Nowadays the effective and fast detection of fruit defects is one of the main concerns for fruit selling companies. This paper presents a new approach that classifies fruit surface defects in color and texture using Radial Basis Probabilistic Neural Networks (RBPNN). The texture and gray features of defect area are extracted by computing a gray level co-occurrence matrix and then defect areas are classified by the applied RBPNN solution.

Automatic classification of fruit defects based on co-occurrence matrix and neural networks

CAPIZZI, GIACOMO;LO SCIUTO, GRAZIA;NAPOLI, CHRISTIAN;TRAMONTANA, EMILIANO ALESSIO;
2015-01-01

Abstract

Nowadays the effective and fast detection of fruit defects is one of the main concerns for fruit selling companies. This paper presents a new approach that classifies fruit surface defects in color and texture using Radial Basis Probabilistic Neural Networks (RBPNN). The texture and gray features of defect area are extracted by computing a gray level co-occurrence matrix and then defect areas are classified by the applied RBPNN solution.
978-836081065-1
Pattern recognition; neural networks; Texture analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/96529
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