OSA's Digital Library

Applied Optics

Applied Optics


  • Vol. 38, Iss. 8 — Mar. 10, 1999
  • pp: 1317–1324

Method of target detection in images by moment analysis of correlation peaks

Robert S. Caprari  »View Author Affiliations

Applied Optics, Vol. 38, Issue 8, pp. 1317-1324 (1999)

View Full Text Article

Enhanced HTML    Acrobat PDF (135 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Automatic target detection and recognition in images often is attempted by use of a linear correlation filter (matched filter), whose output is interpreted by a single pointwise detector (detection based on only one point). I examine a technique for significantly improving the performance of this target detection approach by supplementing the pointwise detector with several neighborhood correlation peak detectors (detection based on a domain of many points extending over much of the peak). The neighborhood detectors extract peak shape information through a moment analysis of correlation plane peaks. I describe the design of statistically quasi-optimal correlation peak discriminators based on second-order geometric moments.

© 1999 Optical Society of America

OCIS Codes
(100.1160) Image processing : Analog optical image processing
(100.2000) Image processing : Digital image processing
(100.4550) Image processing : Correlators
(100.5010) Image processing : Pattern recognition

Original Manuscript: March 31, 1998
Revised Manuscript: September 2, 1998
Published: March 10, 1999

Robert S. Caprari, "Method of target detection in images by moment analysis of correlation peaks," Appl. Opt. 38, 1317-1324 (1999)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. G. Schils, D. Sweeney, “Optical processor for recognition of three-dimensional targets viewed from any direction,” J. Opt. Soc. Am. A 5, 1309–1321 (1988). [CrossRef]
  2. T. Walsh, M. Giles, “Statistical filtering of time-sequenced peak correlation responses for distortion invariant recognition of multiple input objects,” Opt. Eng. 29, 1052–1064 (1990). [CrossRef]
  3. H. Caulfield, “Optical processing of optical correlation plane data,” in Optical Pattern Recognition, H.-K. Liu, ed., Proc. SPIE1053, 93–95 (1989). [CrossRef]
  4. W. Crowe, “Optical morphological processing of optical correlator signals,” in Photonics for Processors, Neural Networks, and Memories, J. Horner, B. Javidi, S. Kowel, W. Miceli, eds., Proc. SPIE2026, 297–301 (1993). [CrossRef]
  5. D. Montera, S. Rogers, D. Ruck, M. Oxley, “Object tracking through adaptive correlation,” Opt. Eng. 33, 294–302 (1994). [CrossRef]
  6. B. Gutmann, T. Wolf, H. Weber, J. Ferrè-Borrull, S. Bosch, S. Vallmitjana, “Improvement of the discrimination capability of correlation techniques by the use of fuzzy logic,” in Vision Systems: New Image Processing Techniques, P. Réfrégier, ed., Proc. SPIE2785, 83–94 (1996).
  7. B. Draayer, G. Carhart, M. Giles, “Optimum classification of correlation-plane data by Bayesian decision theory,” Appl. Opt. 33, 3034–3049 (1994). [CrossRef] [PubMed]
  8. J. Booth, “Automatic post processing of correlation planeimagery,” in Optical Pattern Recognition VI, D. Casasent, T.-H. Chao, eds., Proc. SPIE2490, 108–116 (1995). [CrossRef]
  9. P. Miller, R. Caprari, “Demonstration of improved automatic target recognition system performance by moment analysis of correlation peaks,” Appl. Opt. 38, 1325–1331 (1999). [CrossRef]
  10. M.-K. Hu, “Pattern recognition by moment invariants,” Proc. IRE 49, 1428 (1961).
  11. M.-K. Hu, “Visual pattern recognition by moment invariants,” IRE Trans. Inf. Theory IT-8, 179–187 (1962).
  12. R. Prokop, A. Reeves, “A survey of moment-based techniques for unoccluded object representation and recognition,” CVGIP: Graph. Models Image Process. 54, 438–460 (1992). [CrossRef]
  13. L. Wang, G. Healey, “Illumination and geometry invariant recognition of texture in color images,” in Proceedings of the 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE Computer Society Press, Los Alamitos, Calif., 1996), pp. 419–424.
  14. H. Goldstein, Classical Mechanics, 2nd ed. (Addison-Wesley, Reading, Mass., 1980).
  15. H. Poor, An Introduction to Signal Detection and Estimation, 2nd ed. (Springer-Verlag, New York, 1994). [CrossRef]
  16. C. Helstrom, Elements of Signal Detection and Estimation (Prentice-Hall, Englewood Cliffs, N.J., 1995).

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.


Fig. 1 Fig. 2 Fig. 3

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited