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Erratum to: Methods for evaluating medical tests and biomarkers

Diagnostic and prognostic research 2017年第1期1卷 11页

作者： Gowri Gopalakrishna Miranda Langendam Rob Scholten Patrick Bossuyt Mariska Leeflang Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Richard Riley Marie Westwood Jos Kleinen Gary Collins Hans Reitsma Karel Moons Antonia Zapf Annika Hoyer Katharina Kramer Oliver Kuss J Ensor J J Deeks E C Martin R D Riley Gerta Rücker Susanne Steinhauser Martin Schumacher Joie Ensor Kym Snell Brian Willis Thomas Debray Jon Deeks Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Jan Verbakel Sabine Van Huffel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O'Sullivan Rafael Perera Brian Nicholson Hannah L Bromley Tracy E Roberts Adele Francis Denniis Petrie G Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Doug Altman William Rosenberg Peter Selby Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Anne Rutjes Johannes Reitsma Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Daan Nieboer Bavo De Cock Micael J Pencina Ewout W Steyerberg Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Andrew Mumford Zoe Plummer Kurtis Lee Barnaby Reeves Chris Rogers Veerle Verheyden Gianni D Angelini Gavin J Murphy Jeremy Huddy Melody Ni Katherine Good Graham Cooke 1Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 2Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands. 234Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 49Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 3Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK. 4EPPI-Centre Department of Social Science University College London London UK. 5Division of Health and Social Care Research King's College London London UK. 6College of Computer and Information Science Northeastern University Boston USA. 13University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK. 275Institute of Applied Health Research University of Birmingham Birmingham UK. 9Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 10School of Social and Community Medicine University of Bristol Bristol UK. 179Institute of Applied Health Research University of Birmingham Birmingham UK. 12Kleijnen Systematic Reviews Ltd York UK. 25Research Institute for Primary Care and Health Sciences Keele University Keele UK. 188Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK. 191Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 237Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 18Department of Medical Statistics University Medical Center Göttingen Göttingen Germany. 19Institute for B

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

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Design, implementation and evaluation of a motion control scheme for mobile platforms with high uncertainties

Design, implementation and evaluation of a motion control sc...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Mustafa Mashali Redwan Alqasemi Sudeep Sarkar Rajiv Dubey University of South Florida Tampa Florida USA Research lead at Center for Assistive Rehabilitation and Robotics Technologies at University of South Florida Department of Computer Science and Engineering University of South Florida Department of Mechanical Engineering University of South Florida

In this work, we present a motion control scheme for a robotic mobile platform using low-cost vision sensor to update encoder values. We track the pose of a power wheelchair using wheel encoders along with a Microsoft Kinect camera. Two methods of pose estimation are implemented and tested. These methods are a) encoder-based odometry and b)ICP(Iterative Closest Point)-based updated odometry. We evaluate the performance of each method using precise wheelchair pose ground truth data acquired via a state-of-the-art VICON® system with eight motion capture cameras. Offline data processing is performed to refine the ICP parameters and estimate the covariance matrices of the Kalman filter. The offline data processing results demonstrate that our ICP-based updated odometry has very accurate pose tracking. By implementing our control scheme, the position error is improved by a factor of 15 and the localization orientation error is improved by a factor of 13. In online implementation, there was 4 times improvement for both position and orientation angle estimation. To demonstrate the robustness of our approach, we apply it for online obstacle avoidance. A wheelchair-mounted robotic arm (WMRA) is also included in this platform and will be used for future work on combined mobility and manipulation control with sensor assistance.

关键词： Wheelchairs Robot sensing systems Iterative closest point algorithm Wheels Estimation Motion control Covariance matrices

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A novel telerobotic method for human-in-the-loop assisted grasping based on intention recognition

A novel telerobotic method for human-in-the-loop assisted gr...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Karan Khokar Redwan Alqasemi Sudeep Sarkar Kyle Reed Rajiv Dubey University of South Florida Tampa Florida USA Research Professor at Center for Assistive Rehabilitation and Robotics Technologies at the University of South Florida Department of Computer Science and Engineering at the University of South Florida Department of Mechanical Engineering at the University of South Florida

In this work, we present a methodology for enabling a robot to identify an object and grasp configuration of interest and assist the human teleoperating the robot, to grasp the object. The identification is carried out in real-time by detecting the motion intention of the human as they are teleoperating the remote robotic arm towards the object and the grasp configuration. Simultaneously, depending on the detected object and grasp configuration, the human user is assisted to translate and orient the remote arm gripper in order to preshape and grasp the object. The complete process occurs with the human teleoperating the arm, and without them having to interact with another interface. Motion intention recognition is carried out by using Hidden Markov Models (HMMs), trained offline by preshape trials performed by a skilled teleoperator. The environment is unstructured and comprises of a number of objects, each with multiple grasp configurations. Experimental tests on healthy human subjects have validated our intention recognition based assistance method. They show that the method allows objects to be grasped and placed 48% faster, and with much ease compared to unassisted teleoperation. Moreover, we have proved that the model for intention recognition, trained by a skilled teleoperator, can be used by novice users to efficiently execute a grasping task in teleoperation.

关键词： Hidden Markov models Vectors Grippers Grasping Teleoperators Training

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ESICM LIVES 2016: part one Milan, Italy. 1-5 October 2016 Abstracts

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INTENSIVE CARE MEDICINE EXPERIMENTAL 2016年第SUPPL 1期4卷 1-1页

作者： [Anonymous] Academic Medical Center University of Amsterdam Amsterdam Netherlands UMCU Utrecht Netherlands Autonomous University of Barcelona Barcelona Spain Hospital São Francisco Xavier Lisbon Portugal CHU Henri Mondor Medical ICU Créteil France Université de Paris Est-Créteil Groupe de Recherche Clinique CARMAS Créteil France Hôpital Tenon APHP Medical and Surgical ICU Paris France Université Paris Diderot Sorbonne Paris Cité UMR 1153 Paris France Interdepartmental Division of Critical Care University of Toronto Toronto Canada University Health Network University of Toronto Critical Care Medicine Toronto Canada University of Milan - Bicocca Health Sciences School of Medicine and Surgery Monza Italy St Michael’s Hospital University of Toronto Anesthesia Toronto Canada CHU d’Angers Medical ICU Angers France Interdepartmental Division of Critical Care University of Toronto Saint Michael’s Hospital and Keenan Research Centre Toronto Canada University of Birmingham Institute for Inflammation and Ageing Birmingham UK University of Sheffield Mathematics and Statistics Help Centre Sheffield UK Heart of England NHS Foundation Trust Intensive Care Medicine Birmingham UK Department of Surgery University Hospitals Birmingham NHS Trust Birmingham UK University Hospitals Birmingham NHS Trust Anaesthesia and Critical Care Medicine Birmingham UK Queen’s University of Belfast Wellcome-Wolfson Institute for Experimental Medicine Belfast UK Royal Victoria Hospital Intensive Care Medicine Belfast UK University of Warwick Warwick Clinical Trials Unit Coventry UK University of Bari ‘Aldo Moro’ Bari Italy Hospital Israelita Albert Einstein São Paulo Brazil Mayo Clinic Jacksonville USA Mahidol University Faculty of Tropical Medicine Bangkok Thailand University Medical Center Utrecht Intensive Care Utrecht Netherlands University Medical Center Utrecht Julius Center for Health Sciences and Primary Care Utrecht Netherlands Academic Medical Center University of Amste

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Sparse motion segmentation using propagation of feature labels

Sparse motion segmentation using propagation of feature labe...

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8th International Conference on computer vision Theory and Applications, VISAPP 2013

作者： Sangi, Pekka Hannuksela, Jari Heikkilä, Janne Silvén, Olli Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu Oulu Finland

ISBN: (纸本)9789898565471

The paper considers the problem of extracting background and foreground motions from image sequences based on the estimated displacements of a small set of image blocks. As a novelty, the uncertainty of local motion estimates is analyzed and exploited in the fitting of parametric object motion models which is done within a competitive framework. Prediction of patch labels is based on the temporal propagation of labeling information from seed points in spatial proximity. Estimates of local displacements are then used to predict the object motions which provide a starting point for iterative refinement. Experiments with both synthesized and real image sequences show the potential of the approach as a tool for tracking based online motion segmentation.

关键词： Uncertainty analysis

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5th International Symposium on Focused Ultrasound Abstracts

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JOURNAL OF THERAPEUTIC ULTRASOUND 2016年第SUPPL 1期4卷 1-1页

作者： [Anonymous] Rambam Health Care Campus Haifa Israel Stanford University Stanford CA USA University of Pennsylvania Philadelphia PA USA The Ohio State University Columbus OH USA University of Virginia Charlottesville VA USA Center for Nanomedicine/Wilmer Eye Institute Johns Hopkins University Baltimore MD USA Columbia University New York NY USA Sunnybrook Health Sciences Centre Toronto ON Canada Sunnybrook Research Institute Toronto ON Canada Clem Jones Centre for Ageing Dementia Research Queensland Brain Institute The University of Queensland St Lucia QLD Australia The University of Queensland Brisbane (St Lucia Campus) QLD Australia University of Leuven Leuven Belgium University of Maryland School of Medicine/Baltimore VAMC Baltimore MD USA Research & Development Service VA Maryland Healthcare System and University of Maryland School of Medicine Baltimore MD USA University of Maryland School of Medicine Baltimore MD USA Brigham and Women’s Hospital Boston MA USA Harvard Medical School Boston MA USA Boston University Boston MA USA National Institutes of Health Bethesda MD USA National Institutes of Health Clinical Center Bethesda MD USA University of Utah Salt Lake City UT USA Focused Ultrasound Foundation Charlottesville VA USA Vanderbilt University Nashville TN USA Georgia Institute of Technology Atlanta GA USA Cleveland Clinic Cleveland OH USA Institut Langevin Ondes et Images ESPCI ParisTech CNRS UMR 7587 INSERM U979 Paris France CENIR ICM CNRS U7225 INSERM U975 Paris France University Children’s Hospital Zurich Zurich Switzerland Jeju National University Jeju Republic of Korea Hospital for Sick Children Toronto ON Canada Centre for Image Guided Innovation and Therapeutic Intervention Toronto ON Canada Philips Healthcare Canada Toronto ON Canada Toronto Western Hospital Toronto ON Canada Thunder Bay Regional Research Institute Thunder Bay ON Canada Incheon St. Mary’s Hospital The Catholic University of Korea Incheon Republic of Korea Departme

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Social Adaptation of Robots for Modulating Self-Organization in Animal Societies

Social Adaptation of Robots for Modulating Self-Organization...

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IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, SASOW

作者： Payam Zahadat Michael Bodi Ziad Salem Frank Bonnet Marcelo Elias De Oliveira Francesco Mondada Karlo Griparic Tomislav Haus Stjepan Bogdan Rob Mills Pedro Mariano Luis Correia Olga Kernbach Serge Kernbach Thomas Schmickl Artificial Life Laboratory of the Department of Zoology Karl-Franzens University Graz Austria Computer Engineering Department Aleppo University Syrian Arab Republic Rbotic Systems Laboratory Swiss Federal Institute of Technology (EPFL) Lausanne Switzerland Ecole Polytechnique Federale de Lausanne Lausanne VD CH Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems Zagreb Croatia Universidade de Lisboa Lisboa Lisboa PT Cybertronica Research Research Center of Advanced Robotics and Environmental Science Stuttgart Germany

The goal of the work presented here is to influence the overall behaviour of specific animal societies by integrating computational mechatronic devices (robots) into those societies. To do so, these devices should be accepted by the animals aspart of the society and/or as part of the collectively formed environment. For that, we have developed two sets of robotic hardware for integrating into societies of two different animals: zebra fish and young honeybees. We also developed mechanisms to provide feedback from the behaviours of societies for the controllers of the robotic system. Two different computational methods are then used as the controllers of the robots in simulation and successfully adapted by evolutionary algorithms to influence the simulated animals for desired behaviours. Together, these advances in mechatronic hardware, feedback mechanisms, and controller methodology are laying essential foundations to facilitate experiments on modulating self-organised behaviour in mixed animal -- robot societies.

关键词： Animals Heating Vibrations Robot sensing systems Hardware Biochemistry

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Face spoofing detection using dynamic texture

Face spoofing detection using dynamic texture

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11th Asian Conference on computer vision, ACCV 2012

作者： Komulainen, Jukka Hadid, Abdenour Pietikäinen, Matti Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu P. O. Box 4500 FI-90014 Oulu Finland

ISBN: (纸本)9783642374098

While there is a significant number of works addressing e.g. pose and illumination variation problems in face recognition, the vulnerabilities to spoofing attacks were mostly unexplored until very recently when an increasing attention is started to be paid to this threat. A spoofing attack occurs when a person tries to masquerade as someone else e.g. by wearing a mask to gain illegitimate access and advantages. This work provides the first investigation in research literature on the use of dynamic texture for face spoofing detection. Unlike masks and 3D head models, real faces are indeed non-rigid objects with contractions of facial muscles which result in temporally deformed facial features such as eye lids and lips. Our key idea is to learn the structure and the dynamics of the facial micro-textures that characterise only real faces but not fake ones. Hence, we introduce a novel and appealing approach to face spoofing detection using the spatiotemporal (dynamic texture) extensions of the highly popular local binary pattern approach. We experiment with two publicly available databases consisting of several fake face attacks of different natures under varying conditions and imaging qualities. The experiments show excellent results beyond the state-of-the-art. © 2013 Springer-Verlag.

关键词： Local binary pattern

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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ChaLearn multi-modal gesture recognition 2013: Grand challenge and workshop summary 13

ChaLearn multi-modal gesture recognition 2013: Grand challen...

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2013 15th ACM International Conference on Multimodal Interaction, ICMI 2013

作者： Escalera, Sergio Gonzàlez, Jordi Baró, Xavier Reyes, Miguel Guyon, Isabelle Athitsos, Vassilis Escalante, Hugo Sigal, Leonid Argyros, Antonis Sminchisescu, Cristian Bowden, Richard Sclaroff, Stan Dept. Applied Mathematics Universitat de Barcelona Computer Vision Center Barcelona Spain Dept. Computer Science Univ. Autònoma de Barcelona Computer Vision Center Barcelona Spain EIMT Open University of Catalonia Barcelona Spain ChaLearn Berkeley CA United States Texas University United States INAOE Puebla Mexico Disney Research Pittsburgh PA United States Institute of Computer Science FORTH Creete Greece Lund University Lund Sweden University of Surrey Surrey United Kingdom Department of Computer Science Boston University Boston United States Computer Vision Center UAB Spain

ISBN: (纸本)9781450321297

We organized a Grand Challenge and Workshop on Multi-Modal Gesture Recognition. The MMGR Grand Challenge focused on the recognition of continuous natural gestures from multi-modal data (including RGB, Depth, user mask, Skeletal model, and audio). We made available a large labeled video database of 13,858 gestures from a lexicon of 20 Italian gesture categories recorded with a Kinect™ camera. More than 54 teams participated in the challenge and a final error rate of 12% was achieved by the winner of the competition. Winners of the competition published their work in the workshop of the Challenge. The MMGR Workshop was held at ICMI conference 2013, Sidney. A total of 9 relevant papers with basis on multi-modal gesture recognition were accepted for presentation. This includes multi-modal descriptors, multi-class learning strategies for segmentation and classification in temporal data, as well as relevant applications in the field, including multi-modal Social Signal Processing and multi-modal Human computer Interfaces. Five relevant invited speakers participated in the workshop: Profs. Leonid Signal from Disney research, Antonis Argyros from FORTH, Institute of computer science, Cristian Sminchisescu from Lund University, Richard Bowden from University of Surrey, and Stan Sclaroff from Boston University. They summarized their research in the field and discussed past, current, and future challenges in Multi-Modal Gesture Recognition. © 2013 ACM.

关键词： Gesture recognition

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