Feature Point Matching Using a Hermitian Property Matrix

被引：0

作者：

Haseeb, Muhammad ^{[1
]}

Hancock, Edwin R. ^{[1
]}

机构：

[1] Univ York, Dept Comp Sci, York YO10 5DD, N Yorkshire, England

来源：

SIMILARITY-BASED PATTERN RECOGNITION: FIRST INTERNATIONAL WORKSHOP, SIMBAD 2011 | 2011年 / 7005卷

关键词：

Graph matching; Feature point correspondence; Complex Laplacian; Hermitian matrix; EM algorithm; GRAPH; VECTORS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes the computation of feature point correspondences using the spectra of a Hermitian property matrix. Firstly, a complex Laplacian (Hermitian) matrix is constructed from the Gaussian-weighted distances and the difference of SIFT [10] angles between each pair of points in the two images to be matched. Matches are computed by comparing the complex eigenvectors of the Hermitian property matrices for the two point sets acquired from the two images. Secondly, we embed the complex modal structure within Carcassoni's [12] iterative alignment method to render it more robust to rotation. Our method has been evaluated on both synthetic and real-world data.

引用

页码：321 / 332

页数：12

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