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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 28, Iss. 2 — Jan. 15, 1989
  • pp: 302–305

Comparison of artificial neural networks with pattern recognition and image processing

Jack Y. Jau, Y. Fainman, and Sing H. Lee  »View Author Affiliations


Applied Optics, Vol. 28, Issue 2, pp. 302-305 (1989)
http://dx.doi.org/10.1364/AO.28.000302


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Abstract

This paper presents a comparison between the field of artificial neural network and the field of image processing and pattern recognition. It shows that some of the adaptive processing algorithms for pattern recognition and image processing, in terms of neural networks, can be seen as adaptive heteroassociative and autoassociative memories, respectively. The similarities and differences between these two fields are addressed.

© 1989 Optical Society of America

History
Original Manuscript: March 7, 1987
Published: January 15, 1989

Citation
Jack Y. Jau, Y. Fainman, and Sing H. Lee, "Comparison of artificial neural networks with pattern recognition and image processing," Appl. Opt. 28, 302-305 (1989)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-28-2-302


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References

  1. K. Fukushima, S. Miyake, T. Ito, “Neocognitron: A Neural Network Model for a Mechanism of Visual Pattern Recognition,” IEEE Trans. Syst. Man Cybern. SMC-13, 826 (1983). [CrossRef]
  2. D. E. Rumelhart, D. Zipser, “Feature Discovery by Competitive Learning,” Cognitive Sci. 9, 75 (1985). [CrossRef]
  3. G. A. Carpenter, S. Grossberg, “A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine,” Comput. Vis. Graph. Image Proc. 37, 54 (1987). [CrossRef]
  4. Y. Z. Zhou, R. Chellappa, B. K. Jenkins, “A Novel Approach to Image Restoration Based on a Neural Network,” Presented at IEEE First Annual International Conference on Neural Network, San Diego, CA., June 1987.
  5. R. P. Lippmann, “An Introduction to Computing with Neural Nets,” IEEE Trans. Acoust. Speech Signal Process. ASSP Magazine, 4, 4 (1987).
  6. P. M. Grant, J. P. Sage, “A Comparison of Neural Network and Matched Filter Processing for Detecting Lines in Images,” in Neural Networks for Computing, Snowbird, UT, AIP Conf. Proc. 151 (1986).
  7. K. Fukunaga, Introduction to Statistical Pattern Recognition (Academic, New York, 1972), Chap. 7.
  8. Z-H. Gu, J. R. Leger, S. H. Lee, “Optical Implementation of the Least-Square Linear Mapping Technique for Image Classification,” J. Opt. Soc. Am. 72, 787 (1982). [CrossRef]
  9. J. R. Leger, S. H. Lee, “Image Classification by An Optical Implementation of the Fukunaga-Koontz Transformation,” J. Opt. Soc. Am. 72, 556 (1982). [CrossRef]
  10. A. Papoulis, “A New Algorithm in Spectral Analysis and Band-Limited Extrapolation,” IEEE Trans. Circuit Syst. CAS-22, 735 (1976).
  11. J. A. Cadzow, “An Extrapolation Procedure for Band-Limited Signal,” IEEE Trans. Acoust. Speech Signal Process. ASSP-27, 4 (1979). [CrossRef]
  12. A. V. Oppenheim, M. H. Hayes, J. S. Lim, “Iterative Procedures for Signal Reconstruction from Phase,” Proc. Soc. Photo-Opt. Instrum. Eng. 231, 121 (1980).
  13. D. Youla, “Generalized Image Restoration by the Method of Alternating Orthogonal Projections,” IEEE Trans. Circuit Syst. CAS-25, 694 (1978). [CrossRef]
  14. M. Cohen, S. Grossberg, “Absolute Stability of Global Pattern Formation and Parallel Memory Storage by Competitive Neural Networks,” IEEE Trans. Syst. Man Cybern. SMC-13, 815 (1983). [CrossRef]
  15. N. H. Farhat, D. Psaltis, A. Prata, E. Paek, “Optical Implementation of the Hopfield Model,” Appl. Opt. 24, 1469 (1985). [CrossRef] [PubMed]
  16. J. J. Hopfield, “Neurons with Graded Response have Collective Computational Properties Like Those of Two-State Neurons,” Proc. Natl. Acad. Sci. 81, 3088 (1984). [CrossRef] [PubMed]
  17. A. D. Fisher, C. L. Giles, “Optical Adaptive Associative Computer Architectures,” in Proceedings, IEEE COMPCON (Spring1985), pp. 342–344.
  18. R. Hecht-Nielsen, “Performance Limits of Optical, Electro-optical, and Electronic Neurocomputers,” Proc. Soc. Photo-Opt. Instrum. Eng. 634, 277 (1986).
  19. M. Takeda, J. W. Goodman, “Neural Network for Computation: Number Representation and Programming Complexity,” Appl. Opt. 25, 3033 (1986). [CrossRef] [PubMed]
  20. T. Kohonen, Self-Organization and Associative Memory (Springer-Verlag, Berlin, 1984), Chap. 5.

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