In order to investigate the effectiveness of multivariate image analysis for the evaluation of maize defects, RGB images of maize samples containing different percentages of defective kernels were acquired and then converted into colourgrams, i.e., signals codifying colour-related features. Multivariate analysis of the colourgrams matrix showed a distribution of the acquired samples according to the amount of defective kernels.
Preliminary analysis of RGB images for the identification of defective maize kernels / Orlandi, Giorgia; Calvini, Rosalba; Foca, Giorgia; Ulrici, Alessandro. - (2016), pp. 257-261. (Intervento presentato al convegno 2nd IMEKOFOODS Conference: Promoting Objective and Measurable Food Quality and Safety 2016 tenutosi a Benevento, Italy nel 2-5 ottobre 2016).
Preliminary analysis of RGB images for the identification of defective maize kernels
Giorgia Orlandi;Rosalba Calvini;Giorgia Foca;Alessandro Ulrici
2016
Abstract
In order to investigate the effectiveness of multivariate image analysis for the evaluation of maize defects, RGB images of maize samples containing different percentages of defective kernels were acquired and then converted into colourgrams, i.e., signals codifying colour-related features. Multivariate analysis of the colourgrams matrix showed a distribution of the acquired samples according to the amount of defective kernels.
| File | Dimensione | Formato | |
|---|---|---|---|
|
Orlandi_Paper621_IMEKOFOODS.pdf
Accesso riservato
Tipologia:
AO - Versione originale dell'autore proposta per la pubblicazione
Dimensione
844.4 kB
Formato
Adobe PDF
|
844.4 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris
