An improved SIFT feature matching algorithm

Author

Hua, Yuning ; Lin, Jing ; Lin, Chao

Author_Institution

Dept. of Inf. Sci. & Eng., Shenyangligong Univ., Shenyang, China

fYear

2010

Firstpage

6109

Lastpage

6113

Abstract

For the diversity of feature extraction and the complexity of similarity calculation in the feature-based image registration methods, an improved Scale Invariant Feature Transform (SIFT) feature matching algorithm is proposed. First of all, by using the classic SIFT algorithm, the feature points of the images are extracted. By using the gradients normalized method eigenvector descriptor is formed. Then the feature points are matched according to the Euclidean distance ratio. At last, by using the bilateral matching algorithm, the mismatch points are removed. The experiments show that this method is reliable and practicable.

Keywords

eigenvalues and eigenfunctions; feature extraction; gradient methods; image matching; image registration; scaling phenomena; Euclidean distance ratio; SIFT; bilateral matching; eigenvector; feature extraction; feature matching; gradients normalized method; image registration; scale invariant feature transform; Computer vision; Euclidean distance; Feature extraction; Image registration; Information science; Object recognition; Transforms; SIFT algorithm; image registration; the bilateral matching algorithm; volume normalization;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2010 8th World Congress on

Conference_Location

Jinan

Print_ISBN

978-1-4244-6712-9

Type

conf

DOI

10.1109/WCICA.2010.5554659

Filename

5554659