Automatic classification of physical defects in green coffee beans using CGLCM and SVM

Rayner H.Montes Condori; Juan H.Chuctaya Humari; Christian E. Portugal-Zambrano; Juan C. Gutiérrez-Cáceres; César A. Beltrán-Castañón

doi:10.1109/CLEI.2014.6965169

Automatic classification of physical defects in green coffee beans using CGLCM and SVM

Rayner H.Montes Condori, Juan H.Chuctaya Humari, Christian E. Portugal-Zambrano, Juan C. Gutiérrez-Cáceres, César A. Beltrán-Castañón

Universidad Nacional de San Agustín de Arequipa

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

8 Citas (Scopus)

Resumen

This work is focused on the evaluation of physical coffee beans through a model of automatic classification of defects. The model uses a segmentation step that discriminates the background from the coffee bean image with a follow contours algorithm, then a CGLCM is introduced as features extractor and a Support Vector Machine for the classification task, a database of images has been collected with a total of 3367 images, the classification process used twelve categories of defects, the results of classification showed a accuracy of 86%. Finally a set of conclusions and future works are presented.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2014 Latin American Computing Conference, CLEI 2014
Editores	Pablo Ezzatti, Andrea Delgado
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781479961306
DOI	https://doi.org/10.1109/CLEI.2014.6965169
Estado	Publicada - 21 nov. 2014
Evento	2014 40th Latin American Computing Conference, CLEI 2014 - Montevideo, Uruguay Duración: 15 set. 2014 → 19 set. 2014

Serie de la publicación

Nombre	Proceedings of the 2014 Latin American Computing Conference, CLEI 2014

Conferencia

Conferencia	2014 40th Latin American Computing Conference, CLEI 2014
País/Territorio	Uruguay
Ciudad	Montevideo
Período	15/09/14 → 19/09/14

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10.1109/CLEI.2014.6965169

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Condori, R. H. M., Humari, J. H. C., Portugal-Zambrano, C. E., Gutiérrez-Cáceres, J. C., & Beltrán-Castañón, C. A. (2014). Automatic classification of physical defects in green coffee beans using CGLCM and SVM. En P. Ezzatti, & A. Delgado (Eds.), Proceedings of the 2014 Latin American Computing Conference, CLEI 2014 Artículo 6965169 (Proceedings of the 2014 Latin American Computing Conference, CLEI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLEI.2014.6965169

Condori, Rayner H.Montes ; Humari, Juan H.Chuctaya ; Portugal-Zambrano, Christian E. et al. / Automatic classification of physical defects in green coffee beans using CGLCM and SVM. Proceedings of the 2014 Latin American Computing Conference, CLEI 2014. editor / Pablo Ezzatti ; Andrea Delgado. Institute of Electrical and Electronics Engineers Inc., 2014. (Proceedings of the 2014 Latin American Computing Conference, CLEI 2014).

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title = "Automatic classification of physical defects in green coffee beans using CGLCM and SVM",

abstract = "This work is focused on the evaluation of physical coffee beans through a model of automatic classification of defects. The model uses a segmentation step that discriminates the background from the coffee bean image with a follow contours algorithm, then a CGLCM is introduced as features extractor and a Support Vector Machine for the classification task, a database of images has been collected with a total of 3367 images, the classification process used twelve categories of defects, the results of classification showed a accuracy of 86%. Finally a set of conclusions and future works are presented.",

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author = "Condori, {Rayner H.Montes} and Humari, {Juan H.Chuctaya} and Portugal-Zambrano, {Christian E.} and Guti{\'e}rrez-C{\'a}ceres, {Juan C.} and Beltr{\'a}n-Casta{\~n}{\'o}n, {C{\'e}sar A.}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 40th Latin American Computing Conference, CLEI 2014 ; Conference date: 15-09-2014 Through 19-09-2014",

year = "2014",

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day = "21",

doi = "10.1109/CLEI.2014.6965169",

language = "English",

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Condori, RHM, Humari, JHC, Portugal-Zambrano, CE, Gutiérrez-Cáceres, JC & Beltrán-Castañón, CA 2014, Automatic classification of physical defects in green coffee beans using CGLCM and SVM. En P Ezzatti & A Delgado (eds.), Proceedings of the 2014 Latin American Computing Conference, CLEI 2014., 6965169, Proceedings of the 2014 Latin American Computing Conference, CLEI 2014, Institute of Electrical and Electronics Engineers Inc., 2014 40th Latin American Computing Conference, CLEI 2014, Montevideo, Uruguay, 15/09/14. https://doi.org/10.1109/CLEI.2014.6965169

Automatic classification of physical defects in green coffee beans using CGLCM and SVM. / Condori, Rayner H.Montes; Humari, Juan H.Chuctaya; Portugal-Zambrano, Christian E. et al.
Proceedings of the 2014 Latin American Computing Conference, CLEI 2014. ed. / Pablo Ezzatti; Andrea Delgado. Institute of Electrical and Electronics Engineers Inc., 2014. 6965169 (Proceedings of the 2014 Latin American Computing Conference, CLEI 2014).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Automatic classification of physical defects in green coffee beans using CGLCM and SVM

AU - Condori, Rayner H.Montes

AU - Humari, Juan H.Chuctaya

AU - Portugal-Zambrano, Christian E.

AU - Gutiérrez-Cáceres, Juan C.

AU - Beltrán-Castañón, César A.

PY - 2014/11/21

Y1 - 2014/11/21

N2 - This work is focused on the evaluation of physical coffee beans through a model of automatic classification of defects. The model uses a segmentation step that discriminates the background from the coffee bean image with a follow contours algorithm, then a CGLCM is introduced as features extractor and a Support Vector Machine for the classification task, a database of images has been collected with a total of 3367 images, the classification process used twelve categories of defects, the results of classification showed a accuracy of 86%. Finally a set of conclusions and future works are presented.

AB - This work is focused on the evaluation of physical coffee beans through a model of automatic classification of defects. The model uses a segmentation step that discriminates the background from the coffee bean image with a follow contours algorithm, then a CGLCM is introduced as features extractor and a Support Vector Machine for the classification task, a database of images has been collected with a total of 3367 images, the classification process used twelve categories of defects, the results of classification showed a accuracy of 86%. Finally a set of conclusions and future works are presented.

KW - coffee bean

KW - computer vision

KW - feature extraction

KW - segmentation

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U2 - 10.1109/CLEI.2014.6965169

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BT - Proceedings of the 2014 Latin American Computing Conference, CLEI 2014

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T2 - 2014 40th Latin American Computing Conference, CLEI 2014

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Condori RHM, Humari JHC, Portugal-Zambrano CE, Gutiérrez-Cáceres JC, Beltrán-Castañón CA. Automatic classification of physical defects in green coffee beans using CGLCM and SVM. En Ezzatti P, Delgado A, editores, Proceedings of the 2014 Latin American Computing Conference, CLEI 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 6965169. (Proceedings of the 2014 Latin American Computing Conference, CLEI 2014). doi: 10.1109/CLEI.2014.6965169

Automatic classification of physical defects in green coffee beans using CGLCM and SVM

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