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 language | English |
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Title of host publication | Proceedings of the 2014 Latin American Computing Conference, CLEI 2014 |
Editors | Pablo Ezzatti, Andrea Delgado |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781479961306 |
DOIs | |
State | Published - 21 Nov 2014 |
Event | 2014 40th Latin American Computing Conference, CLEI 2014 - Montevideo, Uruguay Duration: 15 Sep 2014 → 19 Sep 2014 |
Publication series
Name | Proceedings of the 2014 Latin American Computing Conference, CLEI 2014 |
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Conference
Conference | 2014 40th Latin American Computing Conference, CLEI 2014 |
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Country/Territory | Uruguay |
City | Montevideo |
Period | 15/09/14 → 19/09/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- coffee bean
- computer vision
- feature extraction
- segmentation