Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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In this paper, a real time application for visual inspection and classification of cork stoppers is presented. Each cork stopper is represented by a high dimensional set of characteristics corresponding to relevant visual features. We have applied a set of non-parametric and parametric methods in order to compare and evaluate their performance for 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 does not allow precision in the density estimation. We propose a Class-Conditional Independent Component Analysis (CC-ICA) representation of the data that even in low dimensions, performs comparably to standard classification techniques. The method has achieved a success of 98% of correct classification. Our prototype is able to inspect the cork stoppers and classify in 5 quality groups with a speed of 3 objects per second.