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  4. SIFT-EST - a SIFT-based feature matching algorithm using homography estimation
 
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SIFT-EST - a SIFT-based feature matching algorithm using homography estimation

Citation Link: https://doi.org/10.15480/882.3607
Publikationstyp
Conference Paper
Date Issued
2015
Sprache
English
Author(s)
Badr, Arash Shahbaz  
Prapitasari, Luh Putu Ayu  
Grigat, Rolf-Rainer  
Institut
Bildverarbeitungssysteme E-2  
TORE-DOI
10.15480/882.3607
TORE-URI
http://hdl.handle.net/11420/9737
Start Page
504
End Page
511
Citation
Proceedings of the 10th International Conference on Computer Vision Theory and Applications Vol. 3: 504-511 (2015)
Contribution to Conference
10th International Conference on Computer Vision Theory and Applications, VISAPP 2015  
Publisher DOI
10.5220/0005296105040511
Publisher
SCITEPRESS
In this paper, a new feature matching algorithm is proposed and evaluated. This method makes use of features that are extracted by SIFT and aims at reducing the processing time of the matching phase of SIFT. The idea behind this method is to use the information obtained from already detected matches to restrict the range of possible correspondences in the subsequent matching attempts. For this purpose, a few initial matches are used to estimate the homography that relates the two images. Based on this homography, the estimated location of the features of the reference image after transformation to the test image can be specified. This information is used to specify a small set of possible matches for each reference feature based on their distance to the estimated location. The restriction of possible matches leads to a reduction of processing time since the quadratic complexity of the one-to-one matching is undermined. Due to the restrictions of 2D homographies, this method can only be applied to images that are related by pure-rotational transformations or images of planar object.
Subjects
Image Correspondences
Feature Matching
Local Features, SIFT
Homography Estimation
DDC Class
004: Informatik
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by-nc-nd/4.0/
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