Imperial College London

DrTae-KyunKim

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6317tk.kim Website

 
 
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Location

 

1017Electrical EngineeringSouth Kensington Campus

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Summary

 

Summary

Tae-Kyun (T-K) Kim is Associate Professor and the director of Computer Vision and Learning Lab at Imperial College London, UK, since Nov 2010. He is also an adjunct professor at School of Computing, KAIST. He obtained his PhD from Univ. of Cambridge in 2008 and Junior Research Fellowship (governing body) of Sidney Sussex College, Univ. of Cambridge for 2007-2010. His research interests primarily lie in tree structured machine learning, on top of randomized forests and convolutional neural networks, for: articulated 3D hand pose estimation, face analysis and recognition by image sets and videos, 6D object pose estimation, active robot vision, activity recognition, object detection/tracking, which lead to novel active and interactive visual sensing. He has co-authored over 80 academic papers in top-tier conferences and journals in the field, and has co-organised HANDS workshops (in conjunction with CVPR15/CVPR16/ICCV17/ECCV18), and Object Pose workshops (in conjunction with ICCV15/ECCV16/ICCV17/ECCV18). He is 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 KUKA best service robotics paper award at ICRA 2014, and 2016 best paper award by the ASCE Journal of Computing in Civil Engineering, and his co-authored algorithm for face image retrieval is an international standard of MPEG-7 ISO/IEC.


Selected Publications

Journal Articles

Kim T-K, Budvytis I, Cipolla R, 2012, Making a Shallow Network Deep: Conversion of a Boosting Classifier into a Decision Tree by Boolean Optimisation, International Journal of Computer Vision, Vol:100, Pages:203-215

Kim T-K, Stenger B, Kittler J, et al., 2011, Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications, International Journal of Computer Vision, Vol:91, Pages:216-233

Kim T-K, Kittler J, Cipolla R, 2010, On-line Learning of Mutually Orthogonal Subspaces for Face Recognition by Image Sets, Ieee Trans. on Image Processing, Vol:19, Pages:1067-1074

Kim T-K, Cipolla R, 2009, Canonical Correlation Analysis of Video Volume Tensors for Action Categorization and Detection, Ieee Trans. on Pattern Analysis and Machine Intelligence, Vol:31, Pages:1415-1428

Kim T-K, Kittler J, Cipolla R, 2007, Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations, Ieee Transactions on Pattern Analysis and Machine Intelligence (tpami), Vol:29

Kim T-K, Kittler J, 2005, Locally Linear Discriminant Analysis for Multi-modally Distributed Classes for Face Recognition with a Single Model Image, Ieee Transactions on Pattern Analysis and Machine Intelligence (tpami), Vol:27, Pages:318-327

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