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IEEE International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Yimu Ji Shuai Chen Haichang Yao Houzhi Fang Kui Li Shangdong Liu Zhengyuan Xie Kairui Wang School of Computer Science Nanjing University of Posts and Telecommunications Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing Center of HPC China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications School of Computer and Software Nanjing Institute of Industry Technology Nanjing China University of Toronto Toronto Canada BSc Mathematics with Finance University of Liverpool Liverpool United Kingdom

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

At present, there are many excellent genome compression algorithms with high genome compression ratio. However, there is a lack of highly efficient compression algorithms for simultaneous compression of a large number of genomes. This manuscript presents an algorithm, which is called FastLNGC, for simultaneous compression of a large amount of genome data based on multi-thread concurrency. This algorithm is based on the LNGC (Large Number of Genomes Compressor) algorithm, and adopts multi-thread technology to achieve concurrent processing of genome data compression. A large number of experiments show that FastLNGC has better performance on compression of a large number of genes. The source code of FastLNGC is available at https://***/APandaThief/FastLNGC.

关键词： Bioinformatics Genomics Compression algorithms Instruction sets Telecommunications computer science Concurrent computing

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

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NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Poor-prognosis young-onset colorectal cancer is defined by the mesenchymal subtype and can be predicted by integrating molecular and histopathological characteristics

ESMO Gastrointestinal Oncology

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ESMO Gastrointestinal Oncology 2025年 9卷

作者： J. Ke Y. Li L. Qi X. Li W. Wang S. Ten Hoorn Y. Zhu H. Huang F. Gao L. Vermeulen X. Wang Department of Colorectal Surgery The Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China Guangdong Institute of Gastroenterology Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China College of Computer Science and Technology Taiyuan University of Technology Taiyuan China Department of Biomedical Sciences City University of Hong Kong Kowloon Tong Hong Kong SAR Department of Surgery The Chinese University of Hong Kong Sha Tin Hong Kong SAR Li Ka Shing Institute of Health Sciences The Chinese University of Hong Kong Sha Tin Hong Kong SAR Department of Pathology The First Affiliated Hospital of Anhui Medical University Hefei China Laboratory for Experimental Oncology and Radiobiology (LEXOR) Center for Experimental Molecular Medicine (CEMM) Academic Medical Center (AMC) University of Amsterdam Amsterdam The Netherlands National Clinical Research Centre for Geriatric Disorders Department of Geriatrics Xiangya Hospital Central South University Changsha Hunan China State Key Laboratory of Digital Medical Engineering School of Biological Science and Medical Engineering Southeast University Nanjing China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

Background Young-onset colorectal cancer (CRC), affecting individuals <50 years of age, presents a significant health threat worldwide. The molecular and clinical characteristics of young-onset CRC are poorly understood, complicating the development of effective biomarkers for precision oncology. This study aimed to dissect age-dependent molecular heterogeneity of CRC and establish a model for identifying high-risk young-onset patients. Methods We analyzed clinical data for 564 439 patient samples across three large cohorts. For molecular characterizations, a subset of 1874 patient samples was used. A deep learning framework was used to analyze hematoxylin–eosin-stained whole-slide images to quantify Shannon diversity indices (SDIs). Subsequently, a multivariate model, integrating SDI, microsatellite status and promoter methylation of miR-200s, was developed for predicting the consensus molecular subtype (CMS)4-mesenchymal subtype, followed by internal and external clinical validations. Results Young-onset CRC patients exhibited better overall survival but worse relapse-free survival and higher metastasis rates compared with late-onset cases. Molecular subtyping analysis found that young-onset CRC also comprises the same four subtypes (CMS1-4), but the prevalence differs from late-onset CRC. Stratified analysis suggested that the poor outcomes in young-onset CRC were due to higher prevalence of the CMS4-mesenchymal subtype. To predict CMS4, we established an effective risk-scoring model (area under the curve = 0.87) combining molecular and histological markers, with multiple independent validations. Conclusions CRC shows age-dependent molecular heterogeneity, with young-onset cases more frequently presenting the CMS4 subtype. To predict CMS4, we developed and validated a robust risk-scoring model integrating molecular and histological markers, offering a new translatable tool for more optimized management of young-onset patients.

关键词： colorectal cancer young-onset patients consensus molecular subtypes tumor heterogeneity subtype-specific biomarker

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The Unconstrained Ear Recognition Challenge 2019

The Unconstrained Ear Recognition Challenge 2019

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IAPR International Conference on Biometrics (ICB)

作者： Ž. Emeršič A. Kumar S. V. B. S. Harish W. Gutfeter J. N. Khiarak A. Pacut E. Hansley M. Pamplona Segundo S. Sarkar H. J. Park G. P. Nam I.-J. Kim S. G. Sangodkar U. Kacar M. Kirci L. Yuan J. Yuan H. Zhao F. Lu J. Mao X. Zhang D. Yaman F. I. Eyiokur K. B. Özler H. K. Ekenel D. Paul Chowdhury S. Bakshi P. K. Sa B. Majhi P. Peer V. Štruc University of Ljubljana (UL SI) Nitte Mahalinga Adyanthaya Memorial Institute of Technology (NMAM IT IN) Jagadguru Sri Shivarathreeshwara Science & Technology University (JSS STU IN) Research an Academic Computer Network (NASK PL) Warsaw University of Technology (WUT PL) University of South Florida (USF USA) Federal University of Bahia (UFBA BR) Korea Institute of Science and Technology (KIST KR) Indian Institute of Technology Bombay (IITB IN) Istanbul Technical University (ITU TR) University of Science and Technology Beijing (USTB CN) National Institute of Technology Rourkela (NITR IN)

This paper presents a summary of the 2019 Unconstrained Ear Recognition Challenge (UERC), the second in a series of group benchmarking efforts centered around the problem of person recognition from ear images captured in uncontrolled settings. The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i.e. gender and ethnicity. research groups from 12 institutions entered the competition and submitted a total of 13 recognition approaches ranging from descriptor-based methods to deep-learning models. The majority of submissions focused on ensemble based methods combining either representations from multiple deep models or hand-crafted with learned image descriptors. Our analysis shows that methods incorporating deep learning models clearly outperform techniques relying solely on hand-crafted descriptors, even though both groups of techniques exhibit similar behavior when it comes to robustness to various covariates, such presence of occlusions, changes in (head) pose, or variability in image resolution. The results of the challenge also show that there has been considerable progress since the first UERC in 2017, but that there is still ample room for further research in this area.

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How transcranial direct current stimulation facilitates post-stroke rehabilitation

arXiv

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arXiv 2019年

作者： Qi, Feng Hong, Xin Lu, Zhong-Kang Teh, Irvin Teo, Wei-Peng Ang, Kai-Keng Phua, Kok-Soon Guan, Cun-Tai Chew, Effie Nasrallah, Fatima Oxford Centre for Functional MRI of the Brain Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford United Kingdom Clinical Imaging Research Center Agency for Science Technology and Research National University of Singapore Singapore Department of Diagnostic Radiology Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Bioimaging Consortium Agency for Science Technology and Research Singapore Institute for Infocomm Research Agency for Science Technology and Research Singapore Institute for Physical Activity and Nutrition Deakin University Australia Physical Education and Sports Science Academic Group National Institute of Education Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

Current treatments for functional loss of the upper extremity post-stroke remain limited in efficacy, particularly for those with moderate to severe impairment. The non-invasive treatment of transcranial direct current stimulation (tDCS) is a promising intervention technique being tested widely in clinical trials, however, whether tDCS benefits stroke rehabilitation and how tDCS modulates synaptic plasticity are largely unknown. In this study (***, NTC01897025), nineteen chronic stroke patients with moderate-to-severe upper limb disability (Fugl-Meyer assessment = 33.9 ± 8.1, out of 66) were recruited for a two-week treatment of combined bi-hemispherical tDCS (anodal electrode placed over affected M1, while cathode over the unaffected M1) and motor imagery based robotic arm training for stroke rehabilitation. Each patient participated in 10 successive rehabilitation sessions, including 20 minutes real/sham tDCS stimulation, followed by 60 minutes of robot assisted arm movement therapy. The primary outcome measure was the upper extremity component of Fugl-Meyer assessment, while the secondary outcome measures included resting motor threshold (RMT) of stroke affected M1 motor cortex, the whole brain longitudinal activity map, and Granger causality graph derived from task-related functional MRI. The RMT measurements showed tDCS evoked higher excitability (lower RMT) in the motor cortex (DRMTtDCS - DRMTsham = -11.6%, P = 0.04) that enhanced the descending conduction from the lesioned primary motor cortex to the target hand muscle. Granger causality analysis further revealed the enhanced brain circuitry rewiring from lesioned cerebellum to premotor (DFtDCSCer_L->PreM_R = +0.0112 ± 0.0128, P = 0.0304), and from lesioned premotor to primary motor cortex (DFtDCSPreM_R->PriM_R = +0.0077 ± 0.0100, P = 0.0497) in the tDCS group only owing to the newly formed connections close to the anodal electrode. Rebuilding of these critical pathways was clear via the in

关键词： Magnetic resonance imaging

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Corrigendum to “Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models” [Renew Sustain Energy Rev 179 (2023) 113273]

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Renewable and Sustainable Energy Reviews 2023年 185卷

作者： J.M. Schwidtal P. Piccini M. Troncia R. Chitchyan M. Montakhabi C. Francis A. Gorbatcheva T. Capper M.A. Mustafa M. Andoni V. Robu M. Bahloul I.J. Scott T. Mbavarira J.M. España L. Kiesling Department of Industrial Engineering University of Padua Via Giovanni Gradengio 6/a Padova 35131 Italy Centre for Environment and Sustainability University of Surrey Guildford Surrey GU2 7XH United Kingdom Institute for Research in Technology (IIT) Comillas Pontifical University Madrid 28015 Spain Department of Computer Science University of Bristol Bristol BS8 1UB United Kingdom Imec-SMIT Vrije Universiteit Brussel Pleinlaan 9 Brussels 1050 Belgium Garwood Center for Corporate Innovation Haas School of Business University of California Berkeley 2220 Piedmont Ave Berkeley CA 94720 United States Imperial College Business School Imperial College London London SW7 2AZ UK Center of Energy Technologies (CET) Department of Business Development and Technology (BTECH) Aarhus University Birk Centerpark 15 7400 Herning Denmark School of Engineering University of Edinburgh Edinburgh EH9 3FB United Kingdom Energy Institute University College London 14 Upper Woburn Place London WC1H 0NN United Kingdom Tyndall Centre for Climate Change Research School of Engineering University of Manchester Oxford Road Manchester M13 9PL United Kingdom Department of Computer Science University of Manchester Oxford Road Manchester M13 9PL United Kingdom imec-COSIC KU Leuven Kasteelpark Arenberg 10 Bus 2452 Leuven-Heverlee B-3001 Belgium Smart Systems Group School of Engineer and Physical Sciences Heriot-Watt University Edinburgh EH14 4AS United Kingdom James Watt School of Engineering University of Glasgow Glasgow G12 8QQ United Kingdom CWI National Research Institute for Mathematics and Computer Science Amsterdam 1098XG Netherlands International Energy Research Centre Tyndall National Institute Cork Ireland NOVA Information Management School (NOVA IMS) Universidade Nova de Lisboa Campus de Campolide Lisboa 1070-312 Portugal Institute for Innovation and Technology Management Lucerne University of Applied Sciences & Arts Horw 6048 Switzerland Universidad

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Unconstrained biometric recognition based on thermal hand images

Unconstrained biometric recognition based on thermal hand im...

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International Workshop on Biometrics and Forensics (IWBF)

作者： Ewelina Bartuzi Katarzyna Roszczewska Adam Czajka Andrzej Pacut Biometrics Laboratory Research and Academic Computer Network (NASK) Warsaw Poland Department of Computer Science and Engineering University of Notre Dame Notre Dame IN USA Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9781538613672

This paper proposes a biometric recognition method based on thermal images of inner part of the hand, and a database of 21,000 thermal images of both hands acquired by a specialized thermal camera from 70 subjects. The data for each subject was acquired in three different sessions, with two first sessions organized on the same day, and the third session organized approximately two weeks apart. This allowed to analyze the stability of hand temperature in both short-term and long-term horizons. No hand stabilization or positioning devices were used during acquisition, making this setup closer to real-world, unconstrained applications. This required making our method translation-, rotation- and scale-invariant. Two approaches for feature selection and classification are proposed and compared: feature engineering deploying texture descriptors such as Binarized Statistical Image Features (BSIF) and Gabor wavelets, and feature learning based on convolutional neural networks (CNN) trained in different environmental conditions. For within-session scenario we achieved 0.36% and 0.00% of equal error rate (EER) in the first and the second approach, respectively. Between-session EER stands at 27.98% for the first approach and 17.17% for the second one. These results allow for estimation of a short-term stability of hand thermal information. This paper presents the first known to us database of hand thermal images and the first biometric system based solely on hand thermal maps acquired by thermal sensor in unconstrained scenario.

关键词： Feature extraction Thermal sensors Image segmentation Veins Databases Temperature measurement Biomedical imaging

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