Abstract

Background clutter is becoming one of the most important factors affecting the target acquisition performance of electro-optical imaging systems. A novel clutter metric based on sparse representation is proposed in this paper. Based on sparse representation, the similarity vector is defined to describe the similarity between the background and the target in the feature domain, which is a typical feature of the background clutter. This newly proposed metric is applied to the Search_2 data set, and the experiment results show that its prediction correlates well with the detection probability of observers.

© 2011 Optical Society of America

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  1. W. R. Reynolds, “Toward quantifying infrared clutter,” Proc. SPIE 1311, 232–240 (1990).
    [CrossRef]
  2. D. E. Schmieder and M. R. Weathersby, “Detection performance in clutter with variable resolution,” IEEE Trans. Aerosp. Electron. Syst. AES-19, 622–630 (1983).
    [CrossRef]
  3. G. Tidhar, G. Reiter, Z. Avital, and Y. Hadar, “Modeling human search and target acquisition performance: IV. Detection probability in the cluttered environment,” Opt. Eng. 33, 801–808(1994).
    [CrossRef]
  4. J. D’Agostino, W. Lawson, and D. Wilson, “Concepts for search and detection model improvements,” Proc. SPIE 3063, 14–22 (1997).
    [CrossRef]
  5. T. Meitzler, W. Jackson, E. Sohn, and D. Bednarz, “A clutter metric based on texture,” Proceedings of the 36th Midwest Symposium on Circuits and Systems (IEEE, 1993). Vol. 1, pp. 81–87.
  6. T. Meitzler, G. Gerhart, and H. Singh, “A relative clutter metric,” IEEE Trans. Aerosp. Electron. Syst. 34, 968–976 (1998).
    [CrossRef]
  7. T. Meitzler, G. Gerhart, E. Sohn, and H. Singh, “Detection probability using relative clutter in infrared images,” IEEE Trans. Aerosp. Electron. Syst. 34, 955–962 (1998).
    [CrossRef]
  8. C. Yang, J.-Q. Zhang, and X. Xu, “Quaternion phase-correlation-based clutter metric for color images,” Opt. Eng. 46, 127008 (2007).
    [CrossRef]
  9. H. Chang and J. Zhang, “Evaluation of human detection performance using target structure similarity clutter metrics,” Opt. Eng. 45, 096404 (2006).
    [CrossRef]
  10. J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
    [CrossRef]
  11. A. Y. Yang, J. Wright, Y. Ma, and S. S. Sastry, “Feature selection in face recognition: a sparse representation perspective,” Patt. Recog. 43, 331–341 (2010).
    [CrossRef]
  12. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Machine Intell. 31, 210–227 (2009).
    [CrossRef]
  13. K. Huang and S. Aviyente, “Sparse representation for signal classification,” in Advances in Neural Information Processing Systems 19B.Schölkopf , J.Platt, and T.Hofmann, eds. (MIT, 2007) pp. 609–616.
  14. X. Yuan and S. Yan, “Visual classification with multi-task joint sparse representation,” in Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 3493–3500.
    [CrossRef]
  15. E. J. Candès, “Compressive sampling,” in Proceedings of the International Congress of Mathematicians (European Mathematical Society, 2006), Vol. 3, pp. 1433–1452.
  16. R. G. Baraniuk, “Compressive sensing,” IEEE Signal Process. Mag. 24, 118–121 (2007).
    [CrossRef]
  17. A. K. Mishra and B. Mulgrew, “Bistatic SAR ATR using PCA-based features,” Proc. SPIE 6234, 62340U1–62340U9 (2006).
  18. G. N. Ali, P.-J. Chiang, A. K. Mikkilineni, G. T.-C. Chiu, E. J. Delp, and J. P. Allebach, “Application of principal components analysis and Gaussian mixture models to printer identification,” International Conference on Digital Printing Technologies (2004), Vol. 20, 301–305.
  19. D. Donoho and Y. Tsaig, “Extensions of compressed sensing,” Signal Process. 86, 549–571 (2006).
    [CrossRef]
  20. A. Toet, P. Bijl, F. L. Kooi, and J. M. Valeton, “A high-resolution image data set for testing search and detection models,” Report TM-98-A020 (TNO Human Factors Research Institute, 1998).
  21. A. Toet, “Errata in report TNO-TM 1998 A020: A high-resolution image data set for testing search and detection models,” (TNO Human Factors Research Institute, 2001).
  22. D. L. Wilson, “Image-based contrast-to-clutter modeling of detection,” Opt. Eng. 40, 1852–1857 (2001).
    [CrossRef]

2010

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

A. Y. Yang, J. Wright, Y. Ma, and S. S. Sastry, “Feature selection in face recognition: a sparse representation perspective,” Patt. Recog. 43, 331–341 (2010).
[CrossRef]

2009

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Machine Intell. 31, 210–227 (2009).
[CrossRef]

2007

R. G. Baraniuk, “Compressive sensing,” IEEE Signal Process. Mag. 24, 118–121 (2007).
[CrossRef]

C. Yang, J.-Q. Zhang, and X. Xu, “Quaternion phase-correlation-based clutter metric for color images,” Opt. Eng. 46, 127008 (2007).
[CrossRef]

2006

H. Chang and J. Zhang, “Evaluation of human detection performance using target structure similarity clutter metrics,” Opt. Eng. 45, 096404 (2006).
[CrossRef]

A. K. Mishra and B. Mulgrew, “Bistatic SAR ATR using PCA-based features,” Proc. SPIE 6234, 62340U1–62340U9 (2006).

D. Donoho and Y. Tsaig, “Extensions of compressed sensing,” Signal Process. 86, 549–571 (2006).
[CrossRef]

2001

D. L. Wilson, “Image-based contrast-to-clutter modeling of detection,” Opt. Eng. 40, 1852–1857 (2001).
[CrossRef]

1998

T. Meitzler, G. Gerhart, and H. Singh, “A relative clutter metric,” IEEE Trans. Aerosp. Electron. Syst. 34, 968–976 (1998).
[CrossRef]

T. Meitzler, G. Gerhart, E. Sohn, and H. Singh, “Detection probability using relative clutter in infrared images,” IEEE Trans. Aerosp. Electron. Syst. 34, 955–962 (1998).
[CrossRef]

1997

J. D’Agostino, W. Lawson, and D. Wilson, “Concepts for search and detection model improvements,” Proc. SPIE 3063, 14–22 (1997).
[CrossRef]

1994

G. Tidhar, G. Reiter, Z. Avital, and Y. Hadar, “Modeling human search and target acquisition performance: IV. Detection probability in the cluttered environment,” Opt. Eng. 33, 801–808(1994).
[CrossRef]

1990

W. R. Reynolds, “Toward quantifying infrared clutter,” Proc. SPIE 1311, 232–240 (1990).
[CrossRef]

1983

D. E. Schmieder and M. R. Weathersby, “Detection performance in clutter with variable resolution,” IEEE Trans. Aerosp. Electron. Syst. AES-19, 622–630 (1983).
[CrossRef]

Ali, G. N.

G. N. Ali, P.-J. Chiang, A. K. Mikkilineni, G. T.-C. Chiu, E. J. Delp, and J. P. Allebach, “Application of principal components analysis and Gaussian mixture models to printer identification,” International Conference on Digital Printing Technologies (2004), Vol. 20, 301–305.

Allebach, J. P.

G. N. Ali, P.-J. Chiang, A. K. Mikkilineni, G. T.-C. Chiu, E. J. Delp, and J. P. Allebach, “Application of principal components analysis and Gaussian mixture models to printer identification,” International Conference on Digital Printing Technologies (2004), Vol. 20, 301–305.

Avital, Z.

G. Tidhar, G. Reiter, Z. Avital, and Y. Hadar, “Modeling human search and target acquisition performance: IV. Detection probability in the cluttered environment,” Opt. Eng. 33, 801–808(1994).
[CrossRef]

Aviyente, S.

K. Huang and S. Aviyente, “Sparse representation for signal classification,” in Advances in Neural Information Processing Systems 19B.Schölkopf , J.Platt, and T.Hofmann, eds. (MIT, 2007) pp. 609–616.

Baraniuk, R. G.

R. G. Baraniuk, “Compressive sensing,” IEEE Signal Process. Mag. 24, 118–121 (2007).
[CrossRef]

Bednarz, D.

T. Meitzler, W. Jackson, E. Sohn, and D. Bednarz, “A clutter metric based on texture,” Proceedings of the 36th Midwest Symposium on Circuits and Systems (IEEE, 1993). Vol. 1, pp. 81–87.

Bijl, P.

A. Toet, P. Bijl, F. L. Kooi, and J. M. Valeton, “A high-resolution image data set for testing search and detection models,” Report TM-98-A020 (TNO Human Factors Research Institute, 1998).

Candès, E. J.

E. J. Candès, “Compressive sampling,” in Proceedings of the International Congress of Mathematicians (European Mathematical Society, 2006), Vol. 3, pp. 1433–1452.

Chang, H.

H. Chang and J. Zhang, “Evaluation of human detection performance using target structure similarity clutter metrics,” Opt. Eng. 45, 096404 (2006).
[CrossRef]

Chiang, P.-J.

G. N. Ali, P.-J. Chiang, A. K. Mikkilineni, G. T.-C. Chiu, E. J. Delp, and J. P. Allebach, “Application of principal components analysis and Gaussian mixture models to printer identification,” International Conference on Digital Printing Technologies (2004), Vol. 20, 301–305.

Chiu, G. T.-C.

G. N. Ali, P.-J. Chiang, A. K. Mikkilineni, G. T.-C. Chiu, E. J. Delp, and J. P. Allebach, “Application of principal components analysis and Gaussian mixture models to printer identification,” International Conference on Digital Printing Technologies (2004), Vol. 20, 301–305.

D’Agostino, J.

J. D’Agostino, W. Lawson, and D. Wilson, “Concepts for search and detection model improvements,” Proc. SPIE 3063, 14–22 (1997).
[CrossRef]

Delp, E. J.

G. N. Ali, P.-J. Chiang, A. K. Mikkilineni, G. T.-C. Chiu, E. J. Delp, and J. P. Allebach, “Application of principal components analysis and Gaussian mixture models to printer identification,” International Conference on Digital Printing Technologies (2004), Vol. 20, 301–305.

Donoho, D.

D. Donoho and Y. Tsaig, “Extensions of compressed sensing,” Signal Process. 86, 549–571 (2006).
[CrossRef]

Ganesh, A.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Machine Intell. 31, 210–227 (2009).
[CrossRef]

Gerhart, G.

T. Meitzler, G. Gerhart, and H. Singh, “A relative clutter metric,” IEEE Trans. Aerosp. Electron. Syst. 34, 968–976 (1998).
[CrossRef]

T. Meitzler, G. Gerhart, E. Sohn, and H. Singh, “Detection probability using relative clutter in infrared images,” IEEE Trans. Aerosp. Electron. Syst. 34, 955–962 (1998).
[CrossRef]

Hadar, Y.

G. Tidhar, G. Reiter, Z. Avital, and Y. Hadar, “Modeling human search and target acquisition performance: IV. Detection probability in the cluttered environment,” Opt. Eng. 33, 801–808(1994).
[CrossRef]

Huang, K.

K. Huang and S. Aviyente, “Sparse representation for signal classification,” in Advances in Neural Information Processing Systems 19B.Schölkopf , J.Platt, and T.Hofmann, eds. (MIT, 2007) pp. 609–616.

Huang, T. S.

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

Jackson, W.

T. Meitzler, W. Jackson, E. Sohn, and D. Bednarz, “A clutter metric based on texture,” Proceedings of the 36th Midwest Symposium on Circuits and Systems (IEEE, 1993). Vol. 1, pp. 81–87.

Kooi, F. L.

A. Toet, P. Bijl, F. L. Kooi, and J. M. Valeton, “A high-resolution image data set for testing search and detection models,” Report TM-98-A020 (TNO Human Factors Research Institute, 1998).

Lawson, W.

J. D’Agostino, W. Lawson, and D. Wilson, “Concepts for search and detection model improvements,” Proc. SPIE 3063, 14–22 (1997).
[CrossRef]

Ma, Y.

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

A. Y. Yang, J. Wright, Y. Ma, and S. S. Sastry, “Feature selection in face recognition: a sparse representation perspective,” Patt. Recog. 43, 331–341 (2010).
[CrossRef]

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Machine Intell. 31, 210–227 (2009).
[CrossRef]

Mairal, J.

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

Meitzler, T.

T. Meitzler, G. Gerhart, and H. Singh, “A relative clutter metric,” IEEE Trans. Aerosp. Electron. Syst. 34, 968–976 (1998).
[CrossRef]

T. Meitzler, G. Gerhart, E. Sohn, and H. Singh, “Detection probability using relative clutter in infrared images,” IEEE Trans. Aerosp. Electron. Syst. 34, 955–962 (1998).
[CrossRef]

T. Meitzler, W. Jackson, E. Sohn, and D. Bednarz, “A clutter metric based on texture,” Proceedings of the 36th Midwest Symposium on Circuits and Systems (IEEE, 1993). Vol. 1, pp. 81–87.

Mikkilineni, A. K.

G. N. Ali, P.-J. Chiang, A. K. Mikkilineni, G. T.-C. Chiu, E. J. Delp, and J. P. Allebach, “Application of principal components analysis and Gaussian mixture models to printer identification,” International Conference on Digital Printing Technologies (2004), Vol. 20, 301–305.

Mishra, A. K.

A. K. Mishra and B. Mulgrew, “Bistatic SAR ATR using PCA-based features,” Proc. SPIE 6234, 62340U1–62340U9 (2006).

Mulgrew, B.

A. K. Mishra and B. Mulgrew, “Bistatic SAR ATR using PCA-based features,” Proc. SPIE 6234, 62340U1–62340U9 (2006).

Reiter, G.

G. Tidhar, G. Reiter, Z. Avital, and Y. Hadar, “Modeling human search and target acquisition performance: IV. Detection probability in the cluttered environment,” Opt. Eng. 33, 801–808(1994).
[CrossRef]

Reynolds, W. R.

W. R. Reynolds, “Toward quantifying infrared clutter,” Proc. SPIE 1311, 232–240 (1990).
[CrossRef]

Sapiro, G.

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

Sastry, S. S.

A. Y. Yang, J. Wright, Y. Ma, and S. S. Sastry, “Feature selection in face recognition: a sparse representation perspective,” Patt. Recog. 43, 331–341 (2010).
[CrossRef]

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Machine Intell. 31, 210–227 (2009).
[CrossRef]

Schmieder, D. E.

D. E. Schmieder and M. R. Weathersby, “Detection performance in clutter with variable resolution,” IEEE Trans. Aerosp. Electron. Syst. AES-19, 622–630 (1983).
[CrossRef]

Singh, H.

T. Meitzler, G. Gerhart, and H. Singh, “A relative clutter metric,” IEEE Trans. Aerosp. Electron. Syst. 34, 968–976 (1998).
[CrossRef]

T. Meitzler, G. Gerhart, E. Sohn, and H. Singh, “Detection probability using relative clutter in infrared images,” IEEE Trans. Aerosp. Electron. Syst. 34, 955–962 (1998).
[CrossRef]

Sohn, E.

T. Meitzler, G. Gerhart, E. Sohn, and H. Singh, “Detection probability using relative clutter in infrared images,” IEEE Trans. Aerosp. Electron. Syst. 34, 955–962 (1998).
[CrossRef]

T. Meitzler, W. Jackson, E. Sohn, and D. Bednarz, “A clutter metric based on texture,” Proceedings of the 36th Midwest Symposium on Circuits and Systems (IEEE, 1993). Vol. 1, pp. 81–87.

Tidhar, G.

G. Tidhar, G. Reiter, Z. Avital, and Y. Hadar, “Modeling human search and target acquisition performance: IV. Detection probability in the cluttered environment,” Opt. Eng. 33, 801–808(1994).
[CrossRef]

Toet, A.

A. Toet, P. Bijl, F. L. Kooi, and J. M. Valeton, “A high-resolution image data set for testing search and detection models,” Report TM-98-A020 (TNO Human Factors Research Institute, 1998).

A. Toet, “Errata in report TNO-TM 1998 A020: A high-resolution image data set for testing search and detection models,” (TNO Human Factors Research Institute, 2001).

Tsaig, Y.

D. Donoho and Y. Tsaig, “Extensions of compressed sensing,” Signal Process. 86, 549–571 (2006).
[CrossRef]

Valeton, J. M.

A. Toet, P. Bijl, F. L. Kooi, and J. M. Valeton, “A high-resolution image data set for testing search and detection models,” Report TM-98-A020 (TNO Human Factors Research Institute, 1998).

Weathersby, M. R.

D. E. Schmieder and M. R. Weathersby, “Detection performance in clutter with variable resolution,” IEEE Trans. Aerosp. Electron. Syst. AES-19, 622–630 (1983).
[CrossRef]

Wilson, D.

J. D’Agostino, W. Lawson, and D. Wilson, “Concepts for search and detection model improvements,” Proc. SPIE 3063, 14–22 (1997).
[CrossRef]

Wilson, D. L.

D. L. Wilson, “Image-based contrast-to-clutter modeling of detection,” Opt. Eng. 40, 1852–1857 (2001).
[CrossRef]

Wright, J.

A. Y. Yang, J. Wright, Y. Ma, and S. S. Sastry, “Feature selection in face recognition: a sparse representation perspective,” Patt. Recog. 43, 331–341 (2010).
[CrossRef]

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Machine Intell. 31, 210–227 (2009).
[CrossRef]

Xu, X.

C. Yang, J.-Q. Zhang, and X. Xu, “Quaternion phase-correlation-based clutter metric for color images,” Opt. Eng. 46, 127008 (2007).
[CrossRef]

Yan, S.

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

X. Yuan and S. Yan, “Visual classification with multi-task joint sparse representation,” in Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 3493–3500.
[CrossRef]

Yang, A. Y.

A. Y. Yang, J. Wright, Y. Ma, and S. S. Sastry, “Feature selection in face recognition: a sparse representation perspective,” Patt. Recog. 43, 331–341 (2010).
[CrossRef]

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Machine Intell. 31, 210–227 (2009).
[CrossRef]

Yang, C.

C. Yang, J.-Q. Zhang, and X. Xu, “Quaternion phase-correlation-based clutter metric for color images,” Opt. Eng. 46, 127008 (2007).
[CrossRef]

Yuan, X.

X. Yuan and S. Yan, “Visual classification with multi-task joint sparse representation,” in Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 3493–3500.
[CrossRef]

Zhang, J.

H. Chang and J. Zhang, “Evaluation of human detection performance using target structure similarity clutter metrics,” Opt. Eng. 45, 096404 (2006).
[CrossRef]

Zhang, J.-Q.

C. Yang, J.-Q. Zhang, and X. Xu, “Quaternion phase-correlation-based clutter metric for color images,” Opt. Eng. 46, 127008 (2007).
[CrossRef]

IEEE Signal Process. Mag.

R. G. Baraniuk, “Compressive sensing,” IEEE Signal Process. Mag. 24, 118–121 (2007).
[CrossRef]

IEEE Trans. Aerosp. Electron. Syst.

D. E. Schmieder and M. R. Weathersby, “Detection performance in clutter with variable resolution,” IEEE Trans. Aerosp. Electron. Syst. AES-19, 622–630 (1983).
[CrossRef]

T. Meitzler, G. Gerhart, and H. Singh, “A relative clutter metric,” IEEE Trans. Aerosp. Electron. Syst. 34, 968–976 (1998).
[CrossRef]

T. Meitzler, G. Gerhart, E. Sohn, and H. Singh, “Detection probability using relative clutter in infrared images,” IEEE Trans. Aerosp. Electron. Syst. 34, 955–962 (1998).
[CrossRef]

IEEE Trans. Pattern Anal. Machine Intell.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Machine Intell. 31, 210–227 (2009).
[CrossRef]

Opt. Eng.

C. Yang, J.-Q. Zhang, and X. Xu, “Quaternion phase-correlation-based clutter metric for color images,” Opt. Eng. 46, 127008 (2007).
[CrossRef]

H. Chang and J. Zhang, “Evaluation of human detection performance using target structure similarity clutter metrics,” Opt. Eng. 45, 096404 (2006).
[CrossRef]

G. Tidhar, G. Reiter, Z. Avital, and Y. Hadar, “Modeling human search and target acquisition performance: IV. Detection probability in the cluttered environment,” Opt. Eng. 33, 801–808(1994).
[CrossRef]

D. L. Wilson, “Image-based contrast-to-clutter modeling of detection,” Opt. Eng. 40, 1852–1857 (2001).
[CrossRef]

Patt. Recog.

A. Y. Yang, J. Wright, Y. Ma, and S. S. Sastry, “Feature selection in face recognition: a sparse representation perspective,” Patt. Recog. 43, 331–341 (2010).
[CrossRef]

Proc. IEEE

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

Proc. SPIE

J. D’Agostino, W. Lawson, and D. Wilson, “Concepts for search and detection model improvements,” Proc. SPIE 3063, 14–22 (1997).
[CrossRef]

W. R. Reynolds, “Toward quantifying infrared clutter,” Proc. SPIE 1311, 232–240 (1990).
[CrossRef]

A. K. Mishra and B. Mulgrew, “Bistatic SAR ATR using PCA-based features,” Proc. SPIE 6234, 62340U1–62340U9 (2006).

Signal Process.

D. Donoho and Y. Tsaig, “Extensions of compressed sensing,” Signal Process. 86, 549–571 (2006).
[CrossRef]

Other

A. Toet, P. Bijl, F. L. Kooi, and J. M. Valeton, “A high-resolution image data set for testing search and detection models,” Report TM-98-A020 (TNO Human Factors Research Institute, 1998).

A. Toet, “Errata in report TNO-TM 1998 A020: A high-resolution image data set for testing search and detection models,” (TNO Human Factors Research Institute, 2001).

G. N. Ali, P.-J. Chiang, A. K. Mikkilineni, G. T.-C. Chiu, E. J. Delp, and J. P. Allebach, “Application of principal components analysis and Gaussian mixture models to printer identification,” International Conference on Digital Printing Technologies (2004), Vol. 20, 301–305.

K. Huang and S. Aviyente, “Sparse representation for signal classification,” in Advances in Neural Information Processing Systems 19B.Schölkopf , J.Platt, and T.Hofmann, eds. (MIT, 2007) pp. 609–616.

X. Yuan and S. Yan, “Visual classification with multi-task joint sparse representation,” in Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2010), pp. 3493–3500.
[CrossRef]

E. J. Candès, “Compressive sampling,” in Proceedings of the International Congress of Mathematicians (European Mathematical Society, 2006), Vol. 3, pp. 1433–1452.

T. Meitzler, W. Jackson, E. Sohn, and D. Bednarz, “A clutter metric based on texture,” Proceedings of the 36th Midwest Symposium on Circuits and Systems (IEEE, 1993). Vol. 1, pp. 81–87.

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Figures (3)

Fig. 1
Fig. 1

Sensing the background clutter.

Fig. 2
Fig. 2

Full-size target scenes and subregions containing targets as indicated by white outlines in full-size target scenes from Search_2 data set: (a) 5th image, (b) 22nd image, (c) 4th image.

Fig. 3
Fig. 3

Scatter plots of experimental target detection probabilities versus model predictor values. Each sample point represents one test image in Search_2 data set: (a) SV, (b) POE, (c) SRC.

Tables (1)

Tables Icon

Table 1 Performance Comparison between Different Prediction Models

Equations (9)

Equations on this page are rendered with MathJax. Learn more.

x = i = 1 N s i ψ i or x = Ψ s ,
y = Φ x = Φ Ψ s = Θ s ,
argmin s 0 such that Θ s = y .
argmin s 1 such that Θ s = y ,
argmin s 0 subject to x = Ψ S ,
SRC = j = 1 I | s j | ( s j 0 ) ,
x ^ = ˙ P x = P Ψ S = ˙ Θ S R D ,
argmin s 1 such that x ^ = Θ s ,
P D pred = ( X / X 50 ) E 1 + ( X / X 50 ) E ,

Metrics