@inproceedings{904fd0a3b42f434cb9701b7d6a3860ea,
title = "Automatic classification of fruit defects based on Co-occurrence matrix and neural networks",
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.",
keywords = "Co-occurrence matrix, Pattern recognition, Probabilistic neural network, Texture analysis",
author = "Giacomo Capizzi and \{Lo Sciuto\}, Grazia and Christian Napoli and Emiliano Tramontana and Marcin Wozniak",
note = "Publisher Copyright: {\textcopyright} 2015, IEEE.; Federated Conference on Computer Science and Information Systems, FedCSIS 2015 ; Conference date: 13-09-2015 Through 16-09-2015",
year = "2015",
doi = "10.15439/2015F258",
language = "English",
series = "Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "861--867",
editor = "Marcin Paprzycki and Leszek Maciaszek and Maria Ganzha and Leszek Maciaszek",
booktitle = "Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015",
address = "United States",
}