Abstract

Based on the stationary random properties of remote sensing images, a correlation model is proposed to resolve the effects of the image rotation and translation on the correlation value in scene matching. The rotation invariance is achieved by measuring the image rotation with the model and compensating the rotation before the 2D translation scene matching. The input image is rotated from 5° to 5° at an interval of 1° and 11 new images are generated. The 11 new images correlate with all the template images and eleven correlation matrices are obtained. The maximum values of each correlation matrix are picked up and they could follow a fixed curve predicted by the model. Fitting the curve, the rotation corresponding to the estimated peak of the curve is considered to be the rotation of the input image. The rotation measurement of the input image can be as accurate as 0.05°. With an extra 36 rotations of the input image, the measuring range of rotation can be enlarged into ±180°. This method could be very fast and accurate for scene matching in the parallel multichannel holographic optical correlator.

© 2013 Optical Society of America

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References

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2012 (1)

2011 (2)

2010 (3)

2009 (1)

2006 (1)

A. Heifetz, J. T. Shen, J.-K. Lee, R. Tripathi, M. S. Shahriar, and M. Huq, “Translation-invariant object recognition system using an optical correlator and a super-parallel holographic random access memory,” Opt. Eng. 45, 025201 (2006).
[CrossRef]

2005 (1)

H. Zhou, C. Hughlett, J. C. Hanan, T. Lu, and T.-H. Chao, “Development of streamlined OT-MACH-based ATR algorithm for grayscale optical correlator,” Proc. SPIE 5816, 78–83 (2005).
[CrossRef]

2003 (1)

2002 (2)

D. Psaltis, “Coherent optical information systems,” Science 298, 1359–1363 (2002).
[CrossRef]

K. M. Iftekharuddin, C. Rentala, and A. Dani, “Determination of exact rotation angle and discrimination for rotated images,” Opt. Laser Technol. 34, 313–327 (2002).
[CrossRef]

1999 (1)

1996 (1)

B. S. Reddy and B. N. Chatterji, “An FFT-based technique for translation, rotation, and scale-invariant image registration,” IEEE Trans. Image Process. 5, 1266–1271 (1996).
[CrossRef]

1995 (1)

1978 (1)

H. Mostafavi and F. W. Smith, “Image correlation with geometric distortion part 1: acquisition performance,” IEEE Trans. Aerosp. Electron. Syst. AES-14, 487–493 (1978).
[CrossRef]

1966 (1)

L. E. Franks, “A mode for the random video process,” Bell Syst. Tech. J. 45, 609–630 (1966).

Awwal, A.

Awwal, A. A. S.

Bhagatji, A.

Burr, G. W.

Cao, L.

Chao, T.-H.

H. Zhou, C. Hughlett, J. C. Hanan, T. Lu, and T.-H. Chao, “Development of streamlined OT-MACH-based ATR algorithm for grayscale optical correlator,” Proc. SPIE 5816, 78–83 (2005).
[CrossRef]

Chatterji, B. N.

B. S. Reddy and B. N. Chatterji, “An FFT-based technique for translation, rotation, and scale-invariant image registration,” IEEE Trans. Image Process. 5, 1266–1271 (1996).
[CrossRef]

Coufal, H.

Coufal, H. J.

H. J. Coufal, D. Psaltis, and G. T. Sincerbox, Holographic Data Storage (Springer, 2000).

Dani, A.

K. M. Iftekharuddin, C. Rentala, and A. Dani, “Determination of exact rotation angle and discrimination for rotated images,” Opt. Laser Technol. 34, 313–327 (2002).
[CrossRef]

Fard, A.

Feng, H.

Franks, L. E.

L. E. Franks, “A mode for the random video process,” Bell Syst. Tech. J. 45, 609–630 (1966).

Fu, H.

Ge, P.

Goda, K.

Goodman, J. W.

J. W. Goodman, Introduction to Fourier Optics, 2nd ed. (McGraw-Hill, 1996), pp. 305–308.

Gu, C.

Hanan, J. C.

H. Zhou, C. Hughlett, J. C. Hanan, T. Lu, and T.-H. Chao, “Development of streamlined OT-MACH-based ATR algorithm for grayscale optical correlator,” Proc. SPIE 5816, 78–83 (2005).
[CrossRef]

Hanssen, H.

He, Q.

Heifetz, A.

A. Heifetz, J. T. Shen, J.-K. Lee, R. Tripathi, M. S. Shahriar, and M. Huq, “Translation-invariant object recognition system using an optical correlator and a super-parallel holographic random access memory,” Opt. Eng. 45, 025201 (2006).
[CrossRef]

Hughlett, C.

H. Zhou, C. Hughlett, J. C. Hanan, T. Lu, and T.-H. Chao, “Development of streamlined OT-MACH-based ATR algorithm for grayscale optical correlator,” Proc. SPIE 5816, 78–83 (2005).
[CrossRef]

Huq, M.

A. Heifetz, J. T. Shen, J.-K. Lee, R. Tripathi, M. S. Shahriar, and M. Huq, “Translation-invariant object recognition system using an optical correlator and a super-parallel holographic random access memory,” Opt. Eng. 45, 025201 (2006).
[CrossRef]

Iftekharuddin, K.

Iftekharuddin, K. M.

J. S. Shaik and K. M. Iftekharuddin, “Detection and tracking of rotated and scaled targets by use of Hilbert-wavelet transform,” Appl. Opt. 42, 4718–4735 (2003).
[CrossRef]

K. M. Iftekharuddin, C. Rentala, and A. Dani, “Determination of exact rotation angle and discrimination for rotated images,” Opt. Laser Technol. 34, 313–327 (2002).
[CrossRef]

Jalali, B.

Jin, G.

Joseph, J.

Juday, R. D.

B. V. K. V. Kumar, A. Mahalanobis, and R. D. Juday, Correlator Pattern Recognition (Cambridge University, 2005), pp. 357–367.

Karim, M.

Kim, S. H.

Kobras, S.

Kumar, B. V. K. V.

B. V. K. V. Kumar, A. Mahalanobis, and R. D. Juday, Correlator Pattern Recognition (Cambridge University, 2005), pp. 357–367.

Lee, J.-K.

A. Heifetz, J. T. Shen, J.-K. Lee, R. Tripathi, M. S. Shahriar, and M. Huq, “Translation-invariant object recognition system using an optical correlator and a super-parallel holographic random access memory,” Opt. Eng. 45, 025201 (2006).
[CrossRef]

Li, Q.

Lien, J.-R.

Lu, T.

H. Zhou, C. Hughlett, J. C. Hanan, T. Lu, and T.-H. Chao, “Development of streamlined OT-MACH-based ATR algorithm for grayscale optical correlator,” Proc. SPIE 5816, 78–83 (2005).
[CrossRef]

Mahalanobis, A.

B. V. K. V. Kumar, A. Mahalanobis, and R. D. Juday, Correlator Pattern Recognition (Cambridge University, 2005), pp. 357–367.

Manzur, T.

Mostafavi, H.

H. Mostafavi and F. W. Smith, “Image correlation with geometric distortion part 1: acquisition performance,” IEEE Trans. Aerosp. Electron. Syst. AES-14, 487–493 (1978).
[CrossRef]

Neifeld, M.

Psaltis, D.

D. Psaltis, “Coherent optical information systems,” Science 298, 1359–1363 (2002).
[CrossRef]

H. J. Coufal, D. Psaltis, and G. T. Sincerbox, Holographic Data Storage (Springer, 2000).

Reddy, B. S.

B. S. Reddy and B. N. Chatterji, “An FFT-based technique for translation, rotation, and scale-invariant image registration,” IEEE Trans. Image Process. 5, 1266–1271 (1996).
[CrossRef]

Rentala, C.

K. M. Iftekharuddin, C. Rentala, and A. Dani, “Determination of exact rotation angle and discrimination for rotated images,” Opt. Laser Technol. 34, 313–327 (2002).
[CrossRef]

Serati, S.

Shahriar, M. S.

A. Heifetz, J. T. Shen, J.-K. Lee, R. Tripathi, M. S. Shahriar, and M. Huq, “Translation-invariant object recognition system using an optical correlator and a super-parallel holographic random access memory,” Opt. Eng. 45, 025201 (2006).
[CrossRef]

Shaik, J. S.

Shen, J. T.

A. Heifetz, J. T. Shen, J.-K. Lee, R. Tripathi, M. S. Shahriar, and M. Huq, “Translation-invariant object recognition system using an optical correlator and a super-parallel holographic random access memory,” Opt. Eng. 45, 025201 (2006).
[CrossRef]

Sincerbox, G. T.

H. J. Coufal, D. Psaltis, and G. T. Sincerbox, Holographic Data Storage (Springer, 2000).

Singh, K.

Smith, F. W.

H. Mostafavi and F. W. Smith, “Image correlation with geometric distortion part 1: acquisition performance,” IEEE Trans. Aerosp. Electron. Syst. AES-14, 487–493 (1978).
[CrossRef]

Stork, D.

Tan, Q.

Tripathi, R.

A. Heifetz, J. T. Shen, J.-K. Lee, R. Tripathi, M. S. Shahriar, and M. Huq, “Translation-invariant object recognition system using an optical correlator and a super-parallel holographic random access memory,” Opt. Eng. 45, 025201 (2006).
[CrossRef]

Wang, S.

Xu, Z.

Zeller, J.

Zhou, H.

H. Zhou, C. Hughlett, J. C. Hanan, T. Lu, and T.-H. Chao, “Development of streamlined OT-MACH-based ATR algorithm for grayscale optical correlator,” Proc. SPIE 5816, 78–83 (2005).
[CrossRef]

Appl. Opt. (7)

Bell Syst. Tech. J. (1)

L. E. Franks, “A mode for the random video process,” Bell Syst. Tech. J. 45, 609–630 (1966).

IEEE Trans. Aerosp. Electron. Syst. (1)

H. Mostafavi and F. W. Smith, “Image correlation with geometric distortion part 1: acquisition performance,” IEEE Trans. Aerosp. Electron. Syst. AES-14, 487–493 (1978).
[CrossRef]

IEEE Trans. Image Process. (1)

B. S. Reddy and B. N. Chatterji, “An FFT-based technique for translation, rotation, and scale-invariant image registration,” IEEE Trans. Image Process. 5, 1266–1271 (1996).
[CrossRef]

J. Opt. Soc. Am. A (1)

Opt. Eng. (1)

A. Heifetz, J. T. Shen, J.-K. Lee, R. Tripathi, M. S. Shahriar, and M. Huq, “Translation-invariant object recognition system using an optical correlator and a super-parallel holographic random access memory,” Opt. Eng. 45, 025201 (2006).
[CrossRef]

Opt. Express (1)

Opt. Laser Technol. (1)

K. M. Iftekharuddin, C. Rentala, and A. Dani, “Determination of exact rotation angle and discrimination for rotated images,” Opt. Laser Technol. 34, 313–327 (2002).
[CrossRef]

Opt. Lett. (1)

Proc. SPIE (1)

H. Zhou, C. Hughlett, J. C. Hanan, T. Lu, and T.-H. Chao, “Development of streamlined OT-MACH-based ATR algorithm for grayscale optical correlator,” Proc. SPIE 5816, 78–83 (2005).
[CrossRef]

Science (1)

D. Psaltis, “Coherent optical information systems,” Science 298, 1359–1363 (2002).
[CrossRef]

Other (3)

B. V. K. V. Kumar, A. Mahalanobis, and R. D. Juday, Correlator Pattern Recognition (Cambridge University, 2005), pp. 357–367.

H. J. Coufal, D. Psaltis, and G. T. Sincerbox, Holographic Data Storage (Springer, 2000).

J. W. Goodman, Introduction to Fourier Optics, 2nd ed. (McGraw-Hill, 1996), pp. 305–308.

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Figures (12)

Fig. 1.
Fig. 1.

(a) Remote sensing image and (b) a binary and thinned copy of (a). The image under the white frames (both solid and dash) are for correlations. (c) A vector generated by the 1D cross correlations and (d) a correlation matrix generated by the 2D cross correlations.

Fig. 2.
Fig. 2.

(a) Input image and its rotated copies (under the solid and dashed frames, respectively) and (b) the model predictions by Eq. (4) and the real image correlations.

Fig. 3.
Fig. 3.

Flow chart for the image rotation and translation measurement algorithms.

Fig. 4.
Fig. 4.

(a) Reference image and the template images and (b) the original input image and the rotated input image.

Fig. 5.
Fig. 5.

Schematic for the rotation measurement of the input image with respect to the reference image.

Fig. 6.
Fig. 6.

Experimental setup of HOC: PBS, polarizing beam splitter; SLM, spatial light modulator; ST1 and ST2, shutters; L1L6, lens; M, mirror; λ/2, half-wave plate.

Fig. 7.
Fig. 7.

Correlation matrices captured by the CCD as the white image and the rotated input images at various angles are sent into the HOC. The symbol “” means the image is rotated counterclockwise.

Fig. 8.
Fig. 8.

With the original input image rotating at an interval of 1° from 5° to 5° and sent into the HOC, 11 correlation matrices are obtained. The maxima of each correlation matrix follow a predicted curve and are used for estimating the rotation angle of the input image.

Fig. 9.
Fig. 9.

Estimation of the real rotation angle of the input image by fitting a predicted curve with the extracted correlation values.

Fig. 10.
Fig. 10.

Rotation-corrected input image is sent into the HOC to correlate with all the template images. A correlation matrix is outputted.

Fig. 11.
Fig. 11.

Correlation results for an input image with its 360 rotated copies. The input image has a rotation of 123°.

Fig. 12.
Fig. 12.

Theoretical and measured rotation angles of the input images.

Tables (1)

Tables Icon

Table 1. Prediction Errors of Eq. (4) for the Correlation Values

Equations (6)

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

c1(Δx,Δy)=E[f(x,y)f(x+Δx,y+Δy)]=a×exp(α|Δx|β|Δy|)+b,
fd(x⃗)=f(Ax⃗),
c2(Δθ)1|det(IA)|,
c2(Δθ)=1sin|Δθ|+γ.
c3(Δx,Δy,Δθ)=a×exp(α|Δx|β|Δy|)+bsin|Δθ|+γ.
c32(Δθ=θreal+3°)=a1c32(Δθ=θreal+2°)=a2c32(Δθ=θreal+1°)=a3.

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