There are many challenges in image interpretation and understanding, one of which is the uncertainty in image analysis. Strong and powerful modelling tools are needed to describe the objects in the images. Artificial intelligence (AI) plays a significantly important role in the design of a robust tool for image representation.
Using some examples from our work on uncertainty analysis, this lecture demonstrates how AI can stimulate new concepts or the development of dealing with complicated problems, thus leading us to novel adventures.
About the speaker
Prof Huiyu Zhou currently is a Professor of Machine Learning at University of Leicester, UK. He was awarded a PhD degree in Computer Vision from Heriot-Watt University, UK. His research is sponsored by UK EPSRC, AHRC, ESRC, STFC, MRC, EU, Royal Society, Leverhulme Trust, Puffin Trust, Alzheimer’s Research UK, Invest NI and industry.
He has published over 350 peer-reviewed papers in the field (e.g. Nature and IEEE Transactions). He was the recipient of “CVIU 2012 Most Cited Paper Award”, “MIUA 2020 Best Paper Award”, “ICPRAM 2016 Best Paper Award”. Prof Zhou takes many external commitments, serving as the Editor-in-Chief of Recent Advances in Electrical & Electronic Engineering, and Associate Editor of “IEEE Transaction on Human-Machine Systems”, “IEEE Journal of Biomedical and Health Informatics”, “Pattern Recognition”, “PeerJ Computer Science” and “IEEE Access”, and Area Chair of IJCAI and BMVC.
He is a Technical Committee member in IEEE SMC Society, “Robotics Task Force” and “Biometrics Task Force” of the Intelligent Systems Applications Technical Committee, IEEE Computational Intelligence Society.