Iterative methods for solving the Gabor expansion: considerations of convergence
- 1 April 1992
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 1 (2) , 243-244
- https://doi.org/10.1109/83.136600
Abstract
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.Keywords
This publication has 3 references indexed in Scilit:
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- Complete discrete 2-D Gabor transforms by neural networks for image analysis and compressionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
- Gabor's expansion of a signal into Gaussian elementary signalsProceedings of the IEEE, 1980