检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning

TechRxiv

引用

TechRxiv 2019年

作者： Huang, Zhongling Dumitru, Corneliu Octavian Pan, Zongxu Lei, Bin Datcu, Mihai The Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Huairou District Beijing101408 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Wessling 82234 Germany

The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging. Given a large-scale SAR land cover dataset collected from TerraSAR-X images with a hierarchical three-level annotation of 150 categories and comprising more than 100,000 patches, three main challenges in automatically interpreting SAR images of highly imbalanced classes, geographic diversity, and label noise are addressed. In this letter, a deep transfer learning method is proposed based on a similarly annotated optical land cover dataset (NWPU-RESISC45). Besides, a top-2 smooth loss function with cost-sensitive parameters was introduced to tackle the label noise and imbalanced classes’ problems. The proposed method shows high efficiency in transferring information from a similarly annotated remote sensing dataset, a robust performance on highly imbalanced classes, and is alleviating the over-fitting problem caused by label noise. What’s more, the learned deep model has a good generalization for other SAR-specific tasks, such as MSTAR target recognition with a state-of-the-art classification accuracy of 99.46%. © 2019, CC BY.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

What, Where and How to Transfer in SAR Target Recognition Based on Deep CNNs

arXiv

引用

arXiv 2019年

作者： Huang, Zhongling Pan, Zongxu Lei, Bin School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Huairou District Beijing101408 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Institute of Electronics Chinese Academy of Sciences Beijing100190 China

Deep convolutional neural networks (DCNNs) have attracted much attention in remote sensing recently. Compared with the large-scale annotated dataset in natural images, the lack of labeled data in remote sensing becomes an obstacle to train a deep network very well, especially in SAR image interpretation. Transfer learning provides an effective way to solve this problem by borrowing the knowledge from the source task to the target task. In optical remote sensing application, a prevalent mechanism is to fine-tune on an existing model pre-trained with a large-scale natural image dataset, such as ImageNet. However, this scheme does not achieve satisfactory performance for SAR application because of the prominent discrepancy between SAR and optical images. In this paper, we attempt to discuss three issues that are seldom studied before in detail: (1) what network and source tasks are better to transfer to SAR targets, (2) in which layer are transferred features more generic to SAR targets and (3) how to transfer effectively to SAR targets recognition. Based on the analysis, a transitive transfer method via multi-source data with domain adaptation is proposed in this paper to decrease the discrepancy between the source data and SAR targets. Several experiments are conducted on OpenSARShip. The results indicate that the universal conclusions about transfer learning in natural images cannot be completely applied to SAR targets, and the analysis of what and where to transfer in SAR target recognition is helpful to decide how to transfer more effectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A HYBRID NEURAL NETWORK FOR CHROMA INTRA PREDICTION

A HYBRID NEURAL NETWORK FOR CHROMA INTRA PREDICTION

引用

IEEE International Conference on Image processing

作者： Yue Li Li Li Zhu Li Jianchao Yang Ning Xu Dong Liu Houqiang Li CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China University of Missouri Kansas City MO USA Snapchat Inc. CA USA

For chroma intra prediction, previous methods exemplified by the Linear Model method (LM) usually assume a linear correlation between the luma and chroma components in a coding block. This assumption is inaccurate for complex image content or large blocks, and restricts the prediction accuracy. In this paper, we propose a chroma intra prediction method by exploiting both spatial and cross-channel correlations using a hybrid neural network. Specifically, we utilize a convolutional neural network to extract features from the reconstructed luma samples of the current block, as well as utilize a fully connected network to extract features from the neighboring reconstructed luma and chroma samples. The extracted twofold features are then fused to predict the chroma samples-Cb and Cr simultaneously. The proposed chroma intra prediction method is integrated into HEVC. Preliminary results show that, compared with HEVC plus LM, the proposed method achieves on average 0.2%, 3.1% and 2.0% BD-rate reduction on Y, Cb and Cr components, respectively, under All-Intra configuration.

关键词： Feature extraction Neural networks Kernel Correlation Image reconstruction Encoding Prediction methods

来源：评论

学校读者我要写书评

暂无评论

A3Net: Adversarial-and-attention network for machine reading comprehension

arXiv

引用

arXiv 2018年

作者： Wang, Jiuniu Fu, Xingyu Xu, Guangluan Wu, Yirong Chen, Ziyan Wei, Yang Jin, Li Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics CAS Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

In this paper, we introduce Adversarial-and-attention Network (A3Net) for Machine Reading Comprehension. This model extends existing approaches from two perspectives. First, adversarial training is applied to several target variables within the model, rather than only to the inputs or embeddings. We control the norm of adversarial perturbations according to the norm of original target variables, so that we can jointly add perturbations to several target variables during training. As an effective regularization method, adversarial training improves robustness and generalization of our model. Second, we propose a multi-layer attention network utilizing three kinds of high-efficiency attention mechanisms. Multi-layer attention conducts interaction between question and passage within each layer, which contributes to reasonable representation and understanding of the model. Combining these two contributions, we enhance the diversity of dataset and the information extracting ability of the model at the same time. Meanwhile, we construct A3Net for the WebQA dataset. Results show that our model outperforms the state-of-the-art models (improving Fuzzy Score from 73.50% to 77.0%). Copyright © 2018, The Authors. All rights reserved.

关键词： Network layers

来源：评论

学校读者我要写书评

暂无评论

Identity Regularized Sparse Representation for Automatic Target Recognition in Sar Images

Identity Regularized Sparse Representation for Automatic Tar...

引用

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Zongxu Pan Lei Liu Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

An identity regularized sparse representation (IRSR) based SAR target recognition method is proposed in this paper. The method aims to find a transformation that can map the data to a transformed space, in which targets from the same class are close with each other, no matter the distance of them in the original space. This identity constraint can be formulated as a ℓ 1 -norm minimization problem. By decoupling the problem into the sparse coding problem and the dictionary learning problem, the solution can be obtained iteratively. The solution is simply the weighted average of the sparse coding of all training data. Experimental results demonstrate that the proposed method is superior to several related methods.

关键词： Synthetic aperture radar Target recognition Linear programming Airplanes Remote sensing Support vector machines Minimization

来源：评论

学校读者我要写书评

暂无评论

SALIENT SEED EXTRACTION BASED TARGET DETECTION IN SAR IMAGES

SALIENT SEED EXTRACTION BASED TARGET DETECTION IN SAR IMAGES

引用

IEEE International geoscience and Remote Sensing Symposium

作者： Zongxu Pan Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

A salient seed extraction based target detection method is proposed in this paper, aiming to distinguish target points from background points in SAR images. Different from recent superpixel based method which generates superpixels firstly, and for each superpixel decides whether it belongs to part of a target. The proposed method employs a salient point to region scheme. At first, salient seeds are extracted by mean-shift and region feature based approach. Then, pixels are assigned to the most similar seed and those assigned to the salient seeds are extracted to form the foreground region. Finally, constant false alarm rate (CFAR) operation is employed to detect the target points from the foreground region. The effectiveness of the proposed method is validated by comparing with five state-of-the-art methods on TerraSAR-X images.

关键词： Target detection Salient seed extraction Synthetic aperture radar (SAR)

来源：评论

学校读者我要写书评

暂无评论

Analysis and Prediction of Vehicle Exhaust Emission Using ANN 36

Analysis and Prediction of Vehicle Exhaust Emission Using AN...

引用

第36届中国控制会议

作者： Zhenyi Xu Yu Kang Wenjun Lv Department of Automation and State Key Laboratory of Fire Science University of Science and Technology of China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper focused on the analysis of vehicle emission based on the Hefei remote sensing data during the last three *** we propose a three-layer artificial neural network model for predicting vehicle exhaust emission using remote sensing ***,we take adaptive-lasso algorithm to analyze the various factors from the emission data,and determine the principal ***,after doing principal components analysis and selecting algorithm and architecture,the Back-Propagation neural network model with 7-12-1 architecture was established as the optimal ***,we give the prediction results on the testing data-set and prove the potentiality and validity of the proposed method in the prediction of vehicle exhaust emission.

关键词： Vehicle Exhaust Emission Adaptive-Lasso Artificial Neural Network

来源：评论

学校读者我要写书评

暂无评论

A TOPSAR Calibration Method For processing System Of GF3 Next Generation

A TOPSAR Calibration Method For Processing System Of GF3 Nex...

引用

Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Di Yin Bing Han Jili Sun Aiping Chen Liangbo Zhao Xinzhe Yuan Lihua Zhong Yuxin Hu University of Chinese Academy of Sciences Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China Beijing Institute of Telemetry and Telecommunications Technology Beijing China Chinese Academy of Space Technology China Beijing China National Satellite Ocean Application Service State Oceanic Administration Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

TOPSAR is an earth-imaging technique, which can provide wide swath coverage. The paper introduces a TOPSAR focusing and calibrating experiment based on the TOPSAR data acquired by Gaofen3(GF3). In this paper, we firstly derive the processor calibration factors under the demands of keeping signal energy invariant. After that, we fully analyze the impact of antenna electronic steering on TOPSAR products. Aimed to be applied to TOPSAR mode processing system of a SAR satellite, the next generation of GF3, calibration methods to processor and electronic steering was proposed in this paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A DESIGN OF THE COMPLETE POLARIZATION CONVERTER USING DIELECTRIC PERIODIC STRUCTURES

A DESIGN OF THE COMPLETE POLARIZATION CONVERTER USING DIELEC...

引用

IEEE International geoscience and Remote Sensing Symposium

作者： Yueting Zhang Chibiao Ding Bin Lei Weihai Fang Key Laboratory of Geo-spatial Information Processing and Application System Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing General Institute of Electronic Engineering

Polarization converter is used in the applications of the polar SAR observations. There exists coupling between TE and TM modes when plane wave is oblique incident on the surface of dielectric periodic structure, the single TE or TM polarized wave incident will cause TE and TM mixed transmission wave. In some proper incident conditions, complete polarization conversion can be realized between TE and TM mode. In this work, a design of complete polarization converter by using dielectric periodic structure is designed and it is carefully investigated by a method which combines the multimode network theory with the rigorous mode matching method. We revealed TE/TM complete polarization conversion characteristics of dielectric periodic structure, and also analyzed the effects of structure parameters. These investigations provide important guideline for accurate designing new millimeter wave polarization converters.

关键词： Dielectrics Polarization Periodic structures Optical waveguides Microwave filters Reflection

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：