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

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  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]

2009 (1)

2008 (1)

2007 (1)

2005 (4)

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]

F. Crosby, “Signature adaptive target detection and threshold selection for constant false alarm rate,” J. Electron. Imaging 14, 033009 (2005).
[CrossRef]

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]

F. Galland, N. Bertaux, and P. Réfrégier, “Multicomponent image segmentation in homogeneous regions by stochastic complexity minimization,” Patt. Recog. 38, 1926-1936(2005).
[CrossRef]

2004 (4)

2003 (2)

F. Galland, N. Bertaux, and P. Réfrégier, “Minimum description length synthetic aperture radar image segmentation,” IEEE Trans. Image Process. 12, 995-1006 (2003).
[CrossRef]

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]

2001 (1)

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]

2000 (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).

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]

1999 (1)

I. McConnell and C. J. Oliver, “Segmentation-based target detection in SAR,” Proc. SPIE 3869, 45-54 (1999).
[CrossRef]

1998 (1)

I. McConnell and C. J. Oliver, “Comparison of segmentation methods with standard CFAR for point target detection,” Proc. SPIE 3497, 76-87 (1998).
[CrossRef]

1997 (1)

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]

1994 (1)

L. L. Scharf and B. Friedlander, “Matched subspace detectors,” IEEE Trans. Signal Process. 42, 2146-2157 (1994).
[CrossRef]

1992 (1)

J. R. Bunch and R. D. Fierro, “A constant-false-alarm-rate algorithm,” Linear Algebra Appl. 172, 231-241(1992).
[CrossRef]

1989 (1)

Y. Leclerc, “Constructing simple stable descriptions for image partitioning,” Int. J. Comput. Vis. 3, 73-102 (1989).
[CrossRef]

1988 (1)

P. P. Gandhi and S. A. Kassam, “Analysis of CFAR processors in nonhomogeneous background,” IEEE Trans. Aerosp. Electron. Syst. 24, 427-445 (1988).
[CrossRef]

1987 (1)

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]

1985 (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]

Acito, N.

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]

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.

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.

Alam, M. S.

Avishai, T.

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]

Baldacci, A.

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.

Baraniuk, R. G.

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]

Bertaux, N.

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]

F. Galland, N. Bertaux, and P. Réfrégier, “Multicomponent image segmentation in homogeneous regions by stochastic complexity minimization,” Patt. Recog. 38, 1926-1936(2005).
[CrossRef]

F. Galland, N. Bertaux, and P. Réfrégier, “Minimum description length synthetic aperture radar image segmentation,” IEEE Trans. Image Process. 12, 995-1006 (2003).
[CrossRef]

Bhuiyan, S. M. A.

Brosnan, T. M.

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]

Bunch, J. R.

J. R. Bunch and R. D. Fierro, “A constant-false-alarm-rate algorithm,” Linear Algebra Appl. 172, 231-241(1992).
[CrossRef]

Champagnat, F.

Chan, A.

Chen, J. Y.

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]

Choi, H.

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]

Conte, E.

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]

Corsini, G.

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]

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.

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.

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.

Crosby, F.

F. Crosby, “Signature adaptive target detection and threshold selection for constant false alarm rate,” J. Electron. Imaging 14, 033009 (2005).
[CrossRef]

de Figueiredo, R. J. P.

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]

De Maio, A.

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]

Denney, B. S.

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]

Der, S.

Diani, M.

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]

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.

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.

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.

Dom, B.

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.

Farina, A.

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]

Fierro, R. D.

J. R. Bunch and R. D. Fierro, “A constant-false-alarm-rate algorithm,” Linear Algebra Appl. 172, 231-241(1992).
[CrossRef]

Figueiredo, M. A. T.

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]

Foglia, G.

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]

Friedlander, B.

L. L. Scharf and B. Friedlander, “Matched subspace detectors,” IEEE Trans. Signal Process. 42, 2146-2157 (1994).
[CrossRef]

Gagliardi, R. M.

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]

Galland, F.

F. Galland and P. Réfrégier, “Information theory based snake adapted to inhomogeneous intensity variations,” Opt. Lett. 32, 2514-2516 (2007).
[CrossRef]

F. Galland, N. Bertaux, and P. Réfrégier, “Multicomponent image segmentation in homogeneous regions by stochastic complexity minimization,” Patt. Recog. 38, 1926-1936(2005).
[CrossRef]

F. Galland, N. Bertaux, and P. Réfrégier, “Minimum description length synthetic aperture radar image segmentation,” IEEE Trans. Image Process. 12, 995-1006 (2003).
[CrossRef]

Gandhi, P. P.

P. P. Gandhi and S. A. Kassam, “Analysis of CFAR processors in nonhomogeneous background,” IEEE Trans. Aerosp. Electron. Syst. 24, 427-445 (1988).
[CrossRef]

Giovannelli, J. F.

JiCheng, L.

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.

Kanungo, T.

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.

Kassam, S. A.

P. P. Gandhi and S. A. Kassam, “Analysis of CFAR processors in nonhomogeneous background,” IEEE Trans. Aerosp. Electron. Syst. 24, 427-445 (1988).
[CrossRef]

Kendall, M. G.

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.

Khan, J. F.

Ku, W. H.

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.

Kwon, H.

Leclerc, Y.

Y. Leclerc, “Constructing simple stable descriptions for image partitioning,” Int. J. Comput. Vis. 3, 73-102 (1989).
[CrossRef]

Lombardo, P.

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.

Madar, H.

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]

Magraner, E.

Mahalanobis, A.

Margalit, A.

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]

McConnell, I.

I. McConnell and C. J. Oliver, “Segmentation-based target detection in SAR,” Proc. SPIE 3869, 45-54 (1999).
[CrossRef]

I. McConnell and C. J. Oliver, “Comparison of segmentation methods with standard CFAR for point target detection,” Proc. SPIE 3497, 76-87 (1998).
[CrossRef]

Moon, Y. S.

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).

Muise, R. R.

Nasrabadi, N.

Ndili, U.

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]

Niblack, W.

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.

Nowak, R. D.

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]

Oliver, C. J.

I. McConnell and C. J. Oliver, “Segmentation-based target detection in SAR,” Proc. SPIE 3869, 45-54 (1999).
[CrossRef]

I. McConnell and C. J. Oliver, “Comparison of segmentation methods with standard CFAR for point target detection,” Proc. SPIE 3497, 76-87 (1998).
[CrossRef]

Pennucci, G.

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]

Pham, Q. H.

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]

Reed, I. S.

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]

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]

Réfrégier, P.

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]

F. Galland and P. Réfrégier, “Information theory based snake adapted to inhomogeneous intensity variations,” Opt. Lett. 32, 2514-2516 (2007).
[CrossRef]

F. Galland, N. Bertaux, and P. Réfrégier, “Multicomponent image segmentation in homogeneous regions by stochastic complexity minimization,” Patt. Recog. 38, 1926-1936(2005).
[CrossRef]

F. Galland, N. Bertaux, and P. Réfrégier, “Minimum description length synthetic aperture radar image segmentation,” IEEE Trans. Image Process. 12, 995-1006 (2003).
[CrossRef]

Rissanen, J.

J. Rissanen, Stochastic Complexity in Statistical Inquiry (World Scientific, 1989).

Rotman, S. R.

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]

Sadjadi, F. A.

Samson, V.

Scharf, L. L.

L. L. Scharf and B. Friedlander, “Matched subspace detectors,” IEEE Trans. Signal Process. 42, 2146-2157 (1994).
[CrossRef]

Sciotti, M.

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.

Smith, M. J. T.

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]

Soni, T.

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.

Stanfill, S. R.

Steele, D.

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.

Stuart, A.

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.

Succary, R.

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]

Tao, L.

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.

Van Trees, H. L.

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.

Zeidler, J. R.

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.

Zhang, T. X.

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).

ZhengKang, S.

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.

Zuo, Z.

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Figures (11)

Fig. 1
Fig. 1

Three typical infrared images of sky regions [(a)  637 × 233 pixels, (b)  636 × 244 pixels, (c)  638 × 244 pixels] provided by Thales Optronics SA. The white grids that appear in these images delimit the 64 square windows (each one containing 256 pixels) used to compare the different image models implemented in this paper for segmentation and detection tasks.

Fig. 2
Fig. 2

Spectral density (averaged over the 64 square windows of 16 × 16 = 256 pixels shown in Fig. 1) of the difference between the pixel gray level and the mean value estimated in each window using the M C model (column 1), the M L model (column 2), and the M Q model (column 3). Row 1 is related to the image in Fig. 1a, row 2 to the image in Fig. 1b, and row 3 to the one in Fig. 1c.

Fig. 3
Fig. 3

Histograms of the difference between the pixel gray level and the mean value estimated in each square windows of 16 × 16 = 256 pixels shown in Fig. 1 using the M C model (bottom curve), the M L model (middle curve), and the M Q model (top curve), for the image in (a) Fig. 1a, (b) Fig. 1b, and (c) Fig. 1c. For comparison, the Gaussian PDF with the same mean and variance as the samples have been superimposed on the histograms (black dotted curve). For better visualization, the curves associated with the M L and the M Q models have been vertically translated from 0.01 and 0.02, respectively.

Fig. 4
Fig. 4

Evolution of the detection probability as a function of the Pfa for (a) a constant background with μ Ω r = 2.110 4 and σ Ω r = 90 and, for (b) and (c), a linear background with α Ω r = 19 , β Ω r = 4.4 , γ Ω r = 2.010 4 , and σ Ω r = 18 , whose parameters correspond to typical parameter values estimated on the 16 × 16 pixel square windows in Fig. 1a. The region Ω r is a 11 × 11 pixel window and the target (with a 11 dB SNR and a positive contrast) is not centered on the window but is located either d pixels upward (black curves) or d pixels downward (gray curves) from the window center. In (a) and (b), d = 1 pixel and in (c), d = 2 pixels. The target detection has been performed using either the M C model (continuous curves), the M L model (dashed curves) or the M Q model (dotted curves). The crossed curves in (b) and (c) are the average between the two curves obtained with the M C model. The plots have been obtained by averaging 10 5 samples.

Fig. 5
Fig. 5

Detection probability as a function of the region size for different Pfa and for square regions centered on the target location. The Pfa is fixed to (a)  10 3 , (b)  10 4 , and (c)  10 5 , and the SNR of the target (which is centered on the window and has a positive contrast) is equal to 11 dB (bottom curves) and 14 dB (top curves). The plots have been obtained by averaging 10 5 samples and with background parameters set to μ Ω r = 2.110 4 and σ Ω r = 90 when detecting with the M C model; α Ω r = 19 , β Ω r = 4.4 , γ Ω r = 2.010 4 , and σ Ω r = 18 with the M L model; and a Ω r = 2.210 1 , b Ω r = 7.210 2 , c Ω r = 2.010 2 , d Ω r = 6.2 , e Ω r = 6.7 , f Ω r = 2.110 4 , and σ Ω r = 17 with the M Q model, which correspond to typical parameter values estimated on the 16 × 16 pixel square windows in Fig. 1a.

Fig. 6
Fig. 6

Detection probability as a function of the region size N r for different Pfa ( 10 3 , 10 4 , 10 5 ) and for regions with (a) a vertical shape (with a 5 pixel width and a height h so that N r = 5 h ), (b) a square shape, and (c) a horizontal shape (with a 5 pixel height and a width w so that N r = 5 w ). The SNR of the target (which is centered on the window and has a positive contrast) is equal to 13 dB and the plots have been obtained averaging 10 5 samples. The background parameters have been set to μ Ω r = 2.110 4 and σ Ω r = 90 when detecting with the M C model; α Ω r = 19 , β Ω r = 4.4 , γ Ω r = 2.010 4 , and σ Ω r = 18 with the M L model; and a Ω r = 2.210 1 , b Ω r = 7.210 2 , c Ω r = 2.010 2 , d Ω r = 6.2 , e Ω r = 6.7 , f Ω r = 2.110 4 , and σ Ω r = 17 with the M Q model, which correspond to typical parameter values estimated on the 16 × 16 pixel square windows in Fig. 1a.

Fig. 7
Fig. 7

Segmentation results on the images in Fig. 1a (column 1) and in Fig. 1b (column 2) with the M C (row 1), M L (row 2), and M Q (row 3) models.

Fig. 8
Fig. 8

Number of segmented regions containing fewer than N pixels as a function of N, averaged on the three images in Fig. 1 using the M C (solid black line), M L (continuous gray line), or M Q (dashed black line) model.

Fig. 9
Fig. 9

Evolution of the detection probability as a function of the Pfa for four mixed segmentation–detection techniques applied to 64 infrared images extracted from the same sequence as the image in Fig. 1a. The SNR of the targets is set to 11 dB (with a positive contrast). The false alarm and detection probabilities have been estimated in the 431 × 46 pixel portion of the sky shown in Fig. 1a (dashed gray rectangle).

Fig. 10
Fig. 10

Insertion of a 13 dB target (with a positive contrast) in the sky of the infrared image in Fig. 1b. (a) Zoom around the target location. Zoom of the segmentation results obtained on this image using (b) the M C model, (c) the M L model, and (d) the M Q model.

Fig. 11
Fig. 11

GLRT values ρ i 0 j 0 ( U ) obtained in Fig. 10a. A target is detected if ρ i 0 j 0 ( U ) > η Ω r , i 0 j 0 ( U ) , and η Ω r , i 0 j 0 ( U ) is shown by the dashed line so that the Pfa = 10 4 . These values have been plotted for the line that contains the target, using (a) the M C model, (b) the M L model, and (c) the M Q model for both segmentation and detection and (d) using the M C model for segmentation and the M L model for detection. The target is located at column number 131.

Equations (6)

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m Ω r ( C ) [ i , j ] = μ Ω r ,
m Ω r ( L ) [ i , j ] = α Ω r i + β Ω r j + γ Ω r ,
m Ω r ( Q ) [ i , j ] = a Ω r i 2 + b Ω r j 2 + c Ω r i j + d Ω r i + e Ω r j + f Ω r .
ρ i 0 j 0 ( U ) = [ x [ i 0 , j 0 ] m ^ Ω r i 0 j 0 ( U ) [ i 0 , j 0 ] σ ^ Ω r i 0 j 0 ( U ) ] 2 ,
ρ i 0 j 0 ( U ) > η Ω r , i 0 j 0 ( U ) ,
SNR = 10 log 10 [ ( x [ i , j ] m Ω r ( U ) [ i , j ] σ Ω r ) 2 ] ,

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