Self-Organizing Feature Extraction in Recognition of Wood Surface Defects and Color Images

  title={Self-Organizing Feature Extraction in Recognition of Wood Surface Defects and Color Images},
  author={Jouko Lampinen and Seppo Smolander},
A method for constructing classiication features with unsupervised learning is presented. The method is based on clustering of the high dimensional measurements into a small number of features with self-organizing maps. The histograms of the self-organized features are classiied with a multilayer perceptron network, that can pick up the relevant features and feature combinations from the histograms. The method is applied in two industrial problems, color image recognition for selection of… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 18 references

Feature Extractor Giving Distortion Invariant Hierarchical Feature Space, Applications of Arti cial Neural Networks

  • J. Lampinen
  • 1991
Highly Influential
4 Excerpts

Hierarchical classi cation of surface defects on dusty wood boards,Pattern

  • CW.Kim, A. J. Koivo
  • Recognition Letters,
  • 1994
1 Excerpt

Automatic Visual Inspection of Wood Surfaces

  • P. Alapuranen, T. Westman
  • Int. Conf. on Pattern Recognition,
  • 1992
1 Excerpt

Neural network architectures and algorithms: a perspective

  • F. Fogelman Soulie
  • Arti cial Neural Networks,
  • 1991
1 Excerpt

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