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dc.creatorFallas Moya, Fabián
dc.creatorTorres Rojas, Francisco
dc.date.accessioned2018-05-21T21:18:23Z
dc.date.available2018-05-21T21:18:23Z
dc.date.issued2018-02-17
dc.identifier.citationhttps://link.springer.com/chapter/10.1007%2F978-3-319-76261-6_1es_ES
dc.identifier.isbn978-3-319-76260-9
dc.identifier.isbn978-3-319-76261-6
dc.identifier.urihttp://hdl.handle.net/10669/74745
dc.description.abstractAt this time, great effort is being directed toward developing problem-solving technology that mimics human cognitive processes. Research has been done to develop object recognition using Computer Vision for daily tasks such as secure access, traffic management, and robotic behavior. For this research, four different machine learning algorithms have been developed to overcome the computer vision problem of object recognition. Hierarchical temporal memory (HTM) is an emerging technology based on biological methods of the human cortex to learn patterns. This research applied an HTM algorithm to images (video sequences) in order to compare this technique against two others: support vector machines (SVM) and artificial neural networks (ANN). It was concluded that HTM was the most effective.es_ES
dc.description.sponsorshipUniversidad de Costa Rica//UCR/Costa Ricaes_ES
dc.language.isoen_USes_ES
dc.sourceObject Recognition Using Hierarchical Temporal Memory. In: Brito-Loeza C., Espinosa-Romero A. (eds) Intelligent Computing Systems. ISICS 2018. Communications in Computer and Information Science, vol 820. Springer, Chames_ES
dc.subjectMachine learninges_ES
dc.subjectComputer visiones_ES
dc.subjecthierarchical temporal memoryes_ES
dc.titleObject Recognition Using Hierarchical Temporal Memoryes_ES
dc.typeinfo:eu-repo/semantics/bookPartes_ES
dc.typeCapítulo de libroes_ES
dc.identifier.doi10.1007/978-3-319-76261-6_1
dc.description.procedenceUCR::Sedes Regionales::Sede del Caribees_ES
dc.description.procedenceUCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ciencias de la Computación e Informáticaes_ES


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