Morphological/rank neural networks and their adaptive optimal design for image processing

@inproceedings{Pessoa1996MorphologicalrankNN,
  title={Morphological/rank neural networks and their adaptive optimal design for image processing},
  author={L{\'u}cio F. C. Pessoa and Petros Maragos},
  booktitle={ICASSP},
  year={1996}
}
In this paper we formulate a general class of neural network based lters, where each node is a morphological/rank operation. This type of system is computationally eecient since no multiplications are necessary. The introduction of such networks is partially motivated from observations that internal structures of a neuron can generate logic operations. An eecient adaptive optimal design procedure is proposed for these networks, based on the back-propagation algorithm. The procedure is optimal… CONTINUE READING

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References

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Showing 1-9 of 9 references

Morphological systems for character image processing and recognition,

  • P. Yang, P. Maragos
  • Proc. of IEEE ICASSP,
  • 1993
Highly Influential
5 Excerpts

Morphological neural networks: an introduction with applications," Circuits

  • J. L. Davidson, F. Hummer
  • Systems and Signal Processing,
  • 1993
1 Excerpt

Roussel-Ragot, \A uni ed framework for gradient algorithms used for lter adaptation and neural network training," Intl

  • S. Marcos, O. Macchi, C. Vignat, G. Dreyfus, P. L. Per- sonnaz
  • Journal of Circuit The- ory and Applications,
  • 1992
1 Excerpt

frameworkfor gradient algorithms used for lter adaptation andneural network training

  • C. Vignat
  • Journal of Circuit Theory and Applications
  • 1992

Willians, \Learning internal representations by error propaga- tion,

  • D. E. Rumelhart, R.J.G.E. Hinton
  • Parallel Distributed Processing: Explorations in…
  • 1986
1 Excerpt

Image Analysis and Mathematical Morpho- logy

  • J. Serra
  • 1982
1 Excerpt

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