Abstract

What is believed to be the first incoherent snake-based optoelectronic processor that is able to segment an object in a real image is described. The process, based on active contours (snakes), consists of correlating adaptive binary references with the scene image. The proposed optical implementation of algorithms that are already operational numerically opens attractive possibilities for faster processing. Furthermore, this experiment has yielded a new, versatile application for optical processors.

© 2001 Optical Society of America

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References

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  1. A. VanderLugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory 10, 139–145 (1964).
    [CrossRef]
  2. B. Javidi and J. Wang, “Limitation of the classic definition of the correlation signal-to-noise ratio in optical pattern recognition with disjoint signal and scene noise,” Appl. Opt. 31, 6826–6829 (1992).
    [CrossRef] [PubMed]
  3. B. Javidi, P. Réfrégier, and P. Willett, “Optimum receiver design for pattern recognition with nonoverlapping target and scene noise,” Opt. Lett. 18, 1660–1662 (1993).
    [CrossRef] [PubMed]
  4. C. Veronin, K. Priddy, S. Rogers, K. Ayer, M. Kabrisky, and B. Welsh, “Optical image segmentation using neural-based wavelet filtering techniques,” Opt. Eng. 31, 287–294 (1992).
    [CrossRef]
  5. T.-H. Chao, T. Daud, and A. Thakoor, “Fast frame hybrid optoelectronic neural object recognition system,” in International Conference on Applied Modeling and Simulation (International Association of Science and Technology for Development, Calgary, Canada, 1998), pp. 82–86.
  6. W. Feng, Y. Yan, G. Huang, and G. Jin, “Micro-optical multiwavelet element for hybrid texture segmentation processor,” Opt. Eng. 37, 185–188 (1998).
    [CrossRef]
  7. M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vision 1, 321–331 (1988).
    [CrossRef]
  8. O. Germain and P. Réfrégier, “Optimal snake-based segmentation of a random luminance target on a spatially disjoint background,” Opt. Lett. 21, 1845–1847 (1996).
    [CrossRef] [PubMed]
  9. C. Chesnaud, P. Réfrégier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1145–1157 (1999).
    [CrossRef]
  10. P. Réfrégier, O. Germain, and T. Gaidon, “Optimal snake segmentation of target and background with independent Gamma density probabilities, application to speckled and preprocessed images,” Opt. Commun. 137, 382–388 (1997).
    [CrossRef]
  11. L. Bragg, “Lightning calculations with light,” Nature 154, 69–72 (1944).
    [CrossRef]
  12. K. A. Bauchert and S. A. Serati, “High-speed multi-level 512×512 spatial light modulator,” Proc. SPIE 4043, 59–65 (2000).
    [CrossRef]

2000 (1)

K. A. Bauchert and S. A. Serati, “High-speed multi-level 512×512 spatial light modulator,” Proc. SPIE 4043, 59–65 (2000).
[CrossRef]

1999 (1)

C. Chesnaud, P. Réfrégier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1145–1157 (1999).
[CrossRef]

1998 (1)

W. Feng, Y. Yan, G. Huang, and G. Jin, “Micro-optical multiwavelet element for hybrid texture segmentation processor,” Opt. Eng. 37, 185–188 (1998).
[CrossRef]

1997 (1)

P. Réfrégier, O. Germain, and T. Gaidon, “Optimal snake segmentation of target and background with independent Gamma density probabilities, application to speckled and preprocessed images,” Opt. Commun. 137, 382–388 (1997).
[CrossRef]

1996 (1)

1993 (1)

1992 (2)

C. Veronin, K. Priddy, S. Rogers, K. Ayer, M. Kabrisky, and B. Welsh, “Optical image segmentation using neural-based wavelet filtering techniques,” Opt. Eng. 31, 287–294 (1992).
[CrossRef]

B. Javidi and J. Wang, “Limitation of the classic definition of the correlation signal-to-noise ratio in optical pattern recognition with disjoint signal and scene noise,” Appl. Opt. 31, 6826–6829 (1992).
[CrossRef] [PubMed]

1988 (1)

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vision 1, 321–331 (1988).
[CrossRef]

1964 (1)

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

1944 (1)

L. Bragg, “Lightning calculations with light,” Nature 154, 69–72 (1944).
[CrossRef]

Ayer, K.

C. Veronin, K. Priddy, S. Rogers, K. Ayer, M. Kabrisky, and B. Welsh, “Optical image segmentation using neural-based wavelet filtering techniques,” Opt. Eng. 31, 287–294 (1992).
[CrossRef]

Bauchert, K. A.

K. A. Bauchert and S. A. Serati, “High-speed multi-level 512×512 spatial light modulator,” Proc. SPIE 4043, 59–65 (2000).
[CrossRef]

Boulet, V.

C. Chesnaud, P. Réfrégier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1145–1157 (1999).
[CrossRef]

Bragg, L.

L. Bragg, “Lightning calculations with light,” Nature 154, 69–72 (1944).
[CrossRef]

Chao, T.-H.

T.-H. Chao, T. Daud, and A. Thakoor, “Fast frame hybrid optoelectronic neural object recognition system,” in International Conference on Applied Modeling and Simulation (International Association of Science and Technology for Development, Calgary, Canada, 1998), pp. 82–86.

Chesnaud, C.

C. Chesnaud, P. Réfrégier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1145–1157 (1999).
[CrossRef]

Daud, T.

T.-H. Chao, T. Daud, and A. Thakoor, “Fast frame hybrid optoelectronic neural object recognition system,” in International Conference on Applied Modeling and Simulation (International Association of Science and Technology for Development, Calgary, Canada, 1998), pp. 82–86.

Feng, W.

W. Feng, Y. Yan, G. Huang, and G. Jin, “Micro-optical multiwavelet element for hybrid texture segmentation processor,” Opt. Eng. 37, 185–188 (1998).
[CrossRef]

Gaidon, T.

P. Réfrégier, O. Germain, and T. Gaidon, “Optimal snake segmentation of target and background with independent Gamma density probabilities, application to speckled and preprocessed images,” Opt. Commun. 137, 382–388 (1997).
[CrossRef]

Germain, O.

P. Réfrégier, O. Germain, and T. Gaidon, “Optimal snake segmentation of target and background with independent Gamma density probabilities, application to speckled and preprocessed images,” Opt. Commun. 137, 382–388 (1997).
[CrossRef]

O. Germain and P. Réfrégier, “Optimal snake-based segmentation of a random luminance target on a spatially disjoint background,” Opt. Lett. 21, 1845–1847 (1996).
[CrossRef] [PubMed]

Huang, G.

W. Feng, Y. Yan, G. Huang, and G. Jin, “Micro-optical multiwavelet element for hybrid texture segmentation processor,” Opt. Eng. 37, 185–188 (1998).
[CrossRef]

Javidi, B.

Jin, G.

W. Feng, Y. Yan, G. Huang, and G. Jin, “Micro-optical multiwavelet element for hybrid texture segmentation processor,” Opt. Eng. 37, 185–188 (1998).
[CrossRef]

Kabrisky, M.

C. Veronin, K. Priddy, S. Rogers, K. Ayer, M. Kabrisky, and B. Welsh, “Optical image segmentation using neural-based wavelet filtering techniques,” Opt. Eng. 31, 287–294 (1992).
[CrossRef]

Kass, M.

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vision 1, 321–331 (1988).
[CrossRef]

Priddy, K.

C. Veronin, K. Priddy, S. Rogers, K. Ayer, M. Kabrisky, and B. Welsh, “Optical image segmentation using neural-based wavelet filtering techniques,” Opt. Eng. 31, 287–294 (1992).
[CrossRef]

Réfrégier, P.

C. Chesnaud, P. Réfrégier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1145–1157 (1999).
[CrossRef]

P. Réfrégier, O. Germain, and T. Gaidon, “Optimal snake segmentation of target and background with independent Gamma density probabilities, application to speckled and preprocessed images,” Opt. Commun. 137, 382–388 (1997).
[CrossRef]

O. Germain and P. Réfrégier, “Optimal snake-based segmentation of a random luminance target on a spatially disjoint background,” Opt. Lett. 21, 1845–1847 (1996).
[CrossRef] [PubMed]

B. Javidi, P. Réfrégier, and P. Willett, “Optimum receiver design for pattern recognition with nonoverlapping target and scene noise,” Opt. Lett. 18, 1660–1662 (1993).
[CrossRef] [PubMed]

Rogers, S.

C. Veronin, K. Priddy, S. Rogers, K. Ayer, M. Kabrisky, and B. Welsh, “Optical image segmentation using neural-based wavelet filtering techniques,” Opt. Eng. 31, 287–294 (1992).
[CrossRef]

Serati, S. A.

K. A. Bauchert and S. A. Serati, “High-speed multi-level 512×512 spatial light modulator,” Proc. SPIE 4043, 59–65 (2000).
[CrossRef]

Terzopoulos, D.

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vision 1, 321–331 (1988).
[CrossRef]

Thakoor, A.

T.-H. Chao, T. Daud, and A. Thakoor, “Fast frame hybrid optoelectronic neural object recognition system,” in International Conference on Applied Modeling and Simulation (International Association of Science and Technology for Development, Calgary, Canada, 1998), pp. 82–86.

VanderLugt, A.

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

Veronin, C.

C. Veronin, K. Priddy, S. Rogers, K. Ayer, M. Kabrisky, and B. Welsh, “Optical image segmentation using neural-based wavelet filtering techniques,” Opt. Eng. 31, 287–294 (1992).
[CrossRef]

Wang, J.

Welsh, B.

C. Veronin, K. Priddy, S. Rogers, K. Ayer, M. Kabrisky, and B. Welsh, “Optical image segmentation using neural-based wavelet filtering techniques,” Opt. Eng. 31, 287–294 (1992).
[CrossRef]

Willett, P.

Witkin, A.

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vision 1, 321–331 (1988).
[CrossRef]

Yan, Y.

W. Feng, Y. Yan, G. Huang, and G. Jin, “Micro-optical multiwavelet element for hybrid texture segmentation processor,” Opt. Eng. 37, 185–188 (1998).
[CrossRef]

Appl. Opt. (1)

IEEE Trans. Inf. Theory (1)

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

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

C. Chesnaud, P. Réfrégier, and V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1145–1157 (1999).
[CrossRef]

Int. J. Comput. Vision (1)

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vision 1, 321–331 (1988).
[CrossRef]

Nature (1)

L. Bragg, “Lightning calculations with light,” Nature 154, 69–72 (1944).
[CrossRef]

Opt. Commun. (1)

P. Réfrégier, O. Germain, and T. Gaidon, “Optimal snake segmentation of target and background with independent Gamma density probabilities, application to speckled and preprocessed images,” Opt. Commun. 137, 382–388 (1997).
[CrossRef]

Opt. Eng. (2)

W. Feng, Y. Yan, G. Huang, and G. Jin, “Micro-optical multiwavelet element for hybrid texture segmentation processor,” Opt. Eng. 37, 185–188 (1998).
[CrossRef]

C. Veronin, K. Priddy, S. Rogers, K. Ayer, M. Kabrisky, and B. Welsh, “Optical image segmentation using neural-based wavelet filtering techniques,” Opt. Eng. 31, 287–294 (1992).
[CrossRef]

Opt. Lett. (2)

Proc. SPIE (1)

K. A. Bauchert and S. A. Serati, “High-speed multi-level 512×512 spatial light modulator,” Proc. SPIE 4043, 59–65 (2000).
[CrossRef]

Other (1)

T.-H. Chao, T. Daud, and A. Thakoor, “Fast frame hybrid optoelectronic neural object recognition system,” in International Conference on Applied Modeling and Simulation (International Association of Science and Technology for Development, Calgary, Canada, 1998), pp. 82–86.

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Figures (4)

Fig. 1
Fig. 1

Configuration of the optical processor. Plane P contains the correlation of the amplitude distributions located in planes Ps and Pw.

Fig. 2
Fig. 2

Shadow-casting incoherent correlator..

Fig. 3
Fig. 3

(a) Input scene s. (b) Initialization of the snake, with its nodes marked by crosses. (c) Final state of the snake after the optimization of Js,w obtained with the optical processor. (d) Simulation results.

Fig. 4
Fig. 4

(a) Experimental results of the optical snake implementation. (b) Simulation results.

Equations (3)

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

Jw,s=Nwlogm+N-Nwlogm¯,
m=1Nwsw0,
m¯=1N-Nwi=1Nsi-sw0,

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