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dc.contributor.authorBressane, Adriano [UNESP]
dc.contributor.authorFrutuoso Roveda, Jose Arnaldo [UNESP]
dc.contributor.authorGermano Martins, Antonio Cesar [UNESP]
dc.contributor.authorVellasco, MMBR
dc.contributor.authorValdivia, YJT
dc.contributor.authorLopes, H. S.
dc.identifier.citation2015 Latin America Congress On Computational Intelligence (la-cci). New York: Ieee, 6 p., 2015.
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.en
dc.relation.ispartof2015 Latin America Congress On Computational Intelligence (la-cci)
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 identificationen
dc.typeTrabalho apresentado em evento
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationUNESP Univ Estadual Paulista, Environm Sci Grad Program, Sorocaba City, SP, Brazil
dc.description.affiliationUnespUNESP Univ Estadual Paulista, Environm Sci Grad Program, Sorocaba City, SP, Brazil
dc.rights.accessRightsAcesso aberto
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