检索结果-内蒙古大学图书馆

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Statistical physics of learning and inference 27

Statistical physics of learning and inference

引用

27th European Symposium on Artificial Neural Networks, computational intelligence and Machine Learning, ESANN 2019

作者： Biehl, M. Caticha, N. Opper, M. Villmann, T. Univ. of Groningen Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Nijenborgh 9 GroningenNL-9747 AG Netherlands Instituto de Física Universidade de São Paulo Caixa Postal 66318 São Paulo SP05315-970 Brazil Technical University Berlin Department of Electrical Engineering and Computer Science BerlinD-10587 Germany University of Applied Sciences Mittweida Computational Intelligence Group Technikumplatz 17 MittweidaD-09648 Germany

ISBN: (纸本)9782875870650

The exchange of ideas between statistical physics and computer science has been very fruitful and is currently gaining momentum as a consequence of the revived interest in neural networks, machine learning and inference in general. Statistical physics methods complement other approaches to the theoretical understanding of machine learning processes and inference in stochastic modeling. They facilitate, for instance, the study of dynamical and equilibrium properties of randomized training processes in model situations. At the same time, the approach inspires novel and efficient algorithms and facilitates interdisciplinary applications in a variety of scientific and technical disciplines. © 2019 ESANN (***). All rights reserved.

关键词： Statistical Physics

来源：评论

学校读者我要写书评

暂无评论

Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)

arXiv

引用

arXiv 2020年

作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Hussain, Sadiq Khosravi, Abbas Koohestani, Afsaneh Zangooei, Mohammad Hossein Abdar, Moloud Beykikhoshk, Adham Shoeibi, Afshin Zare, Assef Panahiazar, Maryam Nahavandi, Saeid Srinivasan, Dipti Atiya, Amir F. Acharya, U. Rajendra Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran System Administrator Dibrugarh University Assam786004 India University of Texas Dallas United States Applied Artificial Intelligence Institute Deakin University Geelong Australia Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Computational Health Sciences University of California San Francisco United States Dept. of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Department of Computer Engineering Faculty of Engineering Cairo University Cairo12613 Egypt Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread application for this purpose in different fields. One critically important yet less explored aspect is how data and model uncertainties are captured and analyzed. Proper quantification of uncertainty provides valuable information for optimal decision making. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, application of novel deep learning techniques to deal such uncertainties have significantly increased. Copyright © 2020, The Authors. All rights reserved.

关键词： Monte Carlo methods

来源：评论

学校读者我要写书评

暂无评论

Probabilistic load-margin assessment using vine copula and gaussian process emulation

arXiv

引用

arXiv 2020年

作者： Xu, Yijun Karra, Kiran Mili, Lamine Korkali, Mert Chen, Xiao Hu, Zhixiong Bradley Department of Electrical and Computer Engineering Virginia Tech Northern Virginia Center Falls ChurchVA22043 United States Applied Physics Lab Johns Hopkins University BaltimoreMD21218 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Statistics University of California-Santa Cruz Santa CruzCA95064 United States

The increasing penetration of renewable energy along with the variations of the loads bring large uncertainties in the power system states that are threatening the security of power system planning and operation. Facing these challenges, this paper proposes a cost-effective, nonparametric method to quantify the impact of uncertain power injections on the load margins. First, we propose to generate system uncertain inputs via a novel vine copula due to its capability in simulating complex multivariate highly dependent model inputs. Furthermore, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. The simulations conducted on the IEEE 57-bus system, to which correlated renewable generation are attached, reveal the excellent performance of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

来源：评论

学校读者我要写书评

暂无评论

Quantum Simulators: Architectures and Opportunities

引用

PRX Quantum 2021年第1期2卷 017003-017003页

作者： Ehud Altman Kenneth R. Brown Giuseppe Carleo Lincoln D. Carr Eugene Demler Cheng Chin Brian DeMarco Sophia E. Economou Mark A. Eriksson Kai-Mei C. Fu Markus Greiner Kaden R.A. Hazzard Randall G. Hulet Alicia J. Kollár Benjamin L. Lev Mikhail D. Lukin Ruichao Ma Xiao Mi Shashank Misra Christopher Monroe Kater Murch Zaira Nazario Kang-Kuen Ni Andrew C. Potter Pedram Roushan Mark Saffman Monika Schleier-Smith Irfan Siddiqi Raymond Simmonds Meenakshi Singh I.B. Spielman Kristan Temme David S. Weiss Jelena Vučković Vladan Vuletić Jun Ye Martin Zwierlein Department of Physics University of California Berkeley Carolina 94720 USA Department of Electrical and Computer Engineering Department of Physics and Department of Chemistry Duke University Durham North Carolina 27708 USA Center for Computational Quantum Physics Flatiron Institute New York New York 10010 USA Department of Physics Colorado School of Mines Golden Colorado 80401 USA Department of Physics Harvard University Cambridge Massachusetts 02138 USA Department of Physics Chicago University Chicago Illinois 60637 USA Department of Physics and IQUIST University of Illinois Urbana-Champaign Illinois 61801 USA Department of Physics Virgina Tech Blacksburg Virginia 24061 USA Department of Physics University of Wisconsin-Madison Madison Wisconsin 53706 USA Department of Electrical and Computer Engineering and Department of Physics University of Washington Seattle Washington 98195 USA Department of Physics and Astronomy Rice University Houston Texas 77005 USA Joint Quantum Institute and Department of Physics University of Maryland College Park Maryland 20742 USA and IonQ Inc. College Park Maryland 20740 USA Department of Physics and Department of Applied Physics Stanford University Stanford California 94305 USA Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Google Inc. Santa Barbara California 93101 USA Sandia National Laboratory Albuquerque New Mexico 87123 USA Department of Physics Washington University St. Louis Missouri 63130 USA IBM T.J. Watson Research Center Yorktown Heights New York 10598 USA Department of Chemistry and Chemical Biology Harvard University Cambridge Massachusetts 02138 USA Department of Physics The University of Texas at Austin Austin Texas 78712 USA National Institute of Standards and Technology Boulder Colorado 80305 USA Department of Physics The Pennsylvania State University University Park Pennsylvania 16802 USA Department of Electrical Engineering and Ginzto

Quantum simulators are a promising technology on the spectrum of quantum devices from specialized quantum experiments to universal quantum computers. These quantum devices utilize entanglement and many-particle behavior to explore and solve hard scientific, engineering, and computational problems. Rapid development over the last two decades has produced more than 300 quantum simulators in operation worldwide using a wide variety of experimental platforms. Recent advances in several physical architectures promise a golden age of quantum simulators ranging from highly optimized special purpose simulators to flexible programmable devices. These developments have enabled a convergence of ideas drawn from fundamental physics, computer science, and device engineering. They have strong potential to address problems of societal importance, ranging from understanding vital chemical processes, to enabling the design of new materials with enhanced performance, to solving complex computational problems. It is the position of the community, as represented by participants of the National Science Foundation workshop on “Programmable Quantum Simulators,” that investment in a national quantum simulator program is a high priority in order to accelerate the progress in this field and to result in the first practical applications of quantum machines. Such a program should address two areas of emphasis: (1) support for creating quantum simulator prototypes usable by the broader scientific community, complementary to the present universal quantum computer effort in industry; and (2) support for fundamental research carried out by a blend of multi-investigator, multidisciplinary collaborations with resources for quantum simulator software, hardware, and *** document is a summary from a U.S. National Science Foundation supported workshop held on 16–17 September 2019 in Alexandria, VA. Attendees were charged to identify the scientific and community needs, opportunities, and signi

关键词： Quantum information architectures & platforms Quantum simulation

来源：评论

学校读者我要写书评

暂无评论

Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

引用

arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

来源：评论

学校读者我要写书评

暂无评论

Sex Differences in Heart Failure Following Acute Coronary Syndromes

JACC: Advances

引用

JACC: Advances 2023年第3期2卷 100294-100294页

作者： Cenko, Edina Manfrini, Olivia Yoon, Jinsung van der Schaar, Mihaela Bergami, Maria Vasiljevic, Zorana Mendieta, Guiomar Stankovic, Goran Vavlukis, Marija Kedev, Sasko Miličić, Davor Badimon, Lina Bugiardini, Raffaele Laboratory of Epidemiological and Clinical Cardiology Department of Medical and Surgical Sciences University of Bologna Bologna Italy IRCCS Azienda Ospedaliero-Universitaria di Bologna Sant'Orsola Hospital Bologna Italy Google Cloud AI Sunnyvale CA United States Department of Electrical and Computer Engineering University of California Los Angeles Los Angeles CA United States Department of Applied Mathematics and Theoretical Physics and Department of Population Health Cambridge Centre for Artificial Intelligence in Medicine University of Cambridge Cambridge United Kingdom Medical Faculty University of Belgrade Belgrade Serbia Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC) Madrid Spain Clinic of Cardiology University Clinical Centre of Serbia Belgrade Serbia University Clinic for Cardiology Skopje North Macedonia Faculty of Medicine Ss. Cyril and Methodius University in Skopje Skopje North Macedonia Department for Cardiovascular Diseases University Hospital Center Zagreb University of Zagreb Zagreb Croatia Cardiovascular Research Program ICCC IR-IIB Sant Pau Hospital de la Santa Creu i Sant Pau CiberCV-Institute Carlos III Barcelona Spain

Background: There have been conflicting reports regarding outcomes in women presenting with an acute coronary syndrome (ACS). Objectives: The objective of the study was to examine sex-specific differences in 30-day mortality in patients with ACS and acute heart failure (HF) at the time of presentation. Methods: This was a retrospective study of patients included in the International Survey of Acute Coronary Syndromes-ARCHIVES (ISACS-ARCHIVES;NCT04008173). Acute HF was defined as Killip classes ≥2. Participants were stratified according to ACS presentation: ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation ACS (NSTE-ACS). Differences in 30-day mortality and acute HF presentation at admission between sexes were examined using inverse propensity weighting based on the propensity score. Estimates were compared by test of interaction on the log scale. Results: A total of 87,812 patients were included, of whom 30,922 (35.2%) were women. Mortality was higher in women compared with men in those presenting with STEMI (risk ratio [RR]: 1.65;95% CI: 1.56-1.73) and NSTE-ACS (RR: 1.18;95% CI: 1.09-1.28;P_interaction <0.001). Acute HF was more common in women when compared to men with STEMI (RR: 1.24;95% CI: 1.20-1.29) but not in those with NSTE-ACS (RR: 1.02;95% CI: 0.97-1.08) (P_interaction <0.001). The presence of acute HF increased the risk of mortality for both sexes (odds ratio: 6.60;95% CI: 6.25-6.98). Conclusions: In patients presenting with ACS, mortality is higher in women. The presence of acute HF at hospital presentation increases the risk of mortality in both sexes. Women with STEMI are more likely to present with acute HF and this may, in part, explain sex differences in mortality. These findings may be helpful to improve sex-specific personalized risk stratification. © 2023 The Authors

关键词： acute heart failure outcomes sex differences

来源：评论

学校读者我要写书评

暂无评论

An extreme test case for planet formation: a close-in Neptune orbiting an ultracool star

arXiv

引用

arXiv 2023年

作者： Stefánsson, Guðmundur Mahadevan, Suvrath Miguel, Yamila Robertson, Paul Delamer, Megan Kanodia, Shubham Cañas, Caleb Winn, Joshua Ninan, Joe R., Terrien R., Holcomb E., Ford B., Zawadzki B.P., Bowler C., Bender W., Cochran S., Diddams M., Endl C., Fredrick S., Halverson F., Hearty G.J., Hill A., Lin A., Metcalf A., Monson L., Ramsey A., Roy C., Schwab J., Wright G., Zeimann Princeton University Department of Astrophysical Sciences United States Department of Astronomy & Astrophysics The Pennsylvania State University United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Leiden Observatory University of Leiden Netherlands SRON Netherlands Institute for Space Research Netherlands Department of Physics & Astronomy University of California Irvine United States Earth and Planets Laboratory Carnegie Institution for Science United States NASA Goddard Space Flight Center United States Department of Astronomy & Astrophysics Tata Institute of Fundamental Research India Carleton College United States Center for Astrostatistics The Pennsylvania State University United States Institute for Computational & Data Sciences The Pennsylvania State University United States Department of Astronomy The University of Texas at Austin AustinTX78712 United States Steward Observatory The University of Arizona United States Center for Planetary Systems Habitability and McDonald Observatory UT Austin United States Electrical Computer and Energy Engineering University of Colorado United States National Institute of Standards & Technology United States Department of Physics University of Colorado United States Jet Propulsion Laboratory California Institute of Technology United States McDonald Observatory UT Austin United States Space Vehicles Directorate Air Force Research Laboratory United States Space Telescope Science Institute United States Department of Physics and Astronomy Johns Hopkins University United States School of Mathematical and Physical Sciences Macquarie University Australia Penn State Extraterrestrial Intelligence Center The Pennsylvania State University United States Hobby-Eberly Telescope University of Texas Austin AustinTX78712 United States

In current theories of planet formation, close-orbiting planets as massive as Neptune are expected to be very rare around low-mass stars. We report the discovery of a Neptune-mass planet orbiting the 'ultracool' star LHS 3154, which is nine times less massive than the Sun. The planet's orbital period is 3.7 days and its minimum mass is 13.2 Earth masses, giving it the largest known planet-to-star mass ratio among short-period planets ( Copyright © 2023, The Authors. All rights reserved.

关键词： Planets

来源：评论

学校读者我要写书评

暂无评论

CUID: A New Study Of Perceived Image Quality And Its Subjective Assessment

CUID: A New Study Of Perceived Image Quality And Its Subject...

引用

IEEE International Conference on Image Processing

作者： Lucie Lévêque Ji Yang Xiaohan Yang Pengfei Guo Kenneth Dasalla Leida Li Yingying Wu Hantao Liu Laboratory of Ergonomics and Cognitive Sciences applied to Transport Gustave Eiffel University Lyon France School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China School of Computational Science Zhongkai University of Agriculture and Engineering Guangzhou China Department of Computer Science University of Bath Bath United Kingdom School of Artificial Intelligence Xidian University Xi’an China School of Mathematics Cardiff University Cardiff United Kingdom

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Research on image quality assessment (IQA) remains limited mainly due to our incomplete knowledge about human visual perception. Existing IQA algorithms have been designed or trained with insufficient subjective data with a small degree of stimulus variability. This has led to challenges for those algorithms to handle complexity and diversity of real-world digital content. Perceptual evidence from human subjects serves as a grounding for the development of advanced IQA algorithms. It is thus critical to acquire reliable subjective data with controlled perception experiments that faithfully reflect human behavioural responses to distortions in visual signals. In this paper, we present a new study of image quality perception where subjective ratings were collected in a controlled lab environment. We investigate how quality perception is affected by a combination of different categories of images and different types and levels of distortions. The database will be made publicly available to facilitate calibration and validation of IQA algorithms.

关键词： Image quality Measurement Visualization Databases Distortion Reliability Visual perception

来源：评论

学校读者我要写书评

暂无评论

CUID: A new study of perceived image quality and its subjective assessment

arXiv

引用

arXiv 2020年

作者： Lévêque, Lucie Yang, Ji Yang, Xiaohan Guo, Pengfei Dasalla, Kenneth Li, Leida Wu, Yingying Liu, Hantao Laboratory Ergonomics and Cognitive Sciences applied to Transport Gustave Eiffel University Lyon France School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China School of Computational Science Zhongkai University of Agriculture and Engineering Guangzhou China Department of Computer Science University of Bath Bath United Kingdom School of Artificial Intelligence Xidian University Xi’an China School of Mathematics Cardiff University Cardiff United Kingdom

关键词： Vision

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：