Abstract
The architecture of a biologically motivated visual-information processor that can perform a variety of tasks associated with the early stages of machine vision is described. The computational operations performed by the processor emulate the spatiotemporal information-processing capabilities of certain neural-activity fields found along the human visual pathway. The state-space model of the neurovision processor is a two-dimensional nural network of densely interconnected nonlinear processing elements PE's. An individual PE represents the dynamic activity exhibited by a spatially localized population of excitatory and inhibitory nerve cells. Each PE may receive inputs from an external signal space as well as from the neighboring PE's within the network. The information embedded within the signal space is extracted by the feedforward subnet. The feedback subnet of the neurovision processor generates useful steady-state and temporal-response characteristics that can be used for spatiotemporal filtering, short-term visual memory, spatiotemporal stabilization, competitive feedback interaction, and content-addressable memory. To illustrate the versatility of the multitask processor design for machine-vision applications, a computer simulation of a simplified vision system for filtering, storing, and classifying noisy gray-level images in presented.
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References
H.R. Wilson and J.D. Cowan, “A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue”, Kybernetik, Vol. 13, pp. 55–80, 1973.
L. Uhr, “Psychological motivation and underlying concepts”, in Structured Computer Vision, S. Tanimoto and A. Klinger, (eds.), Academic Press: New York, 1980, pp. 1–30.
S.P. Levitan, C.C. Weems, A.R. Hanson, and E.H. Riseman, “The UMass image understanding architecture”, in Parallel Computer Vision, L. Uhr,ed., Academic Press: New York pp. 215–248.1987
S. Amari, “Mathematical foundations of neurocomputing”, Proc. IEEE, vol 78, pp. 1443–1462, 1990.
M.M. Gupta and G.K. Knopf, “A multi-task visual information processor with a biologically motivated design”, J Vis. Commun. Image Rep., vol. 3, No. 3, pp. 230–246, 1992.
M.M. Gupta and G.K. Knopf, “A multi-task neuro-vision processor with extensive feedback and feedforward connections,” in Image Processing, K.-H. Taou, ed., Proc. Soc. Photo-Opt. Instrum. Eng., vol 1606, pp. 482–495, 1991.
K. Kishimoto and S. Amari, “Existence and stability of local excitations in homogenous neural fields”, J. Math. Biol., vol. 7, pp. 303–318, 1979.
G.K. Knopf, “Theoretical studies of a dynamic neurovision processor with a biologically motivated design”, Ph.D. dissertation, University of Saskatchewan, Canada, 1991.
H.R. Wilson and J.D. Cowan, “Excitatory and inhibitory interactions in localized populations of model neurons”, Biophys. J., vol. 12, pp. 1–24, 1972.
S. Grossberg, “Nonlinear neural networks: principles, mechanisms and architectures”, Neural Net., vol. 1, pp. 17–61, 1988.
P.K. Simpson, Artificial Neural Systems, Pergamon Press: New York, 1991.
D.S. Levine, “Neural population modeling and psychology: a review”, Math. Biosci., vol. 66, pp. 1–86, 1983.
R.C. Gonzalez and P. Wintz, Digital Image Processing, Addison-Wesley: Reading MA, 1977.
M.D. Levine, Vision in Man and Machine, McGraw-Hill: New York, 1985.
L. Uhi, “Highly parallel, hierarchical, recognition cone perceptual structures”, in Parallel Computer Vision, L. Uhr, ed., Academic Press: New York: 1987, pp. 249–292.
H. Tunley, “Dynamic image segmentation and optic flow extraction”, in Proc. IEEE Int. Joint conf on Neural Networks, Seattle WA, 1991, vol. 1, pp. 599–604.
H.R. Wilson, “Spatiotemporal characterization of a transient mechanism in the human visual system”, Vis. Res., vol. 20, pp. 443–452, 1980.
P.A. Anninos, B. Beek, T.J. Csermel, E.E. Harth, and G. Pertile, “Dynamics of neural structures” J. Theor. Biol., vol. 26, pp. 121–148, 1980.
A.J. Maren, C.T. Harston, and R.M. Pap, Handbook of Neural Computing Applications, Academic Press: San Diego, CA, 1990.
J.J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities”, Proc. Nat. Acad. Sci., USA, vol. 79, pp. 2554–2558, 1982.
J.J. Hopfield and D.W. Tank, “Computing with neural circuits: a model” Science, vol. 233, pp. 625–633, 1986.
T. Kohonen, Self-Organization and Associative Memory, Springer-Verlag: Berlin, 1984.
K. Fukushima, S. Miyake and T. Ito, “Neo-cognitron: a neural network model for a mechanism of visual pattern recognition”, IEEE Trans. Sys., Man, Cybern., vol. 13, pp. 826–834, 1983.
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Knopf, G.K., Gupta, M.M. Design of a multitask neurovision processor. J Math Imaging Vis 2, 233–250 (1992). https://doi.org/10.1007/BF00118592
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DOI: https://doi.org/10.1007/BF00118592