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Reseach Article

Automatic Image Registration using SIFT-NCC

Published on July 2012 by Vinividyadharan, Subusurendran
Advanced Computing and Communication Technologies for HPC Applications
Foundation of Computer Science USA
ACCTHPCA - Number 4
July 2012
Authors: Vinividyadharan, Subusurendran
b4b0c589-2a70-4cf3-9327-73b5e680493f

Vinividyadharan, Subusurendran . Automatic Image Registration using SIFT-NCC. Advanced Computing and Communication Technologies for HPC Applications. ACCTHPCA, 4 (July 2012), 29-32.

@article{
author = { Vinividyadharan, Subusurendran },
title = { Automatic Image Registration using SIFT-NCC },
journal = { Advanced Computing and Communication Technologies for HPC Applications },
issue_date = { July 2012 },
volume = { ACCTHPCA },
number = { 4 },
month = { July },
year = { 2012 },
issn = 0975-8887,
pages = { 29-32 },
numpages = 4,
url = { /specialissues/accthpca/number4/7576-1030/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Advanced Computing and Communication Technologies for HPC Applications
%A Vinividyadharan
%A Subusurendran
%T Automatic Image Registration using SIFT-NCC
%J Advanced Computing and Communication Technologies for HPC Applications
%@ 0975-8887
%V ACCTHPCA
%N 4
%P 29-32
%D 2012
%I International Journal of Computer Applications
Abstract

Accurate, robust and automatic image registration is critical task in many typical applications that employ multi-sensor and/or multi-date imagery information. The main content of this paper is an algorithm for the registration of digital images. Some multi–sensed or temporal images contain large number of speckles and noise, or image can have some distortion by some means. For these reasons, we need to remove the noises, speckle and to recover from distortion. We register two to find the similarity between the images. This paper discusses techniques for image registration based on SIFT. In this proposed framework we use NCC metrics for optimizing the matching work. Best bin first search using kd tree is used for feature matching and RANSAC is used for outlier elimination.

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Index Terms

Computer Science
Information Sciences

Keywords

Scale Invariant Feature Transform Ncc Ransac Kd Tree Bbf.