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

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Learnable low-rank latent dictionary for subspace clustering

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Pattern Recognition 2021年 120卷

作者： Yesong Xu Shuo Chen Jun Li Lei Luo Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu 210094 China RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan JD Finance America Corporation USA

Recently, Self-Expressive-based Subspace Clustering (SESC) has been widely applied in pattern clustering and machine learning as it aims to learn a representation that can faithfully reflect the correlation between data points. However, most existing SESC methods directly use the original data as the dictionary, which miss the intrinsic structure (e.g., low-rank and nonlinear) of the real-word data. To address this problem, we propose a novel Projection Low-Rank Subspace Clustering (PLRSC) method by integrating feature extraction and subspace clustering into a unified framework. In particular, PLRSC learns a projection transformation to extract the low-dimensional features and utilizes a low-rank regularizer to ensure the informative and important structures of the extracted features. The extracted low-rank features effectively enhance the self-expressive property of the dictionary. Furthermore, we extend PLRSC to a nonlinear version (i.e., NPLRSC) by integrating a nonlinear activator into the projection transformation. NPLRSC cannot only effectively extract features but also guarantee the data structure of the extracted features. The corresponding optimization problem is solved by the Alternating Direction Method (ADM), and we also prove that the algorithm converges to a stationary point. Experimental results on the real-world datasets validate the superior of our model over the existing subspace clustering methods.

关键词： Subspace clustering Low-rank Feature extraction Block diagonal representation

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A Novel LOS Rate Estimation Method Based on images for Strap-down Inertial Guidance

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Journal of Physics: Conference Series 2020年第1期1570卷

作者： Zheng Xu Haibo Luo Bin Hui Zheng Chang Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Tel.: +86-024-2397-0757 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110016 China University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Science Shenyang 110016 China The Key Lab of Image Understanding and Computer Vision Shenyang 110016 China

With the development of technology, precision guided weapon is becoming more and more important in modern war. In order to launch our recent guidance system on medium and small guided weapons, we propose a method to obtain the LOS rate by combining information from both camera and gyroscope. To be specific, we firstly calculate the body LOS angle through transforming the image pixel coordinate system into the image physical coordinate system according to camera internal parameters; then subtract the missile motion information contained in the measurement signal of the seeker and finally the LOS rate is deduced. Comparing with traditional gimballed seekers, our strap-down seekers with camera have significantly reduced costs and influences caused by external environments on platform.

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Neighborhood linear discriminant analysis

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Pattern Recognition 2022年 123卷

作者： Fa Zhu Junbin Gao Jian Yang Ning Ye College of Information Science and Technology Nanjing Forestry University Nanjing 210037 PR China Discipline of Business Analytics University of Sydney Business School University of Sydney NSW 2006 Australia PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and the Jiangsu Key Lab of Image and Video Understanding for Social Security the School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 PR China

Linear Discriminant Analysis (LDA) assumes that all samples from the same class are independently and identically distributed (i.i.d.). LDA may fail in the cases where the assumption does not hold. Particularly when a class contains several clusters (or subclasses), LDA cannot correctly depict the internal structure as the scatter matrices that LDA relies on are defined at the class level. In order to mitigate the problem, this paper proposes a neighborhood linear discriminant analysis (nLDA) in which the scatter matrices are defined on a neighborhood consisting of reverse nearest neighbors. Thus, the new discriminator does not need an i.i.d. assumption. In addition, the neighborhood can be naturally regarded as the smallest subclass, for which it is easier to be obtained than subclass without resorting to any clustering algorithms. The projected directions are sought to make sure that the within-neighborhood scatter as small as possible and the between-neighborhood scatter as large as possible, simultaneously. The experimental results show that nLDA performs significantly better than previous discriminators, such as LDA, LFDA, ccLDA, LM-NNDA, and l 2 , 1 -RLDA.

关键词： Linear discriminant analysis Reverse nearest neighbors Neighborhood linear discriminant analysis Multimodal class

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SoccerNet 2024 Challenges Results

arXiv

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arXiv 2024年

作者： Cioppa, Anthony Giancola, Silvio Somers, Vladimir Joos, Victor Magera, Floriane Held, Jan Ghasemzadeh, Seyed Abolfazl Zhou, Xin Seweryn, Karolina Kowalczyk, Mateusz Mróz, Zuzanna Lukasik, Szymon Halon, Michal Mkhallati, Hassan Deliège, Adrien Hinojosa, Carlos Sanchez, Karen Mansourian, Amir M. Miralles, Pierre Barnich, Olivier De Vleeschouwer, Christophe Alahi, Alexandre Ghanem, Bernard Van Droogenbroeck, Marc Gorski, Adam Clapés, Albert Boiarov, Andrei Afanasiev, Anton Xarles, Artur Scott, Atom Lim, ByoungKwon Yeung, Calvin Gonzalez, Cristian Rüfenacht, Dominic Pacilio, Enzo Deuser, Fabian Altawijri, Faisal Sami Cachón, Francisco Kim, HanKyul Wang, Haobo Choe, Hyeonmin Kim, Hyunwoo J. Kim, Il-Min Kang, Jae-Mo Tursunboev, Jamshid Yang, Jian Hong, Jihwan Lee, Jimin Zhang, Jing Lee, Junseok Zhang, Kexin Habel, Konrad Jiao, Licheng Li, Linyi Gutiérrez-Pérez, Marc Ortega, Marcelo Li, Menglong Lopatto, Milosz Kasatkin, Nikita Nemtsev, Nikolay Oswald, Norbert Udin, Oleg Kononov, Pavel Geng, Pei Alotaibi, Saad Ghazai Kim, Sehyung Ulasen, Sergei Escalera, Sergio Zhang, Shanshan Yang, Shuyuan Moon, Sunghwan Moeslund, Thomas B. Shandyba, Vasyl Golovkin, Vladimir Dai, Wei Chung, WonTaek Liu, Xinyu Zhu, Yongqiang Kim, Youngseo Li, Yuan Yang, Yuting Xiao, Yuxuan Cheng, Zehua Li, Zhihao Belgium King Abdullah University of Science and Technology Saudi Arabia Sportradar Switzerland UCLouvain Belgium EPFL Switzerland EVS Broadcast Equipment Belgium Baidu Research United States NASK National Research Institute Poland Belgium Sharif University of Technology Iran Footovision France Mobius Labs Institut de Robòtica i Informàtica Industrial CSIC-UPC Spain TAHAKOM Saudi Arabia AIBrain Eidos.ai United Kingdom School of Computer Science and Engineering Nanjing University of Science and Technology China Constructor.tech Universitat de Barcelona Spain Computer Vision Center Spain Aalborg University Denmark Kyungpook National University Korea Republic of Intelligent Perception and Image Understanding Lab Xidian University China Germany Korea University Korea Republic of Warsaw University of Technology Poland University of Oxford United Kingdom China Science IntelliCloud Technology Co. Ltd China Robo Space Graduate School of Informatics Nagoya University Japan Tongji University China AGH University of Krakow Poland

The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding. This year, the challenges encompass four vision-based tasks. (1) Ball Action Spotting, focusing on precisely localizing when and which soccer actions related to the ball occur, (2) Dense Video Captioning, focusing on describing the broadcast with natural language and anchored timestamps, (3) Multi-View Foul Recognition, a novel task focusing on analyzing multiple viewpoints of a potential foul incident to classify whether a foul occurred and assess its severity, (4) Game State Reconstruction, another novel task focusing on reconstructing the game state from broadcast videos onto a 2D top-view map of the field. Detailed information about the tasks, challenges, and leaderboards can be found at https://***, with baselines and development kits available at https://***/SoccerNet. © 2024, CC BY.

关键词： HTTP

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SoccerNet 2023 Challenges Results

arXiv

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arXiv 2023年

作者： Cioppa, Anthony Giancola, Silvio Somers, Vladimir Magera, Floriane Zhou, Xin Mkhallati, Hassan Deliège, Adrien Held, Jan Hinojosa, Carlos Mansourian, Amir M. Miralles, Pierre Barnich, Olivier De Vleeschouwer, Christophe Alahi, Alexandre Ghanem, Bernard Van Droogenbroeck, Marc Kamal, Abdullah Maglo, Adrien Clapés, Albert Abdelaziz, Amr Xarles, Artur Orcesi, Astrid Scott, Atom Liu, Bin Lim, Byoungkwon Chen, Chen Deuser, Fabian Yan, Feng Yu, Fufu Shitrit, Gal Wang, Guanshuo Choi, Gyusik Kim, Hankyul Guo, Hao Fahrudin, Hasby Koguchi, Hidenari Ardö, Håkan Salah, Ibrahim Yerushalmy, Ido Muhammad, Iftikar Uchida, Ikuma Be'ery, Ishay Rabarisoa, Jaonary Lee, Jeongae Fu, Jiajun Yin, Jianqin Xu, Jinghang Nang, Jongho Denize, Julien Li, Junjie Zhang, Junpei Kim, Juntae Synowiec, Kamil Kobayashi, Kenji Zhang, Kexin Habel, Konrad Nakajima, Kota Jiao, Licheng Ma, Lin Wang, Lizhi Wang, Luping Li, Menglong Zhou, Mengying Nasr, Mohamed Abdelwahed, Mohamed Liashuha, Mykola Falaleev, Nikolay Oswald, Norbert Jia, Qiong Pham, Quoc-Cuong Song, Ran Hérault, Romain Peng, Rui Chen, Ruilong Liu, Ruixuan Baikulov, Ruslan Fukushima, Ryuto Escalera, Sergio Lee, Seungcheon Chen, Shimin Ding, Shouhong Someya, Taiga Moeslund, Thomas B. Li, Tianjiao Shen, Wei Zhang, Wei Li, Wei Dai, Wei Luo, Weixin Zhao, Wending Zhang, Wenjie Yang, Xinquan Ma, Yanbiao Joo, Yeeun Zeng, Yingsen Gan, Yiyang Zhu, Yongqiang Zhong, Yujie Ruan, Zheng Li, Zhiheng Huang, Zhijian Meng, Ziyu Belgium Saudi Arabia Sportradar Norway UCLouvain Belgium EPFL Switzerland EVS Broadcast Equipment Belgium Baidu Research United States Belgium Sharif University of Technology Iran Footovision France Zewail City of Science Technology and Innovation Egypt Université Paris-Saclay CEA France Universitat de Barcelona Spain Computer Vision Center Spain Nagoya University Japan Research Center for Applied Mathematics and Machine Intelligence Zhejiang Lab China AIBrain United States OPPO Research Institute China Germany Meituan China Tencent Youtu Lab China Amazon Prime Video Sport United States Sogang University Korea Republic of The University of Tokyo Japan Spiideo Sweden University of Tsukuba Japan School of Artificial Intelligence Beijing University of Posts and Telecommunications China Normandie Univ INSA Rouen LITIS France Shanghai Jiao Tong University China Key Laboratory of Intelligent Perception and Image Understanding The Ministry of Education Xidian University China NASK - National Research Institute Poland Robo Space China Tongji University China Sportlight Technology United Kingdom School of Control Science and Engineering Shandong University China lRomul Russia Aalborg University Denmark Turing AI Cultures GmbH Germany Information Systems Technology and Design Singapore University of Technology and Design Singapore Sun Yat-sen University China

The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leader-boards are available on https://***. Baselines and development kits can be found on https://***/SoccerNet. © 2023, CC BY.

关键词： computer vision

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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Technique Report of CVPR 2024 PBDL Challenges

arXiv

引用

arXiv 2024年

作者： Lu, Xiaoqiang Jiao, Licheng Liu, Fang Liu, Xu Li, Lingling Ma, Wenping Yang, Shuyuan Xie, Haiyang Zhao, Jian Huang, Shihua Cheng, Peng Shen, Xi Wang, Zheng An, Shuai Zhu, Caizhi Li, Xuelong Chen, Linwei Fu, Ying Zhang, Tao Liu, Yu Yan, Chenggang Wang, Zichun Wang, Qinliang Liu, Yang Yu, Xinyue Jia, Sen Zhang, Junpei Chen, Puhua Chen, Xiang Li, Hao Pan, Jinshan Xie, Chuanlong Chen, Hongming Li, Mingrui Deng, Tianchen Huang, Jingwei Li, Yufeng Wan, Fei Xu, Bingxin Cheng, Jian Liu, Hongzhe Xu, Cheng Zou, Yuxiang Pan, Weiguo Dai, Songyin Jiang, Linyan Song, Bingyi An, Zhuoyu Lei, Haibo Luo, Qing Song, Jie Liu, Yuan Li, Qihang Zhang, Haoyuan Wang, Lingfeng Chen, Wei Luo, Aling Li, Cheng Cao, Jun Chen, Shu Dou, Zifei Liu, Xinyu Zhang, Jing Zhang, Kexin Yang, Yuting Zhang, Liwen Xu, Zhe Gou, Dingyong Li, Cong Xu, Senyan Zhang, Yunkang Jiang, Siyuan Liu, Qinglin Yu, Wei Lv, Xiaoqian Li, Jianing Zhang, Shengping Ji, Xiangyang Zou, Yunhao Chen, Yuanpei Zhang, Yuhan Peng, Weihang Gou, Xuejian Zhao, Shizhan Zhang, Yanzhao Yan, Libo Guo, Yuwei Li, Guoxin Gao, Qiong Che, Chenyue Sun, Long School of Artificial Intelligence Xidian University China School of Computer Science Wuhan University China Intellindust China Beijing Forestry University China China Telecom China Harbin Institute of Technology China Northwestern Polytechnical University China China Telecom China Beijing Institute of Technology China Lishui Institute of Hangzhou Dianzi University China Tsinghua University China Intelligent Perception and Image Understanding Lab Xidian University China Nanjing University of Science and Technology China Shenyang Aerospace University China Dalian University of Technology China Shanghai Jiao Tong University China University of Electronic Science and Technology of China China Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing100101 China College of Robotics Beijing Union University China Tencent China Sanechips Technology Co. LTD. China Xiaomi Inc. China ZTE Corporation China University of Science and Technology of China China Peking University China Intelligent Science & Technology Academy of CASIC China

The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to inform and enhance deep learning models, we can develop more robust and accurate vision systems. Physics-based vision aims to invert the processes to recover scene properties such as shape, reflectance, light distribution, and medium properties from images. In recent years, deep learning has shown promising improvements for various vision tasks, and when combined with physics-based vision, these approaches can enhance the robustness and accuracy of vision systems. This technical report summarizes the outcomes of the Physics-Based vision Meets Deep Learning (PBDL) 2024 challenge, held in CVPR 2024 workshop. The challenge consisted of eight tracks, focusing on Low-Light Enhancement and Detection as well as High Dynamic Range (HDR) Imaging. This report details the objectives, methodologies, and results of each track, highlighting the top-performing solutions and their innovative approaches. © 2024, CC BY.

关键词： Machine vision

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AIM 2020 challenge on efficient super-resolution: Methods and results

arXiv

引用

arXiv 2020年

作者： Zhang, Kai Danelljan, Martin Li, Yawei Timofte, Radu Liu, Jie Tang, Jie Wu, Gangshan Zhu, Yu He, Xiangyu Xu, Wenjie Li, Chenghua Leng, Cong Cheng, Jian Wu, Guangyang Wang, Wenyi Liu, Xiaohong Zhao, Hengyuan Kong, Xiangtao He, Jingwen Qiao, Yu Dong, Chao Luo, Xiaotong Chen, Liang Zhang, Jiangtao Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Li, Xiaochuan Lang, Zhiqiang Nie, Jiangtao Wei, Wei Zhang, Lei Muqeet, Abdul Hwang, Jiwon Yang, Subin Kang, JungHeum Bae, Sungho Kim, Yongwoo Chen, Liang Zhang, Jiangtao Luo, Xiaotong Qu, Yanyun Jeon, Geun-Woo Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Marty, Steven Marty, Eric Xiong, Dongliang Chen, Siang Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Wang, Haicheng Bhaskara, Vineeth Levinshtein, Alex Tsogkas, Stavros Jepson, Allan Kong, Xiangzhen Zhao, Tongtong Zhao, Shanshan Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Nan, Nan Liu, Shuai Cai, Jie Meng, Zibo Ding, Jiaming Ho, Chiu Man Wang, Xuehui Yan, Qiong Zhao, Yuzhi Chen, Long Zhang, Jiangtao Luo, Xiaotong Chen, Liang Qu, Yanyun Sun, Long Wang, Wenhao Liu, Zhenbing Lan, Rushi Umer, Rao Muhammad Micheloni, Christian Computer Vision Lab ETH Zurich Switzerland State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China MAICRO China University of Electronic Science and Technology of China China McMaster University Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Xiamen University China Indian Institute of Technology Madras India Nanjing University of Aeronautics and Astronautics Nanjing China School of Computer Science Northwestern Polytechnical University China Kyung Hee University Korea Republic of Sang Myung University Korea Republic of Xianmen University China Yonsei University Korea Republic of ETH Zurich Switzerland Zhejiang University China Qingdao Hi-image Technologies Co. Ltd [Hisense Visual Technology Co.Ltd. China Xidian University China Samsung AI Centre Toronto Canada Dalian Maritime University China China Everbright Bank Co. Ltd China College of Engineering Trivandrum India InnoPeak Technology Inc. United States SenseTime Research Hong Kong Sun Yat-sen University China City University of Hong Kong Hong Kong Guilin University of Electronic Technology Guilin541004 China University of Udine Italy

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor ×4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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Considerations and Recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 — In vivo small-animal imaging

arXiv

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arXiv 2022年

作者： Jelescu, Ileana O. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Schilling, Kurt G. Department of Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Resonance National HIgh Magnetic Field Laboratory TallahasseeFL United States Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany USC Stevens Neuroimaging and Informatics Instit

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY-NC-ND.

关键词： Invertebrates

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