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Rotation-Invariant Pattern Recognition: A Procedure Slightly Inspired on Olfactory System and Based on Kohonen Network

  • M. B. Palermo
  • L. H. A. Monteiro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

A computational scheme for rotation-invariant pattern recognition based on Kohonen neural network is developed. This scheme is slightly inspired on the vertebrate olfactory system, and its goal is to recognize spatiotemporal patterns produced in a two-dimensional cellular automaton that would represent the olfactory bulb activity when submitted to odor stimuli. The recognition occurs through a multi-layer Kohonen network that would represent the olfactory cortex. The recognition is invariant to rotations of the patterns, even when a noise lower than 1% is added.

Keywords

Olfactory Bulb Cellular Automaton Spatiotemporal Pattern Olfactory System Output Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Agarwal, S., Chaudhuri, S.: Determination of aircraft orientation for a vision-based system using artificial neural networks. J. Math. Imaging Vis. 8, 255–269 (1998)CrossRefGoogle Scholar
  2. 2.
    Antonucci, M., Tirozzi, B., Yarunin, N.D., Dotsenko, V.S.: Numerical-simulation of neural networks with translation and rotation-invariant pattern-recognition. Int. J. Mod. Phys. B 8, 1529–1541 (1994)CrossRefGoogle Scholar
  3. 3.
    Breakspear, M.: Perception of odors by a nonlinear model of the olfactory bulb. Int. J. Bifurcat. Chaos 11, 101–124 (2001)Google Scholar
  4. 4.
    Chen, G.Y., Bui, T.D., Krzyzak, A.: Rotation invariant pattern recognition using ridgelets, wavelet cycle-spinning and Fourier features. Pattern Recognition 38, 2314–2322 (2005)CrossRefGoogle Scholar
  5. 5.
    Chiu, C.F., Wu, C.Y.: The design of rotation-invariant pattern recognition using the silicon retina. IEEE J. Solid-State Circuits 32, 526–534 (1997)CrossRefGoogle Scholar
  6. 6.
    Claverol, E.T., Brown, A.D., Chad, J.E.: A large-scale simulation of the piriform cortex by a cell automaton-based network model. IEEE Trans. Biomed. Eng. 49, 921–935 (2002)CrossRefGoogle Scholar
  7. 7.
    Dotsenko, V.S.: Neural networks: translation-, rotation- and scale-invariant pattern recognition. J. Phys. A: Math. Gen. 21, L783–L787 (1988)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Freeman, W.J.: Simulation of chaotic EEG patterns with a dynamic model of the olfactory system. Biol. Cybern. 56, 139–150 (1987)CrossRefGoogle Scholar
  9. 9.
    Freeman, W.J.: The physiology of perception. Sci. Am. 264(2), 78–85 (1991)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Greenberg, J.M., Hassard, B.D., Hasting, S.P.: Pattern formation and periodic structures in systems modeled by reaction-diffusion equations. Bull. Math. Soc. 84, 1296–1327 (1978)MATHCrossRefGoogle Scholar
  11. 11.
    Kohonen, T.: The self-organizing map. Proc. IEEE 78, 1464–1480 (1990)CrossRefGoogle Scholar
  12. 12.
    Korsching, S.: Olfactory maps and odor images. Curr. Opin. Neurobiol. 12, 387–392 (2002)CrossRefGoogle Scholar
  13. 13.
    Lledo, P.M., Gheusi, G., Vincent, J.D.: Information processing in the mammalian olfactory system. Physiol. Rev. 85, 281–317 (2005)CrossRefGoogle Scholar
  14. 14.
    Sachse, S., Galizia, C.G.: Role of inhibition for temporal and spatial odor representation in olfactory output neurons: a calcium imaging study. J. Neurophysiol. 87, 1106–1117 (2002)Google Scholar
  15. 15.
    Sheng, Y., Lejeune, C.: Invariant pattern recogniton using Fourier-Mellin transforms and neural networks. J. Optics (Paris) 22, 223–228 (1991)Google Scholar
  16. 16.
    Wolfram, S.: Theory and Applications of Cellular Automata. World Scientific, Singapore (1986)MATHGoogle Scholar
  17. 17.
    Zalevsky, Z., Mendlovic, D., Garcia, J.: Invariant pattern recognition by use of wavelength multiplexing. Appl. Opt. 36, 1059–1063 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • M. B. Palermo
    • 1
  • L. H. A. Monteiro
    • 1
    • 2
  1. 1.Universidade Presbiteriana Mackenzie, Pós-graduação em Engenharia Elétrica, Escola de EngenhariaSão PauloBrazil
  2. 2.Departamento de Engenharia de Telecomunicações e Controle, Escola PolitécnicaUniversidade de São PauloSão PauloBrazil

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