Abstract

A dedicated automatic target recognition and tracking optical correlator (OC) system using advanced processing technology has been developed. Rapidly cycling data-cubes with size, shape, and orientation are employed with software algorithms to isolate correlation peaks and enable tracking of targets in maritime environments with future track prediction. The method has been found superior to employing maximum average correlation height filters for which the correlation peak intensity drops off in proportion to the number of training images. The physical dimensions of the OC system may be reduced to as small as 2in.×2in.×3in. (51mm×51mm×76mm) by modifying and minimizing the OC components.

© 2012 Optical Society of America

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References

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2010 (2)

P. Ambs, “Optical Computing: a 60-year adventure,” Adv. Opt. Tech. 2010, 372652 (2010).
[CrossRef]

V. K. Beri, A. Aran, S. Munshi, A. K. Gupta, and V. K. Rastogi, “Enhancing the capabilities of binary phase only filter,” Opt. Laser Technol. 42, 70–80 (2010).
[CrossRef]

2009 (2)

W. S. Qureshi and A. N. Alvi, “Object tracking using MACH filter and optical flow in cluttered scenes and variable lighting conditions,” World Acad. Sci. Engr. Tech. 60, 709–712(2009).

A. Saxena, M. Sun, and A. Y. Ng, “Make3D: learning 3D scene structure from a single still image,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 824–840 (2009).
[CrossRef]

2005 (1)

T. T. Lu, C. L. Hughlett, H. Zhou, T. H. Chao, and J. C. Hanan, “Neural network post-processing of grayscale optical correlator,” Proc. SPIE 5908, 590810 (2005).
[CrossRef]

2003 (1)

H. Zhou, T.-H. Chao, B. Martin, and N. Villaume, “Simulation of miniature optical correlator for future generation of spacecraft precision landing,” Proc. SPIE 5106, 179–185 (2003).
[CrossRef]

1999 (1)

H. Zhou and T. H. Chao, “MACH filter synthesizing for detecting targets in cluttered environment for gray-scale optical correlator,” Proc. SPIE 715, 394–398 (1999).
[CrossRef]

1998 (1)

K. Bauchert and S. Serati, “Data flow architecture for high-speed optical processors,” Proc. SPIE 3386, 50–58(1998).
[CrossRef]

1994 (1)

1990 (1)

B. D. Bock, T. A. Crow, and M. K. Giles, “Design considerations for miniature optical correlation systems that use pixelated input and filter transducers,” Proc. SPIE 1347, 297–309 (1990).
[CrossRef]

1989 (1)

1987 (2)

1986 (1)

V. K. Kumar, “Minimum variance synthetic discriminant function,” J. Opt. Soc. Am. 3, 1579–1584 (1986).
[CrossRef]

1984 (1)

P. D. Gianino and J. L. Horner, “Additional properties of the phase-only correlation filter,” Opt. Eng. 23, 695–697 (1984).

1966 (1)

1964 (1)

A. Vander Lugt, “Signal detection by complex filtering,” IEEE Trans. Inf. Theory 10, 139–145 (1964).
[CrossRef]

Alvi, A. N.

W. S. Qureshi and A. N. Alvi, “Object tracking using MACH filter and optical flow in cluttered scenes and variable lighting conditions,” World Acad. Sci. Engr. Tech. 60, 709–712(2009).

Ambs, P.

P. Ambs, “Optical Computing: a 60-year adventure,” Adv. Opt. Tech. 2010, 372652 (2010).
[CrossRef]

Aran, A.

V. K. Beri, A. Aran, S. Munshi, A. K. Gupta, and V. K. Rastogi, “Enhancing the capabilities of binary phase only filter,” Opt. Laser Technol. 42, 70–80 (2010).
[CrossRef]

Bach, G. W.

Basri, R.

T. Hassner and R. Basri, “Example based 3D reconstruction from single 2D images,” presented at 2006 Conference on Computer Vision and Pattern Recognition WorkshopNew York, New York, 17–22 June 2006.

Bauchert, K.

K. Bauchert and S. Serati, “Data flow architecture for high-speed optical processors,” Proc. SPIE 3386, 50–58(1998).
[CrossRef]

T. Ewing, S. A. Serati, and K. Bauchert, “Optical correlator using four kilohertz analog spatial light modulators,” in Optical Pattern Recognition XV, D. P. Casasent, ed. (SPIE, 2004), pp. 123–133.

Beri, V. K.

V. K. Beri, A. Aran, S. Munshi, A. K. Gupta, and V. K. Rastogi, “Enhancing the capabilities of binary phase only filter,” Opt. Laser Technol. 42, 70–80 (2010).
[CrossRef]

Bock, B. D.

B. D. Bock, T. A. Crow, and M. K. Giles, “Design considerations for miniature optical correlation systems that use pixelated input and filter transducers,” Proc. SPIE 1347, 297–309 (1990).
[CrossRef]

Casasent, D.

Chao, T. H.

T. T. Lu, C. L. Hughlett, H. Zhou, T. H. Chao, and J. C. Hanan, “Neural network post-processing of grayscale optical correlator,” Proc. SPIE 5908, 590810 (2005).
[CrossRef]

H. Zhou and T. H. Chao, “MACH filter synthesizing for detecting targets in cluttered environment for gray-scale optical correlator,” Proc. SPIE 715, 394–398 (1999).
[CrossRef]

Chao, T.-H.

H. Zhou, T.-H. Chao, B. Martin, and N. Villaume, “Simulation of miniature optical correlator for future generation of spacecraft precision landing,” Proc. SPIE 5106, 179–185 (2003).
[CrossRef]

Cotirell, D. M.

Cottrell, D. M.

Crow, T. A.

B. D. Bock, T. A. Crow, and M. K. Giles, “Design considerations for miniature optical correlation systems that use pixelated input and filter transducers,” Proc. SPIE 1347, 297–309 (1990).
[CrossRef]

Davis, J. A.

Epperson, J. F.

Ewing, T.

T. Ewing, S. A. Serati, and K. Bauchert, “Optical correlator using four kilohertz analog spatial light modulators,” in Optical Pattern Recognition XV, D. P. Casasent, ed. (SPIE, 2004), pp. 123–133.

Gianino, P. D.

P. D. Gianino and J. L. Horner, “Additional properties of the phase-only correlation filter,” Opt. Eng. 23, 695–697 (1984).

Giles, M. K.

B. D. Bock, T. A. Crow, and M. K. Giles, “Design considerations for miniature optical correlation systems that use pixelated input and filter transducers,” Proc. SPIE 1347, 297–309 (1990).
[CrossRef]

Goodman, J. W.

Gupta, A. K.

V. K. Beri, A. Aran, S. Munshi, A. K. Gupta, and V. K. Rastogi, “Enhancing the capabilities of binary phase only filter,” Opt. Laser Technol. 42, 70–80 (2010).
[CrossRef]

Hanan, J. C.

T. T. Lu, C. L. Hughlett, H. Zhou, T. H. Chao, and J. C. Hanan, “Neural network post-processing of grayscale optical correlator,” Proc. SPIE 5908, 590810 (2005).
[CrossRef]

Hassner, T.

T. Hassner and R. Basri, “Example based 3D reconstruction from single 2D images,” presented at 2006 Conference on Computer Vision and Pattern Recognition WorkshopNew York, New York, 17–22 June 2006.

Horner, J. L.

P. D. Gianino and J. L. Horner, “Additional properties of the phase-only correlation filter,” Opt. Eng. 23, 695–697 (1984).

Hughlett, C. L.

T. T. Lu, C. L. Hughlett, H. Zhou, T. H. Chao, and J. C. Hanan, “Neural network post-processing of grayscale optical correlator,” Proc. SPIE 5908, 590810 (2005).
[CrossRef]

Kumar, B. V. K.

Kumar, V. K.

V. K. Kumar, “Minimum variance synthetic discriminant function,” J. Opt. Soc. Am. 3, 1579–1584 (1986).
[CrossRef]

Lilly, R. A.

Lu, T. T.

T. T. Lu, C. L. Hughlett, H. Zhou, T. H. Chao, and J. C. Hanan, “Neural network post-processing of grayscale optical correlator,” Proc. SPIE 5908, 590810 (2005).
[CrossRef]

Lugt, A. Vander

A. Vander Lugt, “Signal detection by complex filtering,” IEEE Trans. Inf. Theory 10, 139–145 (1964).
[CrossRef]

Mahalanobis, A.

Martin, B.

H. Zhou, T.-H. Chao, B. Martin, and N. Villaume, “Simulation of miniature optical correlator for future generation of spacecraft precision landing,” Proc. SPIE 5106, 179–185 (2003).
[CrossRef]

Munshi, S.

V. K. Beri, A. Aran, S. Munshi, A. K. Gupta, and V. K. Rastogi, “Enhancing the capabilities of binary phase only filter,” Opt. Laser Technol. 42, 70–80 (2010).
[CrossRef]

Ng, A. Y.

A. Saxena, M. Sun, and A. Y. Ng, “Make3D: learning 3D scene structure from a single still image,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 824–840 (2009).
[CrossRef]

Qureshi, W. S.

W. S. Qureshi and A. N. Alvi, “Object tracking using MACH filter and optical flow in cluttered scenes and variable lighting conditions,” World Acad. Sci. Engr. Tech. 60, 709–712(2009).

Rastogi, V. K.

V. K. Beri, A. Aran, S. Munshi, A. K. Gupta, and V. K. Rastogi, “Enhancing the capabilities of binary phase only filter,” Opt. Laser Technol. 42, 70–80 (2010).
[CrossRef]

Saxena, A.

A. Saxena, M. Sun, and A. Y. Ng, “Make3D: learning 3D scene structure from a single still image,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 824–840 (2009).
[CrossRef]

Schamschula, M. P.

Serati, S.

K. Bauchert and S. Serati, “Data flow architecture for high-speed optical processors,” Proc. SPIE 3386, 50–58(1998).
[CrossRef]

Serati, S. A.

T. Ewing, S. A. Serati, and K. Bauchert, “Optical correlator using four kilohertz analog spatial light modulators,” in Optical Pattern Recognition XV, D. P. Casasent, ed. (SPIE, 2004), pp. 123–133.

Sims, S. R. F.

Song, S.

Sun, M.

A. Saxena, M. Sun, and A. Y. Ng, “Make3D: learning 3D scene structure from a single still image,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 824–840 (2009).
[CrossRef]

Vijaya Kumar, B. V. K.

Villaume, N.

H. Zhou, T.-H. Chao, B. Martin, and N. Villaume, “Simulation of miniature optical correlator for future generation of spacecraft precision landing,” Proc. SPIE 5106, 179–185 (2003).
[CrossRef]

Waring, M. A.

Weaver, C. S.

Wolberg, G.

G. Wolberg and S. Zokai, “Robust image registration using log-polar transform,” in Proceedings of the 2000 International Conference of Image Processing (IEEE, 2000), pp. 493–496.
[CrossRef]

Zhou, H.

T. T. Lu, C. L. Hughlett, H. Zhou, T. H. Chao, and J. C. Hanan, “Neural network post-processing of grayscale optical correlator,” Proc. SPIE 5908, 590810 (2005).
[CrossRef]

H. Zhou, T.-H. Chao, B. Martin, and N. Villaume, “Simulation of miniature optical correlator for future generation of spacecraft precision landing,” Proc. SPIE 5106, 179–185 (2003).
[CrossRef]

H. Zhou and T. H. Chao, “MACH filter synthesizing for detecting targets in cluttered environment for gray-scale optical correlator,” Proc. SPIE 715, 394–398 (1999).
[CrossRef]

Zokai, S.

G. Wolberg and S. Zokai, “Robust image registration using log-polar transform,” in Proceedings of the 2000 International Conference of Image Processing (IEEE, 2000), pp. 493–496.
[CrossRef]

Adv. Opt. Tech. (1)

P. Ambs, “Optical Computing: a 60-year adventure,” Adv. Opt. Tech. 2010, 372652 (2010).
[CrossRef]

Appl. Opt. (5)

IEEE Trans. Inf. Theory (1)

A. Vander Lugt, “Signal detection by complex filtering,” IEEE Trans. Inf. Theory 10, 139–145 (1964).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

A. Saxena, M. Sun, and A. Y. Ng, “Make3D: learning 3D scene structure from a single still image,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 824–840 (2009).
[CrossRef]

J. Opt. Soc. Am. (1)

V. K. Kumar, “Minimum variance synthetic discriminant function,” J. Opt. Soc. Am. 3, 1579–1584 (1986).
[CrossRef]

Opt. Eng. (1)

P. D. Gianino and J. L. Horner, “Additional properties of the phase-only correlation filter,” Opt. Eng. 23, 695–697 (1984).

Opt. Laser Technol. (1)

V. K. Beri, A. Aran, S. Munshi, A. K. Gupta, and V. K. Rastogi, “Enhancing the capabilities of binary phase only filter,” Opt. Laser Technol. 42, 70–80 (2010).
[CrossRef]

Proc. SPIE (5)

K. Bauchert and S. Serati, “Data flow architecture for high-speed optical processors,” Proc. SPIE 3386, 50–58(1998).
[CrossRef]

T. T. Lu, C. L. Hughlett, H. Zhou, T. H. Chao, and J. C. Hanan, “Neural network post-processing of grayscale optical correlator,” Proc. SPIE 5908, 590810 (2005).
[CrossRef]

B. D. Bock, T. A. Crow, and M. K. Giles, “Design considerations for miniature optical correlation systems that use pixelated input and filter transducers,” Proc. SPIE 1347, 297–309 (1990).
[CrossRef]

H. Zhou, T.-H. Chao, B. Martin, and N. Villaume, “Simulation of miniature optical correlator for future generation of spacecraft precision landing,” Proc. SPIE 5106, 179–185 (2003).
[CrossRef]

H. Zhou and T. H. Chao, “MACH filter synthesizing for detecting targets in cluttered environment for gray-scale optical correlator,” Proc. SPIE 715, 394–398 (1999).
[CrossRef]

World Acad. Sci. Engr. Tech. (1)

W. S. Qureshi and A. N. Alvi, “Object tracking using MACH filter and optical flow in cluttered scenes and variable lighting conditions,” World Acad. Sci. Engr. Tech. 60, 709–712(2009).

Other (4)

T. Hassner and R. Basri, “Example based 3D reconstruction from single 2D images,” presented at 2006 Conference on Computer Vision and Pattern Recognition WorkshopNew York, New York, 17–22 June 2006.

F. Ahmed and I. S. Moskowitz, “The binary phase only filter as an image watermark,” http://www.stormingmedia.us/82/8245/A824564 .

T. Ewing, S. A. Serati, and K. Bauchert, “Optical correlator using four kilohertz analog spatial light modulators,” in Optical Pattern Recognition XV, D. P. Casasent, ed. (SPIE, 2004), pp. 123–133.

G. Wolberg and S. Zokai, “Robust image registration using log-polar transform,” in Proceedings of the 2000 International Conference of Image Processing (IEEE, 2000), pp. 493–496.
[CrossRef]

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Figures (8)

Fig. 1.
Fig. 1.

Optical correlator hardware design showing component spacing (not drawn to scale).

Fig. 2.
Fig. 2.

Experimental correlation output for an X and O pattern with an O filter: (a) X and O pattern (SLM1); (b) O FT filter (SLM2); and (c) correlation output (peaks centered at location of each O).

Fig. 3.
Fig. 3.

Creating BPOFs with MATLAB. (a) Original binary pattern; (b) mirrored (horizontally) image; (c) image padded with ones, centered; (d) FT of padded image (real part) [MATLAB code: F=real(fft2(imag))]; (e) shifting of zero frequency components [Fsh=fftshift(F)]; (e) final binary phase only filter (BPOF) [Fb=sign(Fsh)].

Fig. 4.
Fig. 4.

Relationship between MACH based correlation filters and individual BPOFs for in-plane rotation: (a) jet pattern and filter at 10° rotation (clockwise from horizontal); (b) jet pattern and filter at 20° rotation; (c) jet pattern and filter at 30°; and (d) MACH filter incorporating the three test images.

Fig. 5.
Fig. 5.

Plot showing relationship between the number of training images represented in MACH filter and correlation peak intensity. The blue curve (lower) is the correlation peak intensity for using a single pattern with the MACH filter, while the green curve (upper) is the sum of the peak intensities for each of the patterns that the MACH filter is based on used with that MACH filter.

Fig. 6.
Fig. 6.

Log-polar transformation of binary target image: (a) original image, and (b) log-polar representation.

Fig. 7.
Fig. 7.

Flowchart of optical correlator based target classification, tracking, and track prediction.

Fig. 8.
Fig. 8.

Hardware and software based optical correlator target tracking/track prediction process.

Tables (1)

Tables Icon

Table 1. OC System Parameters Used In Effective Focal Length Calculations

Equations (9)

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

EFL1=p1p2N2λ,
EFL2=p2pcNcλ.
EFL1=f11f12f11+f12d1.
EFL2=f21f22f21+f22d2.
H(ξ,η)=R(ξ,η),
H(ξ,η)=R(ξ,η)|R(ξ,η)|.
f=Sx1m.
Sx=1Ni=1N(XiM)(XiM)*.
H(f)=mαC+βDx+γSx,

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