- Sven Dickinson
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, and is also Vice President, Chief Scientist, and Head of the new Samsung Toronto AI Research Center, which opened in May, 2018. 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 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 currently serves on eight editorial boards, including the role of Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence, and the role of co-editor of the Morgan & Claypool Synthesis Lectures on Computer Vision. He is a Fellow of the International Association for Pattern Recognition (IAPR).
The Role of Symmetry in Human and Computer Vision
Symmetry is one of the most ubiquitous regularities in our natural world. For almost 100 years, human vision researchers have studied how the human vision system has evolved to exploit this powerful regularity as a basis for grouping image features and, for almost 50 years, as a basis for how the human vision system might encode the shape of an object. While computer vision is a much younger discipline, the trajectory is similar, with symmetry playing a major role in both perceptual grouping and object representation. After briefly reviewing some of the milestones in symmetry-based perceptual grouping and object representation/recognition in both human and computer vision, I will articulate some of the research challenges. I will then briefly describe some of our recent efforts to address these challenges, including the detection of symmetry in complex imagery and understanding the role of symmetry in human scene perception. In the last part of my talk, I will step back and briefly talk about some of the technical, societal, ethical, and educational challenges we face as an AI community.