Intelligent visual recognition and classification of cork tiles with neural networks.
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 charge-coupled device (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 feedforward neural networks (NN), employing our GLP(tau) S (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 GLP(tau) S . The NN generalization abilities are discussed and assessed with respect to the NN architectures and the texture feature sets. The reported results are very encouraging with testing rate reaching up to 95%.
Georgieva
Antoniya
A
Nuffield Department of Obstetrics and Gynecology, University of Oxford, Oxford, UK. Antoniya.Georgieva@obs-gyn.ox.ac.uk
Jordanov
Ivan
I
D000704:Analysis of Variance; D001185:Artificial Intelligence; D016002:Discriminant Analysis; D001330:Electronic Data Processing; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual; D010775:Photic Stimulation; D025341:Principal Component Analysis