In 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|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||1.1 Articolo in rivista|