Feature point matching of affine model images using hopfield network

Authors:
Jinsi Tian;Jianbo Su
Affiliations:
Department of Automation, Shanghai Jiao Tong University, Shanghai, China;Department of Automation, Shanghai Jiao Tong University, Shanghai, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

Citing 3
Cited 0

Application of Affine-Invariant Fourier Descriptors to Recognition of 3-D Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
3D motion recovery via affine epipolar geometry

International Journal of Computer Vision
Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an approach to match feature point of a pair of 3- dimensional affine model images. The affine transferring parameters are computed by a set of corresponding feature points, which are obtained based on 2D Hopfield neural network. The design of energy function of the neural network optimizes the matching error of the feature points. Two affine geometric constraints, epipolar and homography are used without the restriction to scene's particularity. A pair of affine model images tests the performance of the proposed method.