Neural networks applied for cork tiles image classification

A. Georgieva*, I. Jordanov, T. Rafik

*Corresponding author for this work

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

Abstract

In this paper a hybrid global optimization method GLPτS is further investigated and applied for feed-forward neural networks supervised learning. The method is initially tested on several benchmark problems and subsequently employed for pattern recognition problem. The proposed technique is used for training Neural Networks (NN) that have to inspect and classify three types of cork tiles images. During the feature extraction phase, statistical textural characteristics are obtained from the tiles' images and then used for training several different NN architectures. Results from the testing phase are discussed and analysed, showing good generalization abilities of the trained NN. Finally, directions of future work are briefly stated.

Original languageEnglish
Title of host publication2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing
PublisherIEEE
Pages232-239
Number of pages8
ISBN (Print)1424407079
DOIs
Publication statusPublished - 4 Jun 2007
Event2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007 - Honolulu, HI, United States
Duration: 1 Apr 20075 Apr 2007

Conference

Conference2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007
Country/TerritoryUnited States
CityHonolulu, HI
Period1/04/075/04/07

Keywords

  • Cork tiles classification
  • Feature extraction
  • Global optimization
  • Image processing
  • Supervised neural networks learning

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