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EnglishIn this paper is proposed, implemented and evaluated a novel radial basis probabilistic neuralnetwork (RBPNN) based classification algorithm for classification fruit surface defects in color andtexture of a very important fruit as orange. The proposed algorithm takes orange images as inputsthen the texture and gray features of defect area are extracted by computing a gray level co-occurrencematrix and the defect areas are classified through an RBPNN-based classifier. Theconducted experiments and the results reveal as the classification accuracy achieved is up to 88%.
| Titolo: | A NOVEL NEURAL NETWORKS-BASED TEXTURE IMAGE PROCESSING ALGORITHM FOR ORANGE DEFECTS CLASSIFICATION | |
| Autori interni: | ||
| Data di pubblicazione: | 2016 | |
| Rivista: | ||
| Handle: | http://hdl.handle.net/20.500.11769/20409 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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