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dc.contributor.authorRaveane, William
dc.contributor.authorGonzález Arrieta, María Angélica 
dc.date.accessioned2017-09-06T09:16:58Z
dc.date.available2017-09-06T09:16:58Z
dc.date.issued2014/06
dc.identifier.citationHybrid Artificial Intelligence Systems. 9th International Conference, HAIS 2014, Salamanca, Spain, June 11-13, 2014. Proceedings. Lecture Notes in Computer Science. Volumen 8480, pp. 365-376.
dc.identifier.isbn978-3-319-07616-4(Print) / 978-3-319-07617-1(Online)
dc.identifier.issn0302-9743(Print) / 1611-3349(Online)
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-07617-1_33
dc.identifier.urihttp://hdl.handle.net/10366/135143
dc.description.abstractA system is presented which combines deep neural networks with discrete inference techniques for the successful recognition of an image. The system presented builds upon the classical sliding window method but applied in parallel over an entire input image. The result is discretized by treating each classified window as a node in a markov random field and applying a minimization of its associated energy levels. Two important benefits are observed with this system: a gain in performance by virtue of the system’s parallel nature, and an improvement in the localization precision due to the inherent connectivity between classified windows.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleHybrid System for Mobile Image Recognition through Convolutional Neural Networks and Discrete Graphical Models
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported