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

1 Citation (Scopus)

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
Title of host publication2006 IEEE Region 5 Conference
Pages249-253
Number of pages5
DOIs
StatePublished - Aug 20 2010
Event2006 IEEE Region 5 Conference - San Antonio, TX, United States
Duration: Apr 7 2006Apr 8 2006

Other

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

Fingerprint

Hopfield neural networks
Correlation methods
Mean square error

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

The Hopfield neural network model for solving affine transformation parameters in the correlation method. / Riyamongkol, Panomkhawn; Zhao, Weizhao.

2006 IEEE Region 5 Conference. 2010. p. 249-253 5507421.

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

Riyamongkol, P & Zhao, W 2010, The Hopfield neural network model for solving affine transformation parameters in the correlation method. in 2006 IEEE Region 5 Conference., 5507421, pp. 249-253, 2006 IEEE Region 5 Conference, San Antonio, TX, United States, 4/7/06. https://doi.org/10.1109/TPSD.2006.5507421
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