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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión 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 originalInglés
Título de la publicación alojadaProceedings of the 2014 Latin American Computing Conference, CLEI 2014
EditoresPablo Ezzatti, Andrea Delgado
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781479961306
DOI
EstadoPublicada - 21 nov. 2014
Evento2014 40th Latin American Computing Conference, CLEI 2014 - Montevideo, Uruguay
Duración: 15 set. 201419 set. 2014

Serie de la publicación

NombreProceedings of the 2014 Latin American Computing Conference, CLEI 2014

Conferencia

Conferencia2014 40th Latin American Computing Conference, CLEI 2014
País/TerritorioUruguay
CiudadMontevideo
Período15/09/1419/09/14

Nota bibliográfica

Publisher Copyright:
© 2014 IEEE.

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