Machine Learning Techniques For Acquiring New Knowledge in Image Tracking

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dc.contributor.author Rodriguez, Blanca en_US
dc.contributor.author Concha, Oscar en_US
dc.contributor.author Garcia, Jesus en_US
dc.contributor.author Molina, Jose en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-02-02T09:59:50Z
dc.date.available 2012-02-02T09:59:50Z
dc.date.issued 2008 en_US
dc.identifier 2009006197 en_US
dc.identifier.citation Rodriguez Blanca et al. 2008, 'Machine Learning Techniques For Acquiring New Knowledge in Image Tracking', Taylor & Francis Inc, vol. 22, no. 3, pp. 266-282. en_US
dc.identifier.issn 0883-9514 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/15180
dc.description.abstract The purpose of this research is to apply data mining (DM) to an optimized surveillance video system with the objective of improving tracking robustness and stability. Specifically, the machine learning has been applied to blob extraction and detection, in order to decide whether a detected blob corresponds to a real target or not. Performance is assessed with an Evaluation function, which has been developed for optimizing the video surveillance system. This Evaluation function measures the quality level reached by the tracking system. en_US
dc.language en_US
dc.publisher Taylor & Francis Inc en_US
dc.relation.isbasedon http://dx.doi.org/10.1080/08839510701821652 en_US
dc.title Machine Learning Techniques For Acquiring New Knowledge in Image Tracking en_US
dc.parent Applied Artificial Intelligence en_US
dc.journal.volume 22 en_US
dc.journal.number 3 en_US
dc.publocation PA, USA en_US
dc.identifier.startpage 266 en_US
dc.identifier.endpage 282 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 0000062869 en_US
dc.personcode 104828 en_US
dc.personcode 0000036047 en_US
dc.personcode 0000036042 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US


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