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 Carlos Gutierrez Caceres; 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 Carlos Gutierrez Caceres, César A. Beltrán-Castañón

Departamento Académico de Ingeniería de Sistemas e Informática

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

7 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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Publisher Copyright:
© 2014 IEEE.

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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., Gutierrez Caceres, 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 [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.",

keywords = "coffee bean, computer vision, feature extraction, segmentation",

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

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Condori, RHM, Humari, JHC, Portugal-Zambrano, CE, Gutierrez Caceres, 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).

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

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AU - Condori, Rayner H.Montes

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AU - Portugal-Zambrano, Christian E.

AU - Gutierrez Caceres, Juan Carlos

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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Condori RHM, Humari JHC, Portugal-Zambrano CE, Gutierrez Caceres 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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