S. Reed, J. Coupland, “Rotation invariance considerations in cascaded linear shift invariant processing,” Asian J. Phys. 8, 421–429 (1999).

S.-C. B. Lo, S.-L. A. Lou, J.-S. Lin, M. T. Freedman, M. V. Chien, S. K. Mun, “Artificial convolution neural network techniques and applications for lung nodule detection,” IEEE Trans. Med. Imaging 14, 711–718 (1995).

[CrossRef]
[PubMed]

E. Barnard, E. C. Botha, “Back-propagation uses prior information efficiently,” IEEE Trans. Neural Networks 4, 794–802 (1993).

[CrossRef]

M. D. Richard, R. P. Lippmann, “Neural network classifiers estimate Bayesian a posteriori probabilities,” Neural Comput. 3, 461–483 (1991).

[CrossRef]

F. Kanaya, S. Miyake, “Bayes statistical behaviour and valid generalization of pattern classifying neural networks,” IEEE Trans. Neural Networks 2, 471–475 (1991).

[CrossRef]

S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, “Optimization by simulated annealing,” Science 220, 671–680 (1983).

[CrossRef]
[PubMed]

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

[CrossRef]

E. Barnard, E. C. Botha, “Back-propagation uses prior information efficiently,” IEEE Trans. Neural Networks 4, 794–802 (1993).

[CrossRef]

E. Barnard, E. C. Botha, “Back-propagation uses prior information efficiently,” IEEE Trans. Neural Networks 4, 794–802 (1993).

[CrossRef]

S.-C. B. Lo, S.-L. A. Lou, J.-S. Lin, M. T. Freedman, M. V. Chien, S. K. Mun, “Artificial convolution neural network techniques and applications for lung nodule detection,” IEEE Trans. Med. Imaging 14, 711–718 (1995).

[CrossRef]
[PubMed]

S. Reed, J. Coupland, “Rotation invariance considerations in cascaded linear shift invariant processing,” Asian J. Phys. 8, 421–429 (1999).

S. Reed, J. Coupland, “Cascaded linear shift invariant processing to improve discrimination and noise tolerance in optical pattern recognition,” in Optical Pattern Recognition IX, D. P. Casasent, T. Chao, eds., Proc. SPIE3386, 272–283 (1998).

[CrossRef]

S.-C. B. Lo, S.-L. A. Lou, J.-S. Lin, M. T. Freedman, M. V. Chien, S. K. Mun, “Artificial convolution neural network techniques and applications for lung nodule detection,” IEEE Trans. Med. Imaging 14, 711–718 (1995).

[CrossRef]
[PubMed]

K. Fukunaga, Introduction to Statistical Pattern Recognition (Academic, New York, 1972).

S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, “Optimization by simulated annealing,” Science 220, 671–680 (1983).

[CrossRef]
[PubMed]

D. E. Rumelhart, G. E. Hinton, R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart, J. L. McLelland, eds. (MIT, Cambridge, Mass., 1986), Vol. 1, pp. 318–362.

F. Kanaya, S. Miyake, “Bayes statistical behaviour and valid generalization of pattern classifying neural networks,” IEEE Trans. Neural Networks 2, 471–475 (1991).

[CrossRef]

S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, “Optimization by simulated annealing,” Science 220, 671–680 (1983).

[CrossRef]
[PubMed]

S.-C. B. Lo, S.-L. A. Lou, J.-S. Lin, M. T. Freedman, M. V. Chien, S. K. Mun, “Artificial convolution neural network techniques and applications for lung nodule detection,” IEEE Trans. Med. Imaging 14, 711–718 (1995).

[CrossRef]
[PubMed]

M. D. Richard, R. P. Lippmann, “Neural network classifiers estimate Bayesian a posteriori probabilities,” Neural Comput. 3, 461–483 (1991).

[CrossRef]

S.-C. B. Lo, S.-L. A. Lou, J.-S. Lin, M. T. Freedman, M. V. Chien, S. K. Mun, “Artificial convolution neural network techniques and applications for lung nodule detection,” IEEE Trans. Med. Imaging 14, 711–718 (1995).

[CrossRef]
[PubMed]

S.-C. B. Lo, S.-L. A. Lou, J.-S. Lin, M. T. Freedman, M. V. Chien, S. K. Mun, “Artificial convolution neural network techniques and applications for lung nodule detection,” IEEE Trans. Med. Imaging 14, 711–718 (1995).

[CrossRef]
[PubMed]

F. Kanaya, S. Miyake, “Bayes statistical behaviour and valid generalization of pattern classifying neural networks,” IEEE Trans. Neural Networks 2, 471–475 (1991).

[CrossRef]

S.-C. B. Lo, S.-L. A. Lou, J.-S. Lin, M. T. Freedman, M. V. Chien, S. K. Mun, “Artificial convolution neural network techniques and applications for lung nodule detection,” IEEE Trans. Med. Imaging 14, 711–718 (1995).

[CrossRef]
[PubMed]

S. Reed, J. Coupland, “Rotation invariance considerations in cascaded linear shift invariant processing,” Asian J. Phys. 8, 421–429 (1999).

S. Reed, J. Coupland, “Cascaded linear shift invariant processing to improve discrimination and noise tolerance in optical pattern recognition,” in Optical Pattern Recognition IX, D. P. Casasent, T. Chao, eds., Proc. SPIE3386, 272–283 (1998).

[CrossRef]

M. D. Richard, R. P. Lippmann, “Neural network classifiers estimate Bayesian a posteriori probabilities,” Neural Comput. 3, 461–483 (1991).

[CrossRef]

D. E. Rumelhart, G. E. Hinton, R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart, J. L. McLelland, eds. (MIT, Cambridge, Mass., 1986), Vol. 1, pp. 318–362.

H. L. Van Trees, Detection, Estimation, and Modulation Theory: Part I (Wiley, New York, 1968).

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

[CrossRef]

S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, “Optimization by simulated annealing,” Science 220, 671–680 (1983).

[CrossRef]
[PubMed]

D. E. Rumelhart, G. E. Hinton, R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart, J. L. McLelland, eds. (MIT, Cambridge, Mass., 1986), Vol. 1, pp. 318–362.

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

[CrossRef]

H. J. Caulfield, W. T. Maloney, “Improved discrimination in optical character recognition,” Appl. Opt. 8, 2354–2356 (1969).

[CrossRef]
[PubMed]

B. Javidi, “Nonlinear joint power spectrum based optical correlation,” Appl. Opt. 28, 2358–2367 (1989).

[CrossRef]
[PubMed]

M. Soriano, C. Saloma, “Improved classification robustness for noisy cell images represented as principal-component projections in a hybrid recognition system,” Appl. Opt. 37, 3628–3638 (1998).

[CrossRef]

S. Reed, J. Coupland, “Rotation invariance considerations in cascaded linear shift invariant processing,” Asian J. Phys. 8, 421–429 (1999).

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

[CrossRef]

S.-C. B. Lo, S.-L. A. Lou, J.-S. Lin, M. T. Freedman, M. V. Chien, S. K. Mun, “Artificial convolution neural network techniques and applications for lung nodule detection,” IEEE Trans. Med. Imaging 14, 711–718 (1995).

[CrossRef]
[PubMed]

E. Barnard, E. C. Botha, “Back-propagation uses prior information efficiently,” IEEE Trans. Neural Networks 4, 794–802 (1993).

[CrossRef]

F. Kanaya, S. Miyake, “Bayes statistical behaviour and valid generalization of pattern classifying neural networks,” IEEE Trans. Neural Networks 2, 471–475 (1991).

[CrossRef]

M. D. Richard, R. P. Lippmann, “Neural network classifiers estimate Bayesian a posteriori probabilities,” Neural Comput. 3, 461–483 (1991).

[CrossRef]

S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, “Optimization by simulated annealing,” Science 220, 671–680 (1983).

[CrossRef]
[PubMed]

H. L. Van Trees, Detection, Estimation, and Modulation Theory: Part I (Wiley, New York, 1968).

We have prepared another study entitled, “Cascaded linear shift-invariant processing in optical pattern recognition.”

K. Fukunaga, Introduction to Statistical Pattern Recognition (Academic, New York, 1972).

D. E. Rumelhart, G. E. Hinton, R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart, J. L. McLelland, eds. (MIT, Cambridge, Mass., 1986), Vol. 1, pp. 318–362.

S. Reed, J. Coupland, “Cascaded linear shift invariant processing to improve discrimination and noise tolerance in optical pattern recognition,” in Optical Pattern Recognition IX, D. P. Casasent, T. Chao, eds., Proc. SPIE3386, 272–283 (1998).

[CrossRef]