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Machine Vision and Applications

, Volume 23, Issue 3, pp 491–499 | Cite as

Multivariate Bayesian cognitive modeling for unsupervised quality control of baked pizzas

  • Sylvio Luiz Mantelli NetoEmail author
  • Daniel Besen de Aguiar
  • Bianca Sens dos Santos
  • Aldo von Wangenheim
Original Paper

Abstract

The present article describes a Bayesian multivariate methodology developed for unsupervised quality control of pizzas based on RGB color attributes. A sensory experiment was done to define the readiness point ground truth. During the validation phase, different pizza samples were baked at a different temperature. The cheese and crust color patterns were statistically compared against the ground truth to check the readiness point. Results show that the proposed methodology presents a good performance demonstrating that color attributes can be used as an unsupervised quality control using traditional statistical methods.

Keywords

Bayesian modeling Multivariate statistics Geometric color space locus 

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Sylvio Luiz Mantelli Neto
    • 1
    Email author
  • Daniel Besen de Aguiar
    • 2
  • Bianca Sens dos Santos
    • 3
  • Aldo von Wangenheim
    • 4
  1. 1.INPE-CCST and UFSC-EGC/INCOD/LEPTENFlorianopolisBrazil
  2. 2.UFSC-INE-INCOD/LEPTENFlorianopolisBrazil
  3. 3.UFSC-EMC-LEPTENFlorianopolisBrazil
  4. 4.UFSC-INE-INCOD/EGCFlorianopolisBrazil

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