@inproceedings{203461093d3c479293901f5e901d28f1,
title = "Shift- and rotation-invariant interpattern heteroassociation model",
abstract = "A shift and rotation invariant neural network using interpattern hetero association (IHA) model is illustrated. To preserve the shift and rotation invariant properties, a set of binarized-encoded circular harmonic expansion (CHE) function at the Fourier domain is used as the training set. The interconnection weight matrix is constructed using an IHA model. By using the shift and symmetric properties of the modulus Fourier spectral, the problem of centering the CHE functions can be avoided. Computer simulations and experimental demonstrations are provided in which we have shown that the shift and rotation invariant properties of the proposed IHA neural net are indeed preserved.",
author = "Yu, {Francis T.} and Uang, {Chii Maw} and Shizhuo Yin",
year = "1993",
month = dec,
day = "1",
language = "English (US)",
isbn = "0819411957",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "Publ by Society of Photo-Optical Instrumentation Engineers",
pages = "464--473",
editor = "Casasent, {David P.}",
booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",
note = "Optical Pattern Recognition IV ; Conference date: 13-04-1993 Through 14-04-1993",
}