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Retrieval of canopy biophysical variables from remote sensing data using contextual information

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Journal of Central South University of Technology 2008年第6期15卷 877-881页

作者：肖志强王锦地梁顺林屈永华万华伟 State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications Chinese Academy of Sciences School of Geography Beijing Normal University Department of Geography University of MarylandCollege Park

In order to improve the accuracy of biophysical parameters retrieved from remotely sensing data, a new algorithm was presented by using spatial contextual to estimate canopy variables from high-resolution remote sensing images. The developed algorithm was used for inversion of leaf area index (LAI) from Enhanced Thematic Mapper Plus (ETM+) data by combining with optimization method to minimize cost functions. The results show that the distribution of LAI is spatially consistent with the false composition imagery from ETM+ and the accuracy of LAI is significantly improved over the results retrieved by the conventional pixelwise retrieval methods, demonstrating that this method can be reliably used to integrate spatial contextual information for inverting LAI from high-resolution remote sensing images.

关键词： inverse problem canopy biophysical variables contextual information leaf area index

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Written discourse analysis in language teaching classrooms: contextual information and language features

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校园英语 2019年第23期 203-204页

作者：路欣天津广播影视职业学院

This assignment is divided into five sections. The first section is the introduction. Section two focuses on some background literature of discourse analysis, Section three discusses the analysis of contextual information and feature of texts in the two extracts from a research article and a textbook. Section four produces a short classroom activity based on the discussed elements on the two extracts. Section 5 is the conclusion.

关键词： language teaching classrooms contextual information language features

Document structure model for survey generation using neural network

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Frontiers of Computer Science 2021年第4期15卷 73-82页

作者： Huiyan XU Zhongqing WANG Yifei ZHANG Xiaolan WENG ZhijianWANG Guodong ZHOU College of Computer and information Hohai UniversityNanjing 210098China School of Computer Science and Technology Huaiyin Normal UniversityHuai’an 223300China Natural Language Processing Lab Soochow UniversitySuzhou 215006China

Survey generation aims to generate a summary from a scientific topic based on related *** structure of papers deeply influences the generative process of survey,especially the relationships between sentence and sentence,paragraph and *** principle,the structure of paper can influence the quality of the ***,we employ the structure of paper to leverage contextual information among sentences in paragraphs to generate a survey for *** particular,we present a neural document structure model for survey *** take paragraphs as units,and model sentences in paragraphs,we then employ a hierarchical model to learn structure among sentences,which can be used to select important and informative sentences to generate *** evaluate our model on scientific document data *** experimental results show that our model is effective,and the generated survey is informative and readable.

关键词： survey generation contextual information document structure

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A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques

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Chinese Journal of Electronics 2016年第2期25卷 334-340页

作者： ZHENG Xiaoyao LUO Yonglong SUN Liping CHEN Fulong College of Territorial Resources and Tourism Anhui Normal University School of Mathematics and Computer Science Anhui Normal University

Recommender system can efficiently alleviate the information overload problem, but it has been trapped in the recommendation accuracy. We proposed a new recommender system which based on matrix factorization techniques. More factors including contextual information, user ratings and item feature are all taken into consideration. Meanwhile the k-modes algorithm is used to reduce the complexity of matrix operations and increase the relevance of the user-item ratings *** with several ma jor existing recommendation approaches, extensive experimental evaluation on publicly available dataset demonstrates that our method enjoys improved recommendation accuracy.

关键词： Recommender system Matrix factorization contextual information Clustering

Knowledge-based bridge detection from SAR images

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Journal of Systems Engineering and Electronics 2009年第5期20卷 929-936页

作者： Wang Wenguang Sun Jinping Hu Rui Mao Shiyi School of Electronic and information Engineering Beihang Univ. Beijing 100191 P. R. China

Automatic bridge detection is an important application of SAR images. Differed from the classical CFAR method, a new knowledge-based bridge detection approach is proposed. The method not only uses the backscattering intensity difference between targets and background but also applies the contextual information and spatial relationship between objects. According to bridges＇ special characteristics and scattering properties in SAR images, the new knowledge-based method includes three processes： river segmentation, potential bridge areas detection and bridge discrimination. The application to AIRSAR data shows that the new method is not sensitive to rivers＇ shape. Moreover, this method can detect bridges successfully when river segmentation is not very exact and is more robust than the radius projection method.

关键词： knowledge-based bridge detection SAR contextual information mathematical morphology.

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Structural Dependence Learning Based on Self-attention for Face Alignment

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Machine Intelligence Research 2024年第3期21卷 514-525页

作者： Biying Li Zhiwei Liu Wei Zhou Haiyun Guo Xin Wen Min Huang Jinqiao Wang Foundation Model Research Center Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100083China Alpha(Beijing)Private Equity Beijing100083China School of Computer Science National University of Defense TechnologyChangsha410073China

Self-attention aggregates similar feature information to enhance the features. However, the attention covers nonface areas in face alignment, which may be disturbed in challenging cases, such as occlusions, and fails to predict landmarks. In addition, the learned feature similarity variance is not large enough in the experiment. To this end, we propose structural dependence learning based on self-attention for face alignment (SSFA). It limits the self-attention learning to the facial range and adaptively builds the significant landmark structure dependency. Compared with other state-of-the-art methods, SSFA effectively improves the performance on several standard facial landmark detection benchmarks and adapts more in challenging cases.

关键词： Computer vision face alignment self-attention facial structure contextual information

LGNet:Local and global representation learning for fast biomedical image segmentation

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Journal of Innovative Optical Health Sciences 2023年第4期16卷 29-39页

作者： Guoping Xu Xuan Zhang Wentao Liao Shangbin Chen Xinglong Wu School of Computer Science&Engineering Hubei Key Laboratory of Intelligent RobotWuhan Institute of TechnologyWuhanHubei 430205P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and TechnologyWuhanHubei 430074P.R.China

Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems,yet still faces many *** on convolutional neural networks(CNNs),medical image segmentation has achieved tremendous ***,owing to the locality of convolution operations,CNNs have the inherent limitation in learning global *** address the limitation in building global context relationship from CNNs,we propose LGNet,a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this ***,we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global *** has two key insights:(1)We bridge two-branch to learn local and global features in an interactive way;(2)we present a novel multi-feature fusion model(MSFFM)to leverage the global contexture information from transformer and the local representational features from *** method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse,ACDC and ***,LGNet achieves the state-of-the-art performance with Dice's indexes of 80.15%on Synapse,of 91.70%on ACDC,and of 95.56%on ***,the inference speed attains at 172 frames per second with 224-224 input *** extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation.

关键词： CNNs transformers segmentation medical image contextual information

Anaphora＇s Creating Irony by the Aid of Context

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Sino-US English Teaching 2015年第9期12卷 647-653页

作者： LIU Yan-chun Harbin Engineering University Harbin China

The core function of anaphora is to serve as a substitute for a certain linguistic component in texts, but it actually has other missions such as creating the rhetorical effect--irony. However, anaphora alone cannot contribute to ironic effect, and only by resorting to other resources like contextual information, can anaphora generate irony. Therefore, Yus＇ s classification of contextual sources is employed to analyze with examples how anaphora creates irony by the aid of context. Ultimately, the conclusion has been drawn that anaphora is able to produce irony with the help of six groups of contextual information classified by Yus except the category--speaker＇s nonverbal behavior and anapbora sometimes has to interact with several groups of contextual information in order to create irony.

关键词： anaphora irony contextual information

A Coupled Visual Representation based on Local Patch and Context for Single Target Tracking

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A Coupled Visual Representation based on Local Patch and Con...