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IEEE International Geoscience and Remote Sensing Symposium

作者： Gang Xiong Lizhe Wang Fang Wang Lijun Wang Shanghai Key Lab. of Intelligent Sensing and Recognition Shanghai Jiao Tong University China Jiangsu Urban and Rural Construction College Changzhou City China Science and Technology on Communication Information Security Control Laboratory China

Based on the two-dimensional multifractional Brownian motion (2D-mFBM) scene model, a novel analytical electromagnetic scattering model considering the scattering characteristics of 2D-mFBM surface is proposed, and then a direct SAR imaging model based on 2D-mFBM is derived. The SAR images are simulated using SAR simulator based on multifractional geometric modeling and electromagnetic modeling. The multifractal spectrum and singularity power spectrum of simulated SAR images are analyzed and compared with those of 2D-mFBM scene model. The simulation and analysis indicates that the SAR image with both HH and VV polarization inherited the multifractal characteristics of 2D-mFBM terrain, and presents similar MFS with the original terrain, which provides valuable reference for the feature extraction of high-resolution SAR remote sensing image.

关键词： Radar polarimetry Fractals Analytical models Electromagnetic scattering Synthetic aperture radar Two dimensional displays Logistics

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NTIRE 2020 Challenge on Image and Video Deblurring

arXiv

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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

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Robust Static Output Feedback Stabilization of Continuous-Time Linear Systems via Enhanced LMI Conditions

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IFAC-PapersOnLine 2020年第2期53卷 4540-4545页

作者： Hassène Gritli Ali Zemouche Safya Belghith Taous-Meriem Laleg-Kirati Higher Institute of Information and Communication Technologies Université de Carthage 1164 Borj Cedria Tunis Tunisia University of Lorraine CRAN CNRS UMR 7039 54400 Cosnes et Romain France Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis Université de Tunis El Manar BP. 37 Le Belvédère 1002 Tunis Tunisia Mathematical and Computational Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia

This paper addresses the problem of Static Output Feedback (SOF) stabilization for continuous-time linear systems subject to norm-bounded parameter uncertainties. Usually this issue leads to the feasibility of a Bilinear Matrix Inequality (BMI), which is difficult to linearize to get non conservative Linear matrix inequality (LMI) conditions. In this paper, by means of some technical lemmas, we transform the BMI into a new LMI with a line search over two scalar variables. The obtained LMI conditions are less conservative than those existing in the literature. Numerical evaluations are presented to show the superiority of the proposed method.

关键词： Continuous-time Linear systems Robust control Parametric uncertainty Static output feedback Linear matrix inequality (LMI)

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Deep high-order supervised hashing

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Optik 2019年 180卷 847-857页

作者： Jing Dong Cheng Qiu Le Sun Jian Xin Zhang Christian Desrosiers Bin Liu Jian Lu Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian 116622 China School of Information and Communication Engineering Dalian University of Technology Dalian 116024 China Laboratory for Imagery Vision and Artificial Intelligence (LIVAI) École de technologie supérieure (ETS) Montreal H3C1K3 Canada International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China

Recently, deep hashing has achieved excellent performance in large-scale image retrieval by simultaneously learning deep features and hash function. However, state-of-the-art methods for this task have so far failed to explore feature statistics higher than first-order. To address this problem, we propose two novel deep high-order supervised hashing (DHoSH) architectures based on point-wise labels (DHoSH-PO) and pair-wise labels (DHoSH-PA), respectively. The core of DHoSH is a trainable layer of bilinear features incorporated into existing deep convolutional neural network (CNN) for end-to-end learning. This layer captures the interaction of local features by bilinear pooling, using correlation to model dependencies of features within the same layer or cross-correlation for features across different layers of the CNN. Furthermore, DHoSH systematically employs the high-order statistics of features of multiple layers. Extensive experiments on commonly used image retrieval benchmarks show that our DHoSH-PO and DHoSH-PA models yield improved accuracy over its first-order counterparts and achieve effective performance for these benchmarks.

关键词： High-order statistics Supervised hashing Bilinear pooling CNN Image retrieval

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MultiplEYE: Creating a multilingual eye-tracking-while-reading corpus 25

MultiplEYE: Creating a multilingual eye-tracking-while-readi...

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Deborah Noemie Jakobi Maja Stegenwallner-Schütz Nora Hollenstein Cui Ding Ramune Kaspere Ana Matić Škorić Eva Pavlinusic Vilus Stefan Frank Marie-Luise Müller Kristine M Jensen de López Nik Kharlamov Hanne B. Søndergaard Knudsen Yevgeni Berzak Ella Lion Irina A. Sekerina Cengiz Acarturk Mohd Faizan Ansari Katarzyna Harezlak Pawel Kasprowski Ana Bautista Lisa Beinborn Anna Bondar Antonia Boznou Leah Bradshaw Jana Mara Hofmann Thyra Krosness Not Battesta Soliva Anila Çepani Kristina Cergol Ana Došen Marijan Palmovic Adelina Çerpja Dalí Chirino Jan Chromý Vera Demberg Iza Škrjanec Nazik Dinçtopal Deniz Dr. Inmaculada Fajardo Mariola Giménez-Salvador Xavier Mínguez-López Maroš Filip Zigmunds Freibergs Jéssica Gomes Andreia Janeiro Paula Luegi João Veríssimo Sasho Gramatikov Jana Hasenäcker Alba Haveriku Nelda Kote Muhammad M. Kamal Hanna Kędzierska Dorota Klimek-Jankowska Sara Kosutar Daniel G. Krakowczyk Izabela Krejtz Marta Łockiewicz Kaidi Lõo Jurgita Motiejūnienė Jamal A. Nasir Johanne Sofie Krog Nedergård Ayşegül Özkan Mikuláš Preininger Loredana Pungă David Robert Reich Chiara Tschirner Špela Rot Andreas Säuberli Jordi Solé-Casals Ekaterina Strati Igor Svoboda Evis Trandafili Spyridoula Varlokosta Mila Vulchanova Lena A. Jäger Department of Computational Linguistics University of Zurich Zurich Switzerland Humboldt-University of Berlin Berlin Germany and University of Koblenz Koblenz Germany Kaunas University of Technology Kaunas Lithuania Faculty of Education and Rehabilitation Sciences Dept of Speech and Language pathology Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Centre for Language Studies Radboud University Nijmegen Netherlands ZPID Leibnitz Institute for Psychology Trier Germany Aalborg University Aalborg Denmark Data and Decision Sciences Technion Haifa Israel Department of Psychology College of Staten Island Staten Island New York USA Cognitive Science Department Jagiellonian University Krakow Poland Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Basque Center on Cognition Brain & Language Donostia Spain Human-Centered Data Science University of Göttingen Göttingen Germany Department of Computational Linguistics University of Zurich Zurich Switzerland and Digital Society Initiative University of Zurich Zurich Switzerland Lab of Psycholinguistics & Neurolinguistics National and Kapodistrian University of Athens Athens Greece Department of Computational Linguistics University of Zurich University of Zurich Switzerland University of Zurich Zurich Switzerland University of Tirana Tirana Albania Faculty of Teacher Education University of Zagreb Zagreb Croatia Hrvatska Croatia and Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia University of Zagreb Zagreb Croatia Dept. of Speech & Lang. Path. Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia Academy of Sciences of Albania Tirana Albania Radboud University Nijmegen Netherlands Institute of Czech Language and Theory of Communication Faculty of Arts Charles University Prague Czech Republic Saarland Universit

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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Publisher Correction: Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants

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Scientific reports 2020年第1期10卷 17650页

作者： Naho Ichikawa Giuseppe Lisi Noriaki Yahata Go Okada Masahiro Takamura Ryu-Ichiro Hashimoto Takashi Yamada Makiko Yamada Tetsuya Suhara Sho Moriguchi Masaru Mimura Yujiro Yoshihara Hidehiko Takahashi Kiyoto Kasai Nobumasa Kato Shigeto Yamawaki Ben Seymour Mitsuo Kawato Jun Morimoto Yasumasa Okamoto Department of Psychiatry and Neurosciences Graduate School of Biomedical Sciences Hiroshima University Hiroshima Japan. ATR Brain Information Communication Research Laboratory Group Kyoto Japan. Institute for Quantum Life Science National Institutes for Quantum and Radiological Science and Technology Chiba Japan. Medical Institute of Developmental Disabilities Research Showa University Tokyo Japan. Department of Functional Brain Imaging Research National Institutes for Quantum and Radiological Science and Technology Chiba Japan. Department of Neuropsychiatry Keio University School of Medicine Tokyo Japan. Department of Psychiatry Kyoto University Graduate School of Medicine Kyoto Japan. Graduate School of Medical and Dental Sciences Tokyo Medical and Dental University Tokyo Japan. Department of Youth Mental Health Graduate School of Medicine The University of Tokyo Tokyo Japan. ATR Brain Information Communication Research Laboratory Group Kyoto Japan. ben@atr.jp. Computational and Biological Learning Lab Cambridge University Cambridge UK. ben@atr.jp. Department of Psychiatry and Neurosciences Graduate School of Biomedical Sciences Hiroshima University Hiroshima Japan. ben@atr.jp.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Real-time 4×28Gb/s transmission over 110-km SMF based on dispersion compensation and wavelength selective switch

Real-time 4×28Gb/s transmission over 110-km SMF based on di...

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Conference on Lasers and Electro-Optics/Pacific Rim, CLEOPR 2018

作者： You, Quan Li, Xiang Luo, Ming Huang, Liyan Lin, Jiasheng Wuhan National Laboratory for Optoelectronics School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China State Key Lab of optic communication Wuhan research institute of post and telecommunication China Accelink Technologies Company Ltd Wuhan China

We experimentally demonstrate real-time 4×28-Gb/s transmission over 110 km single mode fiber. Chromatic dispersion pre-compensation and post-compensation are achieved by dispersion compensation fiber and waveleng... 详细信息

ISBN: (纸本)9781943580453

关键词： Single mode fibers

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New quaternary sequences of even length with optimal auto-correlation

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science China(information sciences) 2018年第2期61卷 125-137页

作者： Wei SU Yang YANG Zhengchun ZHOU Xiaohu TANG School of Economics and Information Engineering Southwestern University of Finance and Economics Science and Technology on Communication Security Laboratory School of Mathematics Southwest Jiaotong University Provincial Key Lab of Information Coding and Transmission Institute of Mobile CommunicationsSouthwest Jiaotong University

Sequences with low auto-correlation property have been applied in code-division multiple access communication systems, radar and cryptography. Using the inverse Gray mapping, a quaternary sequence of even length N can be obtained from two binary sequences of the same length, which are called component sequences. In this paper, using interleaving method, we present several classes of component sequences from twin-prime sequences pairs or GMW sequences pairs given by Tang and Ding in 2010; or two, three or four binary sequences defined by cyclotomic classes of order 4. Hence we can obtain new classes of quaternary sequences, which are different from known ones, since known component sequences are constructed from a pair of binary sequences with optimal auto-correlation or Sidel'nikov sequences.

关键词： binary sequences quaternary sequences Gray mapping interleaving cyclotomy

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AIM 2019 challenge on real-world image super-resolution: Methods and results

arXiv

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arXiv 2019年

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Fritsche, Manuel Gu, Shuhang Purohit, Kuldeep Kandula, Praveen Suin, Maitreya Rajagopalan, A.N. Joon, Nam Hyung Won, Yu Seung Kim, Guisik Kwon, Dokyeong Hsu, Chih-Chung Lin, Chia-Hsiang Huang, Yuanfei Sun, Xiaopeng Lu, Wen Li, Jie Gao, Xinbo Bell-Kligler, Sefi Computer Vision Lab ETH Zurich Indian Institute Of Technology Madras India Image Communication & Signal Processing Laboratory Hanyang University Seoul Korea Republic of CVML Chung-Ang University Department Management Information Systems National Pingtung University of Science and Technology Department of Electrical Engineering National Cheng-Kung University Xidian University Weizmann Institute of Science Israel

This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such images while preserving the low level image characteristics of the source input domain. In Track 2: Target Domain a set of high-quality images is also provided for training, that defines the output domain and desired quality of the superresolved images. To allow for quantitative evaluation, the source input images in both tracks are constructed using artificial, but realistic, image degradations. The challenge is the first of its kind, aiming to advance the state-of-the-art and provide a standard benchmark for this newly emerging task. In total 7 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power

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