Abstract

Linear-correlation amplitude changes when the intensity level of the input image is modified. As recognition is often based on the correlation-peak level, a change of the input illumination may result in a false recognition. We propose an illumination-change compensation by a post processing of the correlation distribution that is based on statistical measures of the correlation histograms. The theoretical background and simulation results are provided in the frame of an actual application in biology.

© 2002 Optical Society of America

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References

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  1. A. B. VanderLugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory, IT-10, 139–145 (1964).
  2. A. Mahalanobis, B. V. K. Vijaya Kumar, D. Casasent, “Minimum average correlation energy filters,” Appl. Opt. 26, 3633–3640 (1987).
    [CrossRef] [PubMed]
  3. B. V. K. Vijaya Kumar, “Tutorial survey of composite filter designs for optical correlators,” Appl. Opt. 31, 4773–4801 (1992).
    [CrossRef]
  4. B. Javidi, “Generalization of the linear matched filter concept to nonlinear matched filters,” Appl. Opt. 29, 1215–1224, (1990).
    [CrossRef] [PubMed]
  5. B. Javidi, J. Wang, “Limitation of the classical 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]
  6. Ph. Réfrégier, B. Javidi, G. Zhang, “Minimum mean-square-error filter for pattern recognition with spatially disjoint signal and scene noise,” Opt. Lett. 18, 1453–1455 (1993).
    [CrossRef] [PubMed]
  7. B. Javidi, F. Parchekani, G. Zhang, “Minimum-mean-square-error filters for detecting a noisy target in background noise,” Appl. Opt. 35, 6964–6975 (1996).
    [CrossRef] [PubMed]
  8. B. Javidi, J. Wang, “Optimum filter for detection of a target in nonoverlapping scene noise,” Appl. Opt. 33, 4454–4458 (1994).
    [CrossRef] [PubMed]
  9. H. Sjöberg, B. Noharet, “Distortion invariant filter for nonoverlapping noise,” Appl. Opt. 37, 6922–6930 (1998).
    [CrossRef]
  10. C. Minetti, F. Dubois, “Reduction in correlation sensitivity to background clutter by the automatic spatial frequency selection algorithm,” Appl. Opt. 35, 1900–1903 (1996).
    [CrossRef] [PubMed]
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    [CrossRef]
  12. P. Garcia-Martinez, C. Ferreira, D. Mendlovic, “Optical non-linear correlation based on nonuniform subband decomposition,” J. Opt. A: Pure Appl. Opt 1, 719–724 (1999).
    [CrossRef]
  13. P. Garcia-Martinez, H. H. Arsenault, S. Roy, “Optical implementation of the sliced orthogonal nonlinear generalized correlation for images degraded by nonoverlapping background noise,” Opt. Commun, 173, 185–193 (2000).
    [CrossRef]
  14. P. Garcia-Martinez, C. Ferreira, J. Garcia, H. Arsenault, “Non-linear rotation-invariant pattern recognition by use ofthe optical morphological correlation,” Appl. Opt. 39, 776–781 (2000).
    [CrossRef]
  15. A Shemer, D. Mendlovic, G. Shabtay, P. Garcia-Martinez, J. Garcia, “Modified morphological correlation based on bit-map representations,” Appl. Opt. 38, 781–787 (1999).
    [CrossRef]
  16. S. Zhang, M. A. Karim, “Illumination-invariant recognition with joint-transform-correlator-based morphological correlation” Appl. Opt. 38, 7228–7237 (1999).
    [CrossRef]
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    [CrossRef] [PubMed]
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    [CrossRef]
  19. F. Dubois, “Automatic spatial frequency selection algorithm for pattern recognition by correlation”, Appl. Opt. 32, 4365–4371 (1993).
    [CrossRef] [PubMed]
  20. A. Papoulis, “Probability, Random Variable and Stochastic Processes, McGraw-Hill, 126–127 (1965).
  21. A. Mahalanobis, B. V. K. Vijaya Kumar, Song, S. R. F. Sims, J. F. Epperson, “Unconstrained correlation filters,” Appl. Opt. 33, 3751–3759 (1994).
    [CrossRef] [PubMed]
  22. A. Mahalanobis, B.V. K. Vijaya Kumar, “Optimality of the maximum average correlation height filter for detection of targets in noise,” Opt. Eng. 36, 2642–2648 (1997).
    [CrossRef]
  23. J. Karlholm, “Generalization of the maximum average correlation height filter,” J. Opt. Soc. Am. A 17, 1399–1406, [2000]
    [CrossRef]

2000 (4)

1999 (3)

1998 (2)

1997 (1)

A. Mahalanobis, B.V. K. Vijaya Kumar, “Optimality of the maximum average correlation height filter for detection of targets in noise,” Opt. Eng. 36, 2642–2648 (1997).
[CrossRef]

1996 (2)

1994 (2)

1993 (2)

1992 (2)

1991 (1)

1990 (1)

1987 (1)

1964 (1)

A. B. VanderLugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory, IT-10, 139–145 (1964).

Arsenault, H.

Arsenault, H. H.

P. Garcia-Martinez, H. H. Arsenault, S. Roy, “Optical implementation of the sliced orthogonal nonlinear generalized correlation for images degraded by nonoverlapping background noise,” Opt. Commun, 173, 185–193 (2000).
[CrossRef]

H. H. Arsenault, D. Lefebvre, “Homomorphic cameo filter for pattern recognition that is invariant with changes in illumination,” Opt. Lett. 25, 1567–1569 (2000).
[CrossRef]

Casasent, D.

Dickey, F. M.

Dubois, F.

Epperson, J. F.

Ferreira, C.

P. Garcia-Martinez, C. Ferreira, J. Garcia, H. Arsenault, “Non-linear rotation-invariant pattern recognition by use ofthe optical morphological correlation,” Appl. Opt. 39, 776–781 (2000).
[CrossRef]

P. Garcia-Martinez, C. Ferreira, D. Mendlovic, “Optical non-linear correlation based on nonuniform subband decomposition,” J. Opt. A: Pure Appl. Opt 1, 719–724 (1999).
[CrossRef]

Garcia, J.

Garcia-Martinez, P.

P. Garcia-Martinez, C. Ferreira, J. Garcia, H. Arsenault, “Non-linear rotation-invariant pattern recognition by use ofthe optical morphological correlation,” Appl. Opt. 39, 776–781 (2000).
[CrossRef]

P. Garcia-Martinez, H. H. Arsenault, S. Roy, “Optical implementation of the sliced orthogonal nonlinear generalized correlation for images degraded by nonoverlapping background noise,” Opt. Commun, 173, 185–193 (2000).
[CrossRef]

P. Garcia-Martinez, C. Ferreira, D. Mendlovic, “Optical non-linear correlation based on nonuniform subband decomposition,” J. Opt. A: Pure Appl. Opt 1, 719–724 (1999).
[CrossRef]

A Shemer, D. Mendlovic, G. Shabtay, P. Garcia-Martinez, J. Garcia, “Modified morphological correlation based on bit-map representations,” Appl. Opt. 38, 781–787 (1999).
[CrossRef]

Goudail, F.

Javidi, B.

Karim, M. A.

Karlholm, J.

Lefebvre, D.

Mahalanobis, A.

Mendlovic, D.

P. Garcia-Martinez, C. Ferreira, D. Mendlovic, “Optical non-linear correlation based on nonuniform subband decomposition,” J. Opt. A: Pure Appl. Opt 1, 719–724 (1999).
[CrossRef]

A Shemer, D. Mendlovic, G. Shabtay, P. Garcia-Martinez, J. Garcia, “Modified morphological correlation based on bit-map representations,” Appl. Opt. 38, 781–787 (1999).
[CrossRef]

Minetti, C.

Noharet, B.

Papoulis, A.

A. Papoulis, “Probability, Random Variable and Stochastic Processes, McGraw-Hill, 126–127 (1965).

Parchekani, F.

Réfrégier, Ph.

Romero, L. A.

Roy, S.

P. Garcia-Martinez, H. H. Arsenault, S. Roy, “Optical implementation of the sliced orthogonal nonlinear generalized correlation for images degraded by nonoverlapping background noise,” Opt. Commun, 173, 185–193 (2000).
[CrossRef]

Shabtay, G.

Shemer, A

Sims, S. R. F.

Sjöberg, H.

Song,

VanderLugt, A. B.

A. B. VanderLugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory, IT-10, 139–145 (1964).

Vijaya Kumar, B. V. K.

Vijaya Kumar, B.V. K.

A. Mahalanobis, B.V. K. Vijaya Kumar, “Optimality of the maximum average correlation height filter for detection of targets in noise,” Opt. Eng. 36, 2642–2648 (1997).
[CrossRef]

Wang, J.

Zhang, G.

Zhang, S.

Appl. Opt. (13)

A. Mahalanobis, B. V. K. Vijaya Kumar, D. Casasent, “Minimum average correlation energy filters,” Appl. Opt. 26, 3633–3640 (1987).
[CrossRef] [PubMed]

B. Javidi, “Generalization of the linear matched filter concept to nonlinear matched filters,” Appl. Opt. 29, 1215–1224, (1990).
[CrossRef] [PubMed]

B. V. K. Vijaya Kumar, “Tutorial survey of composite filter designs for optical correlators,” Appl. Opt. 31, 4773–4801 (1992).
[CrossRef]

F. Dubois, “Automatic spatial frequency selection algorithm for pattern recognition by correlation”, Appl. Opt. 32, 4365–4371 (1993).
[CrossRef] [PubMed]

A. Mahalanobis, B. V. K. Vijaya Kumar, Song, S. R. F. Sims, J. F. Epperson, “Unconstrained correlation filters,” Appl. Opt. 33, 3751–3759 (1994).
[CrossRef] [PubMed]

B. Javidi, J. Wang, “Optimum filter for detection of a target in nonoverlapping scene noise,” Appl. Opt. 33, 4454–4458 (1994).
[CrossRef] [PubMed]

A Shemer, D. Mendlovic, G. Shabtay, P. Garcia-Martinez, J. Garcia, “Modified morphological correlation based on bit-map representations,” Appl. Opt. 38, 781–787 (1999).
[CrossRef]

C. Minetti, F. Dubois, “Reduction in correlation sensitivity to background clutter by the automatic spatial frequency selection algorithm,” Appl. Opt. 35, 1900–1903 (1996).
[CrossRef] [PubMed]

B. Javidi, F. Parchekani, G. Zhang, “Minimum-mean-square-error filters for detecting a noisy target in background noise,” Appl. Opt. 35, 6964–6975 (1996).
[CrossRef] [PubMed]

H. Sjöberg, B. Noharet, “Distortion invariant filter for nonoverlapping noise,” Appl. Opt. 37, 6922–6930 (1998).
[CrossRef]

S. Zhang, M. A. Karim, “Illumination-invariant recognition with joint-transform-correlator-based morphological correlation” Appl. Opt. 38, 7228–7237 (1999).
[CrossRef]

P. Garcia-Martinez, C. Ferreira, J. Garcia, H. Arsenault, “Non-linear rotation-invariant pattern recognition by use ofthe optical morphological correlation,” Appl. Opt. 39, 776–781 (2000).
[CrossRef]

B. Javidi, J. Wang, “Limitation of the classical 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]

IEEE Trans. Inf. Theory (1)

A. B. VanderLugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory, IT-10, 139–145 (1964).

J. Opt. A: Pure Appl. Opt (1)

P. Garcia-Martinez, C. Ferreira, D. Mendlovic, “Optical non-linear correlation based on nonuniform subband decomposition,” J. Opt. A: Pure Appl. Opt 1, 719–724 (1999).
[CrossRef]

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

Opt. Commun, (1)

P. Garcia-Martinez, H. H. Arsenault, S. Roy, “Optical implementation of the sliced orthogonal nonlinear generalized correlation for images degraded by nonoverlapping background noise,” Opt. Commun, 173, 185–193 (2000).
[CrossRef]

Opt. Eng. (1)

A. Mahalanobis, B.V. K. Vijaya Kumar, “Optimality of the maximum average correlation height filter for detection of targets in noise,” Opt. Eng. 36, 2642–2648 (1997).
[CrossRef]

Opt. Lett. (3)

Other (1)

A. Papoulis, “Probability, Random Variable and Stochastic Processes, McGraw-Hill, 126–127 (1965).

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Figures (5)

Fig. 1
Fig. 1

(a) Typical input image at the beginning of the culture, (b) Typical input image at the end of the culture.

Fig. 2
Fig. 2

Image training set. Columns 1 to 4 correspond to response-1 images. Columns 5 to 8 correspond to response-0 images.

Fig. 3
Fig. 3

(a) Correlation peak corresponding to image of figure 1.a. (recognition at the beginning of the culture). From left to right, the maximum detected intensities for the three cells are, respectively, 182, 185, and 190. (b) Correlation intensity histogram corresponding to the image of Fig. 1(a).

Fig. 4
Fig. 4

(a) Correlation peak corresponding to image of Fig. 1(b) (recognition at the end of the culture). The maximum detected intensity is 203. (b) Correlation intensity histogram corresponding to the image of figure 1(b).

Fig. 5
Fig. 5

(a) Input image and correlation peaks in the case of a response 1 at the beginning of the culture. (a.1) Input image (a.2) Correlation peak without pre and post-processing (Test 1) (a.3) Correlation peak with post-processing (Test 2) (b) Input image and correlation peaks in the case of a response 1 at the end of the culture. (b.1) Input image (b.2) Correlation peak without pre and post-processing (Test 1) (b.3) Correlation peak with post-processing (Test 2).

Tables (1)

Tables Icon

Table 1 Average maximum detected correlation intensitiesa

Equations (21)

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xs, t=nps, t,
Ens, t=μ Ens, tns, t=σ2δs-s, t-t.
XU, V=NU, VPU, V.
NU, V=BU, V+B0δU, V,
ENU, V=B0δU, V,
EBU, VBU, V=σ2δU-U, V-V.
μ=1N P0, 0B0.
cs, t=xhs, t=1NU,V-0N-1exp2iπNUs+VtH*U, V×NU, VPU, V.
cs, t=μH*0, 0+1NU,V=0N-1 Ws, t, U, VBU, V,
Ws, t, U, V=exp+2iπNUs+Vt×H*U, VPU, V.
Pcs, t=a1Σ2πexp-a22Σ2,
Ec2s, t=E1N2U1,V1=0N-1 Ws, tU1, V1BU1, V1×U2,V2=0N-1 W* s, t, U2, V2B*U2, V2,
Ec2s, t=1N2U1,V1,U2,V2=0N-1 Ws, t, U1, V1×W*s, t, U2, V2σ2δ×U1-U2, V1-V2,
Σ2=1N2U,V=0N-1 |HU, VPU, V|2σ2.
Pc2s, t=I; I>0+1IΣ2πexp-I22Σ2.
Is, t=Per_RefPer_Obj Is, t,
xs, t=αxs, t
cs, t=α2xhs, t=α2cs, t,
rkq=U,V=0n-1HU, VXkq*U, V
F=U,V=0n-1k,q,i|HU, VXkq*U, V-Ykqi*U, V|2,
G=I1-I0SD1+SD0,

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