Iterative methods for solving the Gabor expansion: considerations of convergence

Iterative methods for solving the Gabor expansion: considerations of convergence

J.G. Daugman's (1988) neural network solution to the Gabor expansion of an image is reformulated as a steepest descent implementation. Nonlinear optimization theory is then applied to select an appropriate convergence factor. Two quasi-Newton-based nonlinear optimization techniques are applied...

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Veröffentlicht in:IEEE transactions on image processing 1992-04, Vol.1 (2), p.243-244
Hauptverfasser: Braithwaite, R.N., Beddoes, M.P.
Format: Artikel
Sprache:eng
Schlagworte:
Convergence
Cost function
Eigenvalues and eigenfunctions
Error correction
Frequency
Image sampling
Image storage
Iterative methods
Lattices
Neural networks
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Zusammenfassung:J.G. Daugman's (1988) neural network solution to the Gabor expansion of an image is reformulated as a steepest descent implementation. Nonlinear optimization theory is then applied to select an appropriate convergence factor. Two quasi-Newton-based nonlinear optimization techniques are applied to improve the convergence for certain types of lattice.< >
ISSN:1057-7149
1941-0042
DOI:10.1109/83.136600