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

662 篇 会议
23 册 图书
22 篇 期刊文献

馆藏范围

707 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

500 篇 工学
- 332 篇 计算机科学与技术...
- 161 篇 软件工程
- 96 篇 测绘科学与技术
- 93 篇 电气工程
- 88 篇 信息与通信工程
- 71 篇 光学工程
- 52 篇 控制科学与工程
- 28 篇 建筑学
- 28 篇 土木工程
- 27 篇 化学工程与技术
- 27 篇 航空宇航科学与技...
- 26 篇 机械工程
- 23 篇 仪器科学与技术
- 22 篇 生物工程
- 21 篇 电子科学与技术（可...
- 19 篇 生物医学工程（可授...
- 15 篇 环境科学与工程（可...
- 12 篇 交通运输工程
- 12 篇 农业工程
225 篇 理学
- 128 篇 物理学
- 83 篇 数学
- 26 篇 化学
- 26 篇 统计学（可授理学、...
- 24 篇 生物学
- 10 篇 地理学
- 10 篇 地球物理学
127 篇 医学
- 125 篇 临床医学
60 篇 管理学
- 43 篇 图书情报与档案管...
- 20 篇 管理科学与工程(可...
15 篇 农学
- 13 篇 作物学
6 篇 军事学
6 篇 艺术学
3 篇 经济学
2 篇 法学
1 篇 教育学
1 篇 文学

主题

194 篇 remote sensing
105 篇 pattern recognit...
78 篇 image processing
37 篇 image segmentati...
31 篇 feature extracti...
25 篇 artificial intel...
25 篇 satellites
19 篇 object detection
19 篇 machine learning
18 篇 training
17 篇 image fusion
17 篇 data mining
17 篇 image analysis
16 篇 image classifica...
16 篇 computer vision
16 篇 image resolution
14 篇 image edge detec...
14 篇 robustness
13 篇 deep learning
13 篇 remote sensing i...

机构

6 篇 tsinghua univers...
6 篇 image processing...
5 篇 university of te...
5 篇 indian institute...
5 篇 dalian universit...
5 篇 university of sy...
5 篇 institute of aut...
5 篇 institute of aut...
5 篇 shenzhen univers...
5 篇 college of infor...
4 篇 institute of ima...
4 篇 sun yat-sen univ...
4 篇 hong kong baptis...
4 篇 southern univers...
3 篇 univ arizona col...
3 篇 mks imaging tech...
3 篇 university of el...
3 篇 peking univ inst...
3 篇 indian institute...
3 篇 national laborat...

作者

8 篇 anon
8 篇 chunhong pan
7 篇 ping guo
5 篇 zhaoxiang zhang
5 篇 hui huang
5 篇 balasubramanian ...
5 篇 jing dong
5 篇 tieniu tan
5 篇 jiwen lu
5 篇 risheng liu
5 篇 wanli ouyang
5 篇 min xu
5 篇 huchuan lu
4 篇 qian yin
4 篇 junwei han
4 篇 jianhuang lai
4 篇 pan chunhong
4 篇 zhang jie
4 篇 yike guo
4 篇 jianguo zhang

语言

688 篇 英文
15 篇 其他
4 篇 中文

检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

共 707 条记录，以下是481-490 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：

Texture detection for image analysis

引用

3rd International conference on Advances in pattern recognition

作者： Chabrier, S Emile, B Rosenberger, C Univ Orleans ENSI Bourges Lab Vis & Robot UPRES EA 2078 F-18020 Bourges France

ISBN: (纸本)3540288333

Many applications such as image compression, pre-processing or segmentation require some information from the regions composing an image. The main objective of this paper is to define a methodology to extract some local information from an image. Each region is characterized in terms of homogeneity (region composed with the same grey-level or a single texture) and its type (textured or uniform). The decision criterion is based on the use of classical texture attributes (cooccurrence matrix and grey-levels moments) and a support vector machine in order to realize the fusion of the different attributes. We then characterize each region considering its type by appropriate features.

关键词： Entropy remote sensing

来源：评论

学校读者我要写书评

暂无评论

A context-sensitive technique based on Support Vector Machines for image classification

引用

1st International conference on pattern recognition and Machine Intelligence

作者： Bovolo, F Bruzzone, L Univ Trent Dept Informat & Commun Technol I-38050 Povo TN Italy

ISBN: (纸本)3540305068

In this paper, a novel context-sensitive classification technique based on Support Vector Machines (CS-SVM) is proposed. This technique aims at exploiting the promising SVM method for classification of 2-D (or n-D) scenes by considering the spatial-context information of the pixel to be analyzed. ne context-based architecture is defined by properly integrating SVMs with a Markov Random Field (MRF) approach. In the design of the resulting system, two main issues have been addressed: i) estimation of the observation term statistic (class-conditional densities) with a proper multiclass SVM architecture;ii) integration of the SVM approach in the framework of MRFs for modeling the prior model of images. Thanks to the effectiveness of the SVM machine learning strategy and to the capability of MRFs to properly model the spatial-contextual information of the scene, the resulting context-sensitive image classification procedure generates regularized classification maps characterized by a high accuracy. Experimental results obtained on Synthetic Aperture Radar (SAR) remote sensing images confirm the effectiveness of the proposed approach.

关键词： image processing

来源：评论

学校读者我要写书评

暂无评论

Landmark extraction, matching and processing for automated image navigation of geostationary weather satellites

Landmark extraction, matching and processing for automated i...

引用

image processing and pattern recognition in remote sensing ii

作者： Kim, Taejung Lee, Tae-Yoon Choi, Hae-Jin Dept. of Geoinformatic Eng. Inha Univ. 253 Yonghyun-Dong Nam-Gu Incheon 40-751 Korea Republic of Satellite Operation and Application Center Korea Aerospace Research Institute 45 Oun-dong Yusong-gu Daejon 305-333 Korea Republic of

This paper addresses the issue on automated registration of images from weather satellites. Traditionally, weather satellite community has employed an approach called landmark detection for automated registration. A ground point or feature with known reference coordinates is defined as landmark. A landmark is matched against a weather satellite image. Based on match results estimated is the mapping function between images and a reference datum. This landmark detection approach has been suffered from the problem of mismatches. If match results contain errors, the accuracy of estimation deteriorates. To overcome this problem, we propose the use of a robust estimation technique called random sample consensus (RANSAC). Through intelligent strategy this robust estimator will distinguish inliers from outliers and establish the mapping function with inliers only. This estimator has been reported to work in land observation satellite applications as well as in many computer vision applications. We will show that the RANSAC can also work for our purpose. We tested our approach using a global coastline database and a GOES-9 image. A global coastline database was processed to generate 30 landmarks. They were matched against a GOES-9 image. Visible inspection revealed that the results contained 13 mismatches. With 30 match results the RANSAC was applied. It identified all 13 mismatches correctly. We can conclude that the RANSAC is able to select correct matches. For reliable automated registration, the RANSAC needs to be incorporated in the landmark detection process of weather satellite images.

关键词： Geostationary satellites

来源：评论

学校读者我要写书评

暂无评论

Bayesian network classification for aster data based on wavelet transformation

Bayesian network classification for aster data based on wave...

引用

International conference on Space Information Technology

作者： Li, Qiqing Cheng, Chengqi Guo, Shide Research Institute of Remote Sensing GIS Peking University 100871 China

ISBN: (纸本)0819460079

In this study, Bayesian networks are considered to be a classifier for the remote sensing image named Aster data, which involves 15 bands. Six. bands, which have different spatial resolutions, are selected to be the attributes in Bayesian network classifier. The sample data from Aster image that is fused by wavelet transform is used to train Bayesian network classifier. Before the above-mentioned processing, the attributes from the transformed image should be normalized by some equal width schemes. Then the learning scheme process is used to acquire the structure of Bayesian networks from the training data set. The relationship of the attributes among all the constituents of the imagery data is mined through the Bayesian networks. To evaluate this classifier, a comprehensive study of the performance is investigated based on the training data set and the independent test data sets. The result shows that Bayesian network performs well on remote sensing imagery data.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Extraction of tree height from large viewing angle aerial images

Extraction of tree height from large viewing angle aerial im...

引用

International conference on Space Information Technology

作者： Li, Chaoyang Yan, Guangjian Liu, Qiang Xiao, Zhiqiang Wang, Jingdi Laboratory of Pattern Recognition and Intelligent System School of Information Engineering Beijing University of Posts and Telecommunications 86-10-58809966 China State Key Laboratory of Remote Sensing Science Beijing Normal University Institute of Remote Sensing Applications Research Center for Remote Sensing School of Geography Beijing Normal University

ISBN: (纸本)0819460079

Due to the complexity and non-regularity of tree shapes, traditional digital photogrammetry using stereo matching method is difficult to obtain the accurate tree height, This fact therefore limits the application of the aerial digital photogrammetry technology in the power line survey. This paper presents a method of tree height extraction from large viewing aerial image using the knowledge of segmented tree crown. This method is based on a rough digital surface model (DSM) of tree crowns and the exterior orientation of the image. The basic steps of this method is that the DSM is first used to find the region of interest in the image based on the exterior orientation, and then the edges of the distinct trees or branches are extracted using image segmentation technology. An algorithm that uses both the rough DSM height information and exterior orientation data to calculate the accurate heights of the segmented trees or branches is presented. The algorithm assumes that most of the trees are upright, and the projection in the large viewing angle images of the crown and branches can therefore be used to calculate their heights relative to the averaged DSM height. Hence, the accurate height of the trees around the rough DSM can be refined. Some experimental results are given with the image captured from multi-angular imaging system mounted on a helicopter in which a Position and Orientation System (POS) is onboard to record the exterior element of the cameras. The experimental results demonstrated that this algorithm can largely improve the accuracy of tree height extraction. The application in power line monitoring system is promising.

关键词： image processing

来源：评论

学校读者我要写书评

暂无评论

A band-weighted landuse classification method for multispectral images

A band-weighted landuse classification method for multispect...

引用

conference on Computer Vision and pattern recognition (CVPR)

作者： Chunhong Pan Gang Wu V. Prinet Qing Yang Songde Ma National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences China

Landuse classification is an important problem in the remote sensing field. It can be used in a wide range of applications. In this paper, we propose a hybrid method fusing edges and regions information for the landuse classification of multispectral images. It mainly includes the steps of image pre-processing, initial segmentation and region merging. Especially, a novel spatial mean shift procedure is proposed so that some information can be extracted and used in the successive steps. Aiming at the multispectral images processing, we also design a band weighting strategy that give a proper weight to each band adoptively according to the region to be processed. Experimental results on the Landsat TM and ETM+ images validate the performance of the proposed method.

关键词： Multispectral imaging remote sensing image segmentation Satellites pattern recognition Rivers image analysis Automation Merging Data mining

来源：评论

学校读者我要写书评

暂无评论

Textural kernel for SVM classification in remote sensing: application to forest fire detection and urban area extraction

Textural kernel for SVM classification in remote sensing: ap...

引用

IEEE International conference on image processing

作者： F. Lafarge X. Descombes J. Zerubia Ariana Project INRIA Sophia-Antipolis France

We present a textural kernel for "support vector machines" classification applied to remote sensing problems. SVMs constitute a method of supervised classification well adapted to deal with data of high dimension, such as images. We introduce kernel functions in order to favor the distinction between our class of interest and the other classes: it gives information of similarity. In our case this similarity is based on radiometric and textural characteristics. One of the main difficulties is to elaborate textural parameters which are relevant and characterize as well as possible the joint distribution of a set of connected pixels. We apply this method to remote sensing problems: the detection of forest fires and the extraction of urban areas in high resolution images.

关键词： Kernel Support vector machines Support vector machine classification remote sensing Fires Urban areas Radiometry Data mining image resolution pattern recognition

来源：评论

学校读者我要写书评

暂无评论

A probabilistic approach for shadows modeling and detection

A probabilistic approach for shadows modeling and detection

引用

IEEE International conference on image processing

作者： N. Bouguila D. Ziou Université de Sherbrook Sherbrooke QUE Canada

The performance of a statistical image processing system depends in large part on the accuracy of the probabilistic model used. This paper presents a robust probabilistic mixture model based on the Dirichlet distribution. An unsupervised algorithm based on MML for learning this mixture is given, too. Experimental results involve shadows modeling and its application to shadows detection in images.

关键词： image processing pattern recognition Mathematical model Robustness Machine learning algorithms Power system modeling Machine learning remote sensing Probability Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

A graph and PNN-based approach to image classification

A graph and PNN-based approach to image classification

引用

International conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Tang Chun-Yan Zhang Bin Luo Key Laboratory of Intelligent Computing and Signal Processing of Anhui University Hefei China

In this paper, a new image classification method is developed. This approach applies graph decomposition and probabilistic neural networks (PNN) to the task of supervised image classification. We use relational graphs to represent image. These graphs are constructed from the feature points of images. Spectra of these graphs are obtained as feature vectors for classification. PNN is adopted to classify image according to the feature vectors. Experimental results show that this method can achieve best result of images classification.

关键词： image classification Neural networks pattern recognition Eigenvalues and eigenfunctions remote sensing image sequences Equations Signal processing Electronic mail image processing

来源：评论

学校读者我要写书评

暂无评论

Applications of Generalized Learning in image recognition

Applications of Generalized Learning in Image Recognition

引用

International conference on Neural Interface and Control

作者： YAO Min, JIANG Zhiwei, YI Wensheng, ZHAO Xiacming College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, P R China Department of Computer, Taizhou University, Taizhou 317000, P R China

Generalized learning model, GLM for short, is a new kind of machine learning model which fuses symbolic learning, connective learning, fuzzy learning, evolutionary learning and statistical learning together. By introducing generalized learning into image recognition, this paper presents a new kind of image recognition model, GLIRM for short. The distinguished advantage of GLIRM is its adaptive learning ability. Through practical application in remote sensing image recognition, satisfactory results have been achieved.

关键词： Generalized learning, pattern recognition, image processing

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71页 << < 45 46 47 48 49 50 51 52 53 54 > >>

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：