dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T15:30:37Z
dc.date.available2018-11-26T15:30:37Z
dc.date.created2018-11-26T15:30:37Z
dc.date.issued2015-01-01
dc.identifier2015 Latin America Congress On Computational Intelligence (la-cci). New York: Ieee, 6 p., 2015.
dc.identifierhttp://hdl.handle.net/11449/158983
dc.identifierWOS:000380396300055
dc.identifier8959637559404206
dc.identifier0000-0002-4899-3983
dc.description.abstractTree species identification is required for many applications. However, current techniques are dependent on the presence of morphological structures such as leaves, which restricts its use in certain situations and seasons. In this context, the use of trunk images can be an alternative. Therefore, the present study developed a pattern recognition based on co-occurrence descriptors, aiming evaluate its performance in the identification of 8 tree species from the Brazilian deciduous native forest, achieving promising results, with precision better than 0.8 for most of them, accuracy equivalent to 0.77 and average area under curve by Receiver Operating Characteristic of 0.88, during the tests with cross-validation sets.
dc.languageeng
dc.publisherIeee
dc.relation2015 Latin America Congress On Computational Intelligence (la-cci)
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjectImage processing
dc.subjectDecision Tree
dc.subjectCo-occurrence descriptors
dc.subjectTrunk images
dc.subjectBrazilian forest
dc.titlePattern recognition in trunk images based on co-occurrence descriptors: a proposal applied to tree species identification
dc.typeActas de congresos


Este ítem pertenece a la siguiente institución