in image segmentation. Recently, over-segmentation algorithm, which clusters points with similar feature into a small group, known as supervoxel here, has been used on RGB-D image or the combination of observations from several calibrated RGB-D cameras to reduce occurrence of clusters crossing object boundaries. This method makes use of both color information and the 3D geometric relationships to describe features and segmented regions. We apply this method to RGB-D images of human body and clothes, so that largely reduce the number of regions that must be considered in following remodel process. Experiments were taken on point clouds captured by Kinect. Because the final goal is to predict object boundaries that represent different part of human and clothes, we further compare the recall with SLIC method for boundaries we have marked previously, and give a suggestion on choosing a better seeding radius in our scenario.

关键词： Three-dimensional displays Image segmentation Image color analysis Clothing Cameras Image reconstruction Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Instance-level coupled subspace learning for fine-grained sketch-based image retrieval 14

Instance-level coupled subspace learning for fine-grained sk...

引用

Computer Vision - ECCV 2016 Workshops, Proceedings

作者： Xu, Peng Yin, Qiyue Qi, Yonggang Song, Yi-Zhe Ma, Zhanyu Wang, Liang Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China SketchX Lab School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

ISBN: (纸本)9783319466033

Fine-grained sketch-based image retrieval (FG-SBIR) is a newly emerged topic in computer vision. The problem is challenging because in addition to bridging the sketch-photo domain gap, it also asks for instance-level discrimination within object categories. Most prior approaches focused on feature engineering and fine-grained ranking, yet neglected an important and central problem: how to establish a finegrained cross-domain feature space to conduct retrieval. In this paper, for the first time we formulate a cross-domain framework specifically designed for the task of FG-SBIR that simultaneously conducts instancelevel retrieval and attribute prediction. Different to conventional phototext cross-domain frameworks that performs transfer on category-level data, our joint multi-view space uniquely learns from the instance-level pair-wise annotations of sketch and photo. More specifically, we propose a joint view selection and attribute subspace learning algorithm to learn domain projection matrices for photo and sketch, respectively. It follows that visual attributes can be extracted from such matrices through projection to build a coupled semantic space to conduct retrieval. Experimental results on two recently released fine-grained photo-sketch datasets show that the proposed method is able to perform at a level close to those of deep models, while removing the need for extensive manual annotations. © Springer International Publishing Switzerland 2016.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Research on Real-time Video Action Classification Based on Three-Dimensional Convolutional Neural Network

Research on Real-time Video Action Classification Based on T...

引用

IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Mingli Yu Chuang Zhang Ming Wu Pattern Recognition and Intelligent System Lab BUPT Beijing China

This paper describes a model for performing action classification in real-time video streaming. This model can simultaneously analyze the spatio-temporal information of video under the constraint of low delay. In addition, in order to prevent the model from judging motionless segments in the video as motion, the model in this article is equipped with the ability of distinguish the segments of motion from the stationary ones. The experimental results show that the model can complete the action classification task with little delay, which ensures that the classification result can be output in real time with the constant input of the video image.

关键词： Streaming media Optical flow Real-time systems Optical buffering Convolutional neural networks Image segmentation

来源：评论

学校读者我要写书评

暂无评论

A Probabilistic Expression system for Fingerprint Identification Findings Based on Stability 7

A Probabilistic Expression System for Fingerprint Identifica...

引用

7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021

作者： Wang, Jingye Zheng, Yixiao Xiong, Weiyu Chen, Junhan Ma, Zhanyu Ma, Rongliang Beijing University of Posts and Telecommunications Pattern Recognition and Intelligent Systems Lab. Beijing100876 China Institute of Forensic Science Ministry of Public Security Beijing100045 China

ISBN: (纸本)9781665405829

Human fingerprints is of critical importance for law enforcement agencies to identify suspects. At present, the number of fingerprint matching feature points is generally used to determine whether two fingerprints are from the same person, and the criteria vary from country to country. This paper establishes a probabilistic expression system from a statistical point of view to give the probability of similarity between two fingerprints, which can be used to characterize the degree of similarity between the two fingerprints. Our method is now being tried for latent fingerprint and tenprints identification conclusion expression in public safety. © 2021 IEEE.

关键词： Palmprint recognition

来源：评论

学校读者我要写书评

暂无评论

Multi-feature content-based product image retrieval based on region of main object

Multi-feature content-based product image retrieval based on...

引用

8th International Conference on Information, Communications and Signal Processing, ICICS 2011

作者： Chai, Lunshao Zhang, Honggang Qin, Zhen Yu, Jie Qi, Yonggang Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China Riverside Lab for Artificial Intelligence Research University of California Riverside Riverside CA United States

ISBN: (纸本)9781457700309

Content-based image retrieval (CBIR) has got an intense interest and seen considerable progress over the last decade. But most of the time it is only applied in lab.ratory. One important reason for this is the diversity of images. Different practical situations call for different taxonomy definitions of images, and lead to very different solutions. At present, and even in the foreseeable future, a general purpose CBIR system is not really possible. However, image search engines oriented at specific domains are feasible in technology and also have the actual demand. With the rapid development of electronic commerce, searching specific product by image has become one of the most attractive related research topics. In this paper, we propose a region-based method fit for the content-based retrieval of product images. The method focuses on two key issues: fast extraction of the main region, in which the product locates, as well as efficient shape and color features extraction. To show the validity of the proposed region-based method, compared experiments are carried out and illustrated on the PI 100 dataset. © 2011 IEEE.

关键词： Content based retrieval

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13页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：