Tae-Kyun Kim, Imperial College of London, United Kingdom

Tae-Kyun (T-K) Kim is Associate Professor and the director of Computer Vision and Learning Lab at Imperial College London, UK, since 2010. He obtained his PhD and Research Fellowship from Univ. of Cambridge in 2008. His research interests primarily lie in machine learning for: articulated hand pose estimation, face analysis and recognition by image sets and videos, 6D object pose estimation, active robot vision, activity recognition, which lead to novel active and interactive visual sensing. He has co-authored 50+ papers in top-tier conferences and journals in the field, and has co-organised the series of HANDS workshop and Object Pose workshop. He was the general chair of BMVC17 in London, and is Associate Editor of Image and Vision Computing Journal, and IPSJ Trans. on Computer Vision and Applications. He received the best paper awards from ICRA14 and 2016 ASCE Journal of Computing in Civil Engineering, and his co-authored algorithm for face image retrieval is MPEG-7 ISO/IEC standard.

3D Hand Pose Estimation for Novel Man-Machine Interface

Fabio Galasso, OSRAM GmbH, Germany

Fabio Galasso heads the Computer Vision Department at OSRAM (Munich, Germany) since 2014, conducting R&D in computer vision, machine learning and multimodal computing, in relation to smart lighting applications. Fabio holds a Master's Degree in Electrical Engineering and Signal Processing from RomaTre University (Italy) and a PhD from the University of Cambridge (UK). He was previously post-doctoral research associate in the Computer Vision Laboratory in Cambridge and at the Max Planck Institute for Informatics in Germany. Fabio is an active member of the computer vision and machine learning community and served recently as area chair at international conferences (ICCV, AVSS, VISAPP), reviewer for journals and conferences (including TPAMI, CVPR, ICCV and ECCV), chair of international workshops on video segmentation (at ECCV conferences).

Computer Vision and Smart Lighting relevant to Assistive Technologies