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16th ACM International Conference on Multimodal Interaction, ICMI 2014

作者： Huang, Xiaohua He, Qiuhai Hong, Xiaopeng Zhao, Guoying Pietikänen, Matti Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu Finland

ISBN: (纸本)9781450328852

Local binary pattern from three orthogonal planes (LBPTOP) has been widely used in emotion recognition in the wild. However, it suffers from illumination and pose changes. This paper mainly focuses on the robustness of LBP-TOP to unconstrained environment. Recent proposed method, spatiotemporal local monogenic binary pattern (STLMBP) [14], was verified to work promisingly in different illumination conditions. Thus this paper proposes an improved spatiotemporal feature descriptor based on STLMBP. The improved descriptor uses not only magnitude and orientation, but also the phase information, which provide complementary information. In detail, the magnitude, orientation and phase images are obtained by using an effective monogenic filter, and multiple feature vectors are finally fused by multiple kernel learning. STLMBP and the proposed method are evaluated in the Acted Facial Expression in the Wild as part of the 2014 Emotion Recognition in the Wild Challenge. They achieve competitive results, with an accuracy gain of 6.35% and 7.65% above the challenge baseline (LBP-TOP) over video. Copyright 2014 ACM.

关键词： Local binary pattern

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BLDC wheel hub motor and motor controller performance test of a concept electric robotic vehicle in HIL according to real driving characteristics

BLDC wheel hub motor and motor controller performance test o...

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International Conference on Electrical and Electronics Engineering, ELECO

作者： Mehmed Yüksel Stefan Losch Sven Kroffke Michael Rohn Frank Kirchner German Research Center for Artificial Intelligence (DFKI) Robotics Innovation Center (RIC) Robert-Hooke-Str. 1 - 28359 Bremen Germany Fraunhofer Institute for Manufacturing Technology and Advanced Materials (IFAM) Bremen Germany German Research Center for Artificial Intelligence (DFKI) Robotics Innovation Center (RIC) Bremen Germany Department of Mathematics and Computer Science University of Bremen Bremen Germany

In this paper we are presenting a method, which is developed as a part of our framework for designing complex robotic vehicle systems, to test a power train of a robotic concept car according to the real driving characteristic from telemetry data gathered from a subset of a pilot electric vehicle fleet in northern Germany in Hardware-in-the-Loop. Our aim is to investigate the driving performance of our modified BLDC wheel hub motor and its motorcontroller under urban area traffic conditions.

关键词： Vehicles Wheels Robots Torque Brushless DC motors

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 1

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BMC Neurosci 2017年第SUPPL 1期18卷 58-58页

作者： Sue Denham Panayiota Poirazi Erik De Schutter Karl Friston Ho Ka Chan Thomas Nowotny Dongqi Han Sungho Hong Sophie Rosay Tanja Wernle Alessandro Treves Sarah Goethals Romain Brette Tomas Van Pottelbergh Rodolphe Sepulchre Alex D. Bird Hermann Cuntz Pedro J. Gonçalves Jan-Matthis Lueckmann Giacomo Bassetto Marcel Nonnenmacher Jakob H. Macke Audrey J. Sederberg Jason N. MacLean Stephanie E. Palmer Ulisse Ferrari Christophe Gardella Olivier Marre Thierry Mora Emina Ibrahimovic Martin Müller Jean-Pascal Pfister Tushar Chauhan Timothée Masquelier Alexandre Montlibert Benoit R. Cottereau Moritz Helias Jannis Schuecker David Dahmen Sven Goedeke Alexandre Hyafil Ainhoa Hermoso-Mendizabal Pavel E. Rueda-Orozco Santiago Jaramillo David Robbe Jaime de la Rocha Marie Rooy Fani Koukouli David DiGregorio Uwe Maskos Boris Gutkin Andrey Yu Verisokin Darya V. Verveyko Dmitry E. Postnov Willy Wong Omid Talakoub Robert Chen Milos Popovic Dmitriy Lisitsyn Eric Drebitz Iris Grothe Sunita Mandon Andreas Kreiter Udo Ernst Peter A. Robinson Xuelong Zhao Kevin M. Aquino John D. Griffiths Grishma Mehta-Pandejee Natasha Gabay James MacLaurin Somwrita Sarkar Tim Kunze Jens Haueisen Thomas R. Knösche Subutai Ahmad Yuwei Cui Marcus Lewis Jeff Hawkins Simona Olmi Spase Petkoski Fabrice Bartolomei Maxime Guye Viktor Jirsa Hazem Toutounji Daniel Durstewitz Matteo Cantarelli Adrian Quintana Boris Marin Matt Earnshaw Padraig Gleeson Robert Court Robert McDougal R. Angus Silver Salvador Dura-Bernal Stephen Larson William W. Lytton Giovanni Idili Lorenzo Posani Simona Cocco Karel Ježek Rémi Monasson School of Psychology Faculty of Health & Human Sciences Plymouth University Plymouth Devon PL4 8AA UK Institute of Molecular Biology and Biotechnology (IMBB) Foundation for Research and Technology-Hellas (FORTH) Heraklion Crete Greece Okinawa Institute of Science and Technology Graduate University Okinawa Japan Wellcome Trust Centre for Neuroimaging Institute of Neurology UCL London WC1N 3BG UK School of Engineering and Informatics University of Sussex Brighton BN1 9QJ UK Graduate School Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Computational Neuroscience Unit Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Cognitive Neuroscience Area Scuola Internazionale Superiore di Studi Avanzati Trieste 34136 Italy Kavli Institute for Systems Neuroscience NTNU Trondheim 7030 Norway Institut de la Vision Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Paris 75012 France Department of Engineering University of Cambridge Cambridge Cambridgeshire CB2 1PZ UK Ernst Strüngmann Institute for Neuroscience Max Planck Society Frankfurt Germany Frankfurt Institute for Advanced Studies Frankfurt Germany Neural Systems Analysis Research Center Caesar An Associate of the Max Planck Society Bonn Germany Max Planck Institute for Biological Cybernetics Tübingen Germany Bernstein Center for Computational Neuroscience Tübingen Germany Present Address: Biomedical Engineering Department Georgia Institute of Technology Atlanta GA 30306 USA Department of Neurobiology University of Chicago Chicago IL 60637 USA Department of Organismal Biology and Anatomy University of Chicago Chicago IL 60637 USA Committee on Computational Neuroscience University of Chicago Chicago IL 60637 USA Institut de la Vision Sorbonne Universités INSERM Paris France Laboratoire de Physique Statistique Ecole Normale Supérieure Paris France Institute of Molecular Life Sciences University of Zürich 8057 Zürich Switzerland Institute of Neuroinformatics Univer

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Active articulation model estimation through interactive perception

Active articulation model estimation through interactive per...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Karol Hausman Scott Niekum Sarah Osentoski Gaurav S. Sukhatme Karol Hausman and Gaurav S. Sukhatme are with the Department of Computer Science University of Southern California Los Angeles CA USA Scott Niekum is with the Robotics Institute Carnegie Mellon University Pittsburgh PA USA School of Computer Science University of Massachusetts Amherst Amherst MA USA Sarah Osentoski is with Robert Bosch Research and Technology Center Palo Alto CA USA

We introduce a particle filter-based approach to representing and actively reducing uncertainty over articulated motion models. The presented method provides a probabilistic model that integrates visual observations with feedback from manipulation actions to best characterize a distribution of possible articulation models. We evaluate several action selection methods to efficiently reduce the uncertainty about the articulation model. The full system is experimentally evaluated using a PR2 mobile manipulator. Our experiments demonstrate that the proposed system allows for intelligent reasoning about sparse, noisy data in a number of common manipulation scenarios.

关键词： Robot sensing systems Visualization Entropy Joints Uncertainty Probabilistic logic

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Total cluster: A person agnostic clustering method for broadcast videos 14

Total cluster: A person agnostic clustering method for broad...

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9th Indian Conference on computer vision, Graphics and Image Processing, ICVGIP 2014

作者： Tapaswi, Makarand Parkhi, Omkar M. Rahtu, Esa Sommerlade, Eric Stiefelhagen, Rainer Zisserman, Andrew Computer Vision for Human Computer Interaction Karlsruhe Institute of Technology Germany Visual Geometry Group Department of Engineering Science University of Oxford United Kingdom Center for Machine Vision Research University of Oulu Finland

ISBN: (纸本)1595930361

The goal of this paper is unsupervised face clustering in edited video material - where face tracks arising from different people are assigned to separate clusters, with one cluster for each person. In particular we explore the extent to which faces can be clustered automatically without making an error. This is a very challenging problem given the variation in pose, lighting and expressions that can occur, and the similarities between different people. The novelty we bring is three fold: first, we show that a form of weak supervision is available from the editing structure of the material - the shots, threads and scenes that are standard in edited video;second, we show that by first clustering within scenes the number of face tracks can be significantly reduced with almost no errors;third, we propose an extension of the clustering method to entire episodes using exemplar SVMs based on the negative training data automatically harvested from the editing structure. The method is demonstrated on multiple episodes from two very different TV series, Scrubs and Buffy. For both series it is shown that we move towards our goal, and also outperform a number of baselines from previous works. Copyright is held by the authors.

关键词： Cluster analysis

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Planning for serendipity

Planning for serendipity

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： T. Chakraborti G. Briggs K. Talamadupula Yu Zhang M. Scheutz D. Smith S. Kambhampati Department of Computer Science Arizona State University Tempe AZ USA HRI Laboratory Tufts University Medford MA USA Cognitive Learning Department at IBM Thomas J. Watson Research Center NY USA Tufts University Medford MA US Autonomous Systems & Robotics Intelligent Systems Division NASA Ames Research Center NY USA

Recently there has been a lot of focus on human robot co-habitation issues that are often orthogonal to many aspects of human-robot teaming;e.g. on producing socially acceptable behaviors of robots and de-conflicting plans of robots and humans in shared environments. However, an interesting offshoot of these settings that has largely been overlooked is the problem of planning for serendipity - i.e. planning for stigmergic collaboration without explicit commitments on agents in co-habitation. In this paper we formalize this notion of planning for serendipity for the first time, and provide an Integer Programming based solution for this problem. Further, we illustrate the different modes of this planning technique on a typical Urban Search and Rescue scenario and show a real-life implementation of the ideas on the Nao Robot interacting with a human colleague.

关键词： Planning Robot kinematics Collaboration Context Robot sensing systems Handover

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Integrated management for network virtualization infrastructure

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Journal of the National Institute of Information and Communications Technology 2015年第2期62卷 33-43页

作者： Yamada, Kazuhisa Nakao, Akihiro Kanada, Yasusi Saida, Yoshinori Amemiya, Koichiro Future Networking Research Group Media Innovation Laboratory NTT Network Innovation Laboratories Network Japan Interfaculty Initiative in Information Studies Graduate School of Interdisciplinary Information Studies University of Tokyo Network Computer Science Japan Network Research Department Center for Technology Innovation - Information and Telecommunications Network Computer Vision Japan System Integration and Services Technologies Management Division Technological Strategy Planning Department NEC Network Japan Software Laboratory Fujitsu Labratories Ltd. Cloud Computing Network Japan

This paper introduces research and development results of extended functions on our network virtualization infrastructure which achieve advanced network virtualization. Our results achieved to meet five requirements which are resource abstraction, resource isolation, resource elasticity, deep programmability and authentication. In addition, we proposed evolvable architecture and extended network virtualization for edge network and terminals. We also deployed our system on JGN-X testbed.

关键词： Virtual reality

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Erratum to: Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions

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International journal of computer assisted radiology and surgery 2016年第5期11卷 847页

作者： Yohannes Kassahun Bingbin Yu Abraham Temesgen Tibebu Danail Stoyanov Stamatia Giannarou Jan Hendrik Metzen Emmanuel Vander Poorten Robotics Innovation Center German Research Center for Artificial Intelligence Robert-Hooke-Str. 1 28359 Bremen Germany. yohannes.kassahun@dfki.de. Faculty 3 - Mathematics and Computer Science University of Bremen Robert-Hooke-Str. 1 28359 Bremen Germany. Centre for Medical Image Computing Department of Computer Science University College London London UK. Hamlyn Centre for Robotic Surgery Imperial College London London UK. Department of Mechanical Engineering University of Leuven Celestijnenlaan 300B 3001 Heverlee Belgium.

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Breast masses identification through pixel-based texture classification

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12th International Workshop on Breast Imaging, IWDM 2014

作者： Torrents-Barrena, Jordina Puig, Domenec Ferre, Maria Melendez, Jaime Diez-Presa, Lorena Arenas, Meritxell Martí, Joan Department of Computer Engineering and Mathematics University Rovira I Virgili Spain Department of Radiology Radboud University Medical Center Netherlands Radiotherapic Oncology Research Group Hospital Universitari Sant Joan de Reus Spain Computer Vision and Robotics Research Institute University of Girona Spain

ISBN: (纸本)9783319078861

Mammographic image analysis plays an important role in computer-aided breast cancer diagnosis. To improve the existing knowledge, this paper proposes a new efficient pixel-based methodology for tumor vs non-tumor classification. The proposed method firstly computes a Gabor feature pool from the mammogram. This feature set is calculated through multi-sized evaluation windows applied to the probabilistic distribution moments, in order to improve the accuracy of the whole system. To deal with a high dimensional data space and a large amount of features, we apply both a linear and non-linear pixel classification stage by using Support Vector Machines (SVMs). The randomness is encoded when training each SVM using randomly sample sets and, in consequence, randomly selected features from the whole feature bank obtained in the first stage. The proposed method has been validated using real mammographic images from well-known databases and its effectiveness is demonstrated in the experimental section. © 2014 Springer International Publishing.

关键词： Pixels

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Control Strategies for a Humanoid Robot to Drive and then Egress a Utility Vehicle for Remote Approach

Control Strategies for a Humanoid Robot to Drive and then Eg...

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IEEE-RAS International Conference on Humanoid Robots

作者： Hyobin Jeong Jaesung Oh Mingeuk Kim Kyungdon Joo In So Kweon Jun-Ho Oh Humanoid Research Center School of Mechanical Aerospace & Systems Engineering Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Robotics and Computer Vision Laboratory School of Mechanical Aerospace & Systems Engineering Department of Electrical Engineering Korea Advanced Institute of Science and Technology Department of Electrical Engineering KAIST

ISBN: (纸本)9781479968862

This paper proposes strategies for the driving and egress of a vehicle with a humanoid robot. To drive the vehicle, the RANSAC method was used to detect obstacles, and the Wagon model was used to control the steering and velocity of the vehicle with only a limited number of sensors which were installed on the humanoid robot. Additionally, a manual teleoperating method was used with the lane projection technique. For the egress motion, gain override and the Cartesian position/force control technique were used to interact with the vehicle structure. To overcome the disadvantages of a highly geared manipulator, a special technique was used that included modelled friction compensation and a non-complementary switching mode. DRC-HUBO+ used the proposed method to perform a vehicle driving and egress task in the DRC finals 2015.

关键词： Humanoid robots EGRESS vehicle Vehicle driving Automobile driving UTILITY VEHICLES control strategy friction compensation DRIVES Steering

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