Abstract

We describe an optoelectronic incoherent multichannel processor that is able to segment an object in a real image. The process is based on an active contour algorithm that has been transposed to optical signal processing to accelerate image processing. This implementation requires exact-valued correlations and thus opens attractive perspectives in terms of optical analog computation. Furthermore, this optical multichannel processor setup encourages incoherent processing with high-resolution images.

© 2003 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. W. Meyer-Eppler, “Die funktionalanalytische Behandlung des Schattenproblems,” Optik (Stuttgart) 1, 465–474 (1946).
  2. F. B. Berger, “Optical cross-correlator,” U.S. patent2,787,188 (1957).
  3. A. VanderLugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory 10, 139–145 (1964).
    [CrossRef]
  4. P. V. C. Hough, “Method and means for recognizing patterns,” U.S. patent3,069,654 (1962).
  5. S. Laut, F. Xu, P. Ambs, Y. Fainman, “A matrix of 64 × 64 computer generated holograms for an optical Hough transform processor,” in Optical Information Science and Technology, (OIST 97): Optical Theory and Neural Networks, A. Mikaelian, ed., Proc. SPIE3402, 22–31 (1998).
    [CrossRef]
  6. N. Michael, R. Arrathoon, “Optoelectronic parallel watershed implementation for segmentation of magnetic resonance brain images,” Appl. Opt. 36, 9269–9286 (1997).
    [CrossRef]
  7. W. Feng, Y. Yan, G. Huang, G. Jin, “Micro-optical multiwavelet element for hybrid texture segmentation processor,” Opt. Eng. 37, 185–188 (1998).
    [CrossRef]
  8. J. Barbé, J. Campos, “Image segmentation with a white light optical correlator,” in 18th Congress of the International Commission for Optics, San Francisco, A. J. Glass, J. W. Goodman, M. Chang, A. H. Guenther, T. Asakura, eds., Proc. SPIE3749, 775–776 (1999).
    [CrossRef]
  9. M. Kass, A. Witkin, D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vision 1, 321–331 (1988).
    [CrossRef]
  10. O. Germain, P. Réfrégier, “Optimal snake-based segmentation of a random luminance target on a spatially disjoint background,” Opt. Lett. 21, 1845–1847 (1996).
    [CrossRef] [PubMed]
  11. C. Chesnaud, P. Réfrégier, V. Boulet, “Statistical region snake-based segmentation adapted to different physical noise models,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1145–1157 (1999).
    [CrossRef]
  12. O. Germain, “Segmentation d’images radar: caractérisation des détecteurs de bord et apport des contours actifs,” Ph.D. dissertation, University of Aix-Marseille III, France (2001).
  13. J. L. Horner, P. Gianino, “Additional properties of the phase-only correlation filter,” Opt. Eng. 23, 695–697 (1984).
  14. Q. Tang, B. Javidi, “Sensitivity of the nonlinear joint transform correlator: experimental investigation,” Appl. Opt. 31, 4016–4024 (1992).
    [CrossRef] [PubMed]
  15. B. V. K. Vijaya Kumar, “Tutorial survey of composite filter designs for optical correlators,” Appl. Opt. 31, 4773–4801 (1992).
    [CrossRef]
  16. L. Bigué, M. Fracès, P. Ambs, “Experimental implementation of a joint transform correlator using synthetic discriminant functions,” Opt. Las. Eng. 23, 93–111 (1995).
    [CrossRef]
  17. E. Hueber, L. Bigué, P. Réfrégier, P. Ambs, “Optical snake-based segmentation processor with a shadow-casting correlator,” Opt. Lett. 26, 1852–1854 (2001).
    [CrossRef]
  18. C. Kervrann, F. Heitz, “Robust tracking of stochastic deformable models in long image sequences,” in Proceedings of IEEE Conference on Computer Vision Pattern Recognition (Seattle, 1994), pp. 724–728.
    [CrossRef]
  19. P. Réfrégier, O. Germain, 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]
  20. M. M. Robertson, “Interpretation of Patterson diagrams,” Nature 152, 411–412 (1943).
    [CrossRef]
  21. L. Bragg, “Lightning calculations with light,” Nature 154, 69–72 (1944).
    [CrossRef]
  22. G. L. Rogers, Noncoherent Optical Processing (Wiley, New York, 1977).
  23. V. Laude, “Corrélation optique optimale et application aux architectures cohérentes et incohérentes,” Ph.D. dissertation, University of Paris XIOrsay (1994).
  24. M. Gedziorowski, J. Garcia, “Programmable opticaldigital processor for rank order and morphological filtering,” Opt. Commun. 119, 207–217 (1995).
    [CrossRef]
  25. D. P. Casasent, G. House, “Implementation issues for a noncoherent optical correlator,” in Optical Pattern Recognition V, D. P. Casasent, T.-H. Chao, eds., Proc. SPIE2237, 179–188 (1994).
    [CrossRef]
  26. V. Laude, “Diffraction analysis of pixelated incoherent shadow casting,” Opt. Commun. 138, 394–402 (1997).
    [CrossRef]
  27. E. L. Green, “Diffraction in lensless correlation,” Appl. Opt. 7, 1237–1239 (1968).
    [CrossRef] [PubMed]
  28. V. Laude, P. Chavel, P. Réfrégier, “Implementation of arbitrary real-valued correlation filters for the shadow-casting incoherent correlator,” Appl. Opt. 35, 5267–5274 (1996).
    [CrossRef] [PubMed]
  29. O. Ruch, P. Réfrégier, “Comparison of Hausdorff distances performance in dissimilarity measurements for silhouette discrimination,” in Automatic Target Recognition XI, F. A. Sadjadi, ed., Proc. SPIE4379, 454–465 (2001).
    [CrossRef]
  30. K. A. Bauchert, S. A. Serati, “High-speed multi-level 512 × 512 spatial light modulator,” in Optical Pattern Recognition XI, D. P. Casasent, T.-H. Chao, eds., Proc. SPIE4043, 59–65 (2000).
    [CrossRef]
  31. P. Ambs, L. Bigué, “Characterization of an analog ferroelectric spatial light modulator. Application to dynamic diffractive optical elements and optical information processing,” in Optoelectronic Information Processing: Optics for Information Systems, 4th Euro-American workshop on optoelectronic Information Processing, P. Réfrégier, B. Javidi, C. Ferreira, S. Vallmitjana, eds., SPIE Critical ReviewCR81, 365–393 (2001).
  32. A. Matwyschuk, P. Ambs, F. Christnacher, “Target tracking correlator assisted by a snake-based optical segmentation method,” Opt. Commun. 219, 125–137 (2003).
    [CrossRef]

2003 (1)

A. Matwyschuk, P. Ambs, F. Christnacher, “Target tracking correlator assisted by a snake-based optical segmentation method,” Opt. Commun. 219, 125–137 (2003).
[CrossRef]

2001 (1)

1999 (1)

C. Chesnaud, P. Réfrégier, 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, G. Jin, “Micro-optical multiwavelet element for hybrid texture segmentation processor,” Opt. Eng. 37, 185–188 (1998).
[CrossRef]

1997 (3)

N. Michael, R. Arrathoon, “Optoelectronic parallel watershed implementation for segmentation of magnetic resonance brain images,” Appl. Opt. 36, 9269–9286 (1997).
[CrossRef]

P. Réfrégier, O. Germain, 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]

V. Laude, “Diffraction analysis of pixelated incoherent shadow casting,” Opt. Commun. 138, 394–402 (1997).
[CrossRef]

1996 (2)

1995 (2)

M. Gedziorowski, J. Garcia, “Programmable opticaldigital processor for rank order and morphological filtering,” Opt. Commun. 119, 207–217 (1995).
[CrossRef]

L. Bigué, M. Fracès, P. Ambs, “Experimental implementation of a joint transform correlator using synthetic discriminant functions,” Opt. Las. Eng. 23, 93–111 (1995).
[CrossRef]

1992 (2)

1988 (1)

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

1984 (1)

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

1968 (1)

1964 (1)

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

1946 (1)

W. Meyer-Eppler, “Die funktionalanalytische Behandlung des Schattenproblems,” Optik (Stuttgart) 1, 465–474 (1946).

1944 (1)

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

1943 (1)

M. M. Robertson, “Interpretation of Patterson diagrams,” Nature 152, 411–412 (1943).
[CrossRef]

Ambs, P.

A. Matwyschuk, P. Ambs, F. Christnacher, “Target tracking correlator assisted by a snake-based optical segmentation method,” Opt. Commun. 219, 125–137 (2003).
[CrossRef]

E. Hueber, L. Bigué, P. Réfrégier, P. Ambs, “Optical snake-based segmentation processor with a shadow-casting correlator,” Opt. Lett. 26, 1852–1854 (2001).
[CrossRef]

L. Bigué, M. Fracès, P. Ambs, “Experimental implementation of a joint transform correlator using synthetic discriminant functions,” Opt. Las. Eng. 23, 93–111 (1995).
[CrossRef]

S. Laut, F. Xu, P. Ambs, Y. Fainman, “A matrix of 64 × 64 computer generated holograms for an optical Hough transform processor,” in Optical Information Science and Technology, (OIST 97): Optical Theory and Neural Networks, A. Mikaelian, ed., Proc. SPIE3402, 22–31 (1998).
[CrossRef]

P. Ambs, L. Bigué, “Characterization of an analog ferroelectric spatial light modulator. Application to dynamic diffractive optical elements and optical information processing,” in Optoelectronic Information Processing: Optics for Information Systems, 4th Euro-American workshop on optoelectronic Information Processing, P. Réfrégier, B. Javidi, C. Ferreira, S. Vallmitjana, eds., SPIE Critical ReviewCR81, 365–393 (2001).

Arrathoon, R.

Barbé, J.

J. Barbé, J. Campos, “Image segmentation with a white light optical correlator,” in 18th Congress of the International Commission for Optics, San Francisco, A. J. Glass, J. W. Goodman, M. Chang, A. H. Guenther, T. Asakura, eds., Proc. SPIE3749, 775–776 (1999).
[CrossRef]

Bauchert, K. A.

K. A. Bauchert, S. A. Serati, “High-speed multi-level 512 × 512 spatial light modulator,” in Optical Pattern Recognition XI, D. P. Casasent, T.-H. Chao, eds., Proc. SPIE4043, 59–65 (2000).
[CrossRef]

Berger, F. B.

F. B. Berger, “Optical cross-correlator,” U.S. patent2,787,188 (1957).

Bigué, L.

E. Hueber, L. Bigué, P. Réfrégier, P. Ambs, “Optical snake-based segmentation processor with a shadow-casting correlator,” Opt. Lett. 26, 1852–1854 (2001).
[CrossRef]

L. Bigué, M. Fracès, P. Ambs, “Experimental implementation of a joint transform correlator using synthetic discriminant functions,” Opt. Las. Eng. 23, 93–111 (1995).
[CrossRef]

P. Ambs, L. Bigué, “Characterization of an analog ferroelectric spatial light modulator. Application to dynamic diffractive optical elements and optical information processing,” in Optoelectronic Information Processing: Optics for Information Systems, 4th Euro-American workshop on optoelectronic Information Processing, P. Réfrégier, B. Javidi, C. Ferreira, S. Vallmitjana, eds., SPIE Critical ReviewCR81, 365–393 (2001).

Boulet, V.

C. Chesnaud, P. Réfrégier, 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]

Campos, J.

J. Barbé, J. Campos, “Image segmentation with a white light optical correlator,” in 18th Congress of the International Commission for Optics, San Francisco, A. J. Glass, J. W. Goodman, M. Chang, A. H. Guenther, T. Asakura, eds., Proc. SPIE3749, 775–776 (1999).
[CrossRef]

Casasent, D. P.

D. P. Casasent, G. House, “Implementation issues for a noncoherent optical correlator,” in Optical Pattern Recognition V, D. P. Casasent, T.-H. Chao, eds., Proc. SPIE2237, 179–188 (1994).
[CrossRef]

Chavel, P.

Chesnaud, C.

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

Christnacher, F.

A. Matwyschuk, P. Ambs, F. Christnacher, “Target tracking correlator assisted by a snake-based optical segmentation method,” Opt. Commun. 219, 125–137 (2003).
[CrossRef]

Fainman, Y.

S. Laut, F. Xu, P. Ambs, Y. Fainman, “A matrix of 64 × 64 computer generated holograms for an optical Hough transform processor,” in Optical Information Science and Technology, (OIST 97): Optical Theory and Neural Networks, A. Mikaelian, ed., Proc. SPIE3402, 22–31 (1998).
[CrossRef]

Feng, W.

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

Ferreira, C.

P. Ambs, L. Bigué, “Characterization of an analog ferroelectric spatial light modulator. Application to dynamic diffractive optical elements and optical information processing,” in Optoelectronic Information Processing: Optics for Information Systems, 4th Euro-American workshop on optoelectronic Information Processing, P. Réfrégier, B. Javidi, C. Ferreira, S. Vallmitjana, eds., SPIE Critical ReviewCR81, 365–393 (2001).

Fracès, M.

L. Bigué, M. Fracès, P. Ambs, “Experimental implementation of a joint transform correlator using synthetic discriminant functions,” Opt. Las. Eng. 23, 93–111 (1995).
[CrossRef]

Gaidon, T.

P. Réfrégier, O. Germain, 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]

Garcia, J.

M. Gedziorowski, J. Garcia, “Programmable opticaldigital processor for rank order and morphological filtering,” Opt. Commun. 119, 207–217 (1995).
[CrossRef]

Gedziorowski, M.

M. Gedziorowski, J. Garcia, “Programmable opticaldigital processor for rank order and morphological filtering,” Opt. Commun. 119, 207–217 (1995).
[CrossRef]

Germain, O.

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

O. Germain, “Segmentation d’images radar: caractérisation des détecteurs de bord et apport des contours actifs,” Ph.D. dissertation, University of Aix-Marseille III, France (2001).

Gianino, P.

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

Green, E. L.

Heitz, F.

C. Kervrann, F. Heitz, “Robust tracking of stochastic deformable models in long image sequences,” in Proceedings of IEEE Conference on Computer Vision Pattern Recognition (Seattle, 1994), pp. 724–728.
[CrossRef]

Horner, J. L.

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

Hough, P. V. C.

P. V. C. Hough, “Method and means for recognizing patterns,” U.S. patent3,069,654 (1962).

House, G.

D. P. Casasent, G. House, “Implementation issues for a noncoherent optical correlator,” in Optical Pattern Recognition V, D. P. Casasent, T.-H. Chao, eds., Proc. SPIE2237, 179–188 (1994).
[CrossRef]

Huang, G.

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

Hueber, E.

Javidi, B.

Q. Tang, B. Javidi, “Sensitivity of the nonlinear joint transform correlator: experimental investigation,” Appl. Opt. 31, 4016–4024 (1992).
[CrossRef] [PubMed]

P. Ambs, L. Bigué, “Characterization of an analog ferroelectric spatial light modulator. Application to dynamic diffractive optical elements and optical information processing,” in Optoelectronic Information Processing: Optics for Information Systems, 4th Euro-American workshop on optoelectronic Information Processing, P. Réfrégier, B. Javidi, C. Ferreira, S. Vallmitjana, eds., SPIE Critical ReviewCR81, 365–393 (2001).

Jin, G.

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

Kass, M.

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

Kervrann, C.

C. Kervrann, F. Heitz, “Robust tracking of stochastic deformable models in long image sequences,” in Proceedings of IEEE Conference on Computer Vision Pattern Recognition (Seattle, 1994), pp. 724–728.
[CrossRef]

Laude, V.

V. Laude, “Diffraction analysis of pixelated incoherent shadow casting,” Opt. Commun. 138, 394–402 (1997).
[CrossRef]

V. Laude, P. Chavel, P. Réfrégier, “Implementation of arbitrary real-valued correlation filters for the shadow-casting incoherent correlator,” Appl. Opt. 35, 5267–5274 (1996).
[CrossRef] [PubMed]

V. Laude, “Corrélation optique optimale et application aux architectures cohérentes et incohérentes,” Ph.D. dissertation, University of Paris XIOrsay (1994).

Laut, S.

S. Laut, F. Xu, P. Ambs, Y. Fainman, “A matrix of 64 × 64 computer generated holograms for an optical Hough transform processor,” in Optical Information Science and Technology, (OIST 97): Optical Theory and Neural Networks, A. Mikaelian, ed., Proc. SPIE3402, 22–31 (1998).
[CrossRef]

Matwyschuk, A.

A. Matwyschuk, P. Ambs, F. Christnacher, “Target tracking correlator assisted by a snake-based optical segmentation method,” Opt. Commun. 219, 125–137 (2003).
[CrossRef]

Meyer-Eppler, W.

W. Meyer-Eppler, “Die funktionalanalytische Behandlung des Schattenproblems,” Optik (Stuttgart) 1, 465–474 (1946).

Michael, N.

Réfrégier, P.

E. Hueber, L. Bigué, P. Réfrégier, P. Ambs, “Optical snake-based segmentation processor with a shadow-casting correlator,” Opt. Lett. 26, 1852–1854 (2001).
[CrossRef]

C. Chesnaud, P. Réfrégier, 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, 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]

V. Laude, P. Chavel, P. Réfrégier, “Implementation of arbitrary real-valued correlation filters for the shadow-casting incoherent correlator,” Appl. Opt. 35, 5267–5274 (1996).
[CrossRef] [PubMed]

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

O. Ruch, P. Réfrégier, “Comparison of Hausdorff distances performance in dissimilarity measurements for silhouette discrimination,” in Automatic Target Recognition XI, F. A. Sadjadi, ed., Proc. SPIE4379, 454–465 (2001).
[CrossRef]

P. Ambs, L. Bigué, “Characterization of an analog ferroelectric spatial light modulator. Application to dynamic diffractive optical elements and optical information processing,” in Optoelectronic Information Processing: Optics for Information Systems, 4th Euro-American workshop on optoelectronic Information Processing, P. Réfrégier, B. Javidi, C. Ferreira, S. Vallmitjana, eds., SPIE Critical ReviewCR81, 365–393 (2001).

Robertson, M. M.

M. M. Robertson, “Interpretation of Patterson diagrams,” Nature 152, 411–412 (1943).
[CrossRef]

Rogers, G. L.

G. L. Rogers, Noncoherent Optical Processing (Wiley, New York, 1977).

Ruch, O.

O. Ruch, P. Réfrégier, “Comparison of Hausdorff distances performance in dissimilarity measurements for silhouette discrimination,” in Automatic Target Recognition XI, F. A. Sadjadi, ed., Proc. SPIE4379, 454–465 (2001).
[CrossRef]

Serati, S. A.

K. A. Bauchert, S. A. Serati, “High-speed multi-level 512 × 512 spatial light modulator,” in Optical Pattern Recognition XI, D. P. Casasent, T.-H. Chao, eds., Proc. SPIE4043, 59–65 (2000).
[CrossRef]

Tang, Q.

Terzopoulos, D.

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

Vallmitjana, S.

P. Ambs, L. Bigué, “Characterization of an analog ferroelectric spatial light modulator. Application to dynamic diffractive optical elements and optical information processing,” in Optoelectronic Information Processing: Optics for Information Systems, 4th Euro-American workshop on optoelectronic Information Processing, P. Réfrégier, B. Javidi, C. Ferreira, S. Vallmitjana, eds., SPIE Critical ReviewCR81, 365–393 (2001).

VanderLugt, A.

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

Vijaya Kumar, B. V. K.

Witkin, A.

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

Xu, F.

S. Laut, F. Xu, P. Ambs, Y. Fainman, “A matrix of 64 × 64 computer generated holograms for an optical Hough transform processor,” in Optical Information Science and Technology, (OIST 97): Optical Theory and Neural Networks, A. Mikaelian, ed., Proc. SPIE3402, 22–31 (1998).
[CrossRef]

Yan, Y.

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

Appl. Opt. (5)

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

Nature (2)

M. M. Robertson, “Interpretation of Patterson diagrams,” Nature 152, 411–412 (1943).
[CrossRef]

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

Opt. Commun. (4)

M. Gedziorowski, J. Garcia, “Programmable opticaldigital processor for rank order and morphological filtering,” Opt. Commun. 119, 207–217 (1995).
[CrossRef]

V. Laude, “Diffraction analysis of pixelated incoherent shadow casting,” Opt. Commun. 138, 394–402 (1997).
[CrossRef]

A. Matwyschuk, P. Ambs, F. Christnacher, “Target tracking correlator assisted by a snake-based optical segmentation method,” Opt. Commun. 219, 125–137 (2003).
[CrossRef]

P. Réfrégier, O. Germain, 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)

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

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

Opt. Las. Eng. (1)

L. Bigué, M. Fracès, P. Ambs, “Experimental implementation of a joint transform correlator using synthetic discriminant functions,” Opt. Las. Eng. 23, 93–111 (1995).
[CrossRef]

Opt. Lett. (2)

Optik (Stuttgart) (1)

W. Meyer-Eppler, “Die funktionalanalytische Behandlung des Schattenproblems,” Optik (Stuttgart) 1, 465–474 (1946).

Other (12)

F. B. Berger, “Optical cross-correlator,” U.S. patent2,787,188 (1957).

P. V. C. Hough, “Method and means for recognizing patterns,” U.S. patent3,069,654 (1962).

S. Laut, F. Xu, P. Ambs, Y. Fainman, “A matrix of 64 × 64 computer generated holograms for an optical Hough transform processor,” in Optical Information Science and Technology, (OIST 97): Optical Theory and Neural Networks, A. Mikaelian, ed., Proc. SPIE3402, 22–31 (1998).
[CrossRef]

J. Barbé, J. Campos, “Image segmentation with a white light optical correlator,” in 18th Congress of the International Commission for Optics, San Francisco, A. J. Glass, J. W. Goodman, M. Chang, A. H. Guenther, T. Asakura, eds., Proc. SPIE3749, 775–776 (1999).
[CrossRef]

C. Kervrann, F. Heitz, “Robust tracking of stochastic deformable models in long image sequences,” in Proceedings of IEEE Conference on Computer Vision Pattern Recognition (Seattle, 1994), pp. 724–728.
[CrossRef]

O. Germain, “Segmentation d’images radar: caractérisation des détecteurs de bord et apport des contours actifs,” Ph.D. dissertation, University of Aix-Marseille III, France (2001).

D. P. Casasent, G. House, “Implementation issues for a noncoherent optical correlator,” in Optical Pattern Recognition V, D. P. Casasent, T.-H. Chao, eds., Proc. SPIE2237, 179–188 (1994).
[CrossRef]

G. L. Rogers, Noncoherent Optical Processing (Wiley, New York, 1977).

V. Laude, “Corrélation optique optimale et application aux architectures cohérentes et incohérentes,” Ph.D. dissertation, University of Paris XIOrsay (1994).

O. Ruch, P. Réfrégier, “Comparison of Hausdorff distances performance in dissimilarity measurements for silhouette discrimination,” in Automatic Target Recognition XI, F. A. Sadjadi, ed., Proc. SPIE4379, 454–465 (2001).
[CrossRef]

K. A. Bauchert, S. A. Serati, “High-speed multi-level 512 × 512 spatial light modulator,” in Optical Pattern Recognition XI, D. P. Casasent, T.-H. Chao, eds., Proc. SPIE4043, 59–65 (2000).
[CrossRef]

P. Ambs, L. Bigué, “Characterization of an analog ferroelectric spatial light modulator. Application to dynamic diffractive optical elements and optical information processing,” in Optoelectronic Information Processing: Optics for Information Systems, 4th Euro-American workshop on optoelectronic Information Processing, P. Réfrégier, B. Javidi, C. Ferreira, S. Vallmitjana, eds., SPIE Critical ReviewCR81, 365–393 (2001).

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (5)

Fig. 1
Fig. 1

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

Fig. 2
Fig. 2

Multichannel incoherent shadow-casting correlator with four channels.

Fig. 3
Fig. 3

Complete optical setup of the multichannel incoherent correlator.

Fig. 4
Fig. 4

Strategy applied to the four-channel correlator. Step 1: Odd numbered nodes are moved in the same stochastic direction. Step 2: Deformations that minimize the criterion (nodes 1, 5, and 7) are applied to obtain the new window. The new binary window is displayed in the four channels to measure their criterion. Step 3: Even numbered nodes are moved in a new stochastic direction. Step 4: Same as Step 2 with the validation of the deformation of node number 4.

Fig. 5
Fig. 5

(a) Several multiplexed windows extracted from the 50-iteration segmentation, (b) final contour obtained with the optical processor superimposed on the original scene.

Equations (7)

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

Pμz=kzexpaμβz+fμ,
Jw, s=Nwlogm+N-Nwlog(m¯
m=1Nwsw0
m¯=1N-Nwi=1N si-sw0,
NWNS=AWAS4α2λ2d2,
NW=NS72×94,
Q=WWRef.

Metrics