Abstract

Quality loss during storage is often associated to changes in relevant product colors and/or to the appearanceof new pigments. Computer Vision System (CVS) for non-destructive quality evaluation often relies on human knowledgeprovided by operators to identify these relevant colors and their features. The approach described in this paperautomatically identifies the most significant colors in unevenly colored products to evaluate their quality level. Itsperformance was compared with results obtained by exploiting human training. The new method improved qualityevaluation and reduced the subjectivity and the inconsistency potentially induced by operators.

Autore Pugliese

• Pace Bernardo , Cefola Maria , Attolico Giovanni

Tutti gli autori

• B. Pace; D.P. Cavallo; M. Cefola; G. Attolico

Titolo volume/Rivista

International Journal of Food Processing Technology

Anno di pubblicazione

2017

ISSN

2408-9826

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile