Handwritten feature descriptor methods applied to fruit classification

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dc.contributor.authorMacanhã, Priscila Alves [UNESP]
dc.contributor.authorEler, Danilo Medeiros [UNESP]
dc.contributor.authorGarcia, Rogério Eduardo [UNESP]
dc.contributor.authorMarcílio Junior, Wilson Estécio [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:17:19Z
dc.date.available2018-12-11T17:17:19Z
dc.date.issued2018-01-01
dc.description.abstractSeveral works have presented distinct ways to compute feature descriptor from different applications and domains. A main issue in Computer Vision systems is how to choose the best descriptor for specific domains. Usually, Computer Vision experts try several combination of descriptor until reach a good result of classification, clustering or retrieving – for instance, the best descriptor is that capable of discriminating the dataset images and reach high correct classification rates. In this paper, we used feature descriptors commonly applied in handwritten images to improve the image classification from fruit datasets. We present distinct combinations of Zoning and Character-Edge Distance methods to generate feature descriptor from fruits. The combination of these two descriptor with Discrete Fourier Transform led us to a new approach for acquire features from fruit images. In the experiments, the new approaches are compared with the main descriptors presented in the literature and our best approach of feature descriptors reaches a correct classification rate of 97.5%. Additionally, we also show how to perform a detailed inspection in feature spaces through an image visualization technique based on a similarity trees known as Neigbor Joining (NJ).en
dc.description.affiliationDepartamento de Matemática e Computação Faculdade de Ciências e Tecnologia UNESP – Univ Estadual Paulista Presidente Prudente
dc.description.affiliationUnespDepartamento de Matemática e Computação Faculdade de Ciências e Tecnologia UNESP – Univ Estadual Paulista Presidente Prudente
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 16/11707-6
dc.description.sponsorshipIdFAPESP: 2013/03452-0
dc.format.extent699-705
dc.identifierhttp://dx.doi.org/10.1007/978-3-319-54978-1_87
dc.identifier.citationAdvances in Intelligent Systems and Computing, v. 558, p. 699-705.
dc.identifier.doi10.1007/978-3-319-54978-1_87
dc.identifier.issn2194-5357
dc.identifier.lattes8031012573259361
dc.identifier.orcid0000-0003-1248-528X
dc.identifier.scopus2-s2.0-85040539470
dc.identifier.urihttp://hdl.handle.net/11449/175744
dc.language.isoeng
dc.relation.ispartofAdvances in Intelligent Systems and Computing
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectComputer vision
dc.subjectFeature descriptor
dc.subjectFruit classification
dc.subjectHandwritten character
dc.subjectImage visualization
dc.titleHandwritten feature descriptor methods applied to fruit classificationen
dc.typeTrabalho apresentado em evento
unesp.author.lattes8031012573259361[3]
unesp.author.orcid0000-0003-1248-528X[3]

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Artigos - Matemática e Computação - FCT