3D textural mapping and soft-computing applied to cork quality inspection

Beatriz Paniagua, Miguel A. Vega-Rodríguez, Mike Chantler, Juan A. Gómez-Pulido, Juan M. Sánchez-Pérez

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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. © Springer-Verlag Berlin Heidelberg 2008.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 4th International Symposium, ISVC 2008, Proceedings
Number of pages10
Volume5358 LNCS
EditionPART 1
Publication statusPublished - 2008
Event4th International Symposium on Visual Computing - Las Vegas, NV, United States
Duration: 1 Dec 20083 Dec 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5358 LNCS
ISSN (Print)0302-9743


Conference4th International Symposium on Visual Computing
Abbreviated titleISVC 2008
Country/TerritoryUnited States
CityLas Vegas, NV


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