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dc.contributor.authorKim, Jong-Hwan
dc.contributor.authorJo, Seongsik
dc.contributor.authorLattimer, Brian Y.
dc.date.accessioned2017-09-18T09:35:02Z
dc.date.available2017-09-18T09:35:02Z
dc.date.issued2016-09-20
dc.identifier.citationJong-Hwan Kim, Seongsik Jo, and Brian Y. Lattimer, “Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images,” Journal of Sensors, vol. 2016, Article ID 8410731, 13 pages, 2016. doi:10.1155/2016/8410731
dc.identifier.urihttp://hdl.handle.net/10919/78930
dc.description.abstractLocating a fire inside of a structure that is not in the direct field of view of the robot has been researched for intelligent firefighting robots. By classifying fire, smoke, and their thermal reflections, firefighting robots can assess local conditions, decide a proper heading, and autonomously navigate toward a fire. Long-wavelength infrared camera images were used to capture the scene due to the camera’s ability to image through zero visibility smoke. This paper analyzes motion and statistical texture features acquired from thermal images to discover the suitable features for accurate classification. Bayesian classifier is implemented to probabilistically classify multiple classes, and a multiobjective genetic algorithm optimization is performed to investigate the appropriate combination of the features that have the lowest errors and the highest performance. The distributions of multiple feature combinations that have 6.70% or less error were analyzed and the best solution for the classification of fire and smoke was identified.
dc.format.mimetypeapplication/pdf
dc.language.isoenen_US
dc.publisherHindawien_US
dc.rightsCreative Commons Attribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleFeature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Imagesen_US
dc.typeArticle - Refereed
dc.date.updated2017-09-18T09:35:02Z
dc.description.versionPeer Reviewed
dc.rights.holderCopyright © 2016 Jong-Hwan Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.title.serialJournal of Sensorsen_US
dc.identifier.doihttps://doi.org/10.1155/2016/8410731
dc.type.dcmitypeText


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Creative Commons Attribution 4.0 International
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