Multi-modal RGB-D scene understanding
Commodity RGB-D sensors such as Microsoft Kinect have received significant attention in the recent years due to their low cost and the ability to capture synchronized color images and depth maps in real time. Although the available depth information has been proven to be extremely useful for many visual computing problems, there are still challenges remaining on finding the best way to harness the low-resolution, noisy and unstable depth data and to complement the existing methods using RGB data alone.??In this talk, I will show you a series of works from my group on using unsupervised feature learning for scene labeling, using deep learning technology for RGB-D object recognition and using multi-modal feature learning for scene classification.
Jianfei?received his PhD degree from the University of Missouri-Columbia. He is currently an?Associate Professor?and has served as the?Head of Visual & Interactive Computing Division?and the?Head of Computer Communication Division?at the?School of Computer Engineering,?Nanyang Technological?University, Singapore. His major research interests include visual computing, computer vision, machine learning and multimedia networking. He has published more than 180 technical papers in international conferences and journals. He is a co-recipient of paper awards in ACCV, IEEE ICIP and MMSP. He has been actively participating in program committees of various conferences. He has served as the leading?Technical Program Chair?for?IEEE International Conference on Multimedia & Expo (ICME) 2012?and the leading?General Chair?for?Pacific-rim Conference on Multimedia(PCM) 2012. He is currently an?Associate Editor?for?IEEE Trans on Image Processing (T-IP)?and has served as an Associate Editor for?IEEE Trans on Circuits and Systems for Video Technology (T-CSVT).