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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of the 2014 Latin American Computing Conference, CLEI 2014
EditorsPablo Ezzatti, Andrea Delgado
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479961306
DOIs
StatePublished - 21 Nov 2014
Event2014 40th Latin American Computing Conference, CLEI 2014 - Montevideo, Uruguay
Duration: 15 Sep 201419 Sep 2014

Publication series

NameProceedings of the 2014 Latin American Computing Conference, CLEI 2014

Conference

Conference2014 40th Latin American Computing Conference, CLEI 2014
Country/TerritoryUruguay
CityMontevideo
Period15/09/1419/09/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

  • coffee bean
  • computer vision
  • feature extraction
  • segmentation

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