Overview
We explore machine learning techniques for computer vision. Visual recognition tasks, which are to make machines that see, can be cast as learning problems from image space to semantic labels. We study novel formulations and computationally efficient methods for understanding visual contents from given imagery data, including the topics of object tracking and recognition, gesture and activity analysis, face recognition, semantic segmentation and man-machine interface. Read more about the Imperial Computer Vision and Learning Laboratory.
Funding
Sep 2013-Aug 2014: Industrial (OMRON) grant worth 65K pounds (PI)
May 2013-Apr 2014: Industrial (Samsung) grant worth 95K pounds (PI)
Apr 2011-Mar 2015: NOPTILUS, European Commission FP7 grant worth 450K Euro (PI), taken over from Prof. Petrou since Jan 2013
Jun 2012-May 2014: EPSRC first grant worth 120K pounds (PI)
Jun 2012-May 2013: Industrial (OMRON) grant worth 65K pounds (PI)
Jan 2012-July 2015: College internal grant for the Imperial-NUS joint PhD program, worth 120K pounds (PI)
Oct 2010-Sep 2015: Dept. faculty start-up package worth 115K pounds (PI)
Guest Lectures
Invited talk, Deep learning summit, London, UK, 2016
Invited talk, Korean Conf. on Computer Vision, Ewha Womans University, Seoul, Korea, 2016
Invited talk, Samsung Advanced Institute of Technology, Suwon, Korea, 2016
Invited lecture and lab, BMVA computer vision summer school, Swansea, UK, 2016
Investigator talk, EPSRC program grant kick-off meeting, Univ. of Surrey, Guildford, UK, 2016
Keynote, Korea-Japan joint workshop on Frontiers of Computer Vision, Takayama, Japan, 2016
Invited talk, Department of Computer Science and Engineering, Korea university, Korea, 2016
Invited talk, Department of Computer Science and Engineering, POSTECH, Korea, 2016
Invited talk, ISN group seminar on machine learning and applications, ICL, UK, 2016
Organiser talk, IEEE ICCV Workshop on recovering 6D object pose, Santiago, Chile, 2015
Invited talk, Imperial Robotics Forum showcase, ICL, UK, 2015
Randomised Forests and Tree-structured Algorithms in Computer Vision, Invited tutorial at IAPR Int. Conf. on Machine Vision Applications, Kyoto, Japan, 2013
Machine Learning for Computer Vision: Randomised Decision Forests and Its Novel Applications, UK New Faculty Summit - Computer Vision, Microsoft Research,, Cambridge, UK, 2012
Large-Scale Visual Recognition, Dept. of EE, KAIST, South Korea, 2010
visual codebook learning, Lecture in the course of Engineering Part IB, Paper 8 - Information Engineering Option, Dept. of Engineering, Univ. of Cambridge, Cambridge, UK, 2010
Boosting and Random Forest for Visual Recognition: Boosting and Tree-structured Classifier, Tutorial at IEEE Int. Conf. on Computer Vision, Kyoto, Japan, 2009
Ensemble of classifiers for Visual Recognition, Human Sensing Lab, CMU, Pittsburgh, USA, 2009
Learning multiple classifiers in computer vision, Invited tutorial at Int. Computer Vision Summer School, Sicily, Italy, 2009
Learning Classifiers in Computer Vision, Computer Vision Lab and U-VR Lab, GIST, South Korea, 2008
Face Recognition by Image Sets, CVSSP, Univ. of Surrey, Guildford, UK, 2008