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111-120 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 segmentation technology and its application in digital image processing 2

Image segmentation technology and its application in digital...

引用

2nd IEEE Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2021

作者： Cheng, Yang Li, Bingjie Information Engineering University Zhengzhou Henan450000 China

ISBN: (纸本)9781728190181

In recent years, the level of science and technology in China has been significantly improved. Digital image processing technology is the product of the times with the rapid development of science and technology, and has been applied in many fields. Digital image processing technology includes many practical technologies, and image segmentation technology is one of them. At present, the widely used pattern recognition technology is realized by image segmentation technology. Based on this, this paper mainly studies the application of image segmentation technology in digital image processing. This paper first introduces the methods of image segmentation, including threshold segmentation, clustering segmentation and edge detection segmentation. Secondly, according to a need to extract a road image for image segmentation, and achieved very good results. Finally, this paper mainly analyzes the application of image segmentation in digital image processing. It is found that image segmentation has good application in automatic license plate recognition, biomedical engineering, remote sensing engineering and fire prevention and detection. © 2021 IEEE.

关键词： remote sensing

来源：评论

学校读者我要写书评

暂无评论

A Semantic Segmentation Method of Buildings in remote sensing image Based on Improved UNet 2

A Semantic Segmentation Method of Buildings in Remote Sensin...

引用

2nd International conference on Signal image processing and Communication, ICSIPC 2022

作者： Li, Zhongyu Liu, Yang Kuang, Yin Wang, Huajun Liu, Cheng College of Computer Science Chengdu Normal University Chengdu611130 China College of Geophysics Chengdu University of Technology Chengdu610059 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China Key Laboratory of interior Layout optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Sichuan Chengdu611130 China College of Movie and Media Sichuan Normal University Chengdu610066 China

ISBN: (纸本)9781510657694

Aiming at the problem of model instability and overfitting of deep neural networks with the deepening of the number of network layers, the current mainstream method is to use batch normalization (BN) to alleviate them. However, since the BN method is more sensitive to batch size when the batch size is small, the model performance will be poor. For a relatively large model, due to the limitation of video memory, the batch size cannot take a large value, limiting the model's performance. Because of the dependence of BN on batch size, this paper adopts group normalization (GN) instead of batch normalization (BN) in the UNet network to alleviate the impact of the model on batch size. Then experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet-GN) improves the mean intersection over union (MIoU) and mean pixel accuracy (MPA) by 10.66% and 1.65% respectively compared with the original model (UNet-BN). © 2022 SPIE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

remote sensing image Object Detection Method with Feature Denoising Fusion Module

Remote Sensing Image Object Detection Method with Feature De...

引用

IEEE Advanced Information Technology, Electronic and Automation Control conference (IAEAC)

作者： Penghui Chen Qishen Li Qiufeng Li Zhongyu Wu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

remote sensing object detection is an important research area in computer vision, widely applied in both military and civilian domains. However, challenges in remote sensing image object detection such as large image sizes, complex backgrounds, and significant variations in target scales are prevalent. To address these issues, this paper proposes a new Feature Denoising and Fusion Module (FDFM) aimed at enhancing the accuracy and robustness of object detection. This module comprises a Multi-Scale Denoising Submodule(MDS) and an Attention Optimization Submodule(AOS). The Multi-Scale Denoising Module aims to suppress lower-level texture noise by utilizing higher-level semantic features before the fusion process, reducing the impact of lower-level noise on subsequent multi-scale feature fusion. Meanwhile, the Attention Optimization Module seeks to enhance the precision of self-attention computations within the Multi-Scale Denoising Module without increasing the parameter count. The efficacy of this method was evaluated on public datasets DOTA, VisDrone, VOC and COCO, showing improvements in comparison to baseline models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Rotation Invariant Aerial image Retrieval with Group Convolutional Metric Learning 25

Rotation Invariant Aerial Image Retrieval with Group Convolu...

引用

25th International conference on pattern recognition (ICPR)

作者： Chung, Hyunseung Nam, Woo-Jeoung Lee, Seong-Whan Korea Univ Dept Artificial Intelligence Seoul South Korea Korea Univ Dept Comp & Radio Commun Engn Seoul South Korea

ISBN: (纸本)9781728188089

remote sensing image retrieval (RSIR) is the process of ranking database images depending on the degree of similarity compared to the query image. As the complexity of RSIR increases due to the diversity in shooting range, angle, and location of remote sensors, there is an increasing demand for methods to address these issues and improve retrieval performance. In this work, we introduce a novel method for retrieving aerial images by merging group convolution with attention mechanism and metric learning, resulting in robustness to rotational variations. For refinement and emphasis on important features, we applied channel attention in each group convolution stage. By utilizing the characteristics of group convolution and channel-wise attention, it is possible to acknowledge the equality among rotated but identically located images. The training procedure has two main steps: (i) training the network with Aerial image Dataset (AID) for classification, (ii) fine-tuning the network with triplet-loss for retrieval with Google Earth South Korea and NWPU-RESISC45 datasets. Results show that the proposed method performance exceeds other state-of-the-art retrieval methods in both rotated and original environments. Furthermore, we utilize class activation maps (CAM) to visualize the distinct difference of main features between our method and baseline, resulting in better adaptability in rotated environments.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Forward-Forward Algorithm for Hyperspectral image Classification

Forward-Forward Algorithm for Hyperspectral Image Classifica...

引用

IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Abel A. Reyes-Angulo Sidike Paheding Michigan Technological University Houghton MI Fairfield University Fairfield CT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The back-propagation algorithm has long been the de-facto standard in optimizing weights and biases in neural networks, particularly in cutting-edge deep-learning models. Its widespread adoption in fields like natural language processing, computer vision, and remote sensing has revolutionized automation in various tasks. The popularity of back-propagation stems from its ability to achieve outstanding performance in tasks such as classification, detection, and segmentation. Nevertheless, back-propagation is not without its limitations, encompassing sensitivity to initial conditions, vanishing gradients, overfitting, and computational complexity. The recent introduction of a forward-forward algorithm (FFA), which computes local goodness functions to optimize network parameters, alleviates the dependence on substantial computational resources and the constant need for architectural scaling. This study investigates the application of FFA for hyperspectral image classification. Experimental results and comparative analysis are provided with the use of the traditional back-propagation algorithm. Preliminary results show the potential behind FFA and its promises.

关键词： Backpropagation Training Computer vision Sensitivity Feature extraction Classification algorithms pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Multi-Plant Leaf Disease Detection and Classification using Convolution Neural Network Aad image processing

Multi-Plant Leaf Disease Detection and Classification using ...

引用

Recent Advances in Science and Engineering Technology (ICRASET), International conference on

作者： S S Aishwarya Pushpa Ravikumar I V Chaithra Deeksha Sagar Computer Science and Engineering AIT Adichunchanagiri Institute of Technology Chikmagalur Karnataka INDIA Department of Computer Science and Engineering AIT Adichunchanagiri Institute of Technology Chikmagalur Karnataka INDIA

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Every nation’s basic need is the agricultural products. Disease-ridden leaves have an effect on the nation’s agricultural output and financial resources. This research provides a deep learning based plant leaf disease classification and detection method. The website (Plant Village dataset) provided the photos that were used. Since tomatoes, peppers, and potatoes are the most prevalent plant species worldwide and in Iraq specifically, we have used them as examples of certain plant species in our study. There are $\mathbf{2 0 6 3 6}$ photos of plants and their illnesses in this data set. Plant leaf diseases are sorted into 15 class labels in the proposed system using a convolutional neural network (CNN). Of these classes, $\mathbf{1 2}$ are dedicated to diseases. Several various plants that were found, including three groups for healthy leaves and various microorganisms including fungus. Consequently, we achieved exceptional precision in both training and testing; specifically, we achieved $\mathbf{9 8. 2 9 \%}$ accuracy in training and $\mathbf{9 8. 0 2 9 \%}$ accuracy in testing across all utilized data sets. pattern recognition is necessary in order to diagnose these disorders. Using remote sensing techniques to investigate multi- and hyper-spectral picture captures is a frequent approach in this situation. Techniques that use this strategy frequently use digital image processing capabilities to accomplish their objectives. There are numerous kinds of pattern recognition algorithms that provide accurate disease detection. Plant diseases are currently detected using principal component analysis and back propagation. These algorithms are acquired by neural network training supervision.

关键词： Training Accuracy Neural networks Classification algorithms pattern recognition Convolutional neural networks remote sensing Diseases Testing Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

UAV Visual Localization Technology Based on Heterogenous remote sensing image Matching 22

UAV Visual Localization Technology Based on Heterogenous Rem...

引用

Proceedings of the 2022 5th International conference on Artificial Intelligence and pattern recognition

作者： Haoyang Tang Jiakun Shi Xin Miao Ruichen Wu Dongfang Yang Xi'an University of Posts and Telecommunications China The Rocket Force University of Engineering China

ISBN: (纸本)9781450396899

At present, the positioning function of intelligent UAVs mainly uses GPS technology, and GPS signals are susceptible to environmental and electromagnetic interference factors. In this paper, we combine remote sensing image processing with image matching algorithms to propose a GPS-independent visual localization technique for UAVs. First, the VGG16 network is used as the feature extraction backbone network, and the backbone network is designed and optimized for the characteristics of heterogenous remote sensing images. Secondly, a feature point screening and matching strategy is constructed, by which common feature points between heterogeneous remote sensing images can be screened and used for feature matching. Finally, the remote sensing image containing geographic location information and the UAV aerial image are fed into the network for feature extraction and matching, and the transformation matrix between the aligned images is calculated by the successfully matched feature points, and the transformation matrix is used to complete the mapping from the aerial image to the satellite image, and finally the geographic location information of each pixel can be read from the mapped image to complete the localization.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Design of Speech Device Software using Optical Character recognition Method 4

Design of Speech Device Software using Optical Character Rec...

引用

4th IEEE Asia-Pacific conference on Geoscience, Electronics and remote sensing Technology (AGERS)

作者： Seetharaman, R. Tharun, M. Mole, S. S. Sreeja Anandan, K. Anna Univ Dept ECE CEG Campus Chennai 600025 Tamil Nadu India Christu Jyothi Inst Technol & Sci Dept ECE Jangoan 506167 India TUV SUD South Asia Pvt Ltd PS Div E&E Chennai 600041 Tamil Nadu India

ISBN: (纸本)9781665428071

The purpose of this paper is to perform Optical Character recognition and convert that recognized data (text) to speech. Optical Character recognition is used to detect information from natural images and transfer them into words, symbols, and numbers. Known as computer vision, this is a sub-field of image processing that deals with the "sight and recognition" by a machine. It is a field of research in pattern recognition, artificial intelligence, and machine learning. Text-to-Speech (TTS) is a type of assistive technology that reads digital text aloud. The recent progress of text-to-speech synthesis (TTS) technology has allowed computers to read any written text aloud with a voice that is artificial but almost indistinguishable from real human speech. The Text- to - Speech software enables us to convert the recognized text into the computer-generated simulation of human speech audio. Implementing this software will be of use to several applications beyond just aiding visually challenged but can be used to convert physical documents to word or pdf documents. This can be used to assist virtual teaching, training, or learning for students in various environments.

关键词： OCR Text to Speech pattern recognition CNN

来源：评论

学校读者我要写书评

暂无评论

Cluster Analysis: Unsupervised Classification for Identifying Benign and Malignant Tumors on Whole Slide image of Prostate Cancer 5

Cluster Analysis: Unsupervised Classification for Identifyin...

引用

5th IEEE International conference on image processing Applications and Systems (IPAS)

作者： Bhattacharjee, Subrata Hwang, Yeong-Byn Sumon, Rashadul Islam Rahman, Hafizur Hyeon, Dong-Woo Moon, Damin Carole, Kouayep Sonia Kim, Hee-Cheol Choi, Heung-Kook Inje Univ Dept Comp Engn Gimhae South Korea Inje Univ Dept Digital Antiaging Healthcare Gimhae South Korea JLK Artificial Intelligence R&D Ctr Seoul South Korea

ISBN: (纸本)9781665462198

Recently, many fields have widely used cluster analysis: psychology, biology, statistics, pattern recognition, information retrieval, machine learning, and data mining. Diagnosis of histopathological images of prostate cancer is one of the routine tasks for pathologists and it is challenging for pathologists to analyze the formation of glands and tumors based on the Gleason grading system. In this study, unsupervised classification has been performed for differentiating malignant (cancerous) from benign (non-cancerous) tumors. Therefore, the unsupervised-based computer-aided diagnosis (CAD) technique would be of great benefit in easing the workloads of pathologists. This technique is used to find meaningful clustering objects (i.e., individuals, entities, patterns, or cases) and identify useful patterns. Radiomic-based features were extracted for cluster analysis using the gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and gray-level size zone matrix (GLSZM) techniques. Multi-clustering techniques used for the unsupervised classification are K-means clustering, K-medoids clustering, Agglomerative Hierarchical (AH) clustering, Gaussian mixture model (GMM) clustering, and Spectral clustering. The quality of the clustering algorithms was determined using Purity, Silhouettes, Adjusted Rand, Fowlkes Mallows, and Calinski Harabasz (CH) scores. However, the best-performing algorithm (i.e., K-means) has been applied to predict and annotate the cancerous regions in the whole slide image (WSI) to compare with the pathologist annotation.

关键词： unsupervised classification prostate cancer radiomic texture feature histopathological

来源：评论

学校读者我要写书评

暂无评论

HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves

HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classif...

引用

IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： William Michael Laprade Pawel Pieta Svetlana Kutuzova Jesper Cairo Westergaard Mads Nielsen Svend Christensen Anders Bjorholm Dahl Department of Applied Mathematics and Computer Science Technical University of Denmark Department of Computer Science University of Copenhagen Department of Plant and Environmental Sciences University of Copenhagen

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hyperspectral imaging is a widely used method in remote sensing, particularly for use in airborne and satellite-based land surveillance. Its versatility is, however, much larger and has also seen usage in everything ranging from food processing and surveillance to astronomy and waste sorting. It is also gaining inroads with agricultural research. With most available datasets focusing on per-pixel classification, there is, however, a potential for hyperspectral whole-image analysis, but there is a severe lack of datasets for whole-image analysis. To help fill this gap and facilitate methodological development in whole-image hyperspectral image analysis, we introduce the Hy-perLeaf2024 dataset. The dataset consists of 2410 hyper-spectral images of wheat leaves, along with associated classification and regression targets at both the leaf level and the plot level. In addition to the dataset, we also provide experiments showing the importance of pretraining and highlighting the future research direction in whole-image hyper-spectral image analysis.

关键词： Adaptation models image analysis image coding Surveillance conferences Focusing Distance measurement

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 << < 8 9 10 11 12 13 14 15 16 17 > >>

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：