V. Laude, A. Grunnet-Jepsen, S. Tonda, “Input image spectral density estimation for real-time adaption of correlation filters,” Opt. Eng. 38, 672–676 (1999).

[CrossRef]

S. Tan, R. C. D. Young, J. D. Richardson, C. R. Chatwin, “A pattern recognition Wiener filter for realistic clutter backgrounds,” Opt. Commun. 172, 193–202 (1999).

[CrossRef]

S. R. DeGraaf, “SAR imaging via Modern 2-D spectral estimation methods,” IEEE Trans. Image Process. 7, 729–761 (1998).

[CrossRef]

P. Réfrégier, F. Goudail, T. Gaidon, “Optimal location of random targets in random background: random Markov fields modelization,” Opt. Commun. 128, 211–215 (1996).

[CrossRef]

R. Paget, D. Longstaff, “A nonparametric multiscale Markov random field model for synthesising natural textures, Fourth International Symposium on Signal Processing and its Applications,” 2, 744–747 (1996).

E. Marom, H. Inbar, “New interpretations of Wiener filters for image recognition,” J. Opt. Soc. Am. A 13, 1325–1330 (1996).

[CrossRef]

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

[CrossRef]
[PubMed]

B. V. K. Vijaya Kumar, D. W. Carlson, A. Mahalanobis, “Optimal trade-off synthetic discriminant function filters for arbitrary devices,” Opt. Lett. 19, 1556–1558 (1994).

[CrossRef]

J. Mao, A. K. Jain, “Texture classification and segmentation using multiresolution simultaneous autoregressive models,” Pattern Recogn. 25, 173–188 (1992).

[CrossRef]

J. W. Modestino, J. Zhang, “A Markov random field model-based approach to image interpretation, IEEE Trans. Pattern Anal. Mach. Intell. 14, 606–615 (1992).

[CrossRef]

C. S. Won, H. Derin, Unsupervised segmentation of noisy and textured images using Markov random fields, CVGIP: Graph. Models Image Process. 54, 308–328 (1992).

[CrossRef]

B. Gidas, “A renormalization group approach to image processing problems,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 164–180 (1989).

[CrossRef]

H. Derin, P. A. Kelly, “Discrete-index Markov-type random fields,” Proc. IEEE 77, 1485–1510 (1989).

[CrossRef]

S. Geman, D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984).

[CrossRef]
[PubMed]

R. L. Kashyap, R. Chellappa, “Estimation and choice of neighbors in spatial-interaction models of images,” IEEE Trans. Inf. Theory IT-29, 60–72 (1983).

[CrossRef]

G. R. Cross, A. K. Jain, “Markov random field texture models,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5, 25–39 (1983).

[CrossRef]

J. H. McClellan, “Multidimensional spectral estimation,” Proc. IEEE 70, 1029–1039 (1982).

[CrossRef]

F. R. Hansen, H. Elliott, “Image segmentation using simple Markov random field models,” Comput. Graph. Image Process. 20, 101–132 (1982).

[CrossRef]

A. K. Jain, “Advances in mathematical models for image processing,” Proc. IEEE 69, 502–528 (1981).

[CrossRef]

M. Hassner, J. Slansky, “The use of Markov random fields as models of texture,” Comput. Graph. Image Process. 12, 357–370 (1980).

[CrossRef]

J. W. Woods, “Two-dimensional discrete Markovian fields,” IEEE Trans. Inf. Theory IT-18, 232–240 (1972).

[CrossRef]

J. M. Blackledge, Quantitative Coherent Imaging (Academic, London, 1989).

H. Zhou, T. S. Chao, “MACH filter synthesizing for detecting targets in cluttered environment for gray-scale optical correlator,” in Optical Pattern Recognition X, D. P. Casasent, T.-H. Chao, eds. SPIE3715, 394–398 (1999).

S. Tan, R. C. D. Young, J. D. Richardson, C. R. Chatwin, “A pattern recognition Wiener filter for realistic clutter backgrounds,” Opt. Commun. 172, 193–202 (1999).

[CrossRef]

R. L. Kashyap, R. Chellappa, “Estimation and choice of neighbors in spatial-interaction models of images,” IEEE Trans. Inf. Theory IT-29, 60–72 (1983).

[CrossRef]

R. Chellappa, A. Jain, Markov Random Field-Theory and Applications (Academic, San Diego, Calif., 1993).

G. R. Cross, A. K. Jain, “Markov random field texture models,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5, 25–39 (1983).

[CrossRef]

S. R. DeGraaf, “SAR imaging via Modern 2-D spectral estimation methods,” IEEE Trans. Image Process. 7, 729–761 (1998).

[CrossRef]

C. S. Won, H. Derin, Unsupervised segmentation of noisy and textured images using Markov random fields, CVGIP: Graph. Models Image Process. 54, 308–328 (1992).

[CrossRef]

H. Derin, P. A. Kelly, “Discrete-index Markov-type random fields,” Proc. IEEE 77, 1485–1510 (1989).

[CrossRef]

F. R. Hansen, H. Elliott, “Image segmentation using simple Markov random field models,” Comput. Graph. Image Process. 20, 101–132 (1982).

[CrossRef]

P. Réfrégier, F. Goudail, T. Gaidon, “Optimal location of random targets in random background: random Markov fields modelization,” Opt. Commun. 128, 211–215 (1996).

[CrossRef]

S. Geman, D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984).

[CrossRef]
[PubMed]

S. Geman, D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984).

[CrossRef]
[PubMed]

B. Gidas, “A renormalization group approach to image processing problems,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 164–180 (1989).

[CrossRef]

R. C. Gonzalez, R. E. Woods, Digital Image Processing (Addison-Wesley, Reading, Mass., 1993).

P. Réfrégier, F. Goudail, T. Gaidon, “Optimal location of random targets in random background: random Markov fields modelization,” Opt. Commun. 128, 211–215 (1996).

[CrossRef]

V. Laude, A. Grunnet-Jepsen, S. Tonda, “Input image spectral density estimation for real-time adaption of correlation filters,” Opt. Eng. 38, 672–676 (1999).

[CrossRef]

M. Haindl, “Texture synthesis,” CWI Quaterly 4, 305–331 (1991).

F. R. Hansen, H. Elliott, “Image segmentation using simple Markov random field models,” Comput. Graph. Image Process. 20, 101–132 (1982).

[CrossRef]

M. Hassner, J. Slansky, “The use of Markov random fields as models of texture,” Comput. Graph. Image Process. 12, 357–370 (1980).

[CrossRef]

R. Chellappa, A. Jain, Markov Random Field-Theory and Applications (Academic, San Diego, Calif., 1993).

J. Mao, A. K. Jain, “Texture classification and segmentation using multiresolution simultaneous autoregressive models,” Pattern Recogn. 25, 173–188 (1992).

[CrossRef]

G. R. Cross, A. K. Jain, “Markov random field texture models,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5, 25–39 (1983).

[CrossRef]

A. K. Jain, “Advances in mathematical models for image processing,” Proc. IEEE 69, 502–528 (1981).

[CrossRef]

R. L. Kashyap, R. Chellappa, “Estimation and choice of neighbors in spatial-interaction models of images,” IEEE Trans. Inf. Theory IT-29, 60–72 (1983).

[CrossRef]

H. Derin, P. A. Kelly, “Discrete-index Markov-type random fields,” Proc. IEEE 77, 1485–1510 (1989).

[CrossRef]

V. Laude, A. Grunnet-Jepsen, S. Tonda, “Input image spectral density estimation for real-time adaption of correlation filters,” Opt. Eng. 38, 672–676 (1999).

[CrossRef]

S. Z. Li, Markov Random Fields Modeling in Computer Vision (Springer-Verlag, Berlin, 1995).

R. Paget, D. Longstaff, “A nonparametric multiscale Markov random field model for synthesising natural textures, Fourth International Symposium on Signal Processing and its Applications,” 2, 744–747 (1996).

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

[CrossRef]
[PubMed]

B. V. K. Vijaya Kumar, D. W. Carlson, A. Mahalanobis, “Optimal trade-off synthetic discriminant function filters for arbitrary devices,” Opt. Lett. 19, 1556–1558 (1994).

[CrossRef]

J. Proakis, D. Manolakis, Digital Signal Processing: Principles, Algorithms and Applications (Prentice-Hall, Englewood Cliffs, N.J., 1996).

J. Mao, A. K. Jain, “Texture classification and segmentation using multiresolution simultaneous autoregressive models,” Pattern Recogn. 25, 173–188 (1992).

[CrossRef]

S. L. Marple, Digital Spectral Analysis with ApplicationsPrentice-Hall, Englewood Cliffs, N.J., 1987).

J. H. McClellan, “Multidimensional spectral estimation,” Proc. IEEE 70, 1029–1039 (1982).

[CrossRef]

J. W. Modestino, J. Zhang, “A Markov random field model-based approach to image interpretation, IEEE Trans. Pattern Anal. Mach. Intell. 14, 606–615 (1992).

[CrossRef]

R. Paget, D. Longstaff, “A nonparametric multiscale Markov random field model for synthesising natural textures, Fourth International Symposium on Signal Processing and its Applications,” 2, 744–747 (1996).

J. Proakis, D. Manolakis, Digital Signal Processing: Principles, Algorithms and Applications (Prentice-Hall, Englewood Cliffs, N.J., 1996).

P. Réfrégier, F. Goudail, T. Gaidon, “Optimal location of random targets in random background: random Markov fields modelization,” Opt. Commun. 128, 211–215 (1996).

[CrossRef]

P. Réfrégier, “Optimal trade-off filters for noise robustness, sharpness of the correlation peak, and Horner efficiency,” Opt. Lett. 16, 829–831 (1991).

[CrossRef]
[PubMed]

P. Réfrégier, “Filter design for optical pattern recognition: Multicriteria optimization approach,” Opt. Lett. 15, 854–856 (1990).

[CrossRef]
[PubMed]

S. Tan, R. C. D. Young, J. D. Richardson, C. R. Chatwin, “A pattern recognition Wiener filter for realistic clutter backgrounds,” Opt. Commun. 172, 193–202 (1999).

[CrossRef]

M. Hassner, J. Slansky, “The use of Markov random fields as models of texture,” Comput. Graph. Image Process. 12, 357–370 (1980).

[CrossRef]

P. Birch, S. Tan, R. Young, T. Koukoulas, F. Claret-Tournier, D. Budgett, C. Chatwin, “Experimental implementation of a Wiener filter in a hybrid digital-optical correlator,” Opt. Lett. 26, 494–496 (2001).

[CrossRef]

S. Tan, R. C. D. Young, J. D. Richardson, C. R. Chatwin, “A pattern recognition Wiener filter for realistic clutter backgrounds,” Opt. Commun. 172, 193–202 (1999).

[CrossRef]

V. Laude, A. Grunnet-Jepsen, S. Tonda, “Input image spectral density estimation for real-time adaption of correlation filters,” Opt. Eng. 38, 672–676 (1999).

[CrossRef]

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

[CrossRef]
[PubMed]

B. V. K. Vijaya Kumar, D. W. Carlson, A. Mahalanobis, “Optimal trade-off synthetic discriminant function filters for arbitrary devices,” Opt. Lett. 19, 1556–1558 (1994).

[CrossRef]

B. V. K. Vijaya Kumar, L. Hassebrook, “Performance measures for correlation filters,” Appl. Opt. 29, 2997–3006 (1990).

[CrossRef]

B. V. K. Vijaya Kumar, “Minimum variance synthetic discriminant functions,” J. Opt. Soc. Am. A 3, 1579–1585 (1986).

[CrossRef]

N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series, (Wiley, New York, 1949).

C. S. Won, H. Derin, Unsupervised segmentation of noisy and textured images using Markov random fields, CVGIP: Graph. Models Image Process. 54, 308–328 (1992).

[CrossRef]

J. W. Woods, “Two-dimensional discrete Markovian fields,” IEEE Trans. Inf. Theory IT-18, 232–240 (1972).

[CrossRef]

R. C. Gonzalez, R. E. Woods, Digital Image Processing (Addison-Wesley, Reading, Mass., 1993).

S. Tan, R. C. D. Young, J. D. Richardson, C. R. Chatwin, “A pattern recognition Wiener filter for realistic clutter backgrounds,” Opt. Commun. 172, 193–202 (1999).

[CrossRef]

J. W. Modestino, J. Zhang, “A Markov random field model-based approach to image interpretation, IEEE Trans. Pattern Anal. Mach. Intell. 14, 606–615 (1992).

[CrossRef]

H. Zhou, T. S. Chao, “MACH filter synthesizing for detecting targets in cluttered environment for gray-scale optical correlator,” in Optical Pattern Recognition X, D. P. Casasent, T.-H. Chao, eds. SPIE3715, 394–398 (1999).

R. Paget, D. Longstaff, “A nonparametric multiscale Markov random field model for synthesising natural textures, Fourth International Symposium on Signal Processing and its Applications,” 2, 744–747 (1996).

B. V. K. Vijaya Kumar, L. Hassebrook, “Performance measures for correlation filters,” Appl. Opt. 29, 2997–3006 (1990).

[CrossRef]

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

[CrossRef]
[PubMed]

M. Hassner, J. Slansky, “The use of Markov random fields as models of texture,” Comput. Graph. Image Process. 12, 357–370 (1980).

[CrossRef]

F. R. Hansen, H. Elliott, “Image segmentation using simple Markov random field models,” Comput. Graph. Image Process. 20, 101–132 (1982).

[CrossRef]

C. S. Won, H. Derin, Unsupervised segmentation of noisy and textured images using Markov random fields, CVGIP: Graph. Models Image Process. 54, 308–328 (1992).

[CrossRef]

M. Haindl, “Texture synthesis,” CWI Quaterly 4, 305–331 (1991).

S. R. DeGraaf, “SAR imaging via Modern 2-D spectral estimation methods,” IEEE Trans. Image Process. 7, 729–761 (1998).

[CrossRef]

J. W. Woods, “Two-dimensional discrete Markovian fields,” IEEE Trans. Inf. Theory IT-18, 232–240 (1972).

[CrossRef]

R. L. Kashyap, R. Chellappa, “Estimation and choice of neighbors in spatial-interaction models of images,” IEEE Trans. Inf. Theory IT-29, 60–72 (1983).

[CrossRef]

G. R. Cross, A. K. Jain, “Markov random field texture models,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5, 25–39 (1983).

[CrossRef]

J. W. Modestino, J. Zhang, “A Markov random field model-based approach to image interpretation, IEEE Trans. Pattern Anal. Mach. Intell. 14, 606–615 (1992).

[CrossRef]

S. Geman, D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984).

[CrossRef]
[PubMed]

B. Gidas, “A renormalization group approach to image processing problems,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 164–180 (1989).

[CrossRef]

P. Réfrégier, F. Goudail, T. Gaidon, “Optimal location of random targets in random background: random Markov fields modelization,” Opt. Commun. 128, 211–215 (1996).

[CrossRef]

S. Tan, R. C. D. Young, J. D. Richardson, C. R. Chatwin, “A pattern recognition Wiener filter for realistic clutter backgrounds,” Opt. Commun. 172, 193–202 (1999).

[CrossRef]

V. Laude, A. Grunnet-Jepsen, S. Tonda, “Input image spectral density estimation for real-time adaption of correlation filters,” Opt. Eng. 38, 672–676 (1999).

[CrossRef]

H. Inbar, E. Marom, “A priori and adaptive Wiener filtering with joint transform corralators,” Opt. Lett. 20, 1050–1052 (1995).

[CrossRef]

P. Réfrégier, “Optimal trade-off filters for noise robustness, sharpness of the correlation peak, and Horner efficiency,” Opt. Lett. 16, 829–831 (1991).

[CrossRef]
[PubMed]

B. V. K. Vijaya Kumar, D. W. Carlson, A. Mahalanobis, “Optimal trade-off synthetic discriminant function filters for arbitrary devices,” Opt. Lett. 19, 1556–1558 (1994).

[CrossRef]

P. Réfrégier, “Filter design for optical pattern recognition: Multicriteria optimization approach,” Opt. Lett. 15, 854–856 (1990).

[CrossRef]
[PubMed]

P. Birch, S. Tan, R. Young, T. Koukoulas, F. Claret-Tournier, D. Budgett, C. Chatwin, “Experimental implementation of a Wiener filter in a hybrid digital-optical correlator,” Opt. Lett. 26, 494–496 (2001).

[CrossRef]

J. Mao, A. K. Jain, “Texture classification and segmentation using multiresolution simultaneous autoregressive models,” Pattern Recogn. 25, 173–188 (1992).

[CrossRef]

J. H. McClellan, “Multidimensional spectral estimation,” Proc. IEEE 70, 1029–1039 (1982).

[CrossRef]

H. Derin, P. A. Kelly, “Discrete-index Markov-type random fields,” Proc. IEEE 77, 1485–1510 (1989).

[CrossRef]

A. K. Jain, “Advances in mathematical models for image processing,” Proc. IEEE 69, 502–528 (1981).

[CrossRef]

H. Zhou, T. S. Chao, “MACH filter synthesizing for detecting targets in cluttered environment for gray-scale optical correlator,” in Optical Pattern Recognition X, D. P. Casasent, T.-H. Chao, eds. SPIE3715, 394–398 (1999).

S. Z. Li, Markov Random Fields Modeling in Computer Vision (Springer-Verlag, Berlin, 1995).

N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series, (Wiley, New York, 1949).

R. C. Gonzalez, R. E. Woods, Digital Image Processing (Addison-Wesley, Reading, Mass., 1993).

J. M. Blackledge, Quantitative Coherent Imaging (Academic, London, 1989).

S. L. Marple, Digital Spectral Analysis with ApplicationsPrentice-Hall, Englewood Cliffs, N.J., 1987).

J. Proakis, D. Manolakis, Digital Signal Processing: Principles, Algorithms and Applications (Prentice-Hall, Englewood Cliffs, N.J., 1996).

R. Chellappa, A. Jain, Markov Random Field-Theory and Applications (Academic, San Diego, Calif., 1993).