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Applied Optics

Applied Optics


  • Vol. 31, Iss. 8 — Mar. 10, 1992
  • pp: 1030–1040

Optical correlator production system neural net

David Casasent and Elizabeth Botha  »View Author Affiliations

Applied Optics, Vol. 31, Issue 8, pp. 1030-1040 (1992)

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A new neural net is described that can easily and cost-effectively accommodate multiple objects in the field of view in parallel. The use of a correlator achieves shift invariance and accommodates multiple objects in parallel. Distortion-invariant filters provide aspect-invariant distortion. Symbolic encoding, the use of generic object parts, and a production system neural net allow large class problems to be addressed. Optical laboratory data on the production system inputs are provided and emphasized. Test data assume binary inputs, although analog (probability) input neurons are possible.

© 1992 Optical Society of America

Original Manuscript: February 5, 1991
Published: March 10, 1992

David Casasent and Elizabeth Botha, "Optical correlator production system neural net," Appl. Opt. 31, 1030-1040 (1992)

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  1. D. E. Rumelhart, G. E. Hinton, R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed ProcessingD. E. Rumelhart, J. L. McClelland, eds. (MIT Press, Cambridge, Mass., 1986), Vol. 1, pp. 318–362; P. Werbos, Ph.D. dissertation (Harvard University, Cambridge, Mass., 1974).
  2. C. L. Giles, R. D. Griffen, T. Maxwell, “Encoding geometric invariances in higher-order neural networks,” Neural Information Processing Systems, D. Anderson, ed. (American Institute of Physics, New York, 1988), pp. 301–309.
  3. K. Fukushima, “Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by a shift in position,” Biol. Cybern. 36, 193–202 (1980). [CrossRef] [PubMed]
  4. E. Barnard, D. Casasent, “Shift invariance and the neocognitron,” Neural Networks 3, 403–410 (1990). [CrossRef]
  5. E. Barnard, D. Casasent, “Image processing for image understanding with neural nets,” in Proceedings of the International Joint Conference on Neural Networks, IEEE Catalog No. 89CH2765-6 (Institute of Electrical and Electronic Engineers, Washington, D.C., 1989), Vol. 1, pp. I-111–115.
  6. D. Casasent, A. Mahalanobis, “Rule-based symbolic processor for object recognition,” Appl. Opt. 26, 4795–4802 (1987). [CrossRef] [PubMed]
  7. E. Botha, D. Casasent, E. Barnard, “Optical production systems using neural networks and symbolic substitution,” Appl. Opt. 27, 5185–5193 (1988). [CrossRef] [PubMed]
  8. D. Casasent, “Unified synthetic discriminant function computational formulation,” Appl. Opt. 23, 1620–1627 (1984). [CrossRef] [PubMed]
  9. See special issue on optical pattern recognition, Opt. Eng. 29(9) (1990).
  10. D. Casasent, E. Barnard, “Adaptive clustering optical neural net,” Appl. Opt. 29, 2603–2615 (1990). [CrossRef] [PubMed]
  11. E. Barnard, P. Vermeulen, D. Casasent, “Optical correlation CGHs with modulated error diffusion,” Appl. Opt. 28, 5358–5362 (1989). [CrossRef] [PubMed]

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