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

143 篇 会议
9 篇 期刊文献
4 册 图书

馆藏范围

156 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

101 篇 工学
- 84 篇 计算机科学与技术...
- 47 篇 测绘科学与技术
- 23 篇 软件工程
- 15 篇 电气工程
- 8 篇 信息与通信工程
- 6 篇 光学工程
- 6 篇 控制科学与工程
- 4 篇 仪器科学与技术
- 4 篇 建筑学
- 3 篇 机械工程
- 3 篇 化学工程与技术
- 2 篇 冶金工程
- 2 篇 电子科学与技术（可...
- 2 篇 土木工程
- 2 篇 交通运输工程
- 2 篇 环境科学与工程（可...
- 1 篇 动力工程及工程热...
- 1 篇 地质资源与地质工...
- 1 篇 矿业工程
26 篇 理学
- 17 篇 数学
- 6 篇 物理学
- 6 篇 统计学（可授理学、...
- 4 篇 地球物理学
- 3 篇 化学
- 2 篇 地质学
- 1 篇 生物学
11 篇 医学
- 11 篇 临床医学
5 篇 管理学
- 4 篇 图书情报与档案管...
2 篇 教育学
- 2 篇 教育学
2 篇 艺术学
- 2 篇 设计学（可授艺术学...
1 篇 农学
1 篇 军事学

主题

46 篇 remote sensing
21 篇 feature extracti...
17 篇 pattern recognit...
13 篇 training
11 篇 image segmentati...
9 篇 satellites
7 篇 support vector m...
7 篇 buildings
6 篇 indexes
6 篇 synthetic apertu...
5 篇 urban areas
5 篇 object detection
5 篇 deep learning
5 篇 optimization
5 篇 semantics
5 篇 image reconstruc...
5 篇 hyperspectral im...
4 篇 optical imaging
4 篇 task analysis
4 篇 three-dimensiona...

机构

6 篇 beijing univ che...
5 篇 college of infor...
4 篇 chinese acad sci...
3 篇 wuhan univ sch r...
3 篇 university of ch...
2 篇 mississippi stat...
2 篇 institute of rem...
2 篇 dynamic vision a...
2 篇 univ chinese aca...
2 篇 bilkent universi...
2 篇 yellow river con...
2 篇 department of co...
2 篇 department of el...
2 篇 image processing...
2 篇 faculty itc univ...
2 篇 chinese acad sci...
2 篇 zhengzhou instit...
2 篇 kyoto univ grad ...
2 篇 chinese acad sci...
2 篇 institute of rem...

作者

5 篇 li wei
4 篇 wei li
3 篇 du qian
3 篇 michaelsen eckar...
3 篇 stilla uwe
3 篇 shan jie
2 篇 zhong yanfei
2 篇 michael schmitt
2 篇 myasnikov v.v.
2 篇 xiang li
2 篇 roscher ribana
2 篇 younan nicolas h...
2 篇 e. kawaguchi
2 篇 qian du
2 篇 friedhelm schwen...
2 篇 rezatofighi hami...
2 篇 yuan yuan
2 篇 schindler konrad
2 篇 zhang bing
2 篇 reid ian

语言

153 篇 英文
2 篇 中文
1 篇 俄文

检索条件"任意字段=5th IAPR Workshop on Pattern Recognition in Remote Sensing"

共 156 条记录，以下是1-10 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：

Image Augmentations in Planetary Science: Implications in Self-Supervised Learning and Weakly-Supervised Segmentation on Mars 26

Image Augmentations in Planetary Science: Implications in Se...

引用

26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Kossmann, Dominik Matei, Arthur Wilhelm, thorsten Fink, Gernot A. TU Dortmund Univ Dept Comp Sci Dortmund Germany TU Dortmund Univ Dept Elect Engn & Informat Technol Dortmund Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Research on the use of augmentations in physically constrained remote sensing scenarios, like the analysis of Martian surface data, is largely unexplored. In this work we present an analysis on how reasonable augmentation strategies can be selected which are class agnostic and respect physical plausibility in supervised and weakly-supervised tasks. Additionally, we present the first results of self-supervised learning on Martian surface data, discuss the importance of physically plausible augmentations in the context of self-supervised learning, specifically contrastive learning, and provide a comprehensive overview of the generalization properties induced by different augmentation strategies with the help of geomorphic maps.

关键词： Mars Image segmentation Self-supervised learning pattern recognition Task analysis remote sensing

来源：评论

学校读者我要写书评

暂无评论

Embedding Spatial Relations in Visual Question Answering for remote sensing 26

Embedding Spatial Relations in Visual Question Answering for...

引用

26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Faure, Maxime Lobry, Sylvain Kurtz, Camille Wendling, Laurent Univ Paris Cite LIPADE F-75006 Paris France

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

remote sensing images carry a wealth of information that is not easily accessible to end-users as it requires strong technical skills and knowledge. Visual Question Answering (VQA), a task that aims at answering an open-ended question in natural language from an image, can provide an easier access to this information. Considering the geographical information contained in remote sensing images, questions often embed an important spatial aspect, for instance regarding the relative position of two objects. Our objective is to better model the spatial relations in the construction of a ground-truth database of image/question/answer triplets and to assess the capacity a VQA model has to answer these questions. In this article, we propose to use histograms of forces to model the directional spatial relations between geo-localized objects. this allows a finer modeling of ambiguous relationships between objects and to provide different levels of assessment of a relation (e.g. object A is slightly/strictly to the west of object B). Using this new dataset, we evaluate the performances of a classical VQA model and propose a curriculum learning strategy to better take into account the varying difficulty of questions embedding spatial relations. With this approach, we show an improvement in the performances of our model, highlighting the interest of embedding spatial relations in VQA for remote sensing applications.

关键词： Training Visualization Histograms Feature extraction Question answering (information retrieval) Spatial databases pattern recognition

来源：评论

学校读者我要写书评

暂无评论

remote sensing Image Scene Classification via Multi-Level Representation Learning 26

Remote Sensing Image Scene Classification via Multi-Level Re...

引用

26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Fu, Wei Yang, Lishuang Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

remote sensing image scene classification (RSSC), which assigns semantic labels to remote sensing images, is very important for remote sensing image interpretation. thanks to the rapid development of deep learning, RSSC achieves significant breakthroughs by the use of convolutional neural network (CNN). However, CNN relies on local receptive fields and is difficult to capture long-range and global scene information. Moreover, the information of salient objects, which contributes to discriminate the category of scenes (e.g., airplanes indicate the airport scene), should be also exploited. To address this issue, a deep learning method, named multi-level representation learning (MLRL), is proposed to collaboratively extract pixel-level, patch-level, and object-level features, which respectively contain local, global, and object-oriented information. Specifically, pixel-level features are obtained by pixel-wise convolution operations within a CNN. Patch-level features are achieved by a patch-wise self-attention network. Object-level features are acquired by applying a CNN to a cropped sub-image, which conveys important information of salient objects. To this end, a three-branch network structure to respectively extract above features, is built. Finally, a decision fusion method is adopted to integrate multi-level features, and gives rise to refined classification results. Experiments conducted on widely-used datasets demonstrate the effectiveness of the proposed method.

关键词： Deep learning Representation learning Measurement Image analysis Fuses Semantics Feature extraction

来源：评论

学校读者我要写书评

暂无评论

12th workshop on pattern recognition in remote sensing

引用

Photogrammetric Record 2022年第178期37卷 250-251页

来源：评论

学校读者我要写书评

暂无评论

Intensity-Sensitive Similarity Indexes for Image Quality Assessment 26

Intensity-Sensitive Similarity Indexes for Image Quality Ass...

引用

26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Li, Xiaotong Armour, Wesley Univ Oxford Dept Engn Sci Oxford E Res Ctr Oxford England

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the importance of Image quality assessment (IQA) is ever increasing due to the fast paced advances in imaging technology and computer vision. Among the numerous IQA methods, Structural SIMilarity (SSIM) index and its variants are better matched to the perceived quality of the human visual system. However, SSIM methods are insufficiently sensitive, when images contain low information, where the important information only occupies a low proportion of the image while most of the image is noise-like, which is common in scientific data. therefore, we propose two new IQA methods, InTensityWeighted SSIM index and Low-Information Similarity Index, for such low information images. In addition, auxiliary indexes are proposed to assist with the assessment. the application of these new IQA methods to natural images and field-specific images, such as radio astronomical images, medical images, and remote sensing images, are also demonstrated. the results show that our IQA methods perform better than state-of-the-art SSIM methods for differences in high-intensity parts of the input images and have similar performance to that of the original and gradient-based SSIM for differences in low-intensity parts. Different similarity indexes are suitable for different applications, which we demonstrate in our results.

关键词： Image quality Computer vision Sensitivity Visual systems pattern recognition Indexes remote sensing

来源：评论

学校读者我要写书评

暂无评论

11th iapr workshop on pattern recognition in remote sensing and 25th International Conference on pattern recognition

引用

Photogrammetric Record 2020年第172期35卷 554-554页

来源：评论

学校读者我要写书评

暂无评论

AutoLC: Search Lightweight and Top-Performing Architecture for remote sensing Image Land-Cover Classification 26

AutoLC: Search Lightweight and Top-Performing Architecture f...

引用

26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Zheng, Chenyu Wang, Junjue Ma, Ailong Zhong, Yanfei Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ LIESMARS Wuhan Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Land-cover classification has long been a hot and difficult challenge in remote sensing community. With massive High-resolution remote sensing (HRS) images available, manually and automatically designed Convolutional Neural Networks (CNNs) have already shown their great latent capacity on HRS land-cover classification in recent years. Especially, the former can achieve better performance while the latter is able to generate lightweight architecture. Unfortunately, they both have shortcomings. On the one hand, because manual CNNs are almost proposed for natural image processing, it becomes very redundant and inefficient to process HRS images. On the other hand, nascent Neural Architecture Search (NAS) techniques for dense prediction tasks are mainly based on encoder-decoder architecture, and just focus on the automatic design of the encoder, which makes it still difficult to recover the refined mapping when confronting complicated HRS scenes. To overcome their defects and tackle the HRS land-cover classification problems better, we propose AutoLC which combines the advantages of two methods. First, we devise a hierarchical search space and gain the lightweight encoder underlying gradient-based search strategy. Second, we meticulously design a lightweight but top-performing decoder that is adaptive to the searched encoder of itself. Finally, experimental results on the LoveDA land-cover dataset demonstrate that our AutoLC method outperforms the state-of-art manual and automatic methods with much less computational consumption.

关键词： Training Gradient methods Image processing Computer architecture Manuals Search problems Decoding

来源：评论

学校读者我要写书评

暂无评论

A lightweight multi-scale context network for salient object detection in optical remote sensing images 26

A lightweight multi-scale context network for salient object...

引用

26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Lin, Yuhan Sun, Han Liu, Ningzhong Bian, Yetong Cen, Jun Zhou, Huiyu Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH Leics England

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for optical RSIs becomes a huge challenge. However, different from natural scene images (NSIs), the discussion on the optical RSI SOD task still remains scarce. In this paper, we propose a multi-scale context network, namely MSCNet, for SOD in optical RSIs. Specifically, a multi-scale context extraction module is adopted to address the scale variation of salient objects by effectively learning multi-scale contextual information. Meanwhile, in order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism for gradually aggregating and refining the salient regions from the multi-scale context extraction module. Extensive experiments on two benchmarks demonstrate that MSCNet achieves competitive performance with only 3.26M parameters. the code will be available at https://***/NuaaYH/MSCNet.

关键词： Integrated optics Semantics Object detection Optical fiber networks Optical imaging Feature extraction Optical sensors

来源：评论

学校读者我要写书评

暂无评论

Controlled Multi-modal Image Generation for Plant Growth Modeling 26

Controlled Multi-modal Image Generation for Plant Growth Mod...

引用

26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Miranda, Miro Drees, Lukas Roscher, Ribana Univ Bonn IGG Remote Sensing Niebuhrstr 1a D-53113 Bonn Germany Tech Univ Munich Data Sci Earth Observat Lise Meitner Str 9 D-85521 Ottobrunn Germany German Res Ctr Artificial Intelligence DFKI Trippstadter Str 122 D-67663 Kaiserslautern Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Predicting plant development is an important task in precision farming and an essential metric for decision-making by researchers and farmers. In this work, we propose a novel generative modeling technique for plant growth prediction based on conditional generative adversarial networks. We formulate plant growth as an image-to-image translation task and predict the appearance of a plant growth stage as a function of its previous stage. We take into account that plant growth is inherently multi-modal, depending on numerous and highly variable environmental factors, and thus a single input belongs to a distribution of potential outputs. We encode the ambiguity in an interpretable and low-dimensional latent vector space representing the various factors of variation that are influencing plant growth. We use a novel encoder-based data fusion technique and combine information contained in remote sensing imagery of different cropping systems with data containing the factors of variation to adequately model plant growth. this offers several advantages over existing methods: (1) we show that we can model a distribution of potential appearances and simultaneously outperform existing methods in providing more realistic predictions, (2) the complexity of plant growth is more adequately captured, as various factors influencing plant growth can be included, (3) predictions are controllable by being conditioned by an interpretable latent vector representing the factors of variation along with an input image of a previous growth stage.

关键词： Measurement Image synthesis Ecosystems Predictive models Generative adversarial networks pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Multi-View Stereo and Depth Priors Guided NeRF for View Synthesis 26

Multi-View Stereo and Depth Priors Guided NeRF for View Synt...

引用

26th International Conference on pattern recognition / 8th International workshop on Image Mining - theory and Applications (IMTA)

作者： Deng, Wang Zhang, Xuetao Guo, Yu Lu, Zheng Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China China Acad Space Technol Inst Remote Sensing Satellite Xian Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In this paper, we present a new framework for view synthesis of novel view based on Neural Radiance Fields(NeRF). We aim to address two main limitations of NeRF. Firstly, we propose to combine multi-view stereo into NeRF to help construct general neural radiance fields across different scenes. Specifically, We build a MVS-Encoding Feature Volume with average group-wise correlation to aggregate the multi-view appearance and geometry feature for every source view. And then we use an MLP to encode neural radiance fields by using the scene-dependent features interpolated from the MVS-Encoding Feature Volumes. this makes our model can be applied to other unseen scenes without any per-scene fine-tuning, and render realistic images with few images. If more training images are provided, our method can be fine-tuned quickly to render more realistic images. In fine-tuning phase, we propose a depth priors guided sampling method, which can make the model represent more accurate geometry for corresponding scenes and so render high-quality images of novel view. We evaluate our method on three common datasets. the experiment results show that our method performs better than other baselines, neither without or with fine-tuning. And the depth priors guided sampling method can be easily applied on other methods based on Neural Radiance Fields to further improve the quality of rendered images.

关键词： View Synthesis Neural Radiance Fields Multi-View Stereo Depth Priors

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16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：