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.

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

BERNARDO PACE;DARIO PIETRO CAVALLO;MARIA CEFOLA;GIOVANNI ATTOLICO
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.
2015
Istituto di Scienze delle Produzioni Alimentari - ISPA
Istituto di Studi sui Sistemi Intelligenti per l'Automazione - ISSIA - Sede Bari
Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato - STIIMA (ex ITIA)
Computer vision system
non-destructive quality evaluation
self-configuration
automatic colors and features selection
image analysis
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Descrizione: Pace et a.-2015
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/299536
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