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PhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage maps

dc.contributor.authorLeite, Roger A.
dc.contributor.authorSchnorr, Lucas Mello
dc.contributor.authorAlmeida, Jurandy
dc.contributor.authorAlberton, Bruna [UNESP]
dc.contributor.authorMorellato, Leonor Patrícia Cerdeira [UNESP]
dc.contributor.authorTorres, Ricardo da S.
dc.contributor.authorComba, João L.D.
dc.contributor.institutionFederal University of Rio Grande do Sul – UFRGS
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2018-12-11T17:29:46Z
dc.date.available2018-12-11T17:29:46Z
dc.date.issued2016-01-01
dc.description.abstractPhenoVis is framework for the visual phenological analysis of forest ecosystems. It contains the chronological percentage maps (CPM), a novel representation that is capable of discovering additional patterns by encoding percentage distributions of the data. Two types of masks are used in PhenoVis: a community mask , which considers all plant species in the image; and a species mask , associated with a given plant species. Among the several images taken at different times of the day, the image taken at noon is preferred for the analysis because it minimizes shadow effects. Therefore, only one image per day is used. The analysis considers the chromatic co- efficients associated with each pixel in the image. In PhenoVis we associate different colors with each bucket of the percentage histogram. The histogram granularity defines the size of a given bucket of the percentage distribution. The number of buckets is given by the number of colors available, and the range of the distribution is given by the IOI. The percentage map of a single input image consists of a normalized stacked bar chart. The chronological percentage map consists of a sequence of percentage maps stacked in chronological order, from top to bottom (portrait) or left to right (landscape).en
dc.description.affiliationInstitute of Informatics Federal University of Rio Grande do Sul – UFRGS
dc.description.affiliationInstitute of Science and Technology Federal University of São Paulo – UNIFESP
dc.description.affiliationDept. of Botany São Paulo State University – UNESP
dc.description.affiliationInstitute of Computing University of Campinas – UNICAMP
dc.description.affiliationUnespDept. of Botany São Paulo State University – UNESP
dc.format.extent181-195
dc.identifierhttp://dx.doi.org/10.1016/j.ins.2016.08.052
dc.identifier.citationInformation Sciences, v. 372, p. 181-195.
dc.identifier.doi10.1016/j.ins.2016.08.052
dc.identifier.file2-s2.0-84989964888.pdf
dc.identifier.issn0020-0255
dc.identifier.scopus2-s2.0-84989964888
dc.identifier.urihttp://hdl.handle.net/11449/178321
dc.language.isoeng
dc.relation.ispartofInformation Sciences
dc.relation.ispartofsjr1,635
dc.rights.accessRightsAcesso abertopt
dc.sourceScopus
dc.subjectPhenology
dc.subjectRemote sensing
dc.subjectSimilarity ranking
dc.subjectVegetation index
dc.subjectVisual analytics
dc.titlePhenoVis – A tool for visual phenological analysis of digital camera images using chronological percentage mapsen
dc.typeArtigopt
dspace.entity.typePublication
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Biociências, Rio Claropt
unesp.departmentBotânica - IBpt

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