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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 27, Iss. 9 — May. 1, 1988
  • pp: 1886–1892

Multiple degree of freedom object recognition using optical relational graph decision nets

David P. Casasent and Andrew J. Lee  »View Author Affiliations


Applied Optics, Vol. 27, Issue 9, pp. 1886-1892 (1988)
http://dx.doi.org/10.1364/AO.27.001886


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Abstract

Multiple degree of freedom object recognition concerns objects with no stable rest position with all scale, rotation, and aspect distortions possible. We assume that the objects are in a fairly benign background, so that feature extractors are usable. In-plane distortion invariance is provided by use of a polar–log coordinate transform feature space, and out-of-plane distortion invariance is provided by linear discriminant function design. Relational graph decision nets are considered for multiple degree of freedom pattern recognition. The design of Fisher linear discriminant functions and synthetic discriminant functions for use at the nodes of binary and multidecision nets is discussed. Case studies are detailed for two-class and multiclass problems. Simulation results demonstrate the robustness of the processors to quantization of the filter coefficients and to noise.

© 1988 Optical Society of America

History
Original Manuscript: May 20, 1987
Published: May 1, 1988

Citation
David P. Casasent and Andrew J. Lee, "Multiple degree of freedom object recognition using optical relational graph decision nets," Appl. Opt. 27, 1886-1892 (1988)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-27-9-1886


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References

  1. D. Casasent, A. J. Lee, “A Feature Space Rule-Based Optical Relational Graph Processor,” Proc. Soc. Photo-Opt. Instrum. Eng. 625, 234 (1986); D. P. Casasent, A. J. Lee, “Optical Relational-Graph Rule-Based Processor for Structural-Attribute Knowledge Bases,” Appl. Opt. 25, 3065 (1986). [CrossRef] [PubMed]
  2. R. A. Fisher, “The Use of Multiple Measurements in Taxonomic Problems,” Ann. Eugenics 7, 179 (1936). [CrossRef]
  3. D. Casasent, “Unified Synthetic Discriminant Function Computational Formulation,” Appl. Opt. 23, 1620 (1984). [CrossRef] [PubMed]
  4. J. R. Leger, S. H. Lee, “Image Classification by an Optical Implementation of the Fukunaga-Koontz Transform,” J. Opt. Soc. Am. 72, 556 (1982). [CrossRef]
  5. Z-H. Gu, J. R. Leger, S. H. Lee, “Optical Implementation of the Least-Squares Linear Mapping Technique for Image Classification,” J. Opt. Soc. Am. 72, 787 (1982). [CrossRef]
  6. Z-H. Gu, S. H. Lee, “Optical Implementation of the Hotelling Trace Criterion for Image Classification,” Opt. Eng. 23, 727 (1984). [CrossRef]
  7. D. Casasent, H. Okuyama, “A High Dimensionality Pattern Recognition Feature Space,” Proc. Soc. Photo-Opt. Instrum. Eng. 579, 245 (1985).
  8. A. Mahalanobis, D. Casasent, “Large Class Iconic Pattern Recognition: an OCR Case Study,” Proc. Soc. Photo-Opt. Instrum. Eng. 726, 2 (1986).

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