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Adaptive self-configuring computer vision system for quality evaluation of fresh-cut radicchio

Articolo
Data di Pubblicazione:
2015
Abstract:
An innovative Computer Vision System (CVS) that extracts color features discriminating the quality levels occurring during fresh-cut radicchio storage in air or modified atmosphere packaging was proposed. It self-configures the parameters normally set by operators and completely automates the following steps adapting to the specific product at hand: color-chart detection, foreground extraction and color segmentation for features extraction and selection. Results proved the average value of a* 20 over the white part and the percentage of light white with respect to the whole visible surface to be the most discriminating color features to significantly separate (P <= 0.05) the three desired quality levels (high, middle and poor) occurring during fresh-cut radicchio storage 23 (whose true value was verified on the base of ammonium content and human evaluated visual quality). The proposed procedure significantly simplify the CVS design and the optimization of its performance, limiting the subjective human intervention to the minimum.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
Computer vision system; non-destructive quality evaluation; self-configuration; automatic colors and features selection; image analysis
Elenco autori:
Colella, Roberto; Cavallo, DARIO PIETRO; Attolico, Giovanni; Pace, Bernardo; Cefola, Maria
Autori di Ateneo:
ATTOLICO GIOVANNI
CEFOLA MARIA
PACE BERNARDO
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/299536
Pubblicato in:
INNOVATIVE FOOD SCIENCE & EMERGING TECHNOLOGIES
Journal
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URL

https://www.sciencedirect.com/science/article/abs/pii/S1466856415001939
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