Learning vector quantization (LVQ) networks are known good neural classifiers which provide fast and accurate results for many applications. The aim of this work was to test if this network paradigm could be employed for the classification of wood sheet defects. Experiments conducted with LVQ networks have shown that they provide a high degree of discrimination between the different types of defects and potentially can perform defect classification in real time.
SobeyP. J.SempleE. C.Detection and sizing visual features in wood using tonal measures and classification algorithm. Pattern Recognition, 1989, 22(4), 367–380.
3.
ChoT. H.ConnersR. W.AramanP. A.A computer vision system for automated grading of rough hardwood lumber using a knowledge-based approach. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Cambridge, Massachusetts, 1990, pp. 345–350.
PhamD. T.AlcockR. J.Automatic detection of defects on birch wood boards. Proc. Instn Mech. Engrs, Part E, Journal of Process Mechanical Engineering, 1996, 210(E1), 45–52.
6.
LampinenJ.SmolanderS.Self-organising feature extraction in recognition of wood surface defects and color images. Int. J. Pattern Recognition and Artif. Intell., 1996, 10(2), 97–113.
7.
PhamD. T.AlcockR. J.Artificial intelligence techniques for processing segmented images of wood boards. Proc. Instn Mech. Engrs, Part E, Journal of Process Mechanical Engineering, 1998, 212(E2), 119–129.
8.
DrakeP. R.PackianatherM. S.A decision tree of neural networks for classifying images of wood veneer. Int. J. Advd Mfg Technol., 1998, 14, 280–285.
9.
PhamD. T.AlcockR. J.Automated visual inspection of wood boards: Selection of features for defect classification by a neural network. Proc. Instn Mech. Engrs, Part E, Journal of Process Mechanical Engineering, 1999, 213(E4), 231–245.
10.
KohonenT.Self-Organisation and Associative Memory, 3rd edition, 1989 (Springer-Verlag, Berlin).
11.
FlotzingerD.KalcherJ.PfurtschellerG.LVQ-based on-line EEG classification. In Proceedings of International Conference on Artificial Neural Networks and Genetic Algorithms, Innsbruck, Austria, 1993, pp. 161–166.
12.
PhamD. T.OztemelE.Control chart pattern recognition using learning vector quantisation networks. Int. J. Prod. Res., 1994, 32(3), 721–729.
13.
DeSienoD.Adding a conscience to competitive learning. In IEEE International Conference on Neural Networks, San Diego, California, 1988, Vol. 1, pp. 117–124.
