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Novel 3D game-like applications driven by body interactions for learning specific forms of intangible cultural heritage 10

Novel 3D game-like applications driven by body interactions ...

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10th International Conference on Computer Vision Theory and Applications, VISAPP 2015

作者： Yilmaz, E. Uʇurca, D. Şahin, C. Dagnino, F.M. Ott, M. Pozzi, F. Dimitropoulos, K. Tsalakanidou, F. Kitsikidis, A. Al Kork, S.K. Xu, K. Denby, B. Roussel, P. Chawah, P. Buchman, L. Adda-Decker, M. Dupont, S. Picart, B. Tilmanne, J. Alivizatou, M. Hadjileontiadis, L. Charisis, V. Glushkova, A. Volioti, C. Manitsaris, A. Hemery, E. Moutarde, F. Grammalidis, N. Argedor Information Technologies Ankara Turkey Palermo Italy Information Technologies Institute Centre for Research and Technology Hellas Thessaloniki Greece Universite Pierre et Marie Curie - Paris 6 Paris France University Paris 3 Sorbonne Nouvelle Paris France Centre National de la Recherche Scientifique Laboratoire de Phonétique et Phonologie UMR 7018 Paris France Phonetics and Phonology Laboratory LPP-CNRS Paris France Universite de Mons Mons Belgium University College London London United Kingdom Aristotle University of Thessaloniki Thessaloniki Greece MTCG Lab. Dep. of Ap. Informatics University of Macedonia Thessaloniki Greece Robotics Lab. Dep. of Mathématiques et Systèmes MINES ParisTech-ARMINES Paris France Signal Processing and Machine Learning Lab. ESPCI Paris France

ISBN: (纸本)9789897580901

The main objective of the EU FP7 ICT i-Treasures project is to build a public and expandable platform to enable learning and transmission of rare know-how of intangible cultural heritage. A core part of this platform consists of game-like applications able to support teaching and learning processes in the ICH field. We have designed and developed four game-like applications (for Human Beat Box singing, Tsamiko dancing, pottery making and contemporary music composition), each corresponding to one of the ICH use cases of i-Treasures project. A first preliminary version of these applications is currently available for further validation, evaluation and demonstration within the project. We have encountered a number of issues, most of which derive from the peculiarities of the ICH domains addressed by the project, and many have already been resolved / The evaluation results are expected to lead to further optimization of these games.

关键词： Human computer interaction

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Optimizing motion primitives to make symbolic models more predictive

Optimizing motion primitives to make symbolic models more pr...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Andreas Orthey Marc Toussaint Nikolay Jetchev LAAS Université de Toulouse UPS INSA INP ISAE Centre National de la Recherche Scientifique Toulouse France Machine Learning and Robotics Laboratory University of Stuttgart Stuttgart Germany Machine Learning and Robotics Laboratory FU Berlin Berlin Germany

ISBN: (纸本)9781467356404

Solving complex robot manipulation tasks requires to combine motion generation on the geometric level with planning on a symbolic level. On both levels robotics research has developed a variety of mature methodologies, including geometric motion planning and motion primitive learning on the motor level as well as logic reasoning and relational Reinforcement learning methods on the symbolic level. However, their robust integration remains a great challenge. In this paper we approach one aspect of this integration by optimizing the motion primitives on the geometric level to be as consistent as possible with their symbolic predictions. The so optimized motion primitives increase the probability of a “successful” motion-meaning that the symbolic prediction was indeed achieved. Conversely, using these optimized motion primitives to collect new data about the effects of actions the learnt symbolic rules becomes more predictive and deterministic.

关键词： Trajectory Noise Robots Grasping Cost function Linear programming

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Clustering data and imprecise concepts

Clustering data and imprecise concepts

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Weifeng Zhang Zengchang Qin Intelligent Computing and Machine Learning Laboratory School of Automation Science and Electrical Engineering Beihang University China Robotics Institute CMU USA Intelligent Computing and Machine Learning Laboratory School of ASEE Beihang University China

Cluster analysis is the assignment of grouping a set of observations into clusters so that observations in the same cluster are similar in some sense. One of the key features for clustering is how to define a sensible similarity measure. However, classical clustering algorithms have no ability to cluster data instances and imprecise concepts using traditional distance measures. In this paper, we proposed a (dis)similarity measure based on a new knowledge representation framework called label semantics. Based on this new measure, we can automatically cluster data instance and descriptive concepts represented by logical expressions of linguistic labels. Experimental results on a toy problem in image classification demonstrate the effectiveness of the new proposed clustering algorithm. Since the new proposed measure can be extended to measuring distance between any two granularities, the new clustering algorithms can also be extended to clustering data instance and imprecise concepts represented by other granularities.

关键词： Pragmatics Clustering algorithms Semantics Image color analysis Silicon Algorithm design and analysis Humans

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Image scene categorization using multi-bag-of-features

Image scene categorization using multi-bag-of-features

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Weifeng Zhang Zengchang Qin Tao Wan Intelligent Computing and Machine Learning Laboratory (ICMLL) School of ASEE Beihang University China Robotics Institute Carnegie Mellon University USA

Image scene classification, the classification of images into semantic categories, e.g. city, urban, sea, etc, has recently become a vigorous research focus in computer vision for its broad application prospect. In this paper, we propose a novel approach to understand image semantic scene based on multi-bag-of-features. We aim to design an efficient but simple scene classification algorithm via fusing multiple low-level image features. Experimental results demonstrate that the proposed approach offers an effective way to classify the complex image scenes by using a multi-bag-of-features model.

关键词： Support vector machines Books Quantization Image segmentation Wide area networks

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Agent self-assessment: Determining policy quality without execution

Agent self-assessment: Determining policy quality without ex...

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IEEE Symposium on Adaptive Dynamic Programming and Reinforcement learning, (ADPRL)

作者： Alexander Hans Siegmund Duell Steffen Udluft Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Ilmenau Germany Machine Learning Group Berlin Institute of Technology Berlin Germany Intelligent Systems and Control Siemens AG Munich Germany

With the development of data-efficient reinforcement learning (RL) methods, a promising data-driven solution for optimal control of complex technical systems has become available. For the application of RL to a technical system, it is usually required to evaluate a policy before actually applying it to ensure it operates the system safely and within required performance bounds. In benchmark applications one can use the system dynamics directly to measure the policy quality. In real applications, however, this might be too expensive or even impossible. Being unable to evaluate the policy without using the actual system hinders the application of RL to autonomous controllers. As a first step toward agent self-assessment, we deal with discrete MDPs in this paper. We propose to use the value function along with its uncertainty to assess a policy's quality and show that, when dealing with an MDP estimated from observations, the value function itself can be misleading. We address this problem by determining the value function's uncertainty through uncertainty propagation and evaluate the approach using a number of benchmark applications.

关键词： Uncertainty Equations Markov processes Benchmark testing Approximation algorithms machine learning Histograms

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Hybrid moving object detection system based on key frame extraction

Hybrid moving object detection system based on key frame ext...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yu Du Xueyao Wang Shi-Ting Wang Shuangshuang Xue Zengchang Qin Intelligent Computing and Machine Learning Laboratory School of ASEE Beihang University Beijing China School of Automation Science and Electronic Engineering (ASEE) Beihang University Beijing China Robotics Institute Carnegie Mellon University USA

In this paper we construct a hybrid moving object detection system. In this system, we first use the frame difference method to extract key frames in a given video sequence, then use the optical flow method and the HSV background subtraction method to extract the moving objects, respectively. We propose two hybrid methods: Improved Optical Flow method and Improved HSV Background Subtraction method. Experimental results have shown that our proposed system has strong robustness and be effective to detect moving objects in various conditions. We also introduce a human-computer interaction tool for selecting the focus area for users. This system will largely benefit for real-world video surveillance applications.

关键词： Computer vision Lighting Image motion analysis Object detection Optical imaging Accuracy Video sequences

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud Computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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