Daming Shi

Bio

Prof Daming Shi got his first PhD in Mechanical Engineering from the Harbin Institute of Technology, followed by the second PhD in Computer Science from the University of Southampton. Before joining UCA, he worked as a Reader of Theoretical Computer Science at Middlesex University, and Assistant Professor at Nanyang Technological University, Singapore. He was appointed as visiting or distinguished professor by universities across the world including Germany, Australia, South Korea and China.

Recently, Prof Shi founded the Intelligent Virtual Reality Lab in Shenzhen, applying AI to VR products. He was a co-founder of the Pattern Recognition and Machine Intelligence Association (PREMIA), Singapore, and chair of the technical committee on the Intelligent Internet System, IEEE SMC Society.

Prof Shi has completed a number of projects across the world, funded by the Agency for Science, Technology and Research (A*STAR), National Medical Research Council (NMRC), Singapore, National Science Foundation Council (NSFC) China, Ministry of Science and Technology (MOST), China, as well as EU FP7.

So far, he has published two books and over 200 papers in the areas of artificial intelligence and image processing. His publications appear in reputable journals such as IEEE TPAMI, IEEE TIP, and IEEE TFS; as well as major conferences such as CVPR.

Here are ten of his representative publications:

• Orouskhani, M., D. Shi, X. Cheng (2021). A Fuzzy Adaptive Dynamic NSGA-II with Fuzzy-based Borda Ranking Method and Its Application to Multimedia Data Analysis, IEEE Transactions on Fuzzy Systems, 29(1):118-128.
• B. Deng, B., S. Jia, D. Shi (2020), Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 58(2): 1422- 1435.
• Zhu, M., D. Shi, M. Zheng, M. Sadiq (2019). Robust Facial Landmark Detection via Occlusion Adaptive Deep Networks. CVPR 2019: 3486-3496.
• Zhang, Y., D. Shi, J. Gao (2017). Low-Rank-Sparse Subspace Representation for Robust Regression. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii.
• Rahmdel, P. S., D. Shi, R. Comley (2014). Comment on “Collinear Segment Detection Using HT Neighborhoods”, IEEE Transactions on Image Processing, 23(2):952-955.
• Shi, D., Gao, J., Rahmdel, P., Antolovich, M. and Clark, T. (2013). UND: Unite-and-Divide Method in Fourier and Radon Domains for Line Segment Detection, IEEE Transactions on Image Processing, 22(6):2500-2505.
• Shi, D., L. Zheng, J. Liu (2010), Advanced Hough transform using a fractional Fourier method. IEEE Transactions on Image Processing, 19(6):1558-1566.
• Shi, D., and M. N. Nguyen (2010). Fuzzy CMAC with Incremental Bayesian Ying Yang Learning and Dynamic Rule Construction, IEEE Transactions on Systems, Man and Cybernetics, 40(2):548-552.
• Nguyen, M. N., D. Shi and C. Quek (2006). FCMAC-BYY: Fuzzy CMAC using Bayesian Ying-Yang learning, IEEE Transactions on Systems, Man and Cybernetics, Part B, 36(5):1180-1190.
• Shi, D., S. R. Gunn and R. I. Damper, (2003). Handwritten Chinese radical recognition using nonlinear active shape models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2):277-280.