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: | article |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Acceso en línea: | http://hdl.handle.net/20.500.11939/4451 |
| Sumario: | 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 |
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