Segmentation of hyperspectral images for the detection of rotten mandarins
The detection of rotten citrus in packing lines is carried out manually under ultraviolet illumination, which is dangerous for workers. Light emitted by the rotten region of the fruit due to the ultraviolet-induced fluorescence is used by the operator to detect the damages. This procedure is require...
| Autores principales: | , , , , , |
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| Otros Autores: | |
| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Acceso en línea: | http://hdl.handle.net/20.500.11939/5314 |
| _version_ | 1855032259514466304 |
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| author | Gómez-Sanchís, Juan Camps-Valls, G. Moltó, Enrique Gomez-Chova, L. Aleixos, Nuria Blasco, José |
| author2 | Campilho, A. Kamel, M. |
| author_browse | Aleixos, Nuria Blasco, José Campilho, A. Kamel, M. Camps-Valls, G. Gomez-Chova, L. Gómez-Sanchís, Juan Moltó, Enrique |
| author_facet | Campilho, A. Kamel, M. Gómez-Sanchís, Juan Camps-Valls, G. Moltó, Enrique Gomez-Chova, L. Aleixos, Nuria Blasco, José |
| author_sort | Gómez-Sanchís, Juan |
| collection | ReDivia |
| description | The detection of rotten citrus in packing lines is carried out manually under ultraviolet illumination, which is dangerous for workers. Light emitted by the rotten region of the fruit due to the ultraviolet-induced fluorescence is used by the operator to detect the damages. This procedure is required because the low contrast between the damaged and sound skin under visible illumination difficult their detection. We study a set of techniques aimed to detect rottenness in citrus using visible and near infrared lighting trough an hyperspectral imaging system. Methods for selecting a proper set of wavelengths are investigated such as correlation analysis, mutual information, stepwise or genetic algorithms. The image segmentation relies on the combination of band selection techniques and pixel classification methods such as classification and regression trees and linear discriminant analysis. |
| format | article |
| id | ReDivia5314 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| record_format | dspace |
| spelling | ReDivia53142025-04-25T14:52:07Z Segmentation of hyperspectral images for the detection of rotten mandarins Lecture Notes in Computer Science Gómez-Sanchís, Juan Camps-Valls, G. Moltó, Enrique Gomez-Chova, L. Aleixos, Nuria Blasco, José Campilho, A. Kamel, M. The detection of rotten citrus in packing lines is carried out manually under ultraviolet illumination, which is dangerous for workers. Light emitted by the rotten region of the fruit due to the ultraviolet-induced fluorescence is used by the operator to detect the damages. This procedure is required because the low contrast between the damaged and sound skin under visible illumination difficult their detection. We study a set of techniques aimed to detect rottenness in citrus using visible and near infrared lighting trough an hyperspectral imaging system. Methods for selecting a proper set of wavelengths are investigated such as correlation analysis, mutual information, stepwise or genetic algorithms. The image segmentation relies on the combination of band selection techniques and pixel classification methods such as classification and regression trees and linear discriminant analysis. 2017-06-01T10:12:07Z 2017-06-01T10:12:07Z 2008 2008 article Gómez-Sanchis J., Camps-Valls G., Moltó E., Gómez-Chova L., Aleixos N., Blasco J. (2008) Segmentation of Hyperspectral Images for the Detection of Rotten Mandarins. In: Campilho A., Kamel M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg, pp. 1071-1080. 0302-9743; 978-3-540-69811-1 http://hdl.handle.net/20.500.11939/5314 10.1007/978-3-540-69812-8_107 en openAccess Impreso |
| spellingShingle | Gómez-Sanchís, Juan Camps-Valls, G. Moltó, Enrique Gomez-Chova, L. Aleixos, Nuria Blasco, José Segmentation of hyperspectral images for the detection of rotten mandarins |
| title | Segmentation of hyperspectral images for the detection of rotten mandarins |
| title_full | Segmentation of hyperspectral images for the detection of rotten mandarins |
| title_fullStr | Segmentation of hyperspectral images for the detection of rotten mandarins |
| title_full_unstemmed | Segmentation of hyperspectral images for the detection of rotten mandarins |
| title_short | Segmentation of hyperspectral images for the detection of rotten mandarins |
| title_sort | segmentation of hyperspectral images for the detection of rotten mandarins |
| url | http://hdl.handle.net/20.500.11939/5314 |
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