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%.
A NOVEL NEURAL NETWORKS-BASED TEXTURE IMAGE PROCESSING ALGORITHM FOR ORANGE DEFECTS CLASSIFICATION
CAPIZZI, GIACOMO;LO SCIUTO, GRAZIA;NAPOLI, CHRISTIAN;TRAMONTANA, EMILIANO ALESSIO;
2016-01-01
Abstract
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%.File in questo prodotto:
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