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

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