Speaker:
Sven Dickinson
Talk Title:
Symmetry in Human and Computer Vision: a Case Study in Scene Perception
Date and Location:
Wednesday, April 23, 2025
4-5 p.m.
BA 5187 and online. Zoom registration link for virtual attendance.
Reception to follow
There is no registration required to attend this event in person. However, seating is limited, so arriving early is recommended.
Abstract:
Symmetry is one of the most ubiquitous regularities in our natural world. For 100 years, human vision researchers have studied how the human vision system has evolved to exploit this powerful regularity for perceptual grouping. In the computer vision community, early (pre-deep learning) researchers also exploited symmetry, and developed elegant representations for symmetry in support of segmentation, grouping, 3-D reconstruction, and object recognition. In the first part of the talk, I will review our research that draws on a symmetry-based representation in computer vision to identify the important role that symmetry plays in the task of human scene perception. In the second part of the talk, I will review our more recent efforts to leverage these findings in human vision to improve the performance of a deep learning computer vision system addressing the same task. This is part of a new research program that seeks to draw on human vision to better inform the design of computer vision systems.
Biography:
Sven Dickinson received the B.A.Sc. degree in Systems Design Engineering from the University of Waterloo, in 1983, and the M.S. and Ph.D. degrees in Computer Science from the University of Maryland, in 1988 and 1991, respectively. He is Professor and past Chair of the Department of Computer Science at the University of Toronto. From 2018 to 2024, he took a leave from the university to serve as Vice President and inaugural Head of the Samsung Toronto AI Research Center. Prior to that, he was a faculty member at Rutgers University where he held a joint appointment between the Department of Computer Science and the Rutgers Center for Cognitive Science (RuCCS). His research interests revolve around the problem of shape perception in computer vision and, more recently, human vision. He has received the National Science Foundation CAREER award, the Government of Ontario Premiere's Research Excellence Award (PREA), and the Lifetime Research Achievement Award from the Canadian Image Processing and Pattern Recognition Society (CIPPRS). He was the Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence, from 2017-2021, currently serves on seven editorial boards, and is co-editor of the Morgan & Claypool Synthesis Lectures on Computer Vision. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the International Association for Pattern Recognition (IAPR), a member of the IEEE Computer Society Board of Governors, and an IEEE Golden Core Member.