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Bayesian Classification of Cork Stoppers Using Class-Conditional Independent Component Analysis

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3 Author(s)
Vitria, J. ; Departament d''Informatica, Univ. Autonoma de Barcelona ; Bressan, M. ; Radeva, P.

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

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Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on  (Volume:37 ,  Issue: 1 )