OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 41, Iss. 11 — Apr. 8, 2002
  • pp: 2149–2157

Distortion Tolerant Image Recognition Receiver by Use of a Multiple-Hypothesis Method

Sherif Kishk and Bahram Javidi  »View Author Affiliations


Applied Optics, Vol. 41, Issue 11, pp. 2149-2157 (2002)
http://dx.doi.org/10.1364/AO.41.002149


View Full Text Article

Acrobat PDF (4015 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

A multiple-hypothesis method is used to detect a target or a reference signal in the presence of additive noise with unknown statistics. The receiver is designed to detect the target and to be tolerant of the variations in rotation and illumination of the target. A multiple-hypothesis test with unknown-noise parameters is used to locate the target position. The proposed method does not use any specific distortion-invariant-filtering technique, but it relies on a multiple-hypothesis approach. Maximum-likelihood estimates of the illumination constant and the unknown noise parameters are obtained. Computer simulations are presented to evaluate the performance of the receiver for various distorted noisy true-class targets with varying illumination and false-class objects.

© 2002 Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(100.5760) Image processing : Rotation-invariant pattern recognition
(110.2970) Imaging systems : Image detection systems

Citation
Sherif Kishk and Bahram Javidi, "Distortion Tolerant Image Recognition Receiver by Use of a Multiple-Hypothesis Method," Appl. Opt. 41, 2149-2157 (2002)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-41-11-2149


Sort:  Author  |  Year  |  Journal  |  Reset

References

  1. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis (Wiley, New York, 1973).
  2. F. Fukunaga, Introduction to Statistical Pattern Recognition (Academic, New York, 1972).
  3. J. L. Turin, “An introduction to matched filters,” IRE Trans. Inf. Theory IT-6, 311–329 (1960).
  4. J. L. Horner and P. D. Gianino, “Phase-only matched filtering,” Appl. Opt. 23, 812–816 (1984).
  5. A. VanderLugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory, 19, 139–145 (1964).
  6. B. Javidi, P. Réfrégier, and P. Willett, “Optimum receiver design for pattern recognition with nonoverlapping target and scene noise,” Opt. Lett. 18, 1660–1664 (1993).
  7. A. Abu-Naser, N. P. Galatsanos, M. N. Wernick, and D. Schonfeld, “Object recognition based on impulse restoration with use of the expectation-maximization algorithm,” J. Opt. Soc. Am. A 15, 2327–2340 (1998).
  8. C. F. Hester and D. Casasent, “Multi-variant technique for multi-class pattern recognition,” Appl. Opt. 19, 1758–1761 (1980).
  9. R. R. Kallman, “The construction of low noise optical correlation filters,” Appl. Opt. 25, 1032–1033 (1986).
  10. A. Mahalanobis, “Review of correlation filters and their application for scene matching,” in Optoelectronic Devices and Systems for Processing; Critical Reviews of Optical Science Technology, B. Javidi and K. M. Johnson, eds., CR65, SPIE Press, 240–260 (1996).
  11. A. Mahalanobis, “Processing of multi-sensor data using correlation filters,” Algorithms, Devices, and Systems for Optical Information Processing II, B. Javidi and D. Psaltis, eds., Proc. SPIE, 3466, 56–64 (1998).
  12. P. Réfrégier, “Filter design for optimal pattern recognition: multicriteria optimization approach,” Opt. Lett. 15, 854–856 (1990).
  13. L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Synthetic discriminant function filter employing nonlinear space-domain preprocessing on bandpass-filtered images,” Appl. Opt. 37, 2051–2062 (1998).
  14. Y.-N. Hsu and H. H. Arsenault, “Optical Pattern recognition using circular harmonics expansion,” Appl. Opt. 21, 4016–4019 (1982).
  15. D. Casasent and D. Psaltis, “Position, rotation, and scale invariant optical correlations,” Appl. Opt. 15, 1795–1799 (1976).
  16. P. Zi-Liang and E. Dalsgaard, “Synthetic circular harmonic phase-only filter for shift, rotation, and scaling-invariant correlation,” Appl. Opt. 34, 7527–7531 (1995).
  17. S. Haykin, Neural Networks: a Comprehensive Foundation (Prentice Hall, Englewood Cliffs, New Jersey 1998).
  18. W. Li and N. M. Nasrabadi, “Invariant object recognition based on network of cascade RCE nets,” Int. J. Pattern Recognition and Artificial Intelligence, 7, 815–829 (1993).
  19. B. Javidi, and J. Wang, “Optimum distortion-invariant filter for detecting a noisy distorted target in nonoverlapping background noise,” J. Opt. Soc. Am. A 12, 2604–2614 (1995).
  20. N. Towghi and B. Javidi, “Generalized optimum receiver for pattern recognition with multiplicative, additive, and non-overlapping background noise,” J. Opt. Soc. Am. A 15, 1557–1565 (1998).
  21. H. J. Caulfield and W. T. Maloney, “Improved discrimination in optical character recognition(L),” Appl. Opt. 8, 2354–2356 (1969).
  22. A. D. McAulay, “Optical correlator for improving images distorted by atmospheric turbulence,” in Advances in Optical Information Processing IX, D. R. Pape, ed., Proc. SPIE, 4046, 41–47 (2000).
  23. H. H. Arsenault and D. Lefebvre, “Homomorphic cameo filter for pattern recognition that is invariant with changes in illumination,” Opt. Lett. 25, 1567–1569 (2000).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited