Abstract

This paper presents a technique for automatic detection of the targets in forward-looking infrared (FLIR) imagery. Mathematical morphology is applied for the preliminary selection of possible regions of interest (ROI). An efficient clutter rejecter module based on probabilistic neural network is proposed, which is trained by using both target and background features to ensure excellent classification performance by moving the ROI in several directions with respect to the center of the detected target patch. Experimental results using real-life FLIR imagery confirm the excellent performance of the detector and the effectiveness of the proposed clutter rejecter module.

© 2009 Optical Society of America

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    [CrossRef]
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  4. M. S. Alam and A. Bal, “Improved multiple target tracking via global motion compensation and optoelectronic correlation,” IEEE Trans. Ind. Electron. 54, 522-529 (2007).
    [CrossRef]
  5. A. Dawoud, M. S. Alam, A. Bal, and C. Loo, “Target tracking in infrared imagery using weighted composite reference function-based decision fusion,” IEEE Trans. Image Process. 15, 404-410 (2006).
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  7. S. R. F. Sims and A. Mahalanobis, “Performance evaluation of quadratic correlation filters for target detection and discrimination in infrared imagery,” Opt. Eng. 43, 1705-1711 (2004).
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  8. D. Casasent and R. Shenoy, “Feature space trajectory for distorted object classification and pose estimation in SAR,” Opt. Eng. 36, 2719-2728 (1997).
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  32. S. A. Rizvi and N. M. Nasarabadi, “Fusion of FLIR automatic target recognition algorithms,” Information Fusion 4, 247-258(2003).
    [CrossRef]
  33. J. F. Khan and M. S. Alam, “Target detection in cluttered forward-looking infrared imagery,” Opt. Eng. 44, 076404 (2005).
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  34. R. C. Gonzalez and R. E. Woods, Digital Image Processing (Addison-Wesley, 1992).
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2008 (1)

G. S. Gill and J. S. Sohal, “Battlefield decision making: a neural network approach,” J. Theoret. Appl. Inform. Technol. 4, 697-699 (2008).

2007 (1)

M. S. Alam and A. Bal, “Improved multiple target tracking via global motion compensation and optoelectronic correlation,” IEEE Trans. Ind. Electron. 54, 522-529 (2007).
[CrossRef]

2006 (1)

A. Dawoud, M. S. Alam, A. Bal, and C. Loo, “Target tracking in infrared imagery using weighted composite reference function-based decision fusion,” IEEE Trans. Image Process. 15, 404-410 (2006).
[CrossRef]

2005 (2)

A. Bal and M. S. Alam, “Automatic target tracking in FLIR image sequences using intensity variation function and template modeling,” IEEE Trans. Instrum. Meas. 54, 1846-1852 (2005).
[CrossRef]

J. F. Khan and M. S. Alam, “Target detection in cluttered forward-looking infrared imagery,” Opt. Eng. 44, 076404 (2005).
[CrossRef]

2004 (3)

2003 (3)

A. L. Chan, S. Z. Der, and N. M. Nasarabadi, “Multistage infrared target detection,” Opt. Eng. 42, 2746-2754 (2003).
[CrossRef]

A. L. Chan, S. A. Rizvi, and N. M. Nasarabadi, “Dualband FLIR for automatic target recognition,” Information Fusion 4, 35-45 (2003).
[CrossRef]

S. A. Rizvi and N. M. Nasarabadi, “Fusion of FLIR automatic target recognition algorithms,” Information Fusion 4, 247-258(2003).
[CrossRef]

2002 (1)

S. A. Rizvi and N. M. Nasarabadi, “A modular clutter rejection technique for FLIR imagery using region based principal component analysis,” Pattern Recogn. 35, 2895-2904 (2002).
[CrossRef]

1998 (3)

T. C. Wang and N. B. Karayiannis, “Detection of microcalcifications in digital mammograms using wavelets,” IEEE Trans. Med. Imaging 17 (1998).
[CrossRef] [PubMed]

D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998).
[CrossRef]

W. Lie-Chan, S. Z. Der, and N. M. Nasarabadi, “Automatic target recognition using feature-decomposition and data-decomposition modular neural network,” IEEE Trans. Image Process. 7, 1113-1121 (1998).
[CrossRef]

1997 (3)

B. Ernisse, S. K. Rogers, M. P. DeSimio, and R. A. Ranies, “Complete automatic target cuer/recognition system for tactical forward-looking infrared images,” Opt. Eng. 36, 2593-2603 (1997).
[CrossRef]

D. Casasent and R. Shenoy, “Feature space trajectory for distorted object classification and pose estimation in SAR,” Opt. Eng. 36, 2719-2728 (1997).
[CrossRef]

A. Mahalanobis, “Correlation filters for object tracking target re-acquisition and smart aimpoint selection,” Proc. SPIE 3073, 25-32 (1997).
[CrossRef]

1995 (3)

S. Rogers, J. Colombi, C. Martin, J. Gainey, K. Fielding, T. Burns, D. Ruck, M. Kabrisky, and M. Oxley, “Neural networks for automatic target recognition,” Neural Networks 8, 1153-1184 (1995).
[CrossRef]

I. E. Dror, M. Zagaeski, and C. F. Moss, “Three-dimensional target recognition via sonar: a neural network model,” Neural Networks 8, 149-160 (1995).
[CrossRef]

A. Ravichandran and B. Yegnanarayana, “Studies on object recognition from degraded images using neural networks,” Neural Networks 8, 481-488 (1995).
[CrossRef]

1990 (2)

M. W. Roth, “Survey of neural network technology for automatic target recognition,” IEEE Trans. Neural Netw. 1, 28-43(1990).
[CrossRef] [PubMed]

D. F. Specht, “Probabilistic neural networks,” Neural Networks 3, 109-118 (1990).
[CrossRef]

1989 (1)

G. A. Carpenter, “Neural network models for pattern recognition and associative memory,” Neural Networks 2, 243-257 (1989).
[CrossRef]

1988 (1)

I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Commun. Pure Appl. Math. 41, 909-996 (1988).
[CrossRef]

1986 (1)

B. Bhanu, “Automatic target recognition: state of the art survey,” IEEE Trans. Aerosp. Electron. Syst. AES-22, 364-379(1986).
[CrossRef]

1985 (1)

T. R. Crimmins and W. M. Brown, “Image algebra and automatic shape recognition,” IEEE Trans. Aerosp. Electron. Syst. AES-21, 60-69 (1985).
[CrossRef]

Acheroy, M.

D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998).
[CrossRef]

Ahn, J.

B. Bhanu and J. Ahn, “A system for model-based recognition of articulated objects,” in Proceedings of the International Conference on Pattern Recognition (IEEE, 1998), Vol. 2, pp. 1812-1815.

Alam, M. S.

M. S. Alam and A. Bal, “Improved multiple target tracking via global motion compensation and optoelectronic correlation,” IEEE Trans. Ind. Electron. 54, 522-529 (2007).
[CrossRef]

A. Dawoud, M. S. Alam, A. Bal, and C. Loo, “Target tracking in infrared imagery using weighted composite reference function-based decision fusion,” IEEE Trans. Image Process. 15, 404-410 (2006).
[CrossRef]

J. F. Khan and M. S. Alam, “Target detection in cluttered forward-looking infrared imagery,” Opt. Eng. 44, 076404 (2005).
[CrossRef]

A. Bal and M. S. Alam, “Automatic target tracking in FLIR image sequences using intensity variation function and template modeling,” IEEE Trans. Instrum. Meas. 54, 1846-1852 (2005).
[CrossRef]

A. Bal and M. S. Alam, “Dynamic target tracking using fringe-adjusted joint transform correlation and template matching,” Appl. Opt. 43, 4874-4881 (2004).
[CrossRef] [PubMed]

Bal, A.

M. S. Alam and A. Bal, “Improved multiple target tracking via global motion compensation and optoelectronic correlation,” IEEE Trans. Ind. Electron. 54, 522-529 (2007).
[CrossRef]

A. Dawoud, M. S. Alam, A. Bal, and C. Loo, “Target tracking in infrared imagery using weighted composite reference function-based decision fusion,” IEEE Trans. Image Process. 15, 404-410 (2006).
[CrossRef]

A. Bal and M. S. Alam, “Automatic target tracking in FLIR image sequences using intensity variation function and template modeling,” IEEE Trans. Instrum. Meas. 54, 1846-1852 (2005).
[CrossRef]

A. Bal and M. S. Alam, “Dynamic target tracking using fringe-adjusted joint transform correlation and template matching,” Appl. Opt. 43, 4874-4881 (2004).
[CrossRef] [PubMed]

Becanovic, V.

J. Waldemark, V. Becanovic, T. Lindblad, and C. S. Lindsey, “Hybrid neural networks for automatic target recognition,” in Systems, Man, and Cybernetics, IEEE Int. Conf. Computational Cybernetics and Simulation (IEEE, 1997), Vol. 4, pp. 4016-4021.
[CrossRef]

Bhanu, B.

B. Bhanu, “Automatic target recognition: state of the art survey,” IEEE Trans. Aerosp. Electron. Syst. AES-22, 364-379(1986).
[CrossRef]

B. Bhanu and T. Jones, “Image understanding research for automatic target recognition,” in IEEE Trans. Aerospace and Electronic Systems Magazine (IEEE, 1993), pp. 15-22.
[CrossRef]

B. Bhanu and J. Ahn, “A system for model-based recognition of articulated objects,” in Proceedings of the International Conference on Pattern Recognition (IEEE, 1998), Vol. 2, pp. 1812-1815.

Borghys, D.

D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998).
[CrossRef]

Brown, W. M.

T. R. Crimmins and W. M. Brown, “Image algebra and automatic shape recognition,” IEEE Trans. Aerosp. Electron. Syst. AES-21, 60-69 (1985).
[CrossRef]

Burns, T.

S. Rogers, J. Colombi, C. Martin, J. Gainey, K. Fielding, T. Burns, D. Ruck, M. Kabrisky, and M. Oxley, “Neural networks for automatic target recognition,” Neural Networks 8, 1153-1184 (1995).
[CrossRef]

Carpenter, G. A.

G. A. Carpenter, “Neural network models for pattern recognition and associative memory,” Neural Networks 2, 243-257 (1989).
[CrossRef]

Casasent, D.

D. Casasent and R. Shenoy, “Feature space trajectory for distorted object classification and pose estimation in SAR,” Opt. Eng. 36, 2719-2728 (1997).
[CrossRef]

Chan, A. L.

S. Z. Der, A. L. Chan, N. M. Nasarabadi, and H. Kwon, “Automated vehicle detection in forward-looking infrared imagery,” Appl. Opt. 43, 333-348 (2004).
[CrossRef] [PubMed]

A. L. Chan, S. Z. Der, and N. M. Nasarabadi, “Multistage infrared target detection,” Opt. Eng. 42, 2746-2754 (2003).
[CrossRef]

A. L. Chan, S. A. Rizvi, and N. M. Nasarabadi, “Dualband FLIR for automatic target recognition,” Information Fusion 4, 35-45 (2003).
[CrossRef]

Chellappa, R.

S. Z. Der, Q. Zheng, R. Chellappa, B. Redman, and H. Mahmoud, “View based recognition of military vehicles in LADAR imagery using CAD model matching,” in Image Recognition and Classification, Algorithms, Systems and Applications, B. Javidi, ed. (Marcel Dekker, 2002), pp. 151-187.

Chui, C. K.

C. K. Chui, An Introduction to Wavelets (Academic, 1992).

Colombi, J.

S. Rogers, J. Colombi, C. Martin, J. Gainey, K. Fielding, T. Burns, D. Ruck, M. Kabrisky, and M. Oxley, “Neural networks for automatic target recognition,” Neural Networks 8, 1153-1184 (1995).
[CrossRef]

Crimmins, T. R.

T. R. Crimmins and W. M. Brown, “Image algebra and automatic shape recognition,” IEEE Trans. Aerosp. Electron. Syst. AES-21, 60-69 (1985).
[CrossRef]

Daubechies, I.

I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Commun. Pure Appl. Math. 41, 909-996 (1988).
[CrossRef]

I. Daubechies, Ten Lectures on Wavelets (SIAM, 1992).
[CrossRef]

Davies, D.

D. Davies, P. Palmer, and M. Mirmehdi, “Detection and tracking of very small low contrast objects,” in Proceedings of the 9th British Machine Vision Conference (BMVA, 1998), pp. 599-608.

Dawoud, A.

A. Dawoud, M. S. Alam, A. Bal, and C. Loo, “Target tracking in infrared imagery using weighted composite reference function-based decision fusion,” IEEE Trans. Image Process. 15, 404-410 (2006).
[CrossRef]

de Ridder, D.

K. Messer, D. de Ridder, and J. Kittler, “Adaptive texture representation methods for automatic target recognition,” in Proceedings of the 10th British Machine Vision Conference (BMVA, 1999), pp. 443-452.

Der, S. Z.

S. Z. Der, A. L. Chan, N. M. Nasarabadi, and H. Kwon, “Automated vehicle detection in forward-looking infrared imagery,” Appl. Opt. 43, 333-348 (2004).
[CrossRef] [PubMed]

A. L. Chan, S. Z. Der, and N. M. Nasarabadi, “Multistage infrared target detection,” Opt. Eng. 42, 2746-2754 (2003).
[CrossRef]

W. Lie-Chan, S. Z. Der, and N. M. Nasarabadi, “Automatic target recognition using feature-decomposition and data-decomposition modular neural network,” IEEE Trans. Image Process. 7, 1113-1121 (1998).
[CrossRef]

S. Z. Der, Q. Zheng, R. Chellappa, B. Redman, and H. Mahmoud, “View based recognition of military vehicles in LADAR imagery using CAD model matching,” in Image Recognition and Classification, Algorithms, Systems and Applications, B. Javidi, ed. (Marcel Dekker, 2002), pp. 151-187.

DeSimio, M. P.

B. Ernisse, S. K. Rogers, M. P. DeSimio, and R. A. Ranies, “Complete automatic target cuer/recognition system for tactical forward-looking infrared images,” Opt. Eng. 36, 2593-2603 (1997).
[CrossRef]

Dror, I. E.

I. E. Dror, M. Zagaeski, and C. F. Moss, “Three-dimensional target recognition via sonar: a neural network model,” Neural Networks 8, 149-160 (1995).
[CrossRef]

Ernisse, B.

B. Ernisse, S. K. Rogers, M. P. DeSimio, and R. A. Ranies, “Complete automatic target cuer/recognition system for tactical forward-looking infrared images,” Opt. Eng. 36, 2593-2603 (1997).
[CrossRef]

Fielding, K.

S. Rogers, J. Colombi, C. Martin, J. Gainey, K. Fielding, T. Burns, D. Ruck, M. Kabrisky, and M. Oxley, “Neural networks for automatic target recognition,” Neural Networks 8, 1153-1184 (1995).
[CrossRef]

Gainey, J.

S. Rogers, J. Colombi, C. Martin, J. Gainey, K. Fielding, T. Burns, D. Ruck, M. Kabrisky, and M. Oxley, “Neural networks for automatic target recognition,” Neural Networks 8, 1153-1184 (1995).
[CrossRef]

Gill, G. S.

G. S. Gill and J. S. Sohal, “Battlefield decision making: a neural network approach,” J. Theoret. Appl. Inform. Technol. 4, 697-699 (2008).

Gonzalez, R. C.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Addison-Wesley, 1992).

Jones, T.

B. Bhanu and T. Jones, “Image understanding research for automatic target recognition,” in IEEE Trans. Aerospace and Electronic Systems Magazine (IEEE, 1993), pp. 15-22.
[CrossRef]

Joo, T.

V. Tom and T. Joo, “Morphological-based front-end processing for IR-based ATR systems,” Final Report (Atlantic Aerospace Electronics Corporation, 1992).

V. Tom and T. Joo, “Morphological detection for scanning IRST sensor,” Final Report TR-1167-90-1 (Atlantic Aerospace Electronics Corporation, 1990).

Kabrisky, M.

S. Rogers, J. Colombi, C. Martin, J. Gainey, K. Fielding, T. Burns, D. Ruck, M. Kabrisky, and M. Oxley, “Neural networks for automatic target recognition,” Neural Networks 8, 1153-1184 (1995).
[CrossRef]

Karayiannis, N. B.

T. C. Wang and N. B. Karayiannis, “Detection of microcalcifications in digital mammograms using wavelets,” IEEE Trans. Med. Imaging 17 (1998).
[CrossRef] [PubMed]

Khan, J. F.

J. F. Khan and M. S. Alam, “Target detection in cluttered forward-looking infrared imagery,” Opt. Eng. 44, 076404 (2005).
[CrossRef]

Kittler, J.

K. Messer, D. de Ridder, and J. Kittler, “Adaptive texture representation methods for automatic target recognition,” in Proceedings of the 10th British Machine Vision Conference (BMVA, 1999), pp. 443-452.

Kwon, H.

Lanterman, A. D.

A. D. Lanterman, M. I. Miller, and D. L. Snyder, “Automatic target recognition via the simulation of infrared scenes,” in Proceedings of the 6th Annual Ground Target Modeling and Validation Conference (Keweenaw Research Center, Michigan Tech. University, 1995), pp. 195-204.

Lie-Chan, W.

W. Lie-Chan, S. Z. Der, and N. M. Nasarabadi, “Automatic target recognition using feature-decomposition and data-decomposition modular neural network,” IEEE Trans. Image Process. 7, 1113-1121 (1998).
[CrossRef]

Lindblad, T.

J. Waldemark, V. Becanovic, T. Lindblad, and C. S. Lindsey, “Hybrid neural networks for automatic target recognition,” in Systems, Man, and Cybernetics, IEEE Int. Conf. Computational Cybernetics and Simulation (IEEE, 1997), Vol. 4, pp. 4016-4021.
[CrossRef]

Lindsey, C. S.

J. Waldemark, V. Becanovic, T. Lindblad, and C. S. Lindsey, “Hybrid neural networks for automatic target recognition,” in Systems, Man, and Cybernetics, IEEE Int. Conf. Computational Cybernetics and Simulation (IEEE, 1997), Vol. 4, pp. 4016-4021.
[CrossRef]

Loo, C.

A. Dawoud, M. S. Alam, A. Bal, and C. Loo, “Target tracking in infrared imagery using weighted composite reference function-based decision fusion,” IEEE Trans. Image Process. 15, 404-410 (2006).
[CrossRef]

MacDonald, R. S.

L. G. Shapiro, R. S. MacDonald, and S. R. Sternberg, “Shape recognition with mathematical morphology,” in Proc. 8th Int. Conference on Pattern Recognition, Paris, France, 27-31 October 1986.

Mahalanobis, A.

S. R. F. Sims and A. Mahalanobis, “Performance evaluation of quadratic correlation filters for target detection and discrimination in infrared imagery,” Opt. Eng. 43, 1705-1711 (2004).
[CrossRef]

A. Mahalanobis, “Correlation filters for object tracking target re-acquisition and smart aimpoint selection,” Proc. SPIE 3073, 25-32 (1997).
[CrossRef]

Mahmoud, H.

S. Z. Der, Q. Zheng, R. Chellappa, B. Redman, and H. Mahmoud, “View based recognition of military vehicles in LADAR imagery using CAD model matching,” in Image Recognition and Classification, Algorithms, Systems and Applications, B. Javidi, ed. (Marcel Dekker, 2002), pp. 151-187.

Martin, C.

S. Rogers, J. Colombi, C. Martin, J. Gainey, K. Fielding, T. Burns, D. Ruck, M. Kabrisky, and M. Oxley, “Neural networks for automatic target recognition,” Neural Networks 8, 1153-1184 (1995).
[CrossRef]

Messer, K.

K. Messer, D. de Ridder, and J. Kittler, “Adaptive texture representation methods for automatic target recognition,” in Proceedings of the 10th British Machine Vision Conference (BMVA, 1999), pp. 443-452.

Miller, M. I.

A. D. Lanterman, M. I. Miller, and D. L. Snyder, “Automatic target recognition via the simulation of infrared scenes,” in Proceedings of the 6th Annual Ground Target Modeling and Validation Conference (Keweenaw Research Center, Michigan Tech. University, 1995), pp. 195-204.

Mirmehdi, M.

D. Davies, P. Palmer, and M. Mirmehdi, “Detection and tracking of very small low contrast objects,” in Proceedings of the 9th British Machine Vision Conference (BMVA, 1998), pp. 599-608.

Mitchell, O. R.

F. Y. Shih and O. R. Mitchell, “Automated fast recognition and location of arbitrarily shaped objects by image morphology,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1988), pp. 774-779.

Moss, C. F.

I. E. Dror, M. Zagaeski, and C. F. Moss, “Three-dimensional target recognition via sonar: a neural network model,” Neural Networks 8, 149-160 (1995).
[CrossRef]

Nasarabadi, N. M.

S. Z. Der, A. L. Chan, N. M. Nasarabadi, and H. Kwon, “Automated vehicle detection in forward-looking infrared imagery,” Appl. Opt. 43, 333-348 (2004).
[CrossRef] [PubMed]

A. L. Chan, S. Z. Der, and N. M. Nasarabadi, “Multistage infrared target detection,” Opt. Eng. 42, 2746-2754 (2003).
[CrossRef]

S. A. Rizvi and N. M. Nasarabadi, “Fusion of FLIR automatic target recognition algorithms,” Information Fusion 4, 247-258(2003).
[CrossRef]

A. L. Chan, S. A. Rizvi, and N. M. Nasarabadi, “Dualband FLIR for automatic target recognition,” Information Fusion 4, 35-45 (2003).
[CrossRef]

S. A. Rizvi and N. M. Nasarabadi, “A modular clutter rejection technique for FLIR imagery using region based principal component analysis,” Pattern Recogn. 35, 2895-2904 (2002).
[CrossRef]

W. Lie-Chan, S. Z. Der, and N. M. Nasarabadi, “Automatic target recognition using feature-decomposition and data-decomposition modular neural network,” IEEE Trans. Image Process. 7, 1113-1121 (1998).
[CrossRef]

Nguyen, T.

G. Strang and T. Nguyen, Wavelets and Filter Banks (Wellesley Cambridge, 1996).

Oxley, M.

S. Rogers, J. Colombi, C. Martin, J. Gainey, K. Fielding, T. Burns, D. Ruck, M. Kabrisky, and M. Oxley, “Neural networks for automatic target recognition,” Neural Networks 8, 1153-1184 (1995).
[CrossRef]

Palmer, P.

D. Davies, P. Palmer, and M. Mirmehdi, “Detection and tracking of very small low contrast objects,” in Proceedings of the 9th British Machine Vision Conference (BMVA, 1998), pp. 599-608.

Perneel, C.

D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998).
[CrossRef]

Przytula, K. W.

K. W. Przytula and D. Thompson, “Evaluation of neural networks for automatic target recognition,” in Proc. IEEE Conf. Aerospace (IEEE, 1997), Vol 3, pp. 423-439.

Ranies, R. A.

B. Ernisse, S. K. Rogers, M. P. DeSimio, and R. A. Ranies, “Complete automatic target cuer/recognition system for tactical forward-looking infrared images,” Opt. Eng. 36, 2593-2603 (1997).
[CrossRef]

Ravichandran, A.

A. Ravichandran and B. Yegnanarayana, “Studies on object recognition from degraded images using neural networks,” Neural Networks 8, 481-488 (1995).
[CrossRef]

Redman, B.

S. Z. Der, Q. Zheng, R. Chellappa, B. Redman, and H. Mahmoud, “View based recognition of military vehicles in LADAR imagery using CAD model matching,” in Image Recognition and Classification, Algorithms, Systems and Applications, B. Javidi, ed. (Marcel Dekker, 2002), pp. 151-187.

Rizvi, S. A.

S. A. Rizvi and N. M. Nasarabadi, “Fusion of FLIR automatic target recognition algorithms,” Information Fusion 4, 247-258(2003).
[CrossRef]

A. L. Chan, S. A. Rizvi, and N. M. Nasarabadi, “Dualband FLIR for automatic target recognition,” Information Fusion 4, 35-45 (2003).
[CrossRef]

S. A. Rizvi and N. M. Nasarabadi, “A modular clutter rejection technique for FLIR imagery using region based principal component analysis,” Pattern Recogn. 35, 2895-2904 (2002).
[CrossRef]

Rogers, S.

S. Rogers, J. Colombi, C. Martin, J. Gainey, K. Fielding, T. Burns, D. Ruck, M. Kabrisky, and M. Oxley, “Neural networks for automatic target recognition,” Neural Networks 8, 1153-1184 (1995).
[CrossRef]

Rogers, S. K.

B. Ernisse, S. K. Rogers, M. P. DeSimio, and R. A. Ranies, “Complete automatic target cuer/recognition system for tactical forward-looking infrared images,” Opt. Eng. 36, 2593-2603 (1997).
[CrossRef]

Roth, M. W.

M. W. Roth, “Survey of neural network technology for automatic target recognition,” IEEE Trans. Neural Netw. 1, 28-43(1990).
[CrossRef] [PubMed]

Ruck, D.

S. Rogers, J. Colombi, C. Martin, J. Gainey, K. Fielding, T. Burns, D. Ruck, M. Kabrisky, and M. Oxley, “Neural networks for automatic target recognition,” Neural Networks 8, 1153-1184 (1995).
[CrossRef]

Sando, T.

T. Sando, “Modeling highway crashes using Bayesisn belief networks and GIS,” Ph.D dissertation (Florida State University, 2005).

Shapiro, L. G.

L. G. Shapiro, R. S. MacDonald, and S. R. Sternberg, “Shape recognition with mathematical morphology,” in Proc. 8th Int. Conference on Pattern Recognition, Paris, France, 27-31 October 1986.

Shenoy, R.

D. Casasent and R. Shenoy, “Feature space trajectory for distorted object classification and pose estimation in SAR,” Opt. Eng. 36, 2719-2728 (1997).
[CrossRef]

Shih, F. Y.

F. Y. Shih and O. R. Mitchell, “Automated fast recognition and location of arbitrarily shaped objects by image morphology,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1988), pp. 774-779.

Sims, S. R. F.

S. R. F. Sims and A. Mahalanobis, “Performance evaluation of quadratic correlation filters for target detection and discrimination in infrared imagery,” Opt. Eng. 43, 1705-1711 (2004).
[CrossRef]

Snyder, D. L.

A. D. Lanterman, M. I. Miller, and D. L. Snyder, “Automatic target recognition via the simulation of infrared scenes,” in Proceedings of the 6th Annual Ground Target Modeling and Validation Conference (Keweenaw Research Center, Michigan Tech. University, 1995), pp. 195-204.

Sohal, J. S.

G. S. Gill and J. S. Sohal, “Battlefield decision making: a neural network approach,” J. Theoret. Appl. Inform. Technol. 4, 697-699 (2008).

Specht, D. F.

D. F. Specht, “Probabilistic neural networks,” Neural Networks 3, 109-118 (1990).
[CrossRef]

Sternberg, S. R.

L. G. Shapiro, R. S. MacDonald, and S. R. Sternberg, “Shape recognition with mathematical morphology,” in Proc. 8th Int. Conference on Pattern Recognition, Paris, France, 27-31 October 1986.

Strang, G.

G. Strang and T. Nguyen, Wavelets and Filter Banks (Wellesley Cambridge, 1996).

Thompson, D.

K. W. Przytula and D. Thompson, “Evaluation of neural networks for automatic target recognition,” in Proc. IEEE Conf. Aerospace (IEEE, 1997), Vol 3, pp. 423-439.

Tom, V.

V. Tom and T. Joo, “Morphological detection for scanning IRST sensor,” Final Report TR-1167-90-1 (Atlantic Aerospace Electronics Corporation, 1990).

V. Tom and T. Joo, “Morphological-based front-end processing for IR-based ATR systems,” Final Report (Atlantic Aerospace Electronics Corporation, 1992).

Verlinde, P.

D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998).
[CrossRef]

Waldemark, J.

J. Waldemark, V. Becanovic, T. Lindblad, and C. S. Lindsey, “Hybrid neural networks for automatic target recognition,” in Systems, Man, and Cybernetics, IEEE Int. Conf. Computational Cybernetics and Simulation (IEEE, 1997), Vol. 4, pp. 4016-4021.
[CrossRef]

Wang, T. C.

T. C. Wang and N. B. Karayiannis, “Detection of microcalcifications in digital mammograms using wavelets,” IEEE Trans. Med. Imaging 17 (1998).
[CrossRef] [PubMed]

Woods, R. E.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Addison-Wesley, 1992).

Yegnanarayana, B.

A. Ravichandran and B. Yegnanarayana, “Studies on object recognition from degraded images using neural networks,” Neural Networks 8, 481-488 (1995).
[CrossRef]

Zagaeski, M.

I. E. Dror, M. Zagaeski, and C. F. Moss, “Three-dimensional target recognition via sonar: a neural network model,” Neural Networks 8, 149-160 (1995).
[CrossRef]

Zheng, Q.

S. Z. Der, Q. Zheng, R. Chellappa, B. Redman, and H. Mahmoud, “View based recognition of military vehicles in LADAR imagery using CAD model matching,” in Image Recognition and Classification, Algorithms, Systems and Applications, B. Javidi, ed. (Marcel Dekker, 2002), pp. 151-187.

Appl. Opt. (2)

Commun. Pure Appl. Math. (1)

I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Commun. Pure Appl. Math. 41, 909-996 (1988).
[CrossRef]

IEEE Trans. Aerosp. Electron. Syst. (2)

T. R. Crimmins and W. M. Brown, “Image algebra and automatic shape recognition,” IEEE Trans. Aerosp. Electron. Syst. AES-21, 60-69 (1985).
[CrossRef]

B. Bhanu, “Automatic target recognition: state of the art survey,” IEEE Trans. Aerosp. Electron. Syst. AES-22, 364-379(1986).
[CrossRef]

IEEE Trans. Image Process. (2)

A. Dawoud, M. S. Alam, A. Bal, and C. Loo, “Target tracking in infrared imagery using weighted composite reference function-based decision fusion,” IEEE Trans. Image Process. 15, 404-410 (2006).
[CrossRef]

W. Lie-Chan, S. Z. Der, and N. M. Nasarabadi, “Automatic target recognition using feature-decomposition and data-decomposition modular neural network,” IEEE Trans. Image Process. 7, 1113-1121 (1998).
[CrossRef]

IEEE Trans. Ind. Electron. (1)

M. S. Alam and A. Bal, “Improved multiple target tracking via global motion compensation and optoelectronic correlation,” IEEE Trans. Ind. Electron. 54, 522-529 (2007).
[CrossRef]

IEEE Trans. Instrum. Meas. (1)

A. Bal and M. S. Alam, “Automatic target tracking in FLIR image sequences using intensity variation function and template modeling,” IEEE Trans. Instrum. Meas. 54, 1846-1852 (2005).
[CrossRef]

IEEE Trans. Med. Imaging (1)

T. C. Wang and N. B. Karayiannis, “Detection of microcalcifications in digital mammograms using wavelets,” IEEE Trans. Med. Imaging 17 (1998).
[CrossRef] [PubMed]

IEEE Trans. Neural Netw. (1)

M. W. Roth, “Survey of neural network technology for automatic target recognition,” IEEE Trans. Neural Netw. 1, 28-43(1990).
[CrossRef] [PubMed]

Information Fusion (2)

A. L. Chan, S. A. Rizvi, and N. M. Nasarabadi, “Dualband FLIR for automatic target recognition,” Information Fusion 4, 35-45 (2003).
[CrossRef]

S. A. Rizvi and N. M. Nasarabadi, “Fusion of FLIR automatic target recognition algorithms,” Information Fusion 4, 247-258(2003).
[CrossRef]

J. Theoret. Appl. Inform. Technol. (1)

G. S. Gill and J. S. Sohal, “Battlefield decision making: a neural network approach,” J. Theoret. Appl. Inform. Technol. 4, 697-699 (2008).

Neural Networks (5)

D. F. Specht, “Probabilistic neural networks,” Neural Networks 3, 109-118 (1990).
[CrossRef]

S. Rogers, J. Colombi, C. Martin, J. Gainey, K. Fielding, T. Burns, D. Ruck, M. Kabrisky, and M. Oxley, “Neural networks for automatic target recognition,” Neural Networks 8, 1153-1184 (1995).
[CrossRef]

G. A. Carpenter, “Neural network models for pattern recognition and associative memory,” Neural Networks 2, 243-257 (1989).
[CrossRef]

I. E. Dror, M. Zagaeski, and C. F. Moss, “Three-dimensional target recognition via sonar: a neural network model,” Neural Networks 8, 149-160 (1995).
[CrossRef]

A. Ravichandran and B. Yegnanarayana, “Studies on object recognition from degraded images using neural networks,” Neural Networks 8, 481-488 (1995).
[CrossRef]

Opt. Eng. (6)

J. F. Khan and M. S. Alam, “Target detection in cluttered forward-looking infrared imagery,” Opt. Eng. 44, 076404 (2005).
[CrossRef]

D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998).
[CrossRef]

B. Ernisse, S. K. Rogers, M. P. DeSimio, and R. A. Ranies, “Complete automatic target cuer/recognition system for tactical forward-looking infrared images,” Opt. Eng. 36, 2593-2603 (1997).
[CrossRef]

A. L. Chan, S. Z. Der, and N. M. Nasarabadi, “Multistage infrared target detection,” Opt. Eng. 42, 2746-2754 (2003).
[CrossRef]

S. R. F. Sims and A. Mahalanobis, “Performance evaluation of quadratic correlation filters for target detection and discrimination in infrared imagery,” Opt. Eng. 43, 1705-1711 (2004).
[CrossRef]

D. Casasent and R. Shenoy, “Feature space trajectory for distorted object classification and pose estimation in SAR,” Opt. Eng. 36, 2719-2728 (1997).
[CrossRef]

Pattern Recogn. (1)

S. A. Rizvi and N. M. Nasarabadi, “A modular clutter rejection technique for FLIR imagery using region based principal component analysis,” Pattern Recogn. 35, 2895-2904 (2002).
[CrossRef]

Proc. SPIE (1)

A. Mahalanobis, “Correlation filters for object tracking target re-acquisition and smart aimpoint selection,” Proc. SPIE 3073, 25-32 (1997).
[CrossRef]

Other (17)

T. Sando, “Modeling highway crashes using Bayesisn belief networks and GIS,” Ph.D dissertation (Florida State University, 2005).

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Addison-Wesley, 1992).

K. Messer, D. de Ridder, and J. Kittler, “Adaptive texture representation methods for automatic target recognition,” in Proceedings of the 10th British Machine Vision Conference (BMVA, 1999), pp. 443-452.

B. Bhanu and T. Jones, “Image understanding research for automatic target recognition,” in IEEE Trans. Aerospace and Electronic Systems Magazine (IEEE, 1993), pp. 15-22.
[CrossRef]

I. Daubechies, Ten Lectures on Wavelets (SIAM, 1992).
[CrossRef]

C. K. Chui, An Introduction to Wavelets (Academic, 1992).

G. Strang and T. Nguyen, Wavelets and Filter Banks (Wellesley Cambridge, 1996).

D. Davies, P. Palmer, and M. Mirmehdi, “Detection and tracking of very small low contrast objects,” in Proceedings of the 9th British Machine Vision Conference (BMVA, 1998), pp. 599-608.

B. Bhanu and J. Ahn, “A system for model-based recognition of articulated objects,” in Proceedings of the International Conference on Pattern Recognition (IEEE, 1998), Vol. 2, pp. 1812-1815.

S. Z. Der, Q. Zheng, R. Chellappa, B. Redman, and H. Mahmoud, “View based recognition of military vehicles in LADAR imagery using CAD model matching,” in Image Recognition and Classification, Algorithms, Systems and Applications, B. Javidi, ed. (Marcel Dekker, 2002), pp. 151-187.

A. D. Lanterman, M. I. Miller, and D. L. Snyder, “Automatic target recognition via the simulation of infrared scenes,” in Proceedings of the 6th Annual Ground Target Modeling and Validation Conference (Keweenaw Research Center, Michigan Tech. University, 1995), pp. 195-204.

L. G. Shapiro, R. S. MacDonald, and S. R. Sternberg, “Shape recognition with mathematical morphology,” in Proc. 8th Int. Conference on Pattern Recognition, Paris, France, 27-31 October 1986.

F. Y. Shih and O. R. Mitchell, “Automated fast recognition and location of arbitrarily shaped objects by image morphology,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1988), pp. 774-779.

V. Tom and T. Joo, “Morphological detection for scanning IRST sensor,” Final Report TR-1167-90-1 (Atlantic Aerospace Electronics Corporation, 1990).

V. Tom and T. Joo, “Morphological-based front-end processing for IR-based ATR systems,” Final Report (Atlantic Aerospace Electronics Corporation, 1992).

K. W. Przytula and D. Thompson, “Evaluation of neural networks for automatic target recognition,” in Proc. IEEE Conf. Aerospace (IEEE, 1997), Vol 3, pp. 423-439.

J. Waldemark, V. Becanovic, T. Lindblad, and C. S. Lindsey, “Hybrid neural networks for automatic target recognition,” in Systems, Man, and Cybernetics, IEEE Int. Conf. Computational Cybernetics and Simulation (IEEE, 1997), Vol. 4, pp. 4016-4021.
[CrossRef]

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Figures (9)

Fig. 1
Fig. 1

Overview of the approach. GT, ground truth.

Fig. 2
Fig. 2

Potential target detection module.

Fig. 3
Fig. 3

PNN-based target classification system.

Fig. 4
Fig. 4

PNN consisting of p input units, Q pattern units, and K category units.

Fig. 5
Fig. 5

Network architecture.

Fig. 6
Fig. 6

Typical clutter-rejection module.

Fig. 7
Fig. 7

Shifted ROI extraction.

Fig. 8
Fig. 8

Sequence L1720. (a) One sample training image, where the black window contains the target, and the white windows contain the background; (b) test image; (c) morphologically processed image; (d) all the ROI selected by the detection module; (e) final detection result after the application of clutter-rejection module; and (f) PNN output.

Fig. 9
Fig. 9

Sequence M1410: (a) ROI and (b) final detection result. Sequence L1813: (c) ROI and (d) final detection result. Sequence L1815S1: (e) ROI and (f) final detection result. Sequence L2208: (g) ROI and (h) final detection result.

Tables (5)

Tables Icon

Table 1 Regions of Interest Detection Results Comparison for Three Different Shapes for the Structuring Element

Tables Icon

Table 2 Regions of Interest Detection Results Comparison for Different Number of Dilations

Tables Icon

Table 3 Regions of Interest Detection Results

Tables Icon

Table 4 Detection Results Comparison between the Presented Technique and the Algorithm in [33]

Tables Icon

Table 5 Computational Cost Comparison between the Presented Technique and the Algorithm in [33]

Equations (1)

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ft=[ft1ft2ft3ft8],fb=[fb11fb12fb18fb21fb22fb28fb51fb52fb58].

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