Weight Adjustment Rule of Neural Networks for Computing Discrete 2-D Gabor Transforms

Hong Yan; John C. Gore

doi:10.1109/29.60090

Weight Adjustment Rule of Neural Networks for Computing Discrete 2-D Gabor Transforms

Hong Yan
, John C. Gore

Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal

7 Citations (Scopus)

Abstract

Daugman has recently proposed a neural network model for computing the discrete 2-D Gabor transform. We prove here that the weight adjustment rule used in the neural network is equivalent to the use of Jacobi iteration for solving simultaneous linear equations, and we propose more efficient algorithms for solving the problem. © 1990 IEEE

Original language	English
Pages (from-to)	1654-1656
Journal	IEEE Transactions on Acoustics, Speech, and Signal Processing
Volume	38
Issue number	9
DOIs	https://doi.org/10.1109/29.60090
Publication status	Published - Sept 1990
Externally published	Yes

Access Output

10.1109/29.60090

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@article{361138853a6645d294ab91bebb1d67e7,

title = "Weight Adjustment Rule of Neural Networks for Computing Discrete 2-D Gabor Transforms",

abstract = "Daugman has recently proposed a neural network model for computing the discrete 2-D Gabor transform. We prove here that the weight adjustment rule used in the neural network is equivalent to the use of Jacobi iteration for solving simultaneous linear equations, and we propose more efficient algorithms for solving the problem. {\textcopyright} 1990 IEEE",

author = "Hong Yan and Gore, \{John C.\}",

year = "1990",

month = sep,

doi = "10.1109/29.60090",

language = "English",

volume = "38",

pages = "1654--1656",

journal = "IEEE Transactions on Acoustics, Speech, and Signal Processing",

issn = "0096-3518",

publisher = "IEEE",

number = "9",

}

TY - JOUR

T1 - Weight Adjustment Rule of Neural Networks for Computing Discrete 2-D Gabor Transforms

AU - Yan, Hong

AU - Gore, John C.

PY - 1990/9

Y1 - 1990/9

N2 - Daugman has recently proposed a neural network model for computing the discrete 2-D Gabor transform. We prove here that the weight adjustment rule used in the neural network is equivalent to the use of Jacobi iteration for solving simultaneous linear equations, and we propose more efficient algorithms for solving the problem. © 1990 IEEE

AB - Daugman has recently proposed a neural network model for computing the discrete 2-D Gabor transform. We prove here that the weight adjustment rule used in the neural network is equivalent to the use of Jacobi iteration for solving simultaneous linear equations, and we propose more efficient algorithms for solving the problem. © 1990 IEEE

UR - https://www.scopus.com/pages/publications/0025491788

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0025491788&origin=recordpage

U2 - 10.1109/29.60090

DO - 10.1109/29.60090

M3 - RGC 22 - Publication in policy or professional journal

SN - 0096-3518

VL - 38

SP - 1654

EP - 1656

JO - IEEE Transactions on Acoustics, Speech, and Signal Processing

JF - IEEE Transactions on Acoustics, Speech, and Signal Processing

IS - 9

ER -

Weight Adjustment Rule of Neural Networks for Computing Discrete 2-D Gabor Transforms

Abstract

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this