Location and characterization of the stem-calyx area on oranges by computer vision
Three image analysis methods were studied and evaluated to solve the problem of removing long stems attached to mechanically harvested oranges: colour segmentation based on linear discriminant analysis, contour curvature analysis, and a thinning process which involves iterating until the stem become...
| Autores principales: | , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Acceso en línea: | http://hdl.handle.net/20.500.11939/4451 |
| _version_ | 1855491756413419520 |
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| author | Ruiz, L. A. Moltó, Enrique Juste, Florentino Pla, F. Valiente, R. |
| author_browse | Juste, Florentino Moltó, Enrique Pla, F. Ruiz, L. A. Valiente, R. |
| author_facet | Ruiz, L. A. Moltó, Enrique Juste, Florentino Pla, F. Valiente, R. |
| author_sort | Ruiz, L. A. |
| collection | ReDivia |
| description | Three image analysis methods were studied and evaluated to solve the problem of removing long stems attached to mechanically harvested oranges: colour segmentation based on linear discriminant analysis, contour curvature analysis, and a thinning process which involves iterating until the stem becomes a skeleton. These techniques are able to determine the presence or absence of a stem with certainty, to locate the stems from random views with more than 90% accuracy and from profile images with an accuracy ranging from 92.4% to 100% depending on the method used. Finally, determination of the length and cutting point of the stem is achieved with only 3.8% of failures. (C) 1996 Silsoe Research Institute |
| format | Artículo |
| id | ReDivia4451 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| record_format | dspace |
| spelling | ReDivia44512025-04-25T14:43:34Z Location and characterization of the stem-calyx area on oranges by computer vision Ruiz, L. A. Moltó, Enrique Juste, Florentino Pla, F. Valiente, R. Three image analysis methods were studied and evaluated to solve the problem of removing long stems attached to mechanically harvested oranges: colour segmentation based on linear discriminant analysis, contour curvature analysis, and a thinning process which involves iterating until the stem becomes a skeleton. These techniques are able to determine the presence or absence of a stem with certainty, to locate the stems from random views with more than 90% accuracy and from profile images with an accuracy ranging from 92.4% to 100% depending on the method used. Finally, determination of the length and cutting point of the stem is achieved with only 3.8% of failures. (C) 1996 Silsoe Research Institute 2017-06-01T10:10:10Z 2017-06-01T10:10:10Z 1996 JUL 1996 article publishedVersion Ruiz, L.A., Moltó, E., Juste, F., Pla, F., Valiente, R. (1996). Location and characterization of the stem-calyx area on oranges by computer vision. Journal of Agricultural Engineering Research, 64(3), 165-172. 0021-8634 http://hdl.handle.net/20.500.11939/4451 10.1006/jaer.1996.0058 en openAccess Impreso |
| spellingShingle | Ruiz, L. A. Moltó, Enrique Juste, Florentino Pla, F. Valiente, R. Location and characterization of the stem-calyx area on oranges by computer vision |
| title | Location and characterization of the stem-calyx area on oranges by computer vision |
| title_full | Location and characterization of the stem-calyx area on oranges by computer vision |
| title_fullStr | Location and characterization of the stem-calyx area on oranges by computer vision |
| title_full_unstemmed | Location and characterization of the stem-calyx area on oranges by computer vision |
| title_short | Location and characterization of the stem-calyx area on oranges by computer vision |
| title_sort | location and characterization of the stem calyx area on oranges by computer vision |
| url | http://hdl.handle.net/20.500.11939/4451 |
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