Comparison of three algorithms in the classification of table olives by means of computer vision
The classification of table olive in different quality categories is performed depending on the defects in the surface of the fruits. However, the characteristics of every category are not defined. Then, it is necessary to apply learning algorithms that allow the extraction of quality information fr...
| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Acceso en línea: | http://hdl.handle.net/20.500.11939/5138 |
| _version_ | 1855032231914897408 |
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| author | Díaz, R. Gil, L. Serrano, C. Blasco, María A. Moltó, Enrique Blasco, José |
| author_browse | Blasco, José Blasco, María A. Díaz, R. Gil, L. Moltó, Enrique Serrano, C. |
| author_facet | Díaz, R. Gil, L. Serrano, C. Blasco, María A. Moltó, Enrique Blasco, José |
| author_sort | Díaz, R. |
| collection | ReDivia |
| description | The classification of table olive in different quality categories is performed depending on the defects in the surface of the fruits. However, the characteristics of every category are not defined. Then, it is necessary to apply learning algorithms that allow the extraction of quality information from batches previously classified by expert workers. In this research, a colorimetric characterisation of the more common defects has been carried out. An image analysis system has been used to segment the parameter set with the information from the olives quality. Three different algorithms have been applied to classify the olives in four quality categories. The results show that a neural network with a hidden layer is able to classify the olives with an accuracy of over 90%, while partial least squares discriminant and Mahalanobis distance are over 70%. (C) 2003 Elsevier Ltd. All rights reserved. |
| format | article |
| id | ReDivia5138 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| record_format | dspace |
| spelling | ReDivia51382025-04-25T14:45:29Z Comparison of three algorithms in the classification of table olives by means of computer vision Díaz, R. Gil, L. Serrano, C. Blasco, María A. Moltó, Enrique Blasco, José The classification of table olive in different quality categories is performed depending on the defects in the surface of the fruits. However, the characteristics of every category are not defined. Then, it is necessary to apply learning algorithms that allow the extraction of quality information from batches previously classified by expert workers. In this research, a colorimetric characterisation of the more common defects has been carried out. An image analysis system has been used to segment the parameter set with the information from the olives quality. Three different algorithms have been applied to classify the olives in four quality categories. The results show that a neural network with a hidden layer is able to classify the olives with an accuracy of over 90%, while partial least squares discriminant and Mahalanobis distance are over 70%. (C) 2003 Elsevier Ltd. All rights reserved. 2017-06-01T10:11:46Z 2017-06-01T10:11:46Z 2004 JAN 2004 article Diaz, R., Gil, L., Serrano, C., Blasco, M., Moltó, E., Blasco, J. (2004). Comparison of three algorithms in the classification of table olives by means of computer vision. Journal of Food Engineering, 61(1), 101-107. 0260-8774 http://hdl.handle.net/20.500.11939/5138 10.1016/S0260-8774(03)00191-2 en openAccess Impreso |
| spellingShingle | Díaz, R. Gil, L. Serrano, C. Blasco, María A. Moltó, Enrique Blasco, José Comparison of three algorithms in the classification of table olives by means of computer vision |
| title | Comparison of three algorithms in the classification of table olives by means of computer vision |
| title_full | Comparison of three algorithms in the classification of table olives by means of computer vision |
| title_fullStr | Comparison of three algorithms in the classification of table olives by means of computer vision |
| title_full_unstemmed | Comparison of three algorithms in the classification of table olives by means of computer vision |
| title_short | Comparison of three algorithms in the classification of table olives by means of computer vision |
| title_sort | comparison of three algorithms in the classification of table olives by means of computer vision |
| url | http://hdl.handle.net/20.500.11939/5138 |
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