Intelligent visual recognition and classification of cork tiles with neural networks

A. Georgieva, Ivan Jordanov

Research output: Contribution to journalArticlepeer-review


An intelligent machine vision system is investigated and used for pattern recognition and classification of seven different types of cork tiles. The system includes image acquisition with a CCD camera, texture feature generation (co-occurrence matrices and Laws’ masks), analysis and processing of the feature vectors (Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA)), and cork tiles classification with feed-forward Neural Networks (NN), employing our GLPτS (Genetic Low-discrepancy Search) hybrid global optimization method. In addition, the same NN are trained with Backpropagation (BP) and the obtained results are compared with the ones from GLPτS. The NN generalization abilities are discussed and assessed in respect to the NN architectures and the texture feature sets. The reported results are very encouraging with testing rate reaching up to 95%.
Original languageEnglish
Pages (from-to)675-685
Number of pages11
JournalIEEE Transactions on Neural Networks
Issue number4
Publication statusPublished - Apr 2009


  • Machine vision
  • pattern recognition
  • texture feature extraction
  • image processing
  • PCA
  • LDA
  • neural networks
  • optimization methods.


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