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.author | de Sa Jr., Jarbas Joaci M. | |
dc.contributor.author | Backes, André R. | |
dc.contributor.author | Rossatto, Davi Rodrigo [UNESP] | |
dc.contributor.author | Kolb, Rosana Marta [UNESP] | |
dc.contributor.author | Bruno, Odemir M. | |
dc.contributor.institution | Universidade Federal de Uberlândia (UFU) | |
dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
dc.contributor.institution | Instituto de Física de São Carlos | |
dc.date.accessioned | 2014-05-27T11:25:55Z | |
dc.date.available | 2014-05-27T11:25:55Z | |
dc.date.issued | 2011-07-01 | |
dc.description.abstract | Currently, 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.affiliation | Departamento de Ciências Matemáticas e de Computação, Avenida Trabalhador São Carlense, n 400, 13560-970, São Carlos, SP | |
dc.description.affiliation | Faculdade de Computação Universidade Federal de Uberlândia, Campus Santa Mônica, Av. Joao Naves de Avila, n 2121, 38408-100, Uberlândia, MG | |
dc.description.affiliation | Departamento de Ciências Biológicas Faculdade de Ciências e Letras Universidade Estadual Paulista, UNESP, Av. Dom Antônio, 2100, 19806-900, Assis, SP | |
dc.description.affiliation | Instituto de Física de São Carlos, Avenida Trabalhador São Carlense, n 400, 13560-970, São Carlos, SP | |
dc.description.affiliationUnesp | Departamento de Ciências Biológicas Faculdade de Ciências e Letras Universidade Estadual Paulista, UNESP, Av. Dom Antônio, 2100, 19806-900, Assis, SP | |
dc.description.sponsorship | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | |
dc.description.sponsorship | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
dc.description.sponsorshipId | FAPESP: 06/54367-9 | |
dc.description.sponsorshipId | CNPq: 135251/2006 | |
dc.description.sponsorshipId | CNPq: 306628/2007-4 | |
dc.description.sponsorshipId | CNPq: 484474/2007-3 | |
dc.format.extent | 467-479 | |
dc.identifier | http://dx.doi.org/10.1139/b11-038 | |
dc.identifier.citation | Botany, v. 89, n. 7, p. 467-479, 2011. | |
dc.identifier.doi | 10.1139/b11-038 | |
dc.identifier.issn | 1916-2804 | |
dc.identifier.lattes | 9548962911240501 | |
dc.identifier.orcid | 0000-0003-3841-5597 | |
dc.identifier.scopus | 2-s2.0-80051556556 | |
dc.identifier.uri | http://hdl.handle.net/11449/72514 | |
dc.identifier.wos | WOS:000295392800005 | |
dc.language.iso | eng | |
dc.relation.ispartof | Botany | |
dc.relation.ispartofsjr | 0,611 | |
dc.rights.accessRights | Acesso restrito | |
dc.source | Scopus | |
dc.subject | Feature extraction | |
dc.subject | Jeffrey-matusita distance | |
dc.subject | Linear discriminant analysis | |
dc.subject | Plant identification | |
dc.subject | Taxonomy | |
dc.subject | anatomy | |
dc.subject | color | |
dc.subject | computer simulation | |
dc.subject | computer vision | |
dc.subject | cuticle | |
dc.subject | discriminant analysis | |
dc.subject | identification method | |
dc.subject | leaf | |
dc.subject | Neotropic Ecozone | |
dc.subject | phylogenetics | |
dc.subject | phylogeny | |
dc.subject | quantitative analysis | |
dc.subject | savanna | |
dc.subject | taxonomy | |
dc.subject | texture | |
dc.subject | Brazil | |
dc.subject | Magnoliophyta | |
dc.title | Measuring and analyzing color and texture information in anatomical leaf cross sections: An approach using computer vision to aid plant species identification | en |
dc.type | Artigo | |
dcterms.license | http://www.nrcresearchpress.com/page/authors/information/rights | |
dspace.entity.type | Publication | |
unesp.author.lattes | 9548962911240501 | |
unesp.author.orcid | 0000-0003-3841-5597[4] | |
unesp.campus | Universidade Estadual Paulista (UNESP), Faculdade de Ciências e Letras, Assis | pt |
unesp.department | Ciências Biológicas - FCLAS | pt |