检索结果-内蒙古大学图书馆

Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

引用

Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach to Geocaching Event Prognosis Using Spatiotemporal Data

A Novel Approach to Geocaching Event Prognosis Using Spatiot...

引用

International Conference on Advanced Information Networking and Applications Workshops (AINAW)

作者： Tomasz Hachaj Pawel S. Hachaj Marek R. Ogiela Pedagogical University of Krakow Institute of Computer Science and Computer Methods Krakow Poland Cracow University of Technology Institute of Water Engineering and Water Resources Krakow Poland AGH University of Science and Technology Cryptography and Cognitive Informatics Research Group Krakow Poland

In this paper we present the case study on application of data mining techniques like clustering, regression and scoring with heuristic measures for the largest social event prognosis based on spatiotemporal data. The aim of this analysis is to propose the methodology that bases on archival records and is capable to predict the place where the largest social event will take place and who will be a host (organizer) of it. For this goal we utilize the part of geocaching community data. The dataset we used is consisted of 755 events that happened from 2010-01-30 to 2015-09-05 in Poland organized by 282 distinct users. Basing on archival data of past four years we were capable to indicate not only the region in which the biggest event took place but also its host and its place within range of 700 meters. Our new algorithm and its application to geocaching events data is main novelty of this paper.

关键词： Global Positioning System Prognostics and health management Data mining Spatiotemporal phenomena Google Conferences computer science

来源：评论

学校读者我要写书评

暂无评论

Author Correction: New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries

引用

Nature genetics 2019年第6期51卷 1067页

作者： Nick Shrine Anna L Guyatt A Mesut Erzurumluoglu Victoria E Jackson Brian D Hobbs Carl A Melbourne Chiara Batini Katherine A Fawcett Kijoung Song Phuwanat Sakornsakolpat Xingnan Li Ruth Boxall Nicola F Reeve Ma'en Obeidat Jing Hua Zhao Matthias Wielscher Stefan Weiss Katherine A Kentistou James P Cook Benjamin B Sun Jian Zhou Jennie Hui Stefan Karrasch Medea Imboden Sarah E Harris Jonathan Marten Stefan Enroth Shona M Kerr Ida Surakka Veronique Vitart Terho Lehtimäki Richard J Allen Per S Bakke Terri H Beaty Eugene R Bleecker Yohan Bossé Corry-Anke Brandsma Zhengming Chen James D Crapo John Danesh Dawn L DeMeo Frank Dudbridge Ralf Ewert Christian Gieger Amund Gulsvik Anna L Hansell Ke Hao Joshua D Hoffman John E Hokanson Georg Homuth Peter K Joshi Philippe Joubert Claudia Langenberg Xuan Li Liming Li Kuang Lin Lars Lind Nicholas Locantore Jian'an Luan Anubha Mahajan Joseph C Maranville Alison Murray David C Nickle Richard Packer Margaret M Parker Megan L Paynton David J Porteous Dmitry Prokopenko Dandi Qiao Rajesh Rawal Heiko Runz Ian Sayers Don D Sin Blair H Smith María Soler Artigas David Sparrow Ruth Tal-Singer Paul R H J Timmers Maarten Van den Berge John C Whittaker Prescott G Woodruff Laura M Yerges-Armstrong Olga G Troyanskaya Olli T Raitakari Mika Kähönen Ozren Polašek Ulf Gyllensten Igor Rudan Ian J Deary Nicole M Probst-Hensch Holger Schulz Alan L James James F Wilson Beate Stubbe Eleftheria Zeggini Marjo-Riitta Jarvelin Nick Wareham Edwin K Silverman Caroline Hayward Andrew P Morris Adam S Butterworth Robert A Scott Robin G Walters Deborah A Meyers Michael H Cho David P Strachan Ian P Hall Martin D Tobin Louise V Wain Department of Health Sciences University of Leicester Leicester UK. Population Health and Immunity Division The Walter and Eliza Hall Institute of Medical Research Parkville Victoria Australia. Department of Medical Biology University of Melbourne Parkville Victoria Australia. Channing Division of Network Medicine Brigham and Women's Hospital Boston MA USA. Division of Pulmonary and Critical Care Medicine Brigham and Women's Hospital Boston MA USA. Target Sciences GlaxoSmithKline Collegeville PA USA. Department of Medicine Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand. Division of Genetics Genomics and Precision Medicine Department of Medicine University of Arizona Tucson AZ USA. Nuffield Department of Population Health University of Oxford Oxford UK. Medical Research Council Population Health Research Unit University of Oxford Oxford UK. The University of British Columbia Centre for Heart Lung Innovation St Paul's Hospital Vancouver British Columbia Canada. Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Department of Epidemiology and Biostatistics MRC-PHE Centre for Environment & Health School of Public HealthImperial College London London UK. Interfaculty Institute for Genetics and Functional Genomics Department of Functional Genomics University Medicine Greifswald Greifswald Germany. Centre for Global Health Research Usher Institute for Population Health Sciences and Informatics University of Edinburgh Edinburgh UK. Centre for Cardiovascular Sciences Queen's Medical Research Institute University of Edinburgh Edinburgh UK. Department of Biostatistics University of Liverpool Liverpool UK. MRC/BHF Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Flatiron Institute Simons Foundation New York NY USA. Busselton Population Medical Research Institute Sir Charles Gairdner Hospital Ne

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

关键词：

来源：评论

学校读者我要写书评

暂无评论

26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

引用

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

来源：评论

学校读者我要写书评

暂无评论

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

引用

arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

引用

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

来源：评论

学校读者我要写书评

暂无评论

Qualitative Evaluation of Full Body Movements with Gesture Description Language 9

Qualitative Evaluation of Full Body Movements with Gesture D...

引用

9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2015

作者： Hachaj, Tomasz Ogiela, Marek R. Pedagogical University of Krakow Institute of Computer Science and Computer Methods 2 Podchorazych Ave Krakow30-084 Poland AGH University of Science and Technology Cryptography and Cognitive Informatics Research Group 30 Mickiewicza Ave Krakow30-059 Poland

ISBN: (纸本)9781479988730

The motivation for writing this paper is to present the method for qualitative evaluation of full body movements with Gesture Description Language. By qualitative evaluation we understand the possibility of evaluating the similarity between the template of movement defined in GDL script (GDLs) and physical activity performed by observed user. The establishing of this methodology allows us to perform real time evaluation of movements accuracy defined as similarity to the statistically computed templates. The presented methodology can be used not only to perform advanced numerical analysis of movements but also to support multimedia visual feedback to various movements activities in computer applications. The initial evaluation presented in this paper proved that proposed method of qualitative analysis of movements has a potential to be used in practice. © 2015 IEEE.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

Learning from Annotated Video: An Initial Study Based on Oyama Karate Tournament Recordings

Learning from Annotated Video: An Initial Study Based on Oya...

引用

International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)

作者： Tomasz Hachaj Marek R. Ogiela Katarzyna Koptyra Pedagogical University of Krakow Institute of Computer Science and Computer Methods Krakow Poland Cryptography and Cognitive Informatics Research Group AGH University of Science and Technology Krakow

ISBN: (纸本)9781467394741

The most of the up-to-date video data are amateur films consisted of an unbroken sequence of frames recorded from a single camera. In case of karate many even most important world tournaments do not have video broadcasting and the multimedia materials are mostly unprofessional. This type of recording is very difficult to analyze with automatic annotation methods. It is due the fact that a cameraman films the whole fight from stationary position. Only with a help of experienced specialists it is possible to distinguish the karate techniques fighters perform. Due to this fact annotations have to be done mostly manually. We have observed that none of the state of the art papers deal with problem of martial-arts annotated video analysis. It seems that this kind of data might be a very good source of knowledge about a kumite (a fight), a particular fighter and his or her strategies and abilities. In this paper we propose a novel video annotation method that enables both quantitative (numerical) and qualitative (categorical) features calculation. We also present and discuss example results we can obtain from those descriptions with popular data mining methods.

关键词： Multimedia communication Semantics Feature extraction Streaming media Data mining Databases Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Human Actions Modelling and Recognition in Low-Dimensional Feature Space

Human Actions Modelling and Recognition in Low-Dimensional F...

引用

International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA)

作者： Tomasz Hachaj Marek R. Ogiela Katarzyna Koptyra Pedagogical University of Krakow Institute of Computer Science and Computer Methods Krakow Poland AGH University of Science and Technology Cryptography and Cognitive Informatics Research Group Krakow Poland

ISBN: (纸本)9781467383165

The aim of this paper is to prove that it is possible to model the set of multiple human actions in low-dimensional feature space so they can be nearly unambiguously distinguish with the help of classification algorithm. The most important condition that has to be satisfied to solve this problem is selection of proper features sets that fit to a particular actions group. We evaluate our methodology on the dataset consisted of 16 different Oyama Karate techniques performed by two professional sport (black belt) instructors and masters of Oyama Karate. The dataset consisted of 640 actions samples. As a classification algorithm we have used Gesture Description Language. We have used four different angle-based features sets. With that selection we have made actions descriptions that transformed initial motion capture dataset to 6 or 8 dimensional features space with maximally three keyframes. Beside only one class the recognition rate was at level of 88% to even 100%.

关键词： Skeleton Classification algorithms Trajectory Torso Principal component analysis Computational modeling Support vector machines

来源：评论

学校读者我要写书评

暂无评论

On Using Palm and Finger Movements as a Gesture-Based Biometrics

On Using Palm and Finger Movements as a Gesture-Based Biomet...

引用

International Conference on Intelligent Networking and Collaborative Systems (INCOS)

作者： Marcin Piekarczyk Marek R. Ogiela Pedagogical University of Cracow Institute of Computer Science and Computer Methods Krakow Poland AGH University of Science and Technology Cryptography and Cognitive Informatics Research Group Krakow Poland

In this paper we consider possibility of using palm movements as effective behavioral biometric modality. We propose to exploit the hand motion characteristics gathered from 3D sensor device as input data for identification system. Currently for various reasons people are concerned about directly touching the biometric scanners, therefore touch-less and non-invasive approach seems to be useful in practical applications. In the described scheme palm and fingertip positions are tracked in real-time, while the defined gesture is performed. The proposed gesture is composed of the spatially arranged well-known signs like letters and numbers. The applied matching algorithm utilizes a combination of DTW and DCT techniques for comparing data series. The experiments show promising results using the proposed method.

关键词： Thumb Biometrics (access control) Discrete cosine transforms Time series analysis Three-dimensional displays Handwriting recognition

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：