By Topic

Robust SIFT-based feature matching using Kendall's rank correlation measure

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kordelas, G. ; Inf. & Telematics Inst., Thessaloniki, Greece ; Daras, P.

The scale invariant feature transform, SIFT, is one of the most efficient image matching techniques based on local features. It has been applied to various scientific domains such as machine vision, robot navigation, object recognition, etc. In this work, a SIFT improvement is proposed that makes feature matching more robust in the presence of different types of image noise. Thus, Kendall's rank correlation measure is employed to improve the performance of feature matching. Its exploitation reduces the number of erroneous SIFT feature matches without adding significantly to the execution time. The results of the SIFT improvement are validated through matching examples between similar images.

Published in:

Image Processing (ICIP), 2009 16th IEEE International Conference on

Date of Conference:

7-10 Nov. 2009