Perceptually relevant pattern recognition applied to cork quality detection

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

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

1 Citation (Scopus)


This paper demonstrates significant improvement in the performance of a computer vision system by incorporating the results of an experiment on human visual perception. This system was designed to solve a problem existing in the cork industry: the automatic classification of cork samples according to their quality. This is a difficult problem because cork is a natural and heterogeneous material. An eye-tracker was used to analyze the gaze patterns of a human expert trained in cork classification, and the results identified visual features of cork samples used by the expert in making decisions. Variations in lightness of the cork surface proved to be a key feature, and this finding was used to select the features included in the final system: defects in the sample (thresholding), size of the biggest defect (morphological operations), and four Laws textural features, all working on a Neuro-Fuzzy classifier. The results obtained from the final system show lower error rates than previous systems designed for this application. © 2009 Springer Berlin Heidelberg.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 6th International Conference, ICIAR 2009, Proceedings
Number of pages10
Volume5627 LNCS
Publication statusPublished - 2009
Event6th International Conference on Image Analysis and Recognition 2009 - Halifax, Canada
Duration: 6 Jul 20098 Jul 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5627 LNCS
ISSN (Print)0302-9743


Conference6th International Conference on Image Analysis and Recognition 2009
Abbreviated titleICIAR 2009


  • Automated visual inspection system
  • Cork industry
  • Eye tracking
  • Image processing
  • Perceptual features
  • Stopper quality
  • Vision science


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