Wavelet-based spectral descriptors for detection of damage in sunflower seeds

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorSáfadi, Thelma-
Autor(es): dc.creatorKang, Minkyoung-
Autor(es): dc.creatorLeite, Isabel C. C.-
Autor(es): dc.creatorVidaković, Brani-
Data de aceite: dc.date.accessioned2026-02-09T11:41:01Z-
Data de disponibilização: dc.date.available2026-02-09T11:41:01Z-
Data de envio: dc.date.issued2019-09-09-
Data de envio: dc.date.issued2019-09-09-
Data de envio: dc.date.issued2016-07-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/36751-
Fonte completa do material: dc.identifierhttps://www.worldscientific.com/doi/10.1142/S0219691316500272-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1145122-
Descrição: dc.descriptionAnalysis of seeds is essential for determining seed lot quality and hence its sowing value. Although not fully objective, assessing seed quality with radiographic images has been used as alternative to standard laboratory testing. Here, we applied 2D scale-mixing non-decimated wavelet transform for automatic processing of radiographic images of sunflower seeds. From the transformed images several spectral indices are derived. These descriptors involve spectral slopes which are directly connected with the degree of image regularity. A methodology paradigm was developed to analyze the images and classify each seed as damaged or undamaged (slight, full). By considering binary and multinomial supervised classification, the rate of correct classification was found to be 82% for damaged and full seeds, and 57% for damaged, slightly damaged, and full seeds. Although in principle many different transforms can serve as a basis in deriving spectral indices, this particular transform proved out to be sensitive to image anisotropy (by its scale-mixing nature), and stable in computation (by its non-decimated nature).-
Idioma: dc.languageen-
Publicador: dc.publisherWorld Scientific Publishing-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceInternational Journal of Wavelets Multiresolution and Information Processing-
Palavras-chave: dc.subject2D discrete wavelet transform-
Palavras-chave: dc.subjectMultiscale analysis-
Palavras-chave: dc.subjectX-ray image classification-
Palavras-chave: dc.subjectTwo-dimensional discrete wavelet transform-
Palavras-chave: dc.subjectMulti-scale analysis-
Título: dc.titleWavelet-based spectral descriptors for detection of damage in sunflower seeds-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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