Tractability and Attention: Key Roles in Robotic Visual Search

dc.contributor.author Tsotsos, John K. en_US
dc.contributor.corporatename York University en_US
dc.contributor.corporatename Georgia Institute of Technology. Institute for Robotics and Intelligent Machine en_US
dc.date.accessioned 2014-03-13T17:40:18Z
dc.date.available 2014-03-13T17:40:18Z
dc.date.issued 2014-03-05
dc.description Presented on March 5, 2014 from 12:00 pm - 1:00 pm in the TSRB Banquet Hall. en_US
dc.description John K. Tsotsos is the Distinguished Research Professor of Vision Science at York University, where he also holds the NSERC Tier I Canada Research Chair in Computational Vision. Tsotsos also holds adjunct appointments in the departments of Computer Science and Ophthalmology and Vision Sciences at the University of Toronto and at the Toronto Rehabilitation Institute. He was director of the highly respected Centre for Vision Research at York University from 2000 to 2006. His research efforts span the areas of computer vision, computational neuroscience, human vision, artificial intelligence, and robotics. Tsotsos received the Canadian Image Processing and Pattern Recognition Society Award for Research Excellence and Service in 2006, and the first President’s Research Excellence Award given by York University, in 2009. He was elected a fellow of the Royal Society of Canada, Academy of Sciences, Division of Mathematics and Physical Sciences in 2010. Prior to joining York University in 2000, Tsotsos was a professor and associate chair of Computer Science at the University of Toronto, where he began his academic career in 1980. Also, while at the University of Toronto, he was appointed to the Division of Cardiology, Faculty of Medicine, and was a fellow of the Canadian Institute for Advanced Research from 1985-1995. He has published more than 300 papers, six of which have received distinctions. His most recent research monograph, “A Computational Perspective on Visual Attention,” was published by the MIT Press in 2011. en_US
dc.description Runtime: 53:21 minutes. en_US
dc.description.abstract Visual search for objects, locations, or events of interest is a central capability for a robot with real-world utility. This capability cannot be limited to yes-no detection; it must include an ability to measure, describe, and compare within the context of a task. We have been investigating this problem since the late 1980s and regardless of the prevailing trends in machine vision, learning, or robotics, have not found reason to ignore the roles of attention, nor a deep understanding of the computational nature of the problem. This presentation will briefly trace our journey. Along the way, we emphasize a number of major points, including the roots of our approach in issues of tractability, the design and evaluation of our subsumptive search algorithm, the development of the AIM saliency model, the confounding nature of sensor bias, the integration of saliency within the object search algorithm, and the need for an overarching framework for attentive behavior, which we have named “Cognitive Programs.” en_US
dc.format.extent 53:21 minutes
dc.identifier.uri http://hdl.handle.net/1853/51324
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.relation.ispartofseries IRIM Seminar Series
dc.subject Robotics en_US
dc.subject Tractability en_US
dc.subject Visual search en_US
dc.subject Cognitive programs en_US
dc.subject Cognition en_US
dc.subject AIM saliency model en_US
dc.title Tractability and Attention: Key Roles in Robotic Visual Search en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
local.relation.ispartofseries IRIM Seminar Series
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
relation.isSeriesOfPublication 9bcc24f0-cb07-4df8-9acb-94b7b80c1e46
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