A Descriptive Language for Flexible and Robust Object Recognition

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Title A Descriptive Language for Flexible and Robust Object Recognition
Author Lovell, Nathan Hains; Estivill-Castro, Vladimir
Publication Title RoboCup 2004: Robot Soccer World Cup VIII
Editor Daniele Nardi, Martin Riedmiller, Claude Sammut, José Santos-Victor
Year Published 2005
Place of publication Berlin
Publisher Springer
Abstract Object recognition systems contain a large amount of highly specific knowledge tailored to the objects in the domain of interest. Not only does the system require information for each object in the recognition process, it may require entirely different vision processing techniques. Generic programming for vision processing tasks is hard since systems on-board a mobile robots have strong performance requirements. Such issues as keeping up with incoming frames from a camera limit the layers of abstraction that can be applied. This results in software that is customized to the domain at hand, that is difficult to port to other applications and that is not particularly robust to changes in the visual environment. In this paper we describe a high level object definition language that removes the domain specific knowledge from the implementation of the object recognition system. The language has features of object-orientation and logic, being more declarative and less imperative. We present an implementation of the language efficient enough to be used on a Sony AIBO in the Robocup Four-Legged league competition and several illustrations of its use to rapidly adjust to new environments through quickly crafted object definitions.
Peer Reviewed No
Published Yes
Alternative URI http://dx.doi.org/10.1007/b106671
ISBN 3-540-25046-8
Conference name International Symposium RoboCup 2004
Location Lisbon, Portugal
Date From 2004-07-04
Date To 2004-07-05
URI http://hdl.handle.net/10072/22850
Date Accessioned 2006-07-18
Date Available 2009-05-06T08:15:05Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Engineering and Information Technology
Subject PRE2009-Computer Vision
Publication Type Conference Publications (Full Written Paper - Non-Refereed)
Publication Type Code e2

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