Deep Texture Feature Aggregation on Leaf Microscopy Images for Brazilian Plant Species Recognition

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorScience for Life Laboratory-
Autor(es): dc.creatorScabini, Leonardo-
Autor(es): dc.creatorZielinski, Kallil-
Autor(es): dc.creatorFares, Ricardo-
Autor(es): dc.creatorKonuk, Emir-
Autor(es): dc.creatorMiranda, Gisele-
Autor(es): dc.creatorKolb, Rosana-
Autor(es): dc.creatorRibas, Lucas-
Autor(es): dc.creatorBruno, Odemir-
Data de aceite: dc.date.accessioned2025-08-21T20:58:47Z-
Data de disponibilização: dc.date.available2025-08-21T20:58:47Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-05-24-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1145/3674029.3674063-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/300684-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/300684-
Descrição: dc.descriptionIn this work, we explore various computer vision techniques, with a focus on texture recognition approaches, for the task of plant species detection. We particularly emphasize the study of a challenging dataset consisting of 50 Brazilian plant species' leaf midrib cross-sections using microscope images. The research focuses on a recent method named Random Encoding of Aggregated Deep Activation Maps (RADAM) that leverages deep features from pre-trained Convolutional Neural Networks (CNNs) for improved plant species identification. This method demonstrates significant advancement over traditional texture analysis and deep learning approaches, showcasing the potential of combining deep feature engineering with texture analysis for accurate plant species recognition.-
Descrição: dc.descriptionSão Carlos Institute of Physics University of São Paulo-
Descrição: dc.descriptionInstitute of Biosciences Humanities and Exact Sciences São Paulo State University-
Descrição: dc.descriptionDivision of Computational Science and Technology School of Electrical Engineering and Computer Science Kth Science for Life Laboratory-
Descrição: dc.descriptionFaculty of Sciences and Letters São Paulo State University-
Descrição: dc.descriptionInstitute of Biosciences Humanities and Exact Sciences São Paulo State University-
Descrição: dc.descriptionFaculty of Sciences and Letters São Paulo State University-
Formato: dc.format209-213-
Idioma: dc.languageen-
Relação: dc.relationACM International Conference Proceeding Series-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectComputer Vision-
Palavras-chave: dc.subjectDeep Learning-
Palavras-chave: dc.subjectPlant Sciences-
Palavras-chave: dc.subjectTexture Analysis-
Título: dc.titleDeep Texture Feature Aggregation on Leaf Microscopy Images for Brazilian Plant Species Recognition-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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