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| Registros recuperados : 1 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agroindústria de Alimentos. |
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Data corrente: |
16/10/2023 |
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Data da última atualização: |
16/10/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 3 |
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Autoria: |
HIDALGO CHÁVEZ, D. W.; SILVA, F. L. C. DA; PINTO, R. V.; CARVALHO, C. W. P. de; FREITAS-SILVA, O. |
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Afiliação: |
DAVY WILLIAM HIDALGO CHÁVEZ, UFRRJ; FELIPE LEITE COELHO DA SILVA, UFRRJ; RENAN VICENTE PINTO, UFRRJ; CARLOS WANDERLEI PILER DE CARVALHO, CTAA; OTNIEL FREITAS SILVA, CTAA. |
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Título: |
Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
CyTA: Journal of Food, v. 21, n. 1, p. 606-613, 2023. |
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DOI: |
https://doi.org/10.1080/19476337.2023.2263513 |
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Idioma: |
Inglês |
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Conteúdo: |
This article describes simple methods to group images including principal component analysis (PCA) and hierarchical clustering of principal components (HCPC). Images of expanded and low expanded extrudates were processed using two optimization alternatives: a) image size reduction (from 2126 to 25 pixels); and b) grayscale conversion before size reduction. After applying PCA and HCPC, all tests yielded consistently similar results with the same PCA distribution and identical HCPC groups. Furthermore, expanded and low expanded extrudates formed groups with their respective peers. The RAM allocated to images and the time required to process them was reduced from 1727 Mb to less than 5 Mb and from ~ 2000s to just 0.1s, respectively. These results demonstrate the e feasibility of using these two simple multivariate statistical techniques for image classification. |
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Palavras-Chave: |
Image classification. |
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Thesaurus NAL: |
Image analysis; Principal component analysis. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1157235/1/Streamlined-approaches-for-image-classification-using-principal-component-analysis-and-hierarchical-clustering-of-extrudates-from-coffee-and-sorghum-b.pdf
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Marc: |
LEADER 01652naa a2200217 a 4500
001 2157235
005 2023-10-16
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1080/19476337.2023.2263513$2DOI
100 1 $aHIDALGO CHÁVEZ, D. W.
245 $aStreamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends.$h[electronic resource]
260 $c2023
520 $aThis article describes simple methods to group images including principal component analysis (PCA) and hierarchical clustering of principal components (HCPC). Images of expanded and low expanded extrudates were processed using two optimization alternatives: a) image size reduction (from 2126 to 25 pixels); and b) grayscale conversion before size reduction. After applying PCA and HCPC, all tests yielded consistently similar results with the same PCA distribution and identical HCPC groups. Furthermore, expanded and low expanded extrudates formed groups with their respective peers. The RAM allocated to images and the time required to process them was reduced from 1727 Mb to less than 5 Mb and from ~ 2000s to just 0.1s, respectively. These results demonstrate the e feasibility of using these two simple multivariate statistical techniques for image classification.
650 $aImage analysis
650 $aPrincipal component analysis
653 $aImage classification
700 1 $aSILVA, F. L. C. DA
700 1 $aPINTO, R. V.
700 1 $aCARVALHO, C. W. P. de
700 1 $aFREITAS-SILVA, O.
773 $tCyTA: Journal of Food$gv. 21, n. 1, p. 606-613, 2023.
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Registro original: |
Embrapa Agroindústria de Alimentos (CTAA) |
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