Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression

This is what I may use to convert my spectral image , well at least a foundation for it


Abstract:

A three-layered neural network is described for transforming two-dimensional discrete signals into generalized nonorthogonal 2-D Gabor representations for image analysis, segmentation, and compression. These transforms are conjoint spatial/spectral representations, which provide a complete image description in terms of locally windowed 2-D spectral coordinates embedded within global 2-D spatial coordinates. In the present neural network approach, based on interlaminar interactions involving two layers with fixed weights and one layer with adjustable weights, the network finds coefficients for complete conjoint 2-D Gabor transforms without restrictive conditions. In wavelet expansions based on a biologically inspired log-polar ensemble of dilations, rotations, and translations of a single underlying 2-D Gabor wavelet template, image compression is illustrated with ratios up to 20:1. Also demonstrated is image segmentation based on the clustering of coefficients in the complete 2-D Gabor transform

Link to Article:

Published in:
Acoustics, Speech and Signal Processing, IEEE Transactions on  (Volume:36 ,  Issue: 7 )

ISSN :0096-3518

Date of Current Version :06 August 2002Digital Object Identifier :10.1109/29.1644