Bayesian classification of cork stoppers using class-conditional independent component analysis

Jordi Vitrià, Marco Bressan, Petia Radeva

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)


In this paper, a real-time application for visual inspection and classification of cork stoppers is presented. The process of cork inspection and quality grading is based on analyzing a large set of characteristics corresponding to visual features that are related to cork porosity. We have applied a set of nonparametric and parametric classification methods for comparing and evaluating their performance in this real problem. The best results have been achieved using Bayesian classification through probabilistic modeling in a high-dimensional space. In this context, it is well known that high dimensionality represents a serious problem for density estimation. We propose a class-conditional independent component analysis representation of the data that allows an accurate estimation of the data probability density function by factorizing it. The method has achieved a success of 98% of correct classification. © 2007 IEEE.
Original languageEnglish
Pages (from-to)32-38
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Issue number1
Publication statusPublished - 1 Jan 2007


  • Independent component analysis (ICA)
  • Machine vision
  • Object recognition
  • Visual inspection


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