OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 42, Iss. 23 — Aug. 10, 2003
  • pp: 4718–4735

Detection and tracking of rotated and scaled targets by use of Hilbert-wavelet transform

Jahangheer S. Shaik and Khan. M. Iftekharuddin  »View Author Affiliations


Applied Optics, Vol. 42, Issue 23, pp. 4718-4735 (2003)
http://dx.doi.org/10.1364/AO.42.004718


View Full Text Article

Enhanced HTML    Acrobat PDF (2410 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

In a recent work, we demonstrated the usefulness of the Hilbert transform in identifying the in-plane rotation angle between two objects. Here we use the Hilbert-wavelet bases instead of the Hilbert transform in the determination of the exact angle of rotation. We describe the design of the two-dimensional Hilbert-wavelet filter based on the spectral-factorization method to generate a Hilbert-transform pair of orthogonal wavelet bases. We compare the relative performance of the Hilbert transform and the Hilbert wavelet to identify both in-plane and out-of-plane rotation angles. We demonstrate that the Hilbert wavelet offers better rotation-angle determination than the Hilbert transform. We present correlation based rotated and scaled object identification and tracking using Hilbert or Hilbert-wavelet transformed infrared image sequences. We also demonstrate reduced data handling and improved tracking of distorted objects using the Hilbert-wavelet transform.

© 2003 Optical Society of America

OCIS Codes
(100.2960) Image processing : Image analysis
(100.5010) Image processing : Pattern recognition
(100.7410) Image processing : Wavelets

History
Original Manuscript: November 14, 2002
Revised Manuscript: May 29, 2003
Published: August 10, 2003

Citation
Jahangheer S. Shaik and Khan. M. Iftekharuddin, "Detection and tracking of rotated and scaled targets by use of Hilbert-wavelet transform," Appl. Opt. 42, 4718-4735 (2003)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-42-23-4718


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. M. S. Snorrason, H. Ruda, “Image understanding software for hybrid hardware,” Advanced Research Projects Agency (DoD), http://cns-web.bu.edu/pub/snorrason-papers/C9406-finalreport.pdf
  2. M. Boshra, B. Bhanu, “Predicting object recognition performance under data uncertainty, occlusion and clutter,” IEEE international Conference on Image Processing, 3, 556–560, 1998.
  3. K. M. Iftekharuddin, C. Rentala, A. Dani, “Determination of exact rotation angle and discrimination for rotated images,” Opt. Laser Technol. 32, 313–327 (2002). [CrossRef]
  4. D. Casasent, D. Psaltis, “Position, rotation and scale invariant optical correlator,” Appl. Opt. 15, 1795–1799 (1976). [CrossRef] [PubMed]
  5. B. U. Lee, C. M. Kim, R. H. Park, “Error sensitivity of rotation angles in ICP algorithm,” IEEE Pat. Annal. Mach. Intel. 22, 1205–1208 (2000).
  6. IR image dataset, US Army Aviation and Missile Command (AMCOM, 1999.
  7. S. R. F. Sims, “Putting ATR performance on an equal basis—The measurement of knowledge-based distortion and relevant clutter,” U.S. Army Aviation & Missile Command 18, 2631–2635 (1997).
  8. S. R. F. Sims, “Data compression issues in automatic target recognition and measuring of distortion,” Opt. Eng. 36, 2671–2674 (1997). [CrossRef]
  9. C. Stauffer, W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, Colo., 2, 246—252 (1999).
  10. S. L. Diab, M. A. Karim, K. M. Iftekharuddin, “Multiobject detection of targets with fine details, scale and translation variation,” Opt. Eng. 37, 876–883 (1998). [CrossRef]
  11. G. Ravichandran, D. Casasent, “Generalized in plane rotation invariant minimum average correlation energy filter,” Opt. Eng. 30, 1601–1607 (1991). [CrossRef]
  12. K. M. Iftekharuddin, M. A. Razzaque, “Constraints in distortion invariant target recognition system simulation,” in International Conference on Sensor Technology, Y. Zhou, S. Xu, eds., Proc. SPIE, 4414, 20–312000.
  13. J. W. Woods, V. K. Ingle, “Kalman filtering in two dimensions: Further results,” IEEE Trans. Acoustic, Speech, Signal Process. 29, 188–197 (1981). [CrossRef]
  14. E. W. Selesnick, “Hilbert Transform Pair of Wavelet bases”, Invited paper for information systems, Signal Process. Lett 8, 170–173 (2001). [CrossRef]
  15. N. P. Galatsanos, T. Chin, “Restoration of color images by multi channel Kalman filtering,” IEEE Trans. Signal Process. 39, 2237–2252 (1991). [CrossRef]
  16. J. P. Thiran, “Recursive digital filters with maximally flat gropu delay,” IEEE Trans. on Circuit Theory, 18, 659–664 (1971). [CrossRef]
  17. S. Mallat, A Wavelet Tour of Signal Processing (AcademicNew York1998).
  18. P. Abry, Ondelettes et Turbulences (Diderot, Paris, 1997).
  19. R. G. Driggers, P. Cox, T. Edwards, Introduction to Infrared and Electro Optical Systems, (Artech House, Norwood, Mass., 1999)

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