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

Panomkhawn Riyamongkol; Weizhao Zhao

doi:10.1109/TPSD.2006.5507421

Back

Conference proceeding

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

Panomkhawn Riyamongkol and Weizhao Zhao

2006 IEEE Region 5 Conference, pp.249-253

2006-04

DOI: https://doi.org/10.1109/TPSD.2006.5507421

Abstract

Application software

Artificial neural networks

Correlation

Equations

Hopfield neural networks

Image registration

Medical diagnosis

Nervous system

Pixel

Predictive models

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.

Metrics

4 Record Views

See more details

Details

Title: The Hopfield neural network model for solving affine transformation parameters in the correlation method
Creators: Panomkhawn Riyamongkol - Department of Electrical and Computer Engineering, Naresuan University, Thailand
Weizhao Zhao - Department of Neurology, Cerebral Vascular Research Center, University of Miami, USA
Publication Details: 2006 IEEE Region 5 Conference, pp.249-253
Publisher: IEEE
Academic Unit: College of Engineering; CoE - Biomedical Engineering
Language: English
Resource Type: Conference proceeding
Record Identifier: 991031619952702976