Title: 3D Computer Vision with Applications to Autonomous Vehicles

Abstract: In this talk we present our works on 3D computer vision based on RGBD sensing. A visual SLAM system on static and dynamic platforms are described that uses motion prior to obtain accurate motion estimation in metric scale to make dynamic features usable for SLAM on dynamic platforms. When depth info is not available, deep learning is used to perform depth prediction and the predicted depth can be used for RGBD SLAM. In this talk, we will also discuss 3D object detection and tracking that can be used for obstacles avoidance, including approaches to enhance object detection in different environments. The proposed RGBD image processing techniques for SLAM, depth prediction, object detection and object tracking are applied to autonomous driving and the performance are evaluated using publicly available benchmark datasets and experimental datasets we collected for practical driving scenarios in real environments including highways, residential, semi-urban and urban roads.


Prof. Henry Leung
IEEE Fellow, SPIE Fellow
University of Calgary, Canada

Henry Leung is a professor of the Department of Electrical and Software Engineering of the University of Calgary. His current research interests include data analytic, information fusion, machine learning, signal and image processing, robotics, and internet of things. He has published over 350 journal papers and 250 refereed conference papers. Dr. Leung has been the associate editor of various journals such as the IEEE Circuits and Systems Magazine, International Journal on Information Fusion, IEEE Trans. Aerospace and Electronic Systems, IEEE Signal Processing Letters, IEEE Trans. Circuits and Systems, Scientific Reports He has also served as guest editors for the special issue “Intelligent Transportation Systems” for the International Journal on Information Fusion and “Cognitive Sensor Networks” for the IEEE Sensor Journal. He is the editor of the Springer book series on “Information Fusion and Data Science”. He is a Fellow of IEEE and SPIE.


Title: Information Theoretic Learning

Abstract: Information theory has attracted increasing attention in the fields of machine learning and signal processing in recent years. Novel information theoretic approaches have been proposed for different learning problems, such as error entropy estimator for supervised learning with the minimum error entropy (MEE) criterion, and mutual information estimator for representation learning with the information maximization principle. In general, information theoretic quantities can capture higher-order statistics and offer potentially significant performance improvement in the adaptation of linear and nonlinear models. This talk will focus on recent advances in information theoretic learning (ITL), including some new quantities and new algorithms in ITL and applications to various machine learning and signal processing tasks.


Prof. Badong Chen
Institute of Artificial Intelligence and Robotics, XJTU
College of Artificial Intelligence, XJTU
School of Electronic and Inform. Engineering, XJTU

Badong Chen received the B.S. and M.S. degrees in control theory and engineering from Chongqing University, in 1997 and 2003, respectively, and the Ph.D. degree in computer science and technology from Tsinghua University in 2008. He was a Postdoctoral Researcher with Tsinghua University from 2008 to 2010, and a Postdoctoral Associate at the University of Florida Computational NeuroEngineering Laboratory (CNEL) during the period October, 2010 to September, 2012. In 2015, he visited the Nanyang Technological University (NTU) as a visiting research scientist. He also served as a senior research fellow with The Hong Kong Polytechnic University in 2017. Currently he is a professor at the Institute of Artificial Intelligence and Robotics (IAIR), Xi’an Jiaotong University. His research interests are in signal processing, machine learning, artificial intelligence, neural engineering and robotics. He has published 2 books, 4 chapters, and over 200 papers in various journals and conference proceedings. Dr. Chen is an IEEE Senior Member and serves (or has served) as the Technical Committee Member of IEEE SPS Machine Learning for Signal Processing (MLSP), and the Technical Committee Member of IEEE CIS Cognitive and Developmental Systems (CDS), and an Associate Editor (or Editor Board Member) for several international journals including IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Cognitive and Developmental Systems (TCDS), Neural Networks, Journal of The Franklin Institute, and Entropy.