Image Registration Model For Remote Sensing Images
Image registration is the vital technology in computer vision. By developing precise image registration algorithm will meaningfully improve the techniques for the problems in computer vision. Registration process does geometrical alteration that aligns point present in one view of an object with sim...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
European Alliance for Innovation (EAI)
2018-10-01
|
Series: | EAI Endorsed Transactions on Internet of Things |
Subjects: | |
Online Access: | https://eudl.eu/pdf/10.4108/eai.21-12-2018.159333 |
id |
doaj-767096ee3edf4c2598b5d28e55350dc2 |
---|---|
record_format |
Article |
spelling |
doaj-767096ee3edf4c2598b5d28e55350dc22020-11-25T01:20:47ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Internet of Things2414-13992018-10-0141610.4108/eai.21-12-2018.159333Image Registration Model For Remote Sensing ImagesSabeen Gul0Sheeraz Memon1Bushra Naz2Department of Computer System Engineering Mehran University of Engineering and Technology JamshoroDepartment of Computer System Engineering Mehran University of Engineering and Technology JamshoroDepartment of Computer System Engineering Mehran University of Engineering and Technology JamshoroImage registration is the vital technology in computer vision. By developing precise image registration algorithm will meaningfully improve the techniques for the problems in computer vision. Registration process does geometrical alteration that aligns point present in one view of an object with similar points in another view of that object or another object .Steps involved in image registration are feature finding, matching of features, image transformation and resampling. Featurefinding and matching have vital role in accuracy of the process. In this paper we have used SIFT (Scale Invariant Feature Transform) for the feature detection which is invariant to scaling, rotation and noise. KNN nearest neighbor is used for matching similar points and the other efficient method in reducing miss matches in the proposed algorithm is Randomsample consensus method.https://eudl.eu/pdf/10.4108/eai.21-12-2018.159333Image registrationSIFTKNNRANSACHigh Resolution Images |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sabeen Gul Sheeraz Memon Bushra Naz |
spellingShingle |
Sabeen Gul Sheeraz Memon Bushra Naz Image Registration Model For Remote Sensing Images EAI Endorsed Transactions on Internet of Things Image registration SIFT KNN RANSAC High Resolution Images |
author_facet |
Sabeen Gul Sheeraz Memon Bushra Naz |
author_sort |
Sabeen Gul |
title |
Image Registration Model For Remote Sensing Images |
title_short |
Image Registration Model For Remote Sensing Images |
title_full |
Image Registration Model For Remote Sensing Images |
title_fullStr |
Image Registration Model For Remote Sensing Images |
title_full_unstemmed |
Image Registration Model For Remote Sensing Images |
title_sort |
image registration model for remote sensing images |
publisher |
European Alliance for Innovation (EAI) |
series |
EAI Endorsed Transactions on Internet of Things |
issn |
2414-1399 |
publishDate |
2018-10-01 |
description |
Image registration is the vital technology in computer vision. By developing precise image registration algorithm will meaningfully improve the techniques for the problems in computer vision. Registration process does geometrical alteration that aligns point present in one view of an object with similar points in another view of that object or another object .Steps involved in image registration are feature finding, matching of features, image transformation and resampling. Featurefinding and matching have vital role in accuracy of the process. In this paper we have used SIFT (Scale Invariant Feature Transform) for the feature detection which is invariant to scaling, rotation and noise. KNN nearest neighbor is used for matching similar points and the other efficient method in reducing miss matches in the proposed algorithm is Randomsample consensus method. |
topic |
Image registration SIFT KNN RANSAC High Resolution Images |
url |
https://eudl.eu/pdf/10.4108/eai.21-12-2018.159333 |
work_keys_str_mv |
AT sabeengul imageregistrationmodelforremotesensingimages AT sheerazmemon imageregistrationmodelforremotesensingimages AT bushranaz imageregistrationmodelforremotesensingimages |
_version_ |
1725132138238443520 |