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

690 篇 英文
13 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281-290 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：

Multi-task Deep Learning for image Understanding 6

Multi-task Deep Learning for Image Understanding

引用

6th International conference on Soft Computing and pattern recognition (SoCPaR)

作者： Yu, Bo Lane, Ian Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Chinese Acad Sci Grad Univ Beijing 100049 Peoples R China Carnegie Mellon Univ Moffett Field CA 94043 USA

ISBN: (纸本)9781479959341

Deep learning models can obtain state-of-the-art performance across many speech and image processing tasks, often significantly outperforming earlier methods. In this paper, we attempt to further improve the performance of these models by introducing multi-task training, in which a combined deep learning model is trained for two inter-related tasks. We show that by introducing a secondary task (such as shape identification in the object classification task) we are able to significantly improve the performance of the main task for which the model is trained. Using public datasets we evaluated our approach on two image understanding tasks, image segmentation and object classification. On the image segmentation task, we observed that the multi-task model almost doubled the accuracy of segmentation at the pixel-level (from 18.7% to 35.6%) compared to the single task model, and improved the performance of face-detection by 10.2% (from 70.1% to 80.3%). For the object classification task, we observed a 2.1% improvement in classification accuracy (from 91.6% to 93.7%) compared to a single-task model. The proposed multi-task models obtained significantly higher accuracies than previously published results on these datasets, obtaining 22.0% and 6.2% higher accuracies on the face-detetction and object classification tasks respectively. These results demonstrate the effectiveness of multi-task training of deep learning models for image understanding tasks.

关键词： image segmentation deep learning multi-task learning

来源：评论

学校读者我要写书评

暂无评论

Combining SIFT and Individual Entropy Correlation Coefficient for image Registration 6

Combining SIFT and Individual Entropy Correlation Coefficien...

引用

6th Chinese conference on pattern recognition (CCPR)

作者： Liu, Gan Chen, Shengyong Zhou, Xiaolong Wang, Xiaoyan Guan, Qiu Yu, Hui Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Univ Portsmouth Sch Comp Portsmouth Hants England

ISBN: (纸本)9783662456439;9783662456422

image registration is an important topic in many fields including industrial image analysis systems, medical and remote sensing. To improve the registration accuracy, an image registration method that combines scale invariant feature transform and individual entropy correlation coefficient (SIFT-IECC) is proposed in this paper. First, scale invariant feature transform algorithm is applied to extract feature points to construct a transformation model. Then, a rough registration image is obtained according to the transformation model. The individual entropy correlation coefficient is used as the similarity measure to refine the rough registration image. Finally, the experimental results show the superior performance of the proposed SIFT-IECC registration method by comparing with the state-of-the-art methods.

关键词： image registration Scale invariant feature transform Individual entropy correlation coefficient

来源：评论

学校读者我要写书评

暂无评论

Urban road extraction via graph cuts based probability propagation

Urban road extraction via graph cuts based probability propa...

引用

作者： Cheng, Guangliang Wang, Ying Gong, Yongchao Zhu, Feiyun Pan, Chunhong National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science China

ISBN: (纸本)9781479957514

In this paper, we propose a graph cuts (GC) based probability propagation approach to automatically extract road network from complex remote sensing images. First, the support vector machine (SVM) classifier with a sigmoid model is applied to assign each pixel a posterior probability of being labelled as road class, which avoids the weaknesses of hard labels in general SVM. Then a GC based probability propagation algorithm is employed to keep the extracted road results smooth and coherent, which can reduce the connections between roads and road-like objects. Finally, a road-geometrical prior is considered to refine the extraction result, so that the non-road objects in images can be removed. Experimental results on two remote sensing image datasets indicate the validity and effectiveness of our method by comparing with two other approaches. © 2014 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Two-stage subpixel impervious surface coverage estimation: comparing classification and regression trees and artificial neural networks 20

Two-stage subpixel impervious surface coverage estimation: c...

引用

conference on image and Signal processing for remote sensing XX

作者： Bernat, Katarzyna Drzewiecki, Wojciech AGH Univ Sci & Technol Fac Min Surveying & Environm Engn Dept Geoinformat Photogrammetry & Remote Sensing Krakow Poland

ISBN: (纸本)9781628413076

The paper presents accuracy comparison of subpixel classification based on medium resolution Landsat images, performed using machine learning algorithms built on decision and regression trees method (C.5.0/Cubist and Random Forest) and artificial neural networks. The aim of the study was to obtain the pattern of percentage impervious surface coverage, valid for the period of 2009-2010. Imperviousness index map generation was a two-stage procedure. The first step was classification, which divided the study area into categories: i) completely permeable (imperviousness index less than 1%) and ii) fully or partially impervious areas. For pixels classified as impervious, the percentage of impervious surface coverage in pixel area was estimated. The root mean square errors (RMS) of determination of the percentage of the impervious surfaces within a single pixel were 11.0% for C.5.0/Cubist method, 11.3% for Random Forest method and 12.6% using artificial neural networks. The introduction of the initial hard classification into completely permeable areas (with imperviousness index <1%) and impervious areas, allowed to improve the accuracy of imperviousness index estimation on poorly urbanized areas covering large areas of the Dobczyce Reservoir catchment. The effect is also visible on final imperviousness index maps.

关键词： subpixel classification impervious surfaces classification and regression trees artificial neural networks Landsat TM Dobczyce Reservoir

来源：评论

学校读者我要写书评

暂无评论

A novel level set method for remote sensing image based on K-means

A novel level set method for remote sensing image based on K...

引用

2014 International conference on Applied Mechanics, Mechatronics and Intelligent System, AMMIS 2014

作者： Xu, Er Jing Jia, Zhen Hong Wang, Lie Jun Hu, Ying Jie Yang, Jie College of Information science and engineering Xinjiang University Urumchi 830046 China Knowledge Engineering and Research Discovery Institute Auckland University of Technology Auckland New Zealand The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9783038351757

Due to the characteristic of remote sensing image, we propose a novel method based on K-means algorithm also with the improved multi-phrase level set model. Comparing with the classical multi-phase C-V model, the improved model considers the region area information, gradient information and edge detection. Proper use of gradient information can overcome the inaccurate edge localization defects in image segmentation. The edge detection is used for keeping the boundary information better in the evolution process. For the reason of picking up the contour's convergent speed and enable the avoidance of trap. Four stages are constructed. Firstly, a median filtering is applied to smooth the original image and reduce parts of noise. Secondly, the usage of K-means algorithm gains more obvious differences of characteristics. Next, the reconstruction of gradient is obtained by using Sobel operator. Finally, segmentation result is achieved by using an improved method of multi-phase level set image segmentation. Experimental results show that the proposed approach has advantages in rapid and efficient application of remote sensing image segmentation. © (2014) Trans Tech Publications, Switzerland.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Forest vegetation spatial and temporal pattern analysis based on NDVI in Jiuzhaigou 2

Forest vegetation spatial and temporal pattern analysis base...

引用

2nd International conference on Renewable Energy and Environmental Technology (REET)

作者： Qiu, Yi Shu Gao, Jun Zhan, Qi Lin Shanghai Business Sch Shanghai 200235 Peoples R China Shanghai Normal Univ Dept Geog Shanghai 200234 Peoples R China Shanghai GISINFO Technol Co Ltd Shanghai 200233 Peoples R China

ISBN: (纸本)9783038352877

This paper takes Jiuzhaigou area for example, selecting Landsat remote sensing images in phase time about years of 1999, 2002, 2006 and 2007, using ENVI and ArcGIS software for image data processing, making spatial and temporal characteristics and distribution pattern analysis of vegetation index NDVI of Jiuzhaigou forest. The result shows that NDVI of Jiuzhaigou forest has obvious characteristics of spatial and temporal variation. The paper provides the analysis process and reference to the forest vegetation research of other areas.

关键词： forest NDVI spatial and temporal pattern Jiuzhaigou

来源：评论

学校读者我要写书评

暂无评论

Mystery behind similarity measures mse and SSIM

Mystery behind similarity measures mse and SSIM

引用

作者： Palubinskas, Gintautas German Aerospace Center DLR Remote Sensing Technology Institute Oberpfaffenhofen Wessling82234 Germany

ISBN: (纸本)9781479957514

Similarity or distance measures play an important role in various pattern recognition applications such as classification, clustering, change detection, information retrieval, energy minimization and optimization problems. We shall analyze theoretically the two most popular quality measures MSE and SSIM used in image processing by showing their origin, similarities/differences and advantages/drawbacks. Both measures depend on the same parameters: sample means, standard deviations and correlation coefficient. It is shown that SSIM originates from two Dice measures and thus inherit their main drawback - dependence on the absolute mean and standard deviation values. Similarly, MSE depends on the absolute standard deviation values. A new similarity measure Composite quality index based on Means, Standard deviations and Correlation coefficient (CMSC) is proposed inheriting advantages of the both measures but at the same time avoiding their drawbacks. © 2014 IEEE.

关键词： Statistics

来源：评论

学校读者我要写书评

暂无评论

Active Learning Methods for Classification of Hyperspectral remote sensing image

Active Learning Methods for Classification of Hyperspectral ...

引用

10th International conference on Intelligent Computing (ICIC)

作者： Ding, Sheng Li, Bo Fu, Xiaowei Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Peoples R China Wuhan Univ Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan Peoples R China

ISBN: (纸本)9783319093390;9783319093383

Active learning(AL) is an effective method in definition of samples, especially when labeled sample number is small. In this paper, we propose two active learning algorithms, which are Random Sampling (RS) and Margin Sampling(MS) algorithms, the two techniques achieve semiautomatic definition of training samples in remote sensing image classification, starting with a small and representative data set, then according to query criterion, the experts select informative samples to add training data set, the model builds the optimal set of samples which minimizes the classification error. Compared with traditional sample selection methods, the results denote the effectiveness of the proposed AL methods.

关键词： Active learning(AL) Random Sampling Margin Sampling(MS) hyperspectral classification

来源：评论

学校读者我要写书评

暂无评论

Multivalued Component-Tree Filtering

Multivalued Component-Tree Filtering

引用

International conference on pattern recognition

作者： Camille Kurtz Benoit Naegel Nicolas Passat LIPADE Universite Paris Descartes Paris France CNRS Universite de Strasbourg Strasbourg France CReSTIC Universite de Reims Champagne-Ardenne Reims France

We introduce the new notion of multivalued component-tree, that extends the classical component-tree initially devoted to grey-level images, in the mathematical morphology framework. We prove that multivalued component-trees can model images whose values are hierarchically organized. We also show that they can be efficiently built from standard component-tree construction algorithms, and involved in antiextensive filtering procedures. The relevance and usefulness of multivalued component-trees is illustrated by an applicative example on hierarchically classified remote sensing images.

关键词： Level set Computational efficiency Morphology Mathematical model Standards remote sensing image processing

来源：评论

学校读者我要写书评

暂无评论

URBAN ROAD EXTRACTION VIA GRAPH CUTS BASED PROBABILITY PROPAGATION

URBAN ROAD EXTRACTION VIA GRAPH CUTS BASED PROBABILITY PROPA...

引用

IEEE International conference on image processing

作者： Guangliang Cheng Ying Wang Yongchao Gong Feiyun Zhu Chunhong Pan National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science

ISBN: (纸本)9781479957521

关键词： Road extraction Support Vector Machine Sigmoid model Graph cuts Probability propagation

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 << < 25 26 27 28 29 30 31 32 33 34 > >>

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：