Title: Quaternion Neural Networks for Robust Environmental Perception in Adverse Weather Autonomous Driving

Abstract: Reliable environmental perception in autonomous driving remains challenging due to adverse weather conditions degrading image quality and compromising computer vision tasks. While traditional image processing methods struggle to mitigate these effects, this keynote introduces Quaternion Neural Networks (QNNs) as an innovative solution for robust scene understanding in difficult weather conditions. QNNs utilize four-dimensional quaternion numbers to efficiently process multidimensional visual data while capturing intrinsic relationships between color channels and spatial features. This mathematical framework provides several advantages over conventional real-valued neural networks, including reduced parameters, improved generalization, and natural handling of structural dependencies in visual data. This talk will cover the fundamental principles of QNNs, including quaternion convolution operations and recent advancements in quaternion transformers. We will demonstrate their practical application in autonomous driving scenarios, highlighting superior performance in weather effect removal, semantic segmentation, and object detection in foggy, cloudy, and rainy conditions. Experimental results show that QNN-based approaches consistently outperform traditional real-valued networks in preserving scene details and maintaining perception reliability under adverse weather, all while requiring fewer parameters. The presentation will conclude with insights into future research directions and potential applications beyond autonomous driving.

Biography: Dr. Sos Agaian is a Distinguished Professor of Computer Science at the Graduate Center and the College of Staten Island, CUNY. Before joining CUNY, he was the Peter T. Flawn Professor at the University of Texas at San Antonio. He also served as a Visiting Professor at Tufts University and a Lead Scientist at Aware, Inc. in Massachusetts. His research spans computational vision, machine learning, AI, multimedia security, remote sensing, and biomedical imaging. Dr. Agaian has received funding from NSF, DARPA, Google, and other agencies. He has published over 850 articles, 10 books, and 19 book chapters and holds 56 patents/disclosures, many of which have been licensed. He has mentored 45 PhD students and received multiple awards for research and teaching, including the MAEStro Educator of the Year, the Distinguished Research Award, the Innovator of the Year, the Tech Flash Titans-Top Researcher Award, and recognition as an Influential Member of the School of Engineering at Tufts University. He is an Associate Editor for several journals, including the Image Processing Transaction (IEEE) and IEEE Transaction of Cybernetics. He is a fellow of the Society for Imaging Science and Technology (IS&T), the Optical Society of America (SPIE), the American Association for the Advancement of Science (AAAS), Institute of Electrical and Electronics Engineers (IEEE), The Asia-Pacific Artificial Intelligence Association (AAIA), and a Foreign Member of the Armenian National Academy. He has delivered over 30 keynote speeches, 100 invited talks, and co-founded/chaired over 200 international conferences. He has also been a Distinguished IEEE Systems, Man, and Cybernetics Society Lecturer.