I. Kypraios, R. C. D. Young, P. Birch, and C. R. Chatwin, “Object recognition within cluttered scenes employing a hybrid optical neural network (HONN) filter,” Opt. Eng. 43, 1839-1850 (2004).

[CrossRef]

I. Kypraios, R. C. D. Young, and C. R. Chatwin, “Performance assessment of unconstrained hybrid optical neural network (U-HONN) filter for object recognition tasks in clutter,” Proc. SPIE 5437, 51-62 (2004).

[CrossRef]

I. Kypraios, R. C. D Young, P. Birch, and C. Chatwin, “A non-linear training set superposition filter derived by neural network training methods for implementation in a shift invariant optical correlator,” Proc. SPIE 5106, 84-95 (2003).

[CrossRef]

H. Demuth and M. Beale, *Neural Network Toolbox for Use with MATLAB: User's Guide for MATLAB Version 6.5* (MathWorks, Inc., 2002), http://www.mathworks.com.

I. Kypraios, R. Young, and C. R. Chatwin, “An investigation of the non-linear properties of correlation filter synthesis and neural network design,” Asian J. Phys. 11, 313-344 (2002).

E. Stamos, Similarity suppression filter design algorithm, in “Algorithms for designing filters for optical pattern recognition,” D. Phil. Thesis (University College London, 2001), pp. 77-78.

A. Talukder and D. Casasent, “Non-linear features for product inspection,” Proc. SPIE 3715, 32-43 (1999).

[CrossRef]

H. Zhou and T. H. Chao, “MACH filter synthesising for detecting targets in cluttered environment for gray-scale optical correlator,” Proc. SPIE 3715, 394-398 (1999).

[CrossRef]

T. H. Chao, G. Reyes, and Y. Park, “Grayscale optical correlator,” Proc. SPIE 3386, 60-64 (1998).

[CrossRef]

D. Casasent, L. M. Neiberg, and M. A. Sipe, “Feature space trajectory distorted object representation for classification and pose estimation,” Opt. Eng. 37, 914-923 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “In-class distortion tolerance, out-of-class discrimination and clutter resistance of correlation filters that employ a space domain non-linearity applied to wavelet filtered input images,” Proc. SPIE 3386, 111-122 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Nonlinear preprocessing operation for enhancing correlator filter performance in clutter,” Proc. SPIE 3490, 182-186 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Synthetic discriminant function filter employing nonlinear space-domain preprocessing on bandpass-filtered images,” Appl. Opt. 37, 2051-2062 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Application of non-linearity to wavelet-transformed images to improve correlation filter performance,” Appl. Opt. 36, 9212-9224 (1997).

[CrossRef]

A. Mahalanobis and 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]

C. G. Looney, *Pattern Recognition Using Neural Networks-Theory and Algorithms for Engineers and Scientists* (Oxford University Press, 1997).

M. T. Hagan, H. B. Demuth, and M. H. Beale, *Neural Network Design* (PWS Publishing, 1996).

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[CrossRef]
[PubMed]

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

[CrossRef]

R. Beale and T. Jackson, *Neural Computing: An Introduction* (Institute of Physics Publishing, 1990).

[CrossRef]

D. Nguyen and B. Widrow, “Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights,” in *Proceedings of the International Joint Conference on Neural Networks*, (IEEE, 1990), Vol. 3, pp. 21-26.

[CrossRef]

D. Nguyen and B. Widrow, “The truck backer-upper: an example of self-learning in neural networks,” in *Proceedings of the International Joint Conference on Neural Networks* (IEEE, 1989), Vol. 2, pp. 357-363.

[CrossRef]

A. Vander Lugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory 10, 139-145 (1964).

[CrossRef]

H. Demuth and M. Beale, *Neural Network Toolbox for Use with MATLAB: User's Guide for MATLAB Version 6.5* (MathWorks, Inc., 2002), http://www.mathworks.com.

M. T. Hagan, H. B. Demuth, and M. H. Beale, *Neural Network Design* (PWS Publishing, 1996).

R. Beale and T. Jackson, *Neural Computing: An Introduction* (Institute of Physics Publishing, 1990).

[CrossRef]

I. Kypraios, R. C. D. Young, P. Birch, and C. R. Chatwin, “Object recognition within cluttered scenes employing a hybrid optical neural network (HONN) filter,” Opt. Eng. 43, 1839-1850 (2004).

[CrossRef]

I. Kypraios, R. C. D Young, P. Birch, and C. Chatwin, “A non-linear training set superposition filter derived by neural network training methods for implementation in a shift invariant optical correlator,” Proc. SPIE 5106, 84-95 (2003).

[CrossRef]

A. Talukder and D. Casasent, “Non-linear features for product inspection,” Proc. SPIE 3715, 32-43 (1999).

[CrossRef]

D. Casasent, L. M. Neiberg, and M. A. Sipe, “Feature space trajectory distorted object representation for classification and pose estimation,” Opt. Eng. 37, 914-923 (1998).

[CrossRef]

D. Casasent, “Unified synthetic discriminant function computational formulation,” Appl. Opt. 23, 1620-1627 (1984).

[CrossRef]
[PubMed]

H. Zhou and T. H. Chao, “MACH filter synthesising for detecting targets in cluttered environment for gray-scale optical correlator,” Proc. SPIE 3715, 394-398 (1999).

[CrossRef]

T. H. Chao, G. Reyes, and Y. Park, “Grayscale optical correlator,” Proc. SPIE 3386, 60-64 (1998).

[CrossRef]

I. Kypraios, R. C. D Young, P. Birch, and C. Chatwin, “A non-linear training set superposition filter derived by neural network training methods for implementation in a shift invariant optical correlator,” Proc. SPIE 5106, 84-95 (2003).

[CrossRef]

I. Kypraios, R. C. D. Young, P. Birch, and C. R. Chatwin, “Object recognition within cluttered scenes employing a hybrid optical neural network (HONN) filter,” Opt. Eng. 43, 1839-1850 (2004).

[CrossRef]

I. Kypraios, R. C. D. Young, and C. R. Chatwin, “Performance assessment of unconstrained hybrid optical neural network (U-HONN) filter for object recognition tasks in clutter,” Proc. SPIE 5437, 51-62 (2004).

[CrossRef]

I. Kypraios, R. Young, and C. R. Chatwin, “An investigation of the non-linear properties of correlation filter synthesis and neural network design,” Asian J. Phys. 11, 313-344 (2002).

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “In-class distortion tolerance, out-of-class discrimination and clutter resistance of correlation filters that employ a space domain non-linearity applied to wavelet filtered input images,” Proc. SPIE 3386, 111-122 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Nonlinear preprocessing operation for enhancing correlator filter performance in clutter,” Proc. SPIE 3490, 182-186 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Synthetic discriminant function filter employing nonlinear space-domain preprocessing on bandpass-filtered images,” Appl. Opt. 37, 2051-2062 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Application of non-linearity to wavelet-transformed images to improve correlation filter performance,” Appl. Opt. 36, 9212-9224 (1997).

[CrossRef]

I. Kypraios, P. Lei, R. C. D. Young, and C. R. Chatwin, “Object recognition within cluttered scenes employing the modified-hybrid optical neural network filter,” submitted to Pattern Recogn.

H. Demuth and M. Beale, *Neural Network Toolbox for Use with MATLAB: User's Guide for MATLAB Version 6.5* (MathWorks, Inc., 2002), http://www.mathworks.com.

M. T. Hagan, H. B. Demuth, and M. H. Beale, *Neural Network Design* (PWS Publishing, 1996).

M. T. Hagan, H. B. Demuth, and M. H. Beale, *Neural Network Design* (PWS Publishing, 1996).

S. Haykin, *Neural Networks--A Comprehensive Foundation* (Prentice Hall, 1994).

R. Beale and T. Jackson, *Neural Computing: An Introduction* (Institute of Physics Publishing, 1990).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Nonlinear preprocessing operation for enhancing correlator filter performance in clutter,” Proc. SPIE 3490, 182-186 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “In-class distortion tolerance, out-of-class discrimination and clutter resistance of correlation filters that employ a space domain non-linearity applied to wavelet filtered input images,” Proc. SPIE 3386, 111-122 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Synthetic discriminant function filter employing nonlinear space-domain preprocessing on bandpass-filtered images,” Appl. Opt. 37, 2051-2062 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Application of non-linearity to wavelet-transformed images to improve correlation filter performance,” Appl. Opt. 36, 9212-9224 (1997).

[CrossRef]

I. Kypraios, R. C. D. Young, P. Birch, and C. R. Chatwin, “Object recognition within cluttered scenes employing a hybrid optical neural network (HONN) filter,” Opt. Eng. 43, 1839-1850 (2004).

[CrossRef]

I. Kypraios, R. C. D. Young, and C. R. Chatwin, “Performance assessment of unconstrained hybrid optical neural network (U-HONN) filter for object recognition tasks in clutter,” Proc. SPIE 5437, 51-62 (2004).

[CrossRef]

I. Kypraios, R. C. D Young, P. Birch, and C. Chatwin, “A non-linear training set superposition filter derived by neural network training methods for implementation in a shift invariant optical correlator,” Proc. SPIE 5106, 84-95 (2003).

[CrossRef]

I. Kypraios, R. Young, and C. R. Chatwin, “An investigation of the non-linear properties of correlation filter synthesis and neural network design,” Asian J. Phys. 11, 313-344 (2002).

I. Kypraios, P. Lei, R. C. D. Young, and C. R. Chatwin, “Object recognition within cluttered scenes employing the modified-hybrid optical neural network filter,” submitted to Pattern Recogn.

I. Kypraios, P. Lei, R. C. D. Young, and C. R. Chatwin, “Object recognition within cluttered scenes employing the modified-hybrid optical neural network filter,” submitted to Pattern Recogn.

C. G. Looney, *Pattern Recognition Using Neural Networks-Theory and Algorithms for Engineers and Scientists* (Oxford University Press, 1997).

A. Mahalanobis and 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]

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

[CrossRef]
[PubMed]

D. Casasent, L. M. Neiberg, and M. A. Sipe, “Feature space trajectory distorted object representation for classification and pose estimation,” Opt. Eng. 37, 914-923 (1998).

[CrossRef]

D. Nguyen and B. Widrow, “Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights,” in *Proceedings of the International Joint Conference on Neural Networks*, (IEEE, 1990), Vol. 3, pp. 21-26.

[CrossRef]

D. Nguyen and B. Widrow, “The truck backer-upper: an example of self-learning in neural networks,” in *Proceedings of the International Joint Conference on Neural Networks* (IEEE, 1989), Vol. 2, pp. 357-363.

[CrossRef]

T. H. Chao, G. Reyes, and Y. Park, “Grayscale optical correlator,” Proc. SPIE 3386, 60-64 (1998).

[CrossRef]

T. H. Chao, G. Reyes, and Y. Park, “Grayscale optical correlator,” Proc. SPIE 3386, 60-64 (1998).

[CrossRef]

D. Casasent, L. M. Neiberg, and M. A. Sipe, “Feature space trajectory distorted object representation for classification and pose estimation,” Opt. Eng. 37, 914-923 (1998).

[CrossRef]

E. Stamos, Similarity suppression filter design algorithm, in “Algorithms for designing filters for optical pattern recognition,” D. Phil. Thesis (University College London, 2001), pp. 77-78.

A. Talukder and D. Casasent, “Non-linear features for product inspection,” Proc. SPIE 3715, 32-43 (1999).

[CrossRef]

A. Vander Lugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory 10, 139-145 (1964).

[CrossRef]

A. Mahalanobis and 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]

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

[CrossRef]
[PubMed]

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

[CrossRef]

B. V. K. Vijaya Kumar and 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, 1579-1584 (1986).

[CrossRef]

D. Nguyen and B. Widrow, “Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights,” in *Proceedings of the International Joint Conference on Neural Networks*, (IEEE, 1990), Vol. 3, pp. 21-26.

[CrossRef]

D. Nguyen and B. Widrow, “The truck backer-upper: an example of self-learning in neural networks,” in *Proceedings of the International Joint Conference on Neural Networks* (IEEE, 1989), Vol. 2, pp. 357-363.

[CrossRef]

I. Kypraios, R. Young, and C. R. Chatwin, “An investigation of the non-linear properties of correlation filter synthesis and neural network design,” Asian J. Phys. 11, 313-344 (2002).

I. Kypraios, R. C. D Young, P. Birch, and C. Chatwin, “A non-linear training set superposition filter derived by neural network training methods for implementation in a shift invariant optical correlator,” Proc. SPIE 5106, 84-95 (2003).

[CrossRef]

I. Kypraios, R. C. D. Young, and C. R. Chatwin, “Performance assessment of unconstrained hybrid optical neural network (U-HONN) filter for object recognition tasks in clutter,” Proc. SPIE 5437, 51-62 (2004).

[CrossRef]

I. Kypraios, R. C. D. Young, P. Birch, and C. R. Chatwin, “Object recognition within cluttered scenes employing a hybrid optical neural network (HONN) filter,” Opt. Eng. 43, 1839-1850 (2004).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “In-class distortion tolerance, out-of-class discrimination and clutter resistance of correlation filters that employ a space domain non-linearity applied to wavelet filtered input images,” Proc. SPIE 3386, 111-122 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Nonlinear preprocessing operation for enhancing correlator filter performance in clutter,” Proc. SPIE 3490, 182-186 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Synthetic discriminant function filter employing nonlinear space-domain preprocessing on bandpass-filtered images,” Appl. Opt. 37, 2051-2062 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Application of non-linearity to wavelet-transformed images to improve correlation filter performance,” Appl. Opt. 36, 9212-9224 (1997).

[CrossRef]

I. Kypraios, P. Lei, R. C. D. Young, and C. R. Chatwin, “Object recognition within cluttered scenes employing the modified-hybrid optical neural network filter,” submitted to Pattern Recogn.

H. Zhou and T. H. Chao, “MACH filter synthesising for detecting targets in cluttered environment for gray-scale optical correlator,” Proc. SPIE 3715, 394-398 (1999).

[CrossRef]

H. J. Caulfield, “Linear combinations of filters for character recognition: a unified treatment,” Appl. Opt. 19, 3877-3878(1980).

[CrossRef]
[PubMed]

D. Casasent, “Unified synthetic discriminant function computational formulation,” Appl. Opt. 23, 1620-1627 (1984).

[CrossRef]
[PubMed]

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

[CrossRef]

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

[CrossRef]

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

[CrossRef]
[PubMed]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Application of non-linearity to wavelet-transformed images to improve correlation filter performance,” Appl. Opt. 36, 9212-9224 (1997).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Synthetic discriminant function filter employing nonlinear space-domain preprocessing on bandpass-filtered images,” Appl. Opt. 37, 2051-2062 (1998).

[CrossRef]

S. Goyal, N. K. Nishchal, V. K. Beri, and A. K. Gupta, “Wavelet-modified maximum average correlation height filter for rotation invariance that uses chirp encoding in a hybrid digital-optical correlator,” Appl. Opt. 45, 4850-4857 (2006).

[CrossRef]
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[CrossRef]

I. Kypraios, R. Young, and C. R. Chatwin, “An investigation of the non-linear properties of correlation filter synthesis and neural network design,” Asian J. Phys. 11, 313-344 (2002).

A. Vander Lugt, “Signal detection by complex spatial filtering,” IEEE Trans. Inf. Theory 10, 139-145 (1964).

[CrossRef]

A. Mahalanobis and 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]

D. Casasent, L. M. Neiberg, and M. A. Sipe, “Feature space trajectory distorted object representation for classification and pose estimation,” Opt. Eng. 37, 914-923 (1998).

[CrossRef]

I. Kypraios, R. C. D. Young, P. Birch, and C. R. Chatwin, “Object recognition within cluttered scenes employing a hybrid optical neural network (HONN) filter,” Opt. Eng. 43, 1839-1850 (2004).

[CrossRef]

I. Kypraios, P. Lei, R. C. D. Young, and C. R. Chatwin, “Object recognition within cluttered scenes employing the modified-hybrid optical neural network filter,” submitted to Pattern Recogn.

A. Talukder and D. Casasent, “Non-linear features for product inspection,” Proc. SPIE 3715, 32-43 (1999).

[CrossRef]

I. Kypraios, R. C. D Young, P. Birch, and C. Chatwin, “A non-linear training set superposition filter derived by neural network training methods for implementation in a shift invariant optical correlator,” Proc. SPIE 5106, 84-95 (2003).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “In-class distortion tolerance, out-of-class discrimination and clutter resistance of correlation filters that employ a space domain non-linearity applied to wavelet filtered input images,” Proc. SPIE 3386, 111-122 (1998).

[CrossRef]

L. S. Jamal-Aldin, R. C. D. Young, and C. R. Chatwin, “Nonlinear preprocessing operation for enhancing correlator filter performance in clutter,” Proc. SPIE 3490, 182-186 (1998).

[CrossRef]

I. Kypraios, R. C. D. Young, and C. R. Chatwin, “Performance assessment of unconstrained hybrid optical neural network (U-HONN) filter for object recognition tasks in clutter,” Proc. SPIE 5437, 51-62 (2004).

[CrossRef]

T. H. Chao, G. Reyes, and Y. Park, “Grayscale optical correlator,” Proc. SPIE 3386, 60-64 (1998).

[CrossRef]

H. Zhou and T. H. Chao, “MACH filter synthesising for detecting targets in cluttered environment for gray-scale optical correlator,” Proc. SPIE 3715, 394-398 (1999).

[CrossRef]

E. Stamos, Similarity suppression filter design algorithm, in “Algorithms for designing filters for optical pattern recognition,” D. Phil. Thesis (University College London, 2001), pp. 77-78.

C. G. Looney, *Pattern Recognition Using Neural Networks-Theory and Algorithms for Engineers and Scientists* (Oxford University Press, 1997).

S. Haykin, *Neural Networks--A Comprehensive Foundation* (Prentice Hall, 1994).

R. Beale and T. Jackson, *Neural Computing: An Introduction* (Institute of Physics Publishing, 1990).

[CrossRef]

H. Demuth and M. Beale, *Neural Network Toolbox for Use with MATLAB: User's Guide for MATLAB Version 6.5* (MathWorks, Inc., 2002), http://www.mathworks.com.

M. T. Hagan, H. B. Demuth, and M. H. Beale, *Neural Network Design* (PWS Publishing, 1996).

D. Nguyen and B. Widrow, “The truck backer-upper: an example of self-learning in neural networks,” in *Proceedings of the International Joint Conference on Neural Networks* (IEEE, 1989), Vol. 2, pp. 357-363.

[CrossRef]

D. Nguyen and B. Widrow, “Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights,” in *Proceedings of the International Joint Conference on Neural Networks*, (IEEE, 1990), Vol. 3, pp. 21-26.

[CrossRef]