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Keynote Speakers
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Robot Town Project: A Platform for Service Robot in Daily Human Life Environment Tsutomu Hasegawa
Bio-Sketch: Tsutomu Hasegawa received the B.E. degree in 1973 in electronic engineering and the Ph.D. degree in 1987, both from the Tokyo Institute of Technology, Tokyo, Japan.
Robust Computer Vision Techniques and Applications
Bio-Sketch: In-So Kweon is Professor of School of Electrical Engineering and Computer Science, KAIST. In Jun. 2009 at IEEE-CVPR’2009, he achieved Best Student Paper-Runner Up (with O. Duchenne), in Sep. 2008 at International Conference on Control, Automation and Systems (ICCAS 2008), he achieved Student Paper Award (with Jungho Kim). In Oct. 2008 at International Conference on Ubiquitous Robots and Ambient Intelligence 2008 (URAI 2008), he achieved Outstanding Poster Award (with K. Sung). In 2006 at Annual Summer Workshop on Robotics, Korea Robotics Society, he achieved Best Paper Award (with S. Kim). In 2001 at SPIE Photonics Conference, he achieved Poster Paper Award, Boston, USA (with K. Yoon). In 2001 he achieved KAIST Research Award.
Rough Classification: Algorithms and Applications Ngoc Thanh Nguyen, Professor, Ph.D., D.Sc.,
Bio-Sketch: Professor Ngoc Thanh Nguyen (Ph.D., D.Sc.) works at Wroclaw University of Technology, Poland, where he is the head of Knowledge Management Systems Department in the Faculty of Computer Science and Management. His scientific interests consist of knowledge integration methods, collective intelligence, intelligent technologies for conflict resolution, inconsistent knowledge processing, multi-agent systems, and E-learning methods. He has edited 20 special issues in international journals, 6 books and 6 conference proceedings. He is the author of 4 monographs and about 180 other publications. His latest monograph entitled “Advanced Methods for Inconsistent Knowledge Management” has been published by Springer last year. Prof. Nguyen serves as Editor-in-Chief of International Journal of Intelligent Information and Database Systems;Editor-in-Chief of two book series: Advances in Applied Intelligence Technologies and Computational Intelligence and its Applications for IGI Global Publishers (USA); Associate Editor of Neurocomputing, International Journal of Innovative Computing & Information Control, Journal of Information Knowledge System Management and KES Journal; and a Member of Editorial Review Boards of several other prestigious international journals. He serves also as an expert for Ministry of Science and Higher Education and Ministry of Regional Development of Poland in evaluating R&D projects. He is the Chair of KES Symposium Series on Agent and Multi-agent Systems. He has been General Chair or Program Chair of more than 10 international conferences. Prof. Nguyen has been selected as the Vice-President of International Society of Applied Intelligence (ISAI); Senior Member of IEEE and ACM, the largest computer science societies in the world. He is also an expert of European Commission in evaluation research projects and an expert of Polish Ministry of Science and Higher Education and Slovakia Research Agency. He is also the Associate Chair of KES International and many other functions in international societies like IFIP, WIC etc. In 2008 for his activities the President of Poland has rewarded Prof. Nguyen the Bronze Medal for Education. He has obtained also awards from the Polish Ministry of Science and Higher Education and the Rector of Wroclaw University of Technology.
Intelligent Pattern Recognition and Applications to Patrick Wang, Professor & Ph.D., IAPR Fellow
Bio-Sketch: Prof. Patrick S.P. Wang, PhD. IAPR Fellow and IEEE Outstanding Achievement Awardee, and is Tenured Full Professor, Northeastern University, USA, iCORE (Informatics Circle of Research Excellence) Visiting Professor, University of Calgary, Canada, Otto-Von-Guericke Distinguished Guest Professor, Magdeburg University, Germany, Zijiang Visiting Chair, ECNU, Shanghai, China, as well as honorary advisory professor of several key universities in China, including Sichuan University, Xiamen University, East China Normal University, Shanghai, and Guangxi Normal University, Guilin. Prof. Wang received his BSEE from National Chiao Tung University (Jiaotong University), MSEE from National Taiwan University, MSICS from Georgia Institute of Technology, and PhD, Computer Science from Oregon State University. Dr. Wang has published over 23 books, 130 technical papers, 3 USA/European Patents, in PR/AI/TV/Cybernetics/Imaging, and is currently founding Editor-in-Chief of IJPRAI (International Journal of Pattern Recognition and Artificial Intelligence) , and Book Series of MPAI, WSP. In addition to his technical interests, Dr. Wang also published a prose book, “Harvard Meditation Melody”and many articles and poems regarding Du Fu and Li Bai’s poems, Beethoven, Brahms, Mozart and Tchaikovsky’s symphonies, and Bizet, Verdi, Puccini and Rossini’s operas.
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Abstract：Ordinary environment of human daily life is a dynamically changing 3D real space: there exist human beings walking and working around, and the layout of furniture and obstacles may change so frequently that the map becomes obsolete quickly. Although it may be a simple and well-ordered environment for humans, it is too complex to recognize for a conventional self-contained autonomous robot equipped with as many sensors as possible within its limited body. Considering the state-of-the-art, we will not be able to expect a capable robot executing various tasks independently in our daily life environment in near future. 
Abstract: Research in KAIST Robotics and Computer Vision (RCV) Lab. has been focused on developing robust methods concerning important computer vision problems: 3D structure recovery, image processing and object recognition. In this talk, we first present robust methods for finding feature correspondences from an image pair with significant deformation. 
Abstract: Rough classification methods, in general, serve to determining a set of attributes which generate an approximate classification referring to a given classification. In the Pawlak’s concept of rough classification for a given classification C of set U of objects a rough classification is the approximation of C. Assume that classification C is generated by set B of attributes, then the approximation of C is based on determining a proper subset B’ of B such that the classification generated by B’ differs “a little” from C. The small difference between these classifications is illustrated by the difference of their accuracy measures which should not be larger than some threshold. In our approach we consider other problem of rough classification: For a given classification of set U which is generated by set A of attributes, one should determine such minimal set B of attributes from A that the distance between the classification generated by attributes from B and the given classification is minimal. In this talk an approach for using rough classification methods to perform recommendation processes in intelligent e-learning systems is presented. Rough classification in this case is related to inconsistency aspect of knowledge of the system. The inconsistency here appears in two aspects: In the first aspect inconsistency refers to difference of the passed scenarios of similar learners (belonging to the same class of the classification). In this case to determine an opening scenario for a new learner it is needed to calculate the consensus of the passed scenarios of the members of the class. The second aspect of inconsistency refers to the fact that assumed to be similar learners (belonging to the same class of the classification) may have very 
Abstract: This talk deals with some fundamental aspects of biometrics and its applications. It basically includes the following: Overview of Biometric Technology and Applications, Importance of Security: A Scenario of Terrorists Attack,, What are Biometric Technologies? Biometrics: Analysis vs Synthesis, Analysis: Interactive Pattern Recognition Concept, Importance of Measurement and Ambiguity, How it works: Fingerprint Extraction and Matching, Iris, and Facial Analysis, Authentication Applications, Thermal Imaging: Emotion Recognition. Synthesis in biometrics, Modeling and Simulation, and more Examples and Applications of Biomedical Imaging in Interactive Fuzzy Learning Environment. Finally, some future research directions are discussed.