The Hopfield neural network model for solving affine transformation parameters in the correlation method

Panomkhawn Riyamongkol, Weizhao Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, we present the Hopfield neural network model to provide solution for affine transformation parameters in the correlation method. Affine transformation equation is derived to get the mean-square error. The model maps the mean-square error equation with the energy function. When the energy function reaches local minima, the mean-square error is minimized. Outputs of the model will be affine transformation parameters. These parameters are applied in the affine transformation equation to register the corresponding two images.

Original languageEnglish (US)
Title of host publication2006 IEEE Region 5 Conference
PublisherIEEE Computer Society
Pages249-253
Number of pages5
ISBN (Print)1424403596, 9781424403592
DOIs
StatePublished - Jan 1 2006
Event2006 IEEE Region 5 Conference - San Antonio, TX, United States
Duration: Apr 7 2006Apr 8 2006

Publication series

Name2006 IEEE Region 5 Conference

Other

Other2006 IEEE Region 5 Conference
CountryUnited States
CitySan Antonio, TX
Period4/7/064/8/06

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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    Riyamongkol, P., & Zhao, W. (2006). The Hopfield neural network model for solving affine transformation parameters in the correlation method. In 2006 IEEE Region 5 Conference (pp. 249-253). [5507421] (2006 IEEE Region 5 Conference). IEEE Computer Society. https://doi.org/10.1109/TPSD.2006.5507421