Abstract

We present an imaging system that measures the polarimetric state of the light coming from each point of a scene. This system, which determines the four components of the Stokes vector at each spatial location, is based on a liquid-crystal polarization modulator, which makes it possible to acquire four-dimensional Stokes parameter images at a standard video rate. We show that using such polarimetric images instead of simple intensity images can improve target detection and segmentation performance.

© 2004 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. J. E. Solomon, “Polarization imaging,” Appl. Opt. 20, 1537–1544 (1981).
    [CrossRef] [PubMed]
  2. W. G. Egan, W. R. Johnson, V. S. Whitehead, “Terrestrial polarization imagery obtained from the Space Shuttle: characterization and interpretation,” Appl. Opt. 30, 435–442 (1991).
    [CrossRef] [PubMed]
  3. L. B. Wolff, “Polarization camera for computer vision with a beam splitter,” J. Opt. Soc. Am. A 11, 2935–2945 (1994).
    [CrossRef]
  4. J. S. Tyo, M. P. Rowe, E. N. Pugh, N. Engheta, “Target detection in optical scattering media by polarization-difference imaging,” Appl. Opt. 35, 1855–1870 (1996).
    [CrossRef] [PubMed]
  5. F. Goudail, Ph. Réfrégier, “Statistical algorithms for target detection in coherent active polarimetric images,” J. Opt. Soc. Am. A 18, 3049–3060 (2001).
    [CrossRef]
  6. A. F. Sadjadi, C. S. L. Chun, “Automatic detection of small objects from their infrared state-of-polarization vectors,” Opt. Lett. 28, 531–533 (2003).
    [CrossRef] [PubMed]
  7. L. B. Wolff, T. E. Boult, “Constraining object features using a polarization reflectance model,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 635–657 (1991).
    [CrossRef]
  8. P. Terrier, V. DeVlaminck, “Robust and accurate estimate of the orientation of partially polarized light from a camera sensor,” Appl. Opt. 40, 5233–5239 (2001).
    [CrossRef]
  9. T. Rogne, S. Stewart, M. Metzler, “Infrared polarimetry: what, why, how and the way ahead,” presented at the Third NATO IRIS Joint Symposium, Quebec, Canada (October 1998).
  10. A. F. Sadjadi, C. S. L. Chun, “Passive polarimetric IR classification,” IEEE Trans. Aerosp. Electron. Syst. 37, 740–751 (2001).
    [CrossRef]
  11. K. Koshikawa, “A polarimetric approach to shape understanding of glossy objects,” presented at the Sixth International Joint Conference on Artificial Intelligence, Tokyo, Japan (1979).
  12. B. F. Jones, P. T. Fairney, “Recognition of shiny dielectric objects by analyzing the polarization of reflected light,” Image Vision Comput. 7, 253–258 (1989).
    [CrossRef]
  13. L. B. Wolff, “Polarization vision: a new sensory approach to image understanding,” Image Vision Comput. 15, 81–93 (1997).
    [CrossRef]
  14. L. B. Wolff, “Polarization-based material classification from specular reflection,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 1059–1071 (1990).
    [CrossRef]
  15. R. A. Chipman, “Polarimetry,” in Handbook of Optics, M. Bass, ed. (McGraw-Hill, New York, 1995), pp. 22.1–22.33.
  16. O. Ruch, Ph. Réfrégier, “Minimal-complexity segmentation with a polygonal snake adapted to different optical noise models,” Opt. Lett. 41, 977–979 (2001).
    [CrossRef]
  17. J. S. Tyo, “Design of optimal polarimeters: maximization of SNR and minimization of systematic errors,” Appl. Opt. 41, 619–630 (2002).
    [CrossRef] [PubMed]
  18. C. Brosseau, Fundamentals of Polarized Light—A Statistical Approach (Wiley, New York, 1998).
  19. J. J. Drewes, R. A. Chipman, M. H. Smith, “Characterizing polarization controllers with Mueller matrix polarimetry,” in Active and Passive Optical Components for WDM Communication, A. K. Dutta, A. A. S. Awwal, N. K. Dutta, K. Okamoto, eds., Proc. SPIE4532, 462–466 (2001).
    [CrossRef]
  20. J. Zallat, Y. Takakura, M.-Ph. Stoll, “Active imaging polarimetry,” presented at the Physics in Signal and Image Processing Conference 2001, Marseille, France (2001).
  21. H. V. Poor, “Elements of hypothesis testing,” in An Introduction to Signal Detection and Estimation (Springer-Verlag, New York, 1994), pp. 5–39.
    [CrossRef]
  22. V. Pagé, F. Goudail, Ph. Réfrégier, “Improved robustness of target location in nonhomogeneous backgrounds by use of the maximum likelihood ratio test location algorithm,” Opt. Lett. 24, 1383–1385 (1999).
    [CrossRef]
  23. O. Germain, Ph. Réfrégier, “On the bias of the likelihood ratio edge detector for SAR images,” IEEE Trans. Geosci. Remote Sens. 38, 1455–1458 (2000).
    [CrossRef]
  24. O. Germain, Ph. Réfrégier, “Optimal snake-based segmentation of a random luminance target on a spatially disjoint background,” Opt. Lett. 21, 1845–1847 (1996).
    [CrossRef] [PubMed]
  25. C. Chesnaud, Ph. 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]
  26. S. Huard, “Polarized optical wave,” in Polarization of Light (Masson, Paris, 1997), pp. 1–35.

2003 (1)

2002 (1)

2001 (4)

F. Goudail, Ph. Réfrégier, “Statistical algorithms for target detection in coherent active polarimetric images,” J. Opt. Soc. Am. A 18, 3049–3060 (2001).
[CrossRef]

O. Ruch, Ph. Réfrégier, “Minimal-complexity segmentation with a polygonal snake adapted to different optical noise models,” Opt. Lett. 41, 977–979 (2001).
[CrossRef]

P. Terrier, V. DeVlaminck, “Robust and accurate estimate of the orientation of partially polarized light from a camera sensor,” Appl. Opt. 40, 5233–5239 (2001).
[CrossRef]

A. F. Sadjadi, C. S. L. Chun, “Passive polarimetric IR classification,” IEEE Trans. Aerosp. Electron. Syst. 37, 740–751 (2001).
[CrossRef]

2000 (1)

O. Germain, Ph. Réfrégier, “On the bias of the likelihood ratio edge detector for SAR images,” IEEE Trans. Geosci. Remote Sens. 38, 1455–1458 (2000).
[CrossRef]

1999 (2)

V. Pagé, F. Goudail, Ph. Réfrégier, “Improved robustness of target location in nonhomogeneous backgrounds by use of the maximum likelihood ratio test location algorithm,” Opt. Lett. 24, 1383–1385 (1999).
[CrossRef]

C. Chesnaud, Ph. 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]

1997 (1)

L. B. Wolff, “Polarization vision: a new sensory approach to image understanding,” Image Vision Comput. 15, 81–93 (1997).
[CrossRef]

1996 (2)

1994 (1)

1991 (2)

W. G. Egan, W. R. Johnson, V. S. Whitehead, “Terrestrial polarization imagery obtained from the Space Shuttle: characterization and interpretation,” Appl. Opt. 30, 435–442 (1991).
[CrossRef] [PubMed]

L. B. Wolff, T. E. Boult, “Constraining object features using a polarization reflectance model,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 635–657 (1991).
[CrossRef]

1990 (1)

L. B. Wolff, “Polarization-based material classification from specular reflection,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 1059–1071 (1990).
[CrossRef]

1989 (1)

B. F. Jones, P. T. Fairney, “Recognition of shiny dielectric objects by analyzing the polarization of reflected light,” Image Vision Comput. 7, 253–258 (1989).
[CrossRef]

1981 (1)

Boulet, V.

C. Chesnaud, Ph. 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]

Boult, T. E.

L. B. Wolff, T. E. Boult, “Constraining object features using a polarization reflectance model,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 635–657 (1991).
[CrossRef]

Brosseau, C.

C. Brosseau, Fundamentals of Polarized Light—A Statistical Approach (Wiley, New York, 1998).

Chesnaud, C.

C. Chesnaud, Ph. 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]

Chipman, R. A.

J. J. Drewes, R. A. Chipman, M. H. Smith, “Characterizing polarization controllers with Mueller matrix polarimetry,” in Active and Passive Optical Components for WDM Communication, A. K. Dutta, A. A. S. Awwal, N. K. Dutta, K. Okamoto, eds., Proc. SPIE4532, 462–466 (2001).
[CrossRef]

R. A. Chipman, “Polarimetry,” in Handbook of Optics, M. Bass, ed. (McGraw-Hill, New York, 1995), pp. 22.1–22.33.

Chun, C. S. L.

A. F. Sadjadi, C. S. L. Chun, “Automatic detection of small objects from their infrared state-of-polarization vectors,” Opt. Lett. 28, 531–533 (2003).
[CrossRef] [PubMed]

A. F. Sadjadi, C. S. L. Chun, “Passive polarimetric IR classification,” IEEE Trans. Aerosp. Electron. Syst. 37, 740–751 (2001).
[CrossRef]

DeVlaminck, V.

Drewes, J. J.

J. J. Drewes, R. A. Chipman, M. H. Smith, “Characterizing polarization controllers with Mueller matrix polarimetry,” in Active and Passive Optical Components for WDM Communication, A. K. Dutta, A. A. S. Awwal, N. K. Dutta, K. Okamoto, eds., Proc. SPIE4532, 462–466 (2001).
[CrossRef]

Egan, W. G.

Engheta, N.

Fairney, P. T.

B. F. Jones, P. T. Fairney, “Recognition of shiny dielectric objects by analyzing the polarization of reflected light,” Image Vision Comput. 7, 253–258 (1989).
[CrossRef]

Germain, O.

O. Germain, Ph. Réfrégier, “On the bias of the likelihood ratio edge detector for SAR images,” IEEE Trans. Geosci. Remote Sens. 38, 1455–1458 (2000).
[CrossRef]

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

Goudail, F.

Huard, S.

S. Huard, “Polarized optical wave,” in Polarization of Light (Masson, Paris, 1997), pp. 1–35.

Johnson, W. R.

Jones, B. F.

B. F. Jones, P. T. Fairney, “Recognition of shiny dielectric objects by analyzing the polarization of reflected light,” Image Vision Comput. 7, 253–258 (1989).
[CrossRef]

Koshikawa, K.

K. Koshikawa, “A polarimetric approach to shape understanding of glossy objects,” presented at the Sixth International Joint Conference on Artificial Intelligence, Tokyo, Japan (1979).

Metzler, M.

T. Rogne, S. Stewart, M. Metzler, “Infrared polarimetry: what, why, how and the way ahead,” presented at the Third NATO IRIS Joint Symposium, Quebec, Canada (October 1998).

Pagé, V.

Poor, H. V.

H. V. Poor, “Elements of hypothesis testing,” in An Introduction to Signal Detection and Estimation (Springer-Verlag, New York, 1994), pp. 5–39.
[CrossRef]

Pugh, E. N.

Réfrégier, Ph.

F. Goudail, Ph. Réfrégier, “Statistical algorithms for target detection in coherent active polarimetric images,” J. Opt. Soc. Am. A 18, 3049–3060 (2001).
[CrossRef]

O. Ruch, Ph. Réfrégier, “Minimal-complexity segmentation with a polygonal snake adapted to different optical noise models,” Opt. Lett. 41, 977–979 (2001).
[CrossRef]

O. Germain, Ph. Réfrégier, “On the bias of the likelihood ratio edge detector for SAR images,” IEEE Trans. Geosci. Remote Sens. 38, 1455–1458 (2000).
[CrossRef]

V. Pagé, F. Goudail, Ph. Réfrégier, “Improved robustness of target location in nonhomogeneous backgrounds by use of the maximum likelihood ratio test location algorithm,” Opt. Lett. 24, 1383–1385 (1999).
[CrossRef]

C. Chesnaud, Ph. 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]

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

Rogne, T.

T. Rogne, S. Stewart, M. Metzler, “Infrared polarimetry: what, why, how and the way ahead,” presented at the Third NATO IRIS Joint Symposium, Quebec, Canada (October 1998).

Rowe, M. P.

Ruch, O.

O. Ruch, Ph. Réfrégier, “Minimal-complexity segmentation with a polygonal snake adapted to different optical noise models,” Opt. Lett. 41, 977–979 (2001).
[CrossRef]

Sadjadi, A. F.

A. F. Sadjadi, C. S. L. Chun, “Automatic detection of small objects from their infrared state-of-polarization vectors,” Opt. Lett. 28, 531–533 (2003).
[CrossRef] [PubMed]

A. F. Sadjadi, C. S. L. Chun, “Passive polarimetric IR classification,” IEEE Trans. Aerosp. Electron. Syst. 37, 740–751 (2001).
[CrossRef]

Smith, M. H.

J. J. Drewes, R. A. Chipman, M. H. Smith, “Characterizing polarization controllers with Mueller matrix polarimetry,” in Active and Passive Optical Components for WDM Communication, A. K. Dutta, A. A. S. Awwal, N. K. Dutta, K. Okamoto, eds., Proc. SPIE4532, 462–466 (2001).
[CrossRef]

Solomon, J. E.

Stewart, S.

T. Rogne, S. Stewart, M. Metzler, “Infrared polarimetry: what, why, how and the way ahead,” presented at the Third NATO IRIS Joint Symposium, Quebec, Canada (October 1998).

Stoll, M.-Ph.

J. Zallat, Y. Takakura, M.-Ph. Stoll, “Active imaging polarimetry,” presented at the Physics in Signal and Image Processing Conference 2001, Marseille, France (2001).

Takakura, Y.

J. Zallat, Y. Takakura, M.-Ph. Stoll, “Active imaging polarimetry,” presented at the Physics in Signal and Image Processing Conference 2001, Marseille, France (2001).

Terrier, P.

Tyo, J. S.

Whitehead, V. S.

Wolff, L. B.

L. B. Wolff, “Polarization vision: a new sensory approach to image understanding,” Image Vision Comput. 15, 81–93 (1997).
[CrossRef]

L. B. Wolff, “Polarization camera for computer vision with a beam splitter,” J. Opt. Soc. Am. A 11, 2935–2945 (1994).
[CrossRef]

L. B. Wolff, T. E. Boult, “Constraining object features using a polarization reflectance model,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 635–657 (1991).
[CrossRef]

L. B. Wolff, “Polarization-based material classification from specular reflection,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 1059–1071 (1990).
[CrossRef]

Zallat, J.

J. Zallat, Y. Takakura, M.-Ph. Stoll, “Active imaging polarimetry,” presented at the Physics in Signal and Image Processing Conference 2001, Marseille, France (2001).

Appl. Opt. (5)

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

A. F. Sadjadi, C. S. L. Chun, “Passive polarimetric IR classification,” IEEE Trans. Aerosp. Electron. Syst. 37, 740–751 (2001).
[CrossRef]

IEEE Trans. Geosci. Remote Sens. (1)

O. Germain, Ph. Réfrégier, “On the bias of the likelihood ratio edge detector for SAR images,” IEEE Trans. Geosci. Remote Sens. 38, 1455–1458 (2000).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (3)

L. B. Wolff, T. E. Boult, “Constraining object features using a polarization reflectance model,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 635–657 (1991).
[CrossRef]

C. Chesnaud, Ph. 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]

L. B. Wolff, “Polarization-based material classification from specular reflection,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 1059–1071 (1990).
[CrossRef]

Image Vision Comput. (2)

B. F. Jones, P. T. Fairney, “Recognition of shiny dielectric objects by analyzing the polarization of reflected light,” Image Vision Comput. 7, 253–258 (1989).
[CrossRef]

L. B. Wolff, “Polarization vision: a new sensory approach to image understanding,” Image Vision Comput. 15, 81–93 (1997).
[CrossRef]

J. Opt. Soc. Am. A (2)

Opt. Lett. (4)

Other (8)

S. Huard, “Polarized optical wave,” in Polarization of Light (Masson, Paris, 1997), pp. 1–35.

K. Koshikawa, “A polarimetric approach to shape understanding of glossy objects,” presented at the Sixth International Joint Conference on Artificial Intelligence, Tokyo, Japan (1979).

T. Rogne, S. Stewart, M. Metzler, “Infrared polarimetry: what, why, how and the way ahead,” presented at the Third NATO IRIS Joint Symposium, Quebec, Canada (October 1998).

R. A. Chipman, “Polarimetry,” in Handbook of Optics, M. Bass, ed. (McGraw-Hill, New York, 1995), pp. 22.1–22.33.

C. Brosseau, Fundamentals of Polarized Light—A Statistical Approach (Wiley, New York, 1998).

J. J. Drewes, R. A. Chipman, M. H. Smith, “Characterizing polarization controllers with Mueller matrix polarimetry,” in Active and Passive Optical Components for WDM Communication, A. K. Dutta, A. A. S. Awwal, N. K. Dutta, K. Okamoto, eds., Proc. SPIE4532, 462–466 (2001).
[CrossRef]

J. Zallat, Y. Takakura, M.-Ph. Stoll, “Active imaging polarimetry,” presented at the Physics in Signal and Image Processing Conference 2001, Marseille, France (2001).

H. V. Poor, “Elements of hypothesis testing,” in An Introduction to Signal Detection and Estimation (Springer-Verlag, New York, 1994), pp. 5–39.
[CrossRef]

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 (8)

Fig. 1
Fig. 1

Schematic of the proposed Stokes imaging polarimeter.

Fig. 2
Fig. 2

Mueller image of the LCVR. Each subimage represents the image of a coefficient of the Mueller matrix. The gray scale is such that negative values appear in dark colors, zero in medium gray, and positive in lighter gray. All images are normalized with respect to the positive part of image M 00, which thus appears completely white here.

Fig. 3
Fig. 3

Stokes parameter image of three small pieces of transparent cellophane tape on a cardboard background.

Fig. 4
Fig. 4

Histograms of a homogeneous region in the three channels, S 1, S 2, and S 3. The region considered here is the area outlined in white in Fig. 3, channel S 1. A Gaussian with same mean and variance as the corresponding histogram is drawn (dotted curve) over each histogram. Mean m and standard deviation σ in the three channels are m 1 = -2.24 and σ1 = 1.12 in S 1; m 2 = -0.20 and σ2 = 1.46 in S 2; m 3 = -0.40 and σ3 = 1.28 in S 3.

Fig. 5
Fig. 5

Stokes parameter images of two objects.

Fig. 6
Fig. 6

Segmentation of the two objects in Fig. 5, in the reduced Stokes parameter images S¯ = {S 1, S 2, S 3}. Left, initial shape (channel S 1); center, results of the first step of the segmentation process (segmentation with node adding); right, results of the second step of the segmentation process (node pruning).

Fig. 7
Fig. 7

Plane ℛ(τ) obtained with the multitarget GLRT on the image of Fig. 3; maximum of each column and of each line of this plane.

Fig. 8
Fig. 8

Result of the segmentation of the three objects detected in the scene of Fig. 3. Top, initial snake; bottom, result of the segmentation.

Tables (1)

Tables Icon

Table 1 Average Polarimetric Parameters of the Objects Segmented in Fig. 8, a

Equations (8)

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

Sout=MPOLMRδ1MRδ2Sin=MglobalSin,
I=S0out =Aδ1, δ2S0in+Bδ1, δ2S1in+Cδ1, δ2S2in+Dδ1, δ2S3in,
I=MLIGHTS  I1I2IN=A1B1C1D1A2B2C2D2ANBNCNDNS1S2S3S4,
F=i=1NIi-Ai, Bi, Ci, DiS1S2S3S42.
τ, w=k=13-Na logσˆa2k-Nb logσˆb2k+NF logσˆF2k,
τ=maxkRτ, wk.
Jw, k=k=13Nawlogσˆa2kw+Nbwlogσˆb2kw+k log N,
I=S0, P=S12+S22+S321/2/S0, φ=12tan-1S2/S1, χ=12sin-1S3/IP.

Metrics