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, G; Lo Sciuto, G; Napoli, C; Tramontana, E; Wozniak, M. - (2015), pp. 861-867. (Intervento presentato al convegno 2015 Federated Conference on Computer Science and Information Systems (FedCSIS) tenutosi a Lodz; Poland) [10.15439/2015F258].
Automatic classification of fruit defects based on co-occurrence matrix and neural networks
Napoli C
;
2015
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.| File | Dimensione | Formato | |
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