Publicação:
Measuring and analyzing color and texture information in anatomical leaf cross sections: An approach using computer vision to aid plant species identification

dc.contributor.authorde Sa Jr., Jarbas Joaci M.
dc.contributor.authorBackes, André R.
dc.contributor.authorRossatto, Davi Rodrigo [UNESP]
dc.contributor.authorKolb, Rosana Marta [UNESP]
dc.contributor.authorBruno, Odemir M.
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionInstituto de Física de São Carlos
dc.date.accessioned2014-05-27T11:25:55Z
dc.date.available2014-05-27T11:25:55Z
dc.date.issued2011-07-01
dc.description.abstractCurrently, studies on leaf anatomy have provided an important source of characters helping taxonomic, systematic, and phylogenetic studies. These studies strongly rely on measurements of characters (such as tissue thickness) and qualitative information (structures description, presence-absence of structures). In this work, we provide a new computational approach that semiautomates the collection of some quantitative data (cuticle, adaxial epidermis, and total leaf thickness) and accesses a new source of information in leaf cross-section images: the texture and the color of leaf tissues. Our aim was to evaluate this information for plant identification purposes. We successfully tested our system identifying eight species from different phylogenetic positions in the angiosperm phylogeny from the neotropical savanna of central Brazil. The proposed system checks the potential of identifying the species for each extracted measure using the Jeffrey-Matusita distance and composes a feature vector with the most important metrics. A linear discriminant analysis with leave-one-out to classify the samples was used. The experiments achieved a 100% success rate in terms of identifying the studied species accessing the above-described parameters, demonstrating that our computational approach can be a helpful tool for anatomical studies, especially ones devoted to plant identification and systematic studies.en
dc.description.affiliationDepartamento de Ciências Matemáticas e de Computação, Avenida Trabalhador São Carlense, n 400, 13560-970, São Carlos, SP
dc.description.affiliationFaculdade de Computação Universidade Federal de Uberlândia, Campus Santa Mônica, Av. Joao Naves de Avila, n 2121, 38408-100, Uberlândia, MG
dc.description.affiliationDepartamento de Ciências Biológicas Faculdade de Ciências e Letras Universidade Estadual Paulista, UNESP, Av. Dom Antônio, 2100, 19806-900, Assis, SP
dc.description.affiliationInstituto de Física de São Carlos, Avenida Trabalhador São Carlense, n 400, 13560-970, São Carlos, SP
dc.description.affiliationUnespDepartamento de Ciências Biológicas Faculdade de Ciências e Letras Universidade Estadual Paulista, UNESP, Av. Dom Antônio, 2100, 19806-900, Assis, SP
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 06/54367-9
dc.description.sponsorshipIdCNPq: 135251/2006
dc.description.sponsorshipIdCNPq: 306628/2007-4
dc.description.sponsorshipIdCNPq: 484474/2007-3
dc.format.extent467-479
dc.identifierhttp://dx.doi.org/10.1139/b11-038
dc.identifier.citationBotany, v. 89, n. 7, p. 467-479, 2011.
dc.identifier.doi10.1139/b11-038
dc.identifier.issn1916-2804
dc.identifier.lattes9548962911240501
dc.identifier.orcid0000-0003-3841-5597
dc.identifier.scopus2-s2.0-80051556556
dc.identifier.urihttp://hdl.handle.net/11449/72514
dc.identifier.wosWOS:000295392800005
dc.language.isoeng
dc.relation.ispartofBotany
dc.relation.ispartofsjr0,611
dc.rights.accessRightsAcesso restrito
dc.sourceScopus
dc.subjectFeature extraction
dc.subjectJeffrey-matusita distance
dc.subjectLinear discriminant analysis
dc.subjectPlant identification
dc.subjectTaxonomy
dc.subjectanatomy
dc.subjectcolor
dc.subjectcomputer simulation
dc.subjectcomputer vision
dc.subjectcuticle
dc.subjectdiscriminant analysis
dc.subjectidentification method
dc.subjectleaf
dc.subjectNeotropic Ecozone
dc.subjectphylogenetics
dc.subjectphylogeny
dc.subjectquantitative analysis
dc.subjectsavanna
dc.subjecttaxonomy
dc.subjecttexture
dc.subjectBrazil
dc.subjectMagnoliophyta
dc.titleMeasuring and analyzing color and texture information in anatomical leaf cross sections: An approach using computer vision to aid plant species identificationen
dc.typeArtigo
dcterms.licensehttp://www.nrcresearchpress.com/page/authors/information/rights
dspace.entity.typePublication
unesp.author.lattes9548962911240501
unesp.author.orcid0000-0003-3841-5597[4]
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Ciências e Letras, Assispt
unesp.departmentCiências Biológicas - FCLASpt

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