An approach for improve the recognition of defects in coffee beans using retinex algorithms

Rel Guzmán Apaza; Christian E. Portugal-Zambrano; Juan Carlos Gutierrez Caceres; César A. Beltrán-Castañón

doi:10.1109/CLEI.2014.6965102

An approach for improve the recognition of defects in coffee beans using retinex algorithms

Rel Guzmán Apaza, 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

5 Citas (Scopus)

Resumen

This paper describes the development of a system for evaluating the quality of coffee focused on the pre-processing of digital images using an algorithm based on the retinex theory called multi-scale retinex with color restoration (MSRCR). A dataset of images of coffee beans are collected and others techniques for image enhancement are compared, then a color gray-level coocurrence matrix (CGLCM) technique is used for features extraction and a Support Vector Machine (SVM) is used to evaluate results with a set of prepared data, these results shows a good visual quality and better accuracy in classification for MSRCR techniques compared with others, finally 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.2014.6965102
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

Nota bibliográfica

Publisher Copyright:
© 2014 IEEE.

Acceder al documento

10.1109/CLEI.2014.6965102

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Apaza, R. G., Portugal-Zambrano, C. E., Gutierrez Caceres, J. C., & Beltrán-Castañón, C. A. (2014). An approach for improve the recognition of defects in coffee beans using retinex algorithms. En P. Ezzatti, & A. Delgado (Eds.), Proceedings of the 2014 Latin American Computing Conference, CLEI 2014 [6965102] (Proceedings of the 2014 Latin American Computing Conference, CLEI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLEI.2014.6965102

Apaza, Rel Guzmán ; Portugal-Zambrano, Christian E. ; Gutierrez Caceres, Juan Carlos et al. / An approach for improve the recognition of defects in coffee beans using retinex algorithms. 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).

@inproceedings{3c1c16785ab247d588fee694cdb75158,

title = "An approach for improve the recognition of defects in coffee beans using retinex algorithms",

abstract = "This paper describes the development of a system for evaluating the quality of coffee focused on the pre-processing of digital images using an algorithm based on the retinex theory called multi-scale retinex with color restoration (MSRCR). A dataset of images of coffee beans are collected and others techniques for image enhancement are compared, then a color gray-level coocurrence matrix (CGLCM) technique is used for features extraction and a Support Vector Machine (SVM) is used to evaluate results with a set of prepared data, these results shows a good visual quality and better accuracy in classification for MSRCR techniques compared with others, finally conclusions and future works are presented.",

keywords = "CGLCM, coffee beans, Computer vision, image enhancement, industrial quality, MSRCR, retinex, SVM",

author = "Apaza, {Rel Guzm{\'a}n} 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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year = "2014",

month = nov,

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doi = "10.1109/CLEI.2014.6965102",

language = "Ingl{\'e}s",

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Apaza, RG, Portugal-Zambrano, CE, Gutierrez Caceres, JC & Beltrán-Castañón, CA 2014, An approach for improve the recognition of defects in coffee beans using retinex algorithms. En P Ezzatti & A Delgado (eds.), Proceedings of the 2014 Latin American Computing Conference, CLEI 2014., 6965102, 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.2014.6965102

An approach for improve the recognition of defects in coffee beans using retinex algorithms. / Apaza, Rel Guzmán; Portugal-Zambrano, Christian E.; Gutierrez Caceres, Juan Carlos 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. 6965102 (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

TY - GEN

T1 - An approach for improve the recognition of defects in coffee beans using retinex algorithms

AU - Apaza, Rel Guzmán

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 paper describes the development of a system for evaluating the quality of coffee focused on the pre-processing of digital images using an algorithm based on the retinex theory called multi-scale retinex with color restoration (MSRCR). A dataset of images of coffee beans are collected and others techniques for image enhancement are compared, then a color gray-level coocurrence matrix (CGLCM) technique is used for features extraction and a Support Vector Machine (SVM) is used to evaluate results with a set of prepared data, these results shows a good visual quality and better accuracy in classification for MSRCR techniques compared with others, finally conclusions and future works are presented.

AB - This paper describes the development of a system for evaluating the quality of coffee focused on the pre-processing of digital images using an algorithm based on the retinex theory called multi-scale retinex with color restoration (MSRCR). A dataset of images of coffee beans are collected and others techniques for image enhancement are compared, then a color gray-level coocurrence matrix (CGLCM) technique is used for features extraction and a Support Vector Machine (SVM) is used to evaluate results with a set of prepared data, these results shows a good visual quality and better accuracy in classification for MSRCR techniques compared with others, finally conclusions and future works are presented.

KW - CGLCM

KW - coffee beans

KW - Computer vision

KW - image enhancement

KW - industrial quality

KW - MSRCR

KW - retinex

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M3 - Contribución a la conferencia

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T3 - Proceedings of the 2014 Latin American Computing Conference, CLEI 2014

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

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Apaza RG, Portugal-Zambrano CE, Gutierrez Caceres JC, Beltrán-Castañón CA. An approach for improve the recognition of defects in coffee beans using retinex algorithms. En Ezzatti P, Delgado A, editores, Proceedings of the 2014 Latin American Computing Conference, CLEI 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 6965102. (Proceedings of the 2014 Latin American Computing Conference, CLEI 2014). doi: 10.1109/CLEI.2014.6965102

An approach for improve the recognition of defects in coffee beans using retinex algorithms

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