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arXiv 2023年

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany ETH Zürich Switzerland The Shenzhen Campus of Sun Yat-Sen University Sun Yat-Sen University China Bigo Technology Pte. Ltd Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023, CC BY-SA.

关键词： Image enhancement

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Robust visual tracking via combinative deep learning

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Journal of Physics: Conference Series 2018年第1期1074卷

作者： Jia Wen JiWei Zhao PengFei Liu Hongjun Wang The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao P.R.China Information Science and Engineering College Yanshan University Qinhuangdao P.R.China State Key Laboratory of Software Engineering of Hebei Province Yanshan University Qinhuangdao P.R.China

Object tracking is a hot topic in computer vision. In recent years, a large number of trackers has been proposed, in which the deep learning tracker has achieved excellent performance. The real-time capability of the deep learning tracker is not good enough due to the high-complexity of the network structures. This paper proposed an innovative tracking method to solve this problem. There are three important differences between this tracker and the other deep learning trackers. Firstly, the overcomplete basis in the deep learning tracker results in heavy computational cost. In order to reduce the complexity of the network, fewer units are used in the first hidden layer to replace the overcomplete basis. Secondly, a training method combining two observation models is used in the tracking process. The denoising automatic encoder is used in the first layer and the backpropagation is used in the other layers. This can avoid the diffusion of gradients which is caused by BP and adapt to the change of the targets easier. Thirdly, this tracker using adaptive particle filter to track targets. The number of particles is dynamic changes in tracking process. In this paper, we use different kinds of unlabelled datum to train network and initialize observation model. The observation model uses the samples collected in the tracking to adjust dynamically so as to adapt to the target appearance and complex environment. Compared with the existing methods, the results of experiments in different video sequences show that this tracker has a higher speed and the similar accuracy compared.

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A review of advances in image inpainting research

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The Journal of Photographic Science 2024年第5期72卷

作者： Hong-an Li Liuqing Hu Jun Liu Jing Zhang Tian Ma a School of Computer Science and Technology Xi'an University of Science and Technology Xi'an People's Republic of Chinab State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing People's Republic of China a School of Computer Science and Technology Xi'an University of Science and Technology Xi'an People's Republic of China c Shaanxi Technical College of Finance and Economics Xianyang People's Republic of China

ABSTRACTThe aim of image inpainting is to fill in damaged areas according to certain rules based on information about the adjacent positions of missing areas and the overall structure of the image, a technique that plays a key role in various tasks in computer vision. With the rapid development of deep learning, researchers have combined it with image inpainting and achieved excellent performance. To gain insight into the techniques involved, this paper summarizes the latest research advances in the field of image inpainting. Firstly, existing classical image inpainting methods are reviewed, and traditional image inpainting methods and their advantages and disadvantages are introduced. Secondly, three classical network models are outlined, and the image inpainting methods are classified into single-stage, multi-stage and a priori condition-guided approaches according to different network types and model structures. Representative algorithms among them are selected and their important technical improvement ideas are analyzed and summarized. Then, the common datasets commonly used in image inpainting tasks and the evaluation metrics used to evaluate inpainting results are introduced. The paper presents a comprehensive summary of the various algorithms in terms of network models and inpainting methods, and selects representative algorithms for quantitative and qualitative comparative analysis. Finally, the future development trends and research directions have prospected, and the current problems of image inpainting are summarized.

关键词： Image inpainting convolutional neural network generative adversarial network variational self-encoder evaluation metrics dataset

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Research on Image Feature Point Matching Based on ORB and RANSAC Algorithm

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Journal of Physics: Conference Series 2020年第1期1651卷

作者： Hua Zhang Guoxun Zheng Haohai Fu School of Computer Technology and Engineering Changchun Institute of Technology Changchun Jilin 130012 China Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Changchun Jilin 130012 China Jilin Provincial Engineering Research Center of Changbai Ecological Culture Virtual Reality Simulation Changchun Jilin 130012 China

Image matching is one of the basic problems in the field of machine vision research. In order to improve the accuracy of image feature point matching and enhance the anti-interference ability of the algorithm, for the shortcomings of traditional ORB algorithm, an image feature point matching algorithm based on ORB and RANSAC algorithm is proposed. The algorithm can filter out false matches on the basis of coarse matching results, and filter out exact matching points through the homography matrix. Experiments show that this method can effectively improve the accuracy of image matching and shorten the execution time, and it is very robust to image matching with different scales, ambiguities, brightness and rotation.

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Frontoparietal connectivity neurofeedback training for promotion of working memory: An fNIRS study in healthy male participants

arXiv

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arXiv 2020年

作者： Xia, Meiyun Xu, Pengfei Yang, Yuanbin Jiang, Wenyu Wang, Zehua Gu, Xiaolei Yang, Mingxi Li, Deyu Li, Shuyu Dong, Guijun Wang, Ling Wang, Daifa School of Biological Science and Medical Engineering Beihang University Beijing100083 China College of Computer Science Sichuan Normal University Chengdu610101 China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100083 China State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100083 China Department of Rehabilitation Medicine Wangjing Hospital China Academy of Chinese Medical Sciences Beijing100102 China Department of Neurological Rehabilitation Guangxi Jiangbin Hospital Nanning530021 China School of Sports and Health Shandong Sport University Jinan250102 China

Neurofeedback cognitive training is a promising tool used to promote cognitive functions effectively and efficiently. In this study, we investigated a novel functional near-infrared spectroscopy (fNIRS)based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory, involving healthy adults. Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation, as neurofeedback, yielding a strong targeting effect. In the experiment, 10 participants (test group) received three cognitive training sessions of 15 min using fNIRS-based frontoparietal FC as neurofeedback, and another 10 participants served as the control group. Frontoparietal FC was significantly increased in the test group (p = 0.03), and the cognitive functions (memory and attention) were significantly promoted compared with the control group (accuracy of 3-back test: p = 0.0005, reaction time of 3-back test: p = 0.0009). After additional validations on long-term training effect and on different patient populations, the proposed method exhibited considerable potential to be developed as a fast, effective, and widespread training approach for cognitive function enhancement. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain

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Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer

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Chinese Medical Journal 2019年第4期132卷 379-387页

作者： Lei Ding Guang-Wei Liu Bao-Chun Zhao Yun-Peng Zhou Shuai Li Zheng-Dong Zhang Yu-Ting Guo Ai-Qin Li Yun Lu Hong-Wei Yao Wei-Tang Yuan Gui-Ying Wang Dian-Liang Zhang Lei Wang Shandong Key Laboratory of Digital Medicine & Computer Assisted Surgery Qingdao UniversityQingdaoShandong 266003China Department of General Surgery Qingdao Municipal HospitalQingdaoShandong 266011China Department of General Surgery The Sixth Affiliated Hospital of Sun Yat-Sen UniversityGuangzhouGuangdong 510655China. Department of Medical Administration Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdaoShandong 266003China Department of Outpatient Administration Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdaoShandong 266003China Department of Follow-up Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdaoShandong 266003China Department of Gastroenterology Surgery Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdaoShandong 266003China State Key Laboratory of Virtual Reality Technology &Systems Beihang UniversityBeijing 100191China Department of General Surgery Beijing Friendship HospitalCapital Medical University & National Clinical Research Center for Digestive DiseasesBeijing 100050China Department of General Surgery First Affiliated Hospital of Zhengzhou UniversityZhenzhouHenan 450052China Department of General Surgery Fourth Hospital of Hebei Medical UniversityShijiazhuangHebei 050011China

Background: An artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis of metastatic lymph node (LN) in rectal cancer patients. The primary objective of this study was to comprehensively verify its accuracy in clinical use. Methods: Four hundred fourteen patients with rectal cancer discharged between January 2013 and March 2015 were collected from 6 clinical centers, and the magnetic resonance imaging data for pelvic metastatic LNs of each patient was identified by Faster R-CNN. Faster R-CNN based diagnoses were compared with radiologist based diagnoses and pathologist based diagnoses for methodological verification, using correlation analyses and consistency check. For clinical verification, the patients were retrospectively followed up by telephone for 36 months, with post-operative recurrence of rectal cancer as a clinical outcome;recurrence-free survivals of the patients were compared among different diagnostic groups, by methods of Kaplan-Meier and Cox hazards regression model. Results: Significant correlations were observed between any 2 factors among the numbers of metastatic LNs separately diagnosed by radiologists, Faster R-CNN and pathologists, as evidenced by rradiologist-Faster R-CNN of 0.912, rPathologist-radiologist of 0.134, and rPathologist-Faster R-CNN of 0.448 respectively. The value of kappa coefficient in N staging between Faster R-CNN and pathologists was 0.573, and this value between radiologists and pathologists was 0.473. The 3 groups of Faster R-CNN, radiologists and pathologists showed no significant differences in the recurrence-free survival time for stage N0 and N1 patients, but significant differences were found for stage N2 patients. Conclusion: Faster R-CNN surpasses radiologists in the evaluation of pelvic metastatic LNs of rectal cancer, but is not on par with pathologists.

关键词： AI (Artificial Intelligence) Magnetic resonance imaging Pathology Lymph nodes Rectal cancer

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一种基于弱标签的三维模型语义自动标注方法

一种基于弱标签的三维模型语义自动标注方法

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第十二届中国虚拟现实大会

作者： TIAN Feng 田枫 SHENXu-kun 沈旭昆 LIU Xian-mei 刘贤梅 ZHOU Kai 周凯 DU Rui-shan 杜睿山 The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 1001 北京航空航天大学虚拟现实技术与系统国家重点实验室北京 100191 东北石油大学计算机与信息技术学院大庆 163318 The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 1001 北京航空航天大学虚拟现实技术与系统国家重点实验室北京 100191 School of Computer and Information Technology Northeast Petroleum University Daqing 163318 China 东北石油大学计算机与信息技术学院大庆 163318

随着三维应用的普及,三维模型大量产生并广泛传播。由于三维模型广泛应用于计算机辅助设计、三维游戏、电影特效制作等诸多领域,已经形成了大量的三维模型数据库。三维模型语义标注的目的是给出描述其语义的标注词,是三维模型管理和基... 详细信息

随着三维应用的普及,三维模型大量产生并广泛传播。由于三维模型广泛应用于计算机辅助设计、三维游戏、电影特效制作等诸多领域,已经形成了大量的三维模型数据库。三维模型语义标注的目的是给出描述其语义的标注词,是三维模型管理和基于文本的三维检索的关键技术。针对互联网大量存在的弱标签三维模型现状,提出一种基于弱标签的三维模型语义标注方法LPMLL,首先,采用半监督学习方法进行标签传播,得到标注词置信度,达到提升训练集的目的。然后,采用一种基于最大后验概率准则的方法进行多标签学习,得到最终标注词。实验数据表明了该方法的有效性。

关键词：信息处理三维模型语义标注技术弱标签文本检索

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Correction to: Automatic differentiation of Glaucoma visual field from non-glaucoma visual field using deep convolutional neural network

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BMC medical imaging 2019年第1期19卷 40页

作者： Fei Li Zhe Wang Guoxiang Qu Diping Song Ye Yuan Yang Xu Kai Gao Guangwei Luo Zegu Xiao Dennis S C Lam Hua Zhong Yu Qiao Xiulan Zhang Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology Sun Yat-sen University Guangzhou China. SenseTime Group Limited Hong Kong China. Guangdong key lab of Computer Vision & Virtual Reality Multimedia Research Center Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China. C-MER Dennis Lam Eye Hospital Shenzhen China. Department of Ophthalmology The First Affiliated Hospital of Kunming Medical University Kunming China. Department of Ophthalmology The First Affiliated Hospital of Kunming Medical University Kunming China. zhoculist@***. Guangdong key lab of Computer Vision & Virtual Reality Multimedia Research Center Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China. yu.qiao@***. Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology Sun Yat-sen University Guangzhou China. zhangxl2@***.

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Geographic information science in the era of geospatial big data: A cyberspace perspective

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The Innovation 2022年第5期3卷 21-22页

作者： Xintao Liu Min Chen Christophe Claramunt Michael Batty Mei-Po Kwan Ahmad MSenousi Tao Cheng Josef Strobl Arzu Cöltekin John Wilson Temenoujka Bandrova Milan Konecny Paul MTorrens Fengyuan Zhang Li He Jinfeng Wang Carlo Ratti Olaf Kolditz Alexander Klippel Songnian Li Hui Lin Guonian Lu Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic UniversityHong Kong SARKowloonChina Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China The French Naval Academy Research Institute 29240 Lanveoc-PoulmicFrance The Bartlett Centre for Advanced Spatial Analysis(CASA) University College London(UCL)WC1E 6BT LondonUK Institute of Space and Earth Information Science The Chinese University of Hong KongHong Kong SARShatin 999077China Department of Civil Engineering Cairo University12613 CairoEgypt SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College London(UCL)WC1E 6BT LondonUK Department of Geoinformatics University of Salzburg5020 SalzburgAustria Institute of Interactive Technologies(IIT) University of Applied Sciences and Arts Northwestern Switzerland FHNW5210 WindischSwitzerland Spatial Sciences Institute University of Southern CaliforniaLos AngelesCA 90007USA Laboratory on Cartography Department of Photogrammetry and CartographyUniversity of ArchitectureCivil Engineering and Geodesy1164 SofiaBulgaria Laboratory on Geoinformatics and Cartography Department of GeographyMasaryk University61137 BrnoCeskáRepublika Computer Science and Engineering Tandon School&Center for Urban Science+ProgressNew York UniversityNew YorkNY 10012USA School of Humanities and Social Science Xi’an Jiaotong UniversityXi’an 710049China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Urban Studies and Planning Massachusetts Institute of TechnologyCambridgeMA 02139USA Centre for Environmental Research Helmholtz Centre for Environmental Research-UFZ04318 LeipzigGermany Laboratory of Geo-information Science and Remote Sensing Wageningen University&ResearchWageningen 6708the Netherlands Department of Civil Engineering Toronto Metropolitan UniversityTorontoON M5B 2K3Canada School of Geography and Env

The advent of information and communication technology and the Internet of Things have led our society toward a digital *** proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to *** transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIScience).In the last two decades,“cyber thinking”in GIScience has received significant attention from different *** instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.

关键词： computer cyber instance

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基于LOD的地物与地形融合与绘制

基于LOD的地物与地形融合与绘制

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第六届智能CAD与数字娱乐学术会议

作者： Chen Guojun 陈国军 Chen Qinggang 陈庆刚 Cui Baolei 崔保磊 College of Computer and Communication Engineering China University of Petroleum Dongying 257061Ch 中国石油大学计算机与通信工程学院山东东营257061 State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191C 北京航空航天大学虚拟现实技术与系统国家重点实验室北京100191

地表特征物与地形的融合对三维虚拟环境真实感有重要作用。核心问题是特征物模型与地形模型在数据层次的融合。本文针对基于规则网格绘制的地形与地表特征物的融合问题，提出一种通过扩展变化单元格分辨率，用更精细的三角形的描述特征... 详细信息

地表特征物与地形的融合对三维虚拟环境真实感有重要作用。核心问题是特征物模型与地形模型在数据层次的融合。本文针对基于规则网格绘制的地形与地表特征物的融合问题，提出一种通过扩展变化单元格分辨率，用更精细的三角形的描述特征物轮廓，结合视点相关的LOD简化技术建立地形和特征物的多分辨率模型的方法，并增加过渡区域解决由相邻单元格分辨率不一致导致的裂缝问题。实验表明，该方法的融合效果逼真，且对地形实时绘制影响较小。

关键词：数据融合细节层次模型图形绘制虚拟环境

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