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Building Extraction from High-Resolution Remotely Sensed Imagery Based on Multi-subgraph Matching

Journal of the Indian Society of Remote Sensing 2018年第12期46卷 2003-2013页

作者： Wenzao Shi Zhengyuan Mao Jinqing Liu Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application College of Photonic and Electronic Engineering Fujian Normal University Fuzhou People’s Republic of China Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory of Photonics Technology Fujian Normal University Fuzhou People’s Republic of China Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education Fuzhou University Fuzhou People’s Republic of China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou People’s Republic of China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou People’s Republic of China

Building extraction is still a difficult issue in the field of remote sensing. In order to extract the buildings with similar structures efficiently, an algorithm based on multi-subgraph matching is proposed using only the panchromatic high-resolution remotely sensed imagery (RSI). Firstly, scale-invariant feature transform feature is detected within both RSI and building template, and the corresponding graphs are constructed. Then, binary matching rules are defined to reconstruct the graphs to reduce the complexity. At last, according to the homogeneity of the building top, disconnected subgraphs are isolated from the reconstructed graphs. To improve the algorithm accuracy, the matched subgraphs are optimized on the basis of the differences in the structure and size. For verifying the validity of the proposed method, nine representatives are chosen from GF-2 images covering Guangzhou, China. Experimental results show that the precision and recall of the proposed method are 97.73% and 87.16%, respectively, and its overall performance F1 is higher than the three other similar methods.

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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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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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Building area extraction from the high spatial resolution remote sensing imagery

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Earth science Informatics 2018年第1期12卷 19-29页

作者： Shi, Wenzao Mao, Zhengyuan Liu, Jinqing Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou People’s Republic of China Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory for Photonics Technology Fujian Normal University Fuzhou People’s Republic of China College of Photonic and Electronic Engineering Fujian Normal University Fuzhou China Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Fuzhou University Fuzhou People’s Republic of China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou People’s Republic of China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou People’s Republic of China

An approach to building area extraction from high-resolution remote sensing imagery is proposed based on the local gradient orientation density function (LGODF) and the bimodal density function (BDF). Firstly, the LGODF is calculated by moving the window of 35 × 35 based on the gradient magnitude and the gradient direction of each pixel in the image. Then, the BDF is obtained by multiplying the movable bimodal Gaussian mixture function and LGODF in each window. Finally, peaks with difference of 90° are searched for in the BDF, and the central point of the corresponding windows are determined as building pixels. For the validity of the proposed method, seven representative sub-images from PLEIADES images covering Shenzhen China are selected. Experimental results reveal that the precision achieve 94.08% and the recall up to 96.70%.

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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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Creating Knowledge and Wisdom via Big data Analytics: Preface for ITQM 2017

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Procedia Computer science 2017年 122卷 1-9页

作者： Ahuja, Vandana Shi, Yong Khazanchi, Deepak Abidi, Naseem Tian, Yingjie Berg, Daniel Tien, James M. Jaypee Business School A-10 Sector-62 Noida Uttar Pradesh201 307 India School of Economics and Management University of Chinese Academy of Sciences Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha Chinese Academy of Sciences OmahaNE68182 United States College of Engineering University of Miami Coral GablesFL33124 United States

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Stroke genetics informs drug discovery and risk prediction across ancestries (vol 611, pg 115, 2022)

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NATURE 2022年第7938期612卷 E7-E7页

作者： Mishra, Aniket Malik, Rainer Hachiya, Tsuyoshi Jurgenson, Tuuli Namba, Shinichi Posner, Daniel C. Kamanu, Frederick K. Koido, Masaru Le Grand, Quentin Shi, Mingyang He, Yunye Georgakis, Marios K. Caro, Ilana Krebs, Kristi Liaw, Yi-Ching Vaura, Felix C. Lin, Kuang Winsvold, Bendik Slagsvold Srinivasasainagendra, Vinodh Parodi, Livia Bae, Hee-Joon Chauhan, Ganesh Chong, Michael R. Tomppo, Liisa Akinyemi, Rufus Roshchupkin, Gennady V. Habib, Naomi Jee, Yon Ho Thomassen, Jesper Qvist Abedi, Vida Carcel-Marquez, Jara Nygaard, Marianne Leonard, Hampton L. Yang, Chaojie Yonova-Doing, Ekaterina Knol, Maria J. Lewis, Adam J. Judy, Renae L. Ago, Tetsuro Amouyel, Philippe Armstrong, Nicole D. Bakker, Mark K. Bartz, Traci M. Bennett, David A. Bis, Joshua C. Bordes, Constance Borte, Sigrid Cain, Anael Ridker, Paul M. Cho, Kelly Chen, Zhengming Cruchaga, Carlos Cole, John W. de Jager, Phil L. de Cid, Rafael Endres, Matthias Ferreira, Leslie E. Geerlings, Mirjam I. Gasca, Natalie C. Gudnason, Vilmundur Hata, Jun He, Jing Heath, Alicia K. Ho, Yuk-Lam Havulinna, Aki S. Hopewell, Jemma C. Hyacinth, I. Hyacinth Inouye, Michael Jacob, Mina A. Jeon, Christina E. Jern, Christina Kamouchi, Masahiro Keene, Keith L. Kitazono, Takanari Kittner, Steven J. Konuma, Takahiro Kumar, Amit Lacaze, Paul Launer, Lenore J. Lee, Keon-Joo Lepik, Kaido Li, Jiang Li, Liming Manichaikul, Ani Markus, Hugh S. Marston, Nicholas A. Meitinger, Thomas Mitchell, Braxton D. Montellano, Felipe A. Morisaki, Takayuki Mosley, Thomas H. Nalls, Mike A. Nordestgaard, Borge G. O'Donnell, Martin J. Okada, Yukinori Onland-Moret, N. Charlotte Ovbiagele, Bruce Peters, Annette Psaty, Bruce M. Rich, Stephen S. Rosand, Jonathan Sabatine, Marc S. Sacco, Ralph L. Saleheen, Danish Sandset, Else Charlotte Salomaa, Veikko Sargurupremraj, Muralidharan Sasaki, Makoto Satizabal, Claudia L. Schmidt, Carsten O. Shimizu, Atsushi Smith, Nicholas L. Sloane, Kelly L. Sutoh, Yoichi Sun, Yan V. Tanno, Kozo Tiedt, Steffen Tatlisumak, Turgut Torres-Aguila, Nuria P. Tiwari, Hemant K. Tregouet, Bordeaux Population Health Research Center University of Bordeaux Inserm UMR 1219 Bordeaux France Department of Public Health Bordeaux University Hospital Bordeaux France Department of Neurology Memory Clinic Bordeaux University Hospital Bordeaux France Department of Neurology Institute for Neurodegenerative Diseases CHU de Bordeaux Bordeaux France Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Munich Germany Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of Harvard and the Massachusetts Institute of Technology Cambridge MA USA Munich Cluster for Systems Neurology Munich Germany German Center for Neurodegenerative Diseases (DZNE) Munich Germany Iwate Tohoku Medical Megabank Organization Iwate Medical University Iwate Japan Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia Institute of Mathematics and Statistics University of Tartu Tartu Estonia Department of Computational Biology University of Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland University Center for Primary Care and Public Health Lausanne Switzerland Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Department of Genome Informatics Graduate School of Medicine The University of Tokyo Tokyo Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Integrated Frontier Research for Medical Science Division Institute for Open and Transdisciplinary Research Initiatives Osaka University Suita Japan Center for Infectious Disease Education and Research (CiDER) Osaka University Suita Japan Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA TIMI S

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Biogeography-based optimization for cluster analysis

Biogeography-based optimization for cluster analysis

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International Conference on Computer, Communication and Computational sciences, ICCCCS 2016

作者： Wu, Xueyan Wang, Hainan Chen, Zhimin Lu, Zhihai Phillips, Preetha Wang, Shuihua Zhang, Yudong School of Computer Science and Technology Nanjing Normal University NanjingJiangsu210023 China Key Laboratory of Statistical Information Technology & Data Mining State Statistics Bureau ChengduSichuan610225 China School of Computer and Information Engineering Henan Normal University XinxiangHenan453000 China School of Electronic Information Shanghai Dianji University Shanghai200240 China Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education Jilin University ChangchunJilin130012 China Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology GuilinGuangxi541004 China School of Natural Sciences and Mathematics Shepherd University ShepherdstownWV25443 United States West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Department of Electrical Engineering The City College of New York CUNY New YorkNY10031 United States State Key Lab of CAD & CG Zhejiang University HangzhouZhejiang310027 China Manchester Metropolitan University ManchesterM156BH United Kingdom

ISBN: (纸本)9789811037696

With the aim of resolving the issue of cluster analysis more precisely and validly, a new approach was proposed based on biogeography-based optimization (abbreviated as BBO) algorithm. (Method) First, we reformulated the problem with an optimization model based on the variance ratio criterion (VARAC). Then, BBO was presented to search the optimal solution of the VARAC. There are 400 data of four groups in the experimental dataset, which have the degrees of overlapping of three distinct scales. The first one is nonoverlapping, the second one is partial overlapping, and the last is severely overlapping. BBO algorithm was compared with three different state-of-the-art approaches. We ran every algorithm 20 times. In this experiment, our results demonstrate the maximum VARAC values that can be found by BBO. The conclusion is that BBO is predominant which is extremely quick for the issue of clustering analysis. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Cluster analysis

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Image reconstruction algorithm of compressed sensing based on neural dynamics

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Journal of information Hiding and Multimedia Signal Processing 2017年第3期8卷 623-631页

作者： Gan, Zhenhua Xiong, Baoping Zou, Fumin Liao, Lyuchao Du, Min College of Information Science and Engineering Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province China College of Electrical Engineering and Automation Fuzhou University No.2 Xueyuan Road University Town Minhou Fuzhou CityFujian Province China Fujian Provincial Key Laboratory of Big Data Mining and Applications No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province China Key Lab of Automotive Electronics and Electric Drive Technology of Fujian Province No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province China

To improve the recovery performance of compressed sensing (CS) recon- struction, this paper proposed a CS reconstruction model based on neural dynamics optimization algorithm (NDOA) with l1-norm minimization, and applied it to the two- dimensional (2D) image signals to verify the reconstruction effectiveness. Experiments proved the NDOA had performed better in reconstruction than the orthogonal matching pursuit (OMP) and its improved algorithms considering its perfect recovery. When the compressed sampling rate is 0:875 for 64×64 Lena image, the application of NDOA would raise the peak signal to noise ratio (PSNR) by 3:6466dB while decrease the relative error (REOR) by 2:21% in comparison with OMP and SWOMP. It showed that the effective- ness of CS reconstruction of NDOA was evident. At the same time, the adoption of NDOA could realize real-time processing by parallel computing, which would effectively solve real-time problems such as image processing in the realm of high dimensional sparse signals. © 2017.

关键词： Compressed sensing

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