OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 9 — Mar. 20, 2010
  • pp: 1518–1527

Mixed segmentation–detection-based technique for point target detection in nonhomogeneous sky

Emilie Vasquez, Frédéric Galland, Guillaume Delyon, and Philippe Réfrégier  »View Author Affiliations


Applied Optics, Vol. 49, Issue 9, pp. 1518-1527 (2010)
http://dx.doi.org/10.1364/AO.49.001518


View Full Text Article

Enhanced HTML    Acrobat PDF (1400 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

This paper deals with point target detection in infrared images of the sky for which there are local variations of the gray level mean value. We show that considering a simple image model with the gray level mean value varying as a linear or a quadratic function of the pixel coordinates can improve mixed segmentation–detection performance in comparison to homogeneous model-based approaches.

© 2010 Optical Society of America

OCIS Codes
(040.1880) Detectors : Detection
(110.3080) Imaging systems : Infrared imaging

ToC Category:
Detectors

History
Original Manuscript: November 11, 2009
Manuscript Accepted: January 3, 2010
Published: March 10, 2010

Citation
Emilie Vasquez, Frédéric Galland, Guillaume Delyon, and Philippe Réfrégier, "Mixed segmentation-detection-based technique for point target detection in nonhomogeneous sky," Appl. Opt. 49, 1518-1527 (2010)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-49-9-1518


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. A. Margalit, I. S. Reed, and R. M. Gagliardi, “Adaptive optical target detection using correlated images,” IEEE Trans. Aerosp. Electron. Syst. aes-21, 394-405 (1985). [CrossRef]
  2. Y. S. Moon, T. X. Zhang, Z. R. Zuo, and Z. Zuo, “Detection of sea surface small targets in infrared images based on multilevel filter and minimum risk Bayes test,” Int. J. Patt. Recog. Art. Intell. 14, 907-918 (2000).
  3. A. Mahalanobis, R. R. Muise, and S. R. Stanfill, “Quadratic correlation filter design methodology for target detection and surveillance applications,” Appl. Opt. 43, 5198-5205(2004). [CrossRef]
  4. F. A. Sadjadi, “Infrared target detection with probability density functions of wavelet transform subbands,” Appl. Opt. 43, 315-323 (2004). [CrossRef]
  5. S. Der, A. Chan, N. Nasrabadi, and H. Kwon, “Automated vehicle detection in forward-looking infrared imagery,” Appl. Opt. 43, 333-348 (2004). [CrossRef]
  6. J. F. Khan, M. S. Alam, and S. M. A. Bhuiyan, “Automatic target detection in forward-looking infrared imagery via probabilistic neural networks,” Appl. Opt. 48, 464-476 (2009). [CrossRef]
  7. V. Samson, F. Champagnat, and J. F. Giovannelli, “Point target detection and subpixel position estimation in optical imagery,” Appl. Opt. 43, 257-263 (2004). [CrossRef]
  8. H. Madar, T. Avishai, R. Succary, and S. R. Rotman, “Developing a CFAR filter for detecting point targets using a dynamic programming algorithm,” Proc. SPIE 5204, 31-34 (2003). [CrossRef]
  9. T. Soni, J. R. Zeidler, and W. H. Ku, “Detection of point objects in spatially correlated clutter using two dimensional adaptive prediction filtering,” in Conference Record of The Twenty-Sixth Asilomar Conference on Signals, Systems and Computers (IEEE, 1992), pp. 846-851.
  10. B. S. Denney and R. J. P. de Figueiredo, “Optimal point target detection using adaptive auto regressive background prediction,” Proc. SPIE 4048, 46-57 (2000). [CrossRef]
  11. M. Diani, N. Acito, and G. Corsini, “Dim target detection in IR maritime surveillance systems,” in Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2003), pp. 2650-2652.
  12. M. Diani, N. Acito, and G. Corsini, “Airborne threat detection in navy IRST systems,” in IEE Proceedings of Vision, Image and Signal Processing (Institution of Electrical Engineers, 2005), pp. 45-51.
  13. J. Y. Chen and I. S. Reed, “A detection algorithm for optical targets in clutter,” IEEE Trans. Aerosp. Electron. Syst. aes-23, 46-59 (1987). [CrossRef]
  14. F. Crosby, “Signature adaptive target detection and threshold selection for constant false alarm rate,” J. Electron. Imaging 14, 033009 (2005). [CrossRef]
  15. Q. H. Pham, T. M. Brosnan, and M. J. T. Smith, “Sequential digital filters for fast detection of targets in FLIR image data,” Proc. SPIE 3069, 62-73 (1997). [CrossRef]
  16. L. JiCheng, S. ZhengKang, and L. Tao, “Detection of spot target in infrared clutter with morphological filter,” in Proceedings of IEEE Aerospace and Electronics Conference (IEEE, 1996), p. 168-172.
  17. A. De Maio, G. Foglia, E. Conte, and A. Farina, “CFAR behavior of adaptive detectors: an experimental analysis,” IEEE Trans. Aerosp. Electron. Syst. 41, 233-251 (2005). [CrossRef]
  18. P. Lombardo and M. Sciotti, “Segmentation-based technique for ship detection in SAR images,” in IEE Proceedings Radar, Sonar and Navigation (Institution of Electrical Engineers, 2001), pp. 147-159.
  19. P. P. Gandhi and S. A. Kassam, “Analysis of CFAR processors in nonhomogeneous background,” IEEE Trans. Aerosp. Electron. Syst. 24, 427-445 (1988). [CrossRef]
  20. I. McConnell and C. J. Oliver, “Comparison of segmentation methods with standard CFAR for point target detection,” Proc. SPIE 3497, 76-87 (1998). [CrossRef]
  21. I. McConnell and C. J. Oliver, “Segmentation-based target detection in SAR,” Proc. SPIE 3869, 45-54 (1999). [CrossRef]
  22. U. Ndili, R. G. Baraniuk, H. Choi, R. D. Nowak, and M. A. T. Figueiredo, “Coding theoretic approach to segmentation and robust CFAR-detection for ladar images,” Proc. SPIE 4379, 86-94 (2001). [CrossRef]
  23. F. Galland, N. Bertaux, and P. Réfrégier, “Minimum description length synthetic aperture radar image segmentation,” IEEE Trans. Image Process. 12, 995-1006 (2003). [CrossRef]
  24. F. Galland, N. Bertaux, and P. Réfrégier, “Multicomponent image segmentation in homogeneous regions by stochastic complexity minimization,” Patt. Recog. 38, 1926-1936(2005). [CrossRef]
  25. F. Galland and P. Réfrégier, “Information theory based snake adapted to inhomogeneous intensity variations,” Opt. Lett. 32, 2514-2516 (2007). [CrossRef]
  26. M. G. Kendall and A. Stuart, “Estimation: least squares and other methods,” in The Advanced Theory of Statistics (Griffin, 1961), Vol. 2, pp. 75-97.
  27. E. Magraner, N. Bertaux, and P. Réfrégier, “Adaptive log-quadratic approach for target detection in nonhomogeneous backgrounds perturbed with speckle fluctuations,” Opt. Lett. 33, 2821-2823 (2008). [CrossRef]
  28. H. L. Van Trees, “Classical detection and estimation theory,” in Detection, Estimation, and Modulation Theory. Part I: Detection, Estimation, and Linear Modulation Theory (Wiley-Interscience, 1968), pp. 19-165.
  29. J. R. Bunch and R. D. Fierro, “A constant-false-alarm-rate algorithm,” Linear Algebra Appl. 172, 231-241(1992). [CrossRef]
  30. M. Diani, G. Corsini, and A. Baldacci, “Space-time processing for the detection of airborne targets in IR image sequences,” in IEE Proceedings of Vision, Image and Signal Processing (Institution of Electrical Engineers, 2001), pp. 151-157.
  31. L. L. Scharf and B. Friedlander, “Matched subspace detectors,” IEEE Trans. Signal Process. 42, 2146-2157 (1994). [CrossRef]
  32. Y. Leclerc, “Constructing simple stable descriptions for image partitioning,” Int. J. Comput. Vis. 3, 73-102 (1989). [CrossRef]
  33. T. Kanungo, B. Dom, W. Niblack, and D. Steele, “A fast algorithm for MDL-based multi-band image segmentation,” in Proceedings of Computer Vision and Pattern Recognition CVPR (IEEE, 1994), pp. 609-616.
  34. J. Rissanen, Stochastic Complexity in Statistical Inquiry (World Scientific, 1989).
  35. N. Acito, G. Corsini, M. Diani, and G. Pennucci, “Comparative analysis of clutter removal techniques over experimental IR images,” Opt. Eng. 44, 106401 (2005). [CrossRef]

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