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A multistage parallel-hierarchic network as a model of a neuronlike computation scheme

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Cybernetics and Systems Analysis Aims and scope

Abstract

A three-dimensional network and its application to the analysis of images are described. This multilevel architecture studies partial correlations between structural components of an image. An algorithm is proposed that formalizes a new approach to the decomposition of images. An image is transformed so that each pixel contains information on the spatial structure of its neighborhood. The most correlated information is first formed, which ensures the resistance of the algorithm to small structural changes.

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References

  1. D. Hinton, /’’Mapping part: Whole hierarchies into connectionist networks,” Artificial Intelligence,46, No. 1–2, 45–75 (1990).

    Google Scholar 

  2. D. L. Alkon. K. T. Blackwell, G. S. Barbour, et al.,“ Biological plausibility of synaptic associative memory models,” Neural Networks,7, No. 6/7, 1005–1017 (1994).

    Article  MATH  Google Scholar 

  3. A. Lanitis, C. J. Taylor, and T. F. Cootes, “Automatic face identification system using flexible appearance models,” Image and vision computing,13, No. 5, 393–401 (1995).

    Article  Google Scholar 

  4. R. Chellappa, C. L. Wilson, and S. Sirohey, “’Human and machine recognition of faces: a survey,” Proc. of the IEEE,83, 705–740 (1995).

    Article  Google Scholar 

  5. S. Zeki, “A Vision of the brain,” in: Blackwell Sci. PubL Oxford (1993), pp. 43–50.

    Google Scholar 

  6. E. B. Babskii, A. A. Zybkov, G. I. Kositskii, et al., Physiology of Men [in Russian], Meditsina, Moscow (1972).

    Google Scholar 

  7. L. I. Timchenko, “Convergent and diverent processes in natural and artificial networks,” Visnyk VPI, No. 1, 5–10 (1997).

  8. D. H. Huebel, Eye, Brain, and Vision [Russian translation], Mir, Moscow (1990).

    Google Scholar 

  9. D. O. Hebb, The Organization of Behavior, Wiley, New York (1949).

    Google Scholar 

  10. L. I. Timchenko, Y. F. Kutaev, and S. V. Chepornyuk, et al., “A brainlike approach to a multistage hierarchial image,” in: Proc. Image Analysis and Processing (Florence, Italy, Sept. 17–19, 1997), Springer-verlag (1997), pp. 246–253.

  11. F. E. Blum, A. Lazerson, and L. Hofstadter, Brain, Mind, and Behavior, Freeman, New York (1985).

    Google Scholar 

  12. C. J. Shatz, “Impulse activity and the patterning of connection during CNS Development,” Neuron,5, No. 6, 745–758 (1990).

    Article  Google Scholar 

  13. Ya. Abu-Mostafa and D. Psaltis, “Optical neuron-network computers,” Scientific American [Russian translation], No. 5, 42–50 (1987).

  14. S. V. Svechnikov, V. P. Kozhemyako, and L. I. Timchenko, Quasiimpulse-Potential Optoelectronic Elements and Devices of the Logic-Temporal Type," Naukova Dumka, Kiev (1987).

    Google Scholar 

  15. M. A. Grudin, D. M. Harvey, L. I. Timchenko, and P. J. G. Lisboa, “Face recognition method using a multistage hierarchical network,” in: Proc. IEEE Intl. Conf. Acoust., Speech and Signal Proc. ICASSP97 (Munich, Germany, April 21–24, 1997), Vol. 4, Munich (1997), 2545–2548.

    Google Scholar 

  16. M. A. Grudin, L. I. Timchenko, D. M. Harvey, and V. P. Gel, “A system-theoretical approach to multistage hierarchical image processing,” in: Proc. EUROPTO Symp. (Besancon, France, June 11–14, 1996), Vol. 2785 (1996), pp. 225–234.

    Google Scholar 

  17. M. A. Grudin, D. M. Harvey, and L. I. Timchenko, “Multistage hierarchy for fast image analysis,” in: Proc. Eur. Symp. on Satellite Remote Sensing III (Taomina, Italy, 23–26 Sept., 1996), Vol. 2955, Taomina (1996), pp. 120–128.

    Google Scholar 

  18. L. Timchenko, S. Chepornyuk, M. Grudin, et al., “Use of three-dimensional multistage networks for facial image decomposition,” in: Proc. Machine Vision Appl., Architectures, and Systems Integration IV (SPIE Symp., Pittsburgh, USA, Oct. 14–17, 1997), Pitsburg (1997), pp. 55–60.

  19. “V. Gel, L. Timchenko, I. Iwasyuk, et al. ”Neural network method for biosignal processing,“ in: Proc. 3th Int. Conf. Biosignal 96 Processing, Analysis of Biomedical Signals and Images, Brno (1996), pp. 128-130.

  20. L. I. Timchenko, M.A. Grudin, V.P. Gel, et al., “A multistage hierarchical approach to image processing,” in: IEE Colloq. on Mulitdimensional Systems, P. 2.1–2.6 (London, UK, January, 1996), London (1996).

  21. V. P. Kozhemyako, Y. F. Kutaev, L. I. Timchenko, et al., “Use of the Q-transformation method for facial image normalization,” in: Proc. International ICSC/IFAC Symp. on NEURAL COMPUTATION - NC’98 (Vienna, Austria, Sept. 23–25, 1998), Vienna (1998), pp. 287–291.

  22. Y. Kutaev, L. Timchenko, A. Gertsiy, et al., “Compact representation of facial images for identification in a parallel-hierarchical network,” in: Machine Vision Systems for Inspection and Metrology, SPIE Symp. VII, (Boston, USA, Nov. 1–5, 1998), Boston (1998), pp. 157–167.

  23. M. J. L. Orr, “An introduction to radial basis function networks,” Technical report. Center for Cognitive Sci.; Univ. of Edinburgh (Scotland), Edinburgh (1996).

    Google Scholar 

  24. P. Werbos, “Beyond regression: New tools for prediction and analysis in the behavioral sciences,” Ph.D. Thesis in Applied Mathematics, Harvard University, Harvard (1974).

    Google Scholar 

  25. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning Representations by Backpropagation Errors,” Nature, 323, 533–536 (1986).

    Article  Google Scholar 

  26. J. K. Aggarwal and Shishir Shah, “Object recognition and performance bounds,” in: Proc. Image Analysis and Processing (Florence, Italy, Sept. 17–19), Florence (1–997), pp. 343–360.

  27. A. P. Stakhov, Introduction to the Algorithmic Theory of Measurements [in Russian), Sov. Radio, Moscow (1977).

    Google Scholar 

  28. D. J. Hall and G. B. Ball, “ISODATA: A novel method of data analysis and pattern classification,” Tech. Rep., Stanford Res. Inst.. Menlo Park (California) (1965).

    Google Scholar 

  29. T. Kohonen, “Self-organized formation of topologically correct feature maps,” Biol. Cybern., No. 43, 59–69 (1982).

    Google Scholar 

  30. R. Linsker, “Self-organization in a perceptual network,” Computer,21, No. 3, 105–117 (1988).

    Article  Google Scholar 

  31. P. Maragos and R. W. Shafer, “Morphological systems for multidimensional signal processing,” Proc. IEEE.,78, No. 4, 690–710 (1990).

    Article  Google Scholar 

  32. D. M. Harvey, S. P. Kshirsagar, C. A. Hobson, et al., “Digital signal processing system architectures for image processing,” in: Proc. of the 5th Intern. Conf. on Image Processing and Its Applications (Edinburgh, 1995), IEE Conf. Publ., No. 410 (1995), pp. 460–464.

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Authors and Affiliations

  1. Vinnitsa State Technical University, Vinnitsa, Ukraine

    L. I. Timchenko

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  1. L. I. Timchenko
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Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 114–133, March–April, 2000.

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Timchenko, L.I. A multistage parallel-hierarchic network as a model of a neuronlike computation scheme. Cybern Syst Anal 36, 251–267 (2000). https://doi.org/10.1007/BF02678673

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  • DOI: https://doi.org/10.1007/BF02678673

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