25 October 1988 Relaxation Neural Network For Complete Discrete 2-D Gabor Transforms
Author Affiliations +
Proceedings Volume 1001, Visual Communications and Image Processing '88: Third in a Series; (1988) https://doi.org/10.1117/12.969058
Event: Visual Communications and Image Processing III, 1988, Cambridge, MA, United States
It is often desirable in image processing to represent image structure in terms of a set of coefficients on a family of expansion functions. For example, familiar approaches to image coding, feature extraction, image segmentation, statistical and spectral analysis, and compression, involve such methods. It has invariably been necessary that the expansion functions employed comprise an orthogonal basis for the image space, because the problem of obtaining the correct coefficients on a non-orthogonal set of expansion functions is usually arduous if not impossible. Oddly enough, image coding in biological visual systems clearly involves non-orthogonal expansion functions. The receptive field profiles of visual neu-rons with linear response properties have large overlaps and large inner products, and are suggestive of a conjoint (spatial and spectral) "2-D Gabor representation" (Daugman 1980, 1985). The 2-D Gabor transform has useful decorrelating properties and provides a conjoint image description resembling a speech spectrogram, in which local 2-D image regions are analyzed for orientation and spatial frequency content, but its expansion functions are non-orthogonal. This paper describes a three-layered relaxation "neural network" that efficiently computes the correct coefficients for this and other, non-orthogonal, image transforms. Examples of applications which are illustrated include: (1) image compression to below 1.0 bit/pixel, and (2) textural image segmentation based upon the statistics of the 2-D Gabor coefficients found by the relaxation network.
© (1988) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John G. Daugman, John G. Daugman, } "Relaxation Neural Network For Complete Discrete 2-D Gabor Transforms", Proc. SPIE 1001, Visual Communications and Image Processing '88: Third in a Series, (25 October 1988); doi: 10.1117/12.969058; https://doi.org/10.1117/12.969058


Pyramid Coding Of Images Using Visual Criterion
Proceedings of SPIE (October 31 1989)
Orthogonal Pyramid Transforms For Image Coding.
Proceedings of SPIE (October 12 1987)

Back to Top