Object Categorization and the Need for Many-to-Many Matching
Object recognition systems have their roots in the AI community, and originally addressed the problem of object categorization. These early systems, however, were limited by their inability to bridge the representational gap between low-level image features and high-level object models, hindered by the assumption of one-to-one correspondence between image and model features. Over the next thirty years, the mainstream recognition community moved steadily in the direction of exemplar recognition while narrowing the representational gap. The community is now returning to the categorization problem, and faces the same representational gap as its predecessors did. We review the evolution of object recognition systems and argue that bridging this representational gap requires an ability to match image and model features many-to-many. We review three formulations of the many-to-many matching problem as applied to model acquisition and object recognition.
Dr. Sven J. Dickinson
Professor of Computer Science, University of Toronto on April 2, 2018 at 8:00 AM in Engineering Building II, Room 1230.
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 currently Professor of Computer Science at the University of Toronto, where he served as Departmental Vice Chair, from 2003-2006, and as the Associate Professor, from 2000-2007. From 1995-2000, he was an Assistant Professor of Computer Science at Rutgers University, where he also held a joint appointment in the Rutgers Center for Cognitive Science (RuCCS) and membership in the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS). From 1994-1995, he was a Research Assistant Professor in the Rutgers Center for Cognitive Science, and from 1991-1994, a Research Associate at the Artificial Intelligence Laboratory, University of Toronto. He has held affiliations with the MIT Media Laboratory (Visiting Scientist, 1992-1994), the University of Toronto (Visiting Assistant professor, 1994-1997), and the Computer Vision Laboratory of the Center for Automation Research at the University of Maryland (Assistant Research Scientist, 1993-1994 , Visiting Assistant Professor, 1994-1997). Prior to his academic career, he worked in the computer vision industry, designing image processing systems for Grinnell Systems Inc., San Jose, CA, 1983-1984, and optical character recognition systems for DEST, Inc., Milpitas, CA 1984-1985.
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