Abstract
In computational models of the human brain, beneath the symbolic-type reasoning modelled by logic-based formalisms, there is another level of computation. It is implemented in a sub-symbolic substrate, in which operations are carried out by local interactions of simple computing elements, without any central guidance. Such a substrate is apparent in perception. In this paper, we show how a distributed, sub-symbolic organization can be usefully employed in pattern recognition. We present a self-organizing model that automatically learns the topology of an input space based on samples drawn from that space. Experiments are carried out to show that such a model can build a template to be used for character recognition, by autonomously inferring the essential topological features from a set of character images.
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References
W.A.Boothby: An Introduction to Differentiable Manifolds and Riemann Geometry. Academic Press, 1975.
A.Bunde and S.Havlin: Fractals and Disordered Systems. Springer-Verlag, 1991.
G.A.Carpenter and S.Grossberg: Art 2: Self-organization of stable category recognition codes for analog input patterns. Applied Optics, 26:4919–4930, 1987.
L.A.Cooper and R.N.Shepard: Turning something over in the mind. Scientific American, (7):106–114, 1985.
R.Durbin and D.Willsnaw: An analogue approach to the travelling salesman problem using an elastic net method. Nature, 326, April 1987.
S.Geman and D.Geman: Stochastic relazation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721–741, 1984.
H.Haken: Cooperative phenomena in systems far from thermal equilibrium and in nonphysical systems. Reviews of Modern Physics, 47(1):67–121, January 1975.
E.I.Knudsen, S.du Lac, and S.D.Esterly: Computational maps in the brain. Annual Review of Neuroscience, 10:41–65, 1987.
W.Köhler: Gestalt Psychology. Liverlight Publishing Corporation, New York, 1947.
T.Kohonen: The self-organizing map. Proceedings of the IEEE, 78(9):1464–1480, 1990.
B.Kosko: Neural Networks and Fuzzy Systems — A Dynamical Systems Approach to Machine Intelligence. Prentice Hall, Englewood Cliffs, NJ 07632, 1992.
K.S.Lashley: In search of the engram. Society of Experimantal Biology Symposium No 4: Psychological Mechanisms in Animal Behavior, pages 454–455, 468–473, 477–480, 1950.
Y.LeCun, B.Boser, J.S.Denker, D.Henderson, R.E.Howard, W.Hubbard, and L.D.Jackel: Backpropagation applied to handwritten zip code recognition. Neural Computation, 1989.
B.MacLennan: Characteristics of connectionist knowledge representation. Technical Report CS-91-147, Computer Science Department, University of Tennessee, Knoxville TN, November 1991.
B.MacLennan: Field computation in the brain. Technical Report CS-92-174, Computer Science Department, University of Tennessee, Knoxville TN, 1992.
B.J.MacLennan: Flexible computing in the 21st century. Vector Register, 4(3), 1991.
D.Marr and T.Poggio: Cooperative computation of stereo disparity. Science, 194:283–287, October 1976.
J.A.Marshall: Self-organizing neural network for perception of visual motion. Neural Networks, 3:45–74, 1990.
T.M.Martinez, H.J.Ritter, and K.J.Schulten: Three-dimensional neural net for learning visuomoto coordination of a robot arm. IEEE Transactions on Neural Networks, 1(1).
J.T.McIlwain: Large receptive fields and spatial transformations in the visual system. International Review of Physiology, Neurophysiology II, 10:223–248, 1976.
M.Ohlsson: Extensions and explorations of the elastic arms algorithm. Technical Report LU TP 92-28, Department of Theoretical Physics, University of Lund, Sölvegatan 14A S-22362 Lund, Sweden, December 1992. Also submitted to “Computer Physics Communications”.
A.Pellionisz and R.Llinás: Tensor network theory of the metaorganization of functional geometries in the central nervous system. Neuroscience, 16:245–273, 1985.
H.Ritter: Learning with the self-organizing map. In T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, editors, Artificial Neural Networks: Proceedings of the ICANN 91, pages 379–384. International Neural Networks Society, North Holland, June 1991.
R.N.Shepard and J.Metzler: Mental rotation of three-dimensional objects. Science, 171:701–703, February 1971.
C.A.Skarda and W.J.Freeman: How brains make chaos in order to make sense of the world. Behavioral Brain and Science, 10:161–195, 1987.
C.Y.Suen, C.Nadal, R.Legault, T.A.Mai, and L.Lam: Computer recognition of unconstrained handwritten numerals. Proceedings of the IEEE, 80(7):1162–1180, July 1992.
H.Takahashi: A neural net OCR using geometrical and zonal-pattern features. In Proceedings of the First International Conference on Document Analysis and Recognition, Saint Malo, France, September 1991.
B.Widrow: The “rubber-mask” technique-I. pattern measurement and analysis. Pattern Recognition, 5:175–197, 1973.
B.Widrow: The “rubber-mask” technique-II. pattern storage and recognition. Pattern Recognition, 5:199–211, 1973.
D.J.Willshaw and C.von der Marlsburg: How patterned neural connections can be set up by self-organization. Proceedings of the Royal Society of London, B, 194:431–445, 1976.
A.L.Youille, K.Honda, and C.Peterson: Particle tracking by deformable templates. In Proceedings of the International Joint Conference on Neural Networks, pages I-7–I-12, 1991.
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© 1993 Springer-Verlag Berlin Heidelberg
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Santini, S. (1993). Identification of pattern dimensionality by self-organization. In: Roberto, V. (eds) Intelligent Perceptual Systems. Lecture Notes in Computer Science, vol 745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57379-8_9
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DOI: https://doi.org/10.1007/3-540-57379-8_9
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