Abstract
This paper presents a solution to a problem existing in the cork industry: cork stopper/disk classification according to their quality. Cork is a natural and heterogeneous material; therefore, its automatic classification (seven quality classes exist) is very difficult. The solution proposed in this paper combines the extraction of 3D cork features and soft-computing. In order to evaluate the performance of the neuro-fuzzy network designed, we compare its results with other 4 basic classifiers working with the same feature space. In conclusion, our experiments showed that the best results in case of cork quality classification were obtained with the proposed system that works with the following features: depth+intensity combined feature, weighted depth, second depth level feature, root mean square roughness and other three textural features (wavelets). The obtained classification results have highly improved other results reported in similar studies.
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Paniagua, B., Vega-Rodríguez, M.A., Chantler, M., Gómez-Pulido, J.A., Sánchez-Pérez, J.M. (2008). 3D Textural Mapping and Soft-Computing Applied to Cork Quality Inspection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_71
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DOI: https://doi.org/10.1007/978-3-540-89639-5_71
Publisher Name: Springer, Berlin, Heidelberg
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