Please use this identifier to cite or link to this item: http://dspace.uniten.edu.my/jspui/handle/123456789/8007
Title: Affine versus projective transformation for SIFT and RANSAC image matching methods
Authors: Redzuwan, R. 
Radzi, N.A.M. 
Din, N.M. 
Mustafa, I.S. 
Issue Date: 2016
Abstract: Image registration is a process of determining the geometrical transformation that aligns two or more images taken from different viewpoints and sensors at different times. Scale Invariant Feature Transform (SIFT) method has gained more popularity since it extracts the highest number of features and matching points compared to Speeded-Up Robust Feature (SURF) and Harris Corner Detector at little computational cost. In this paper, a combination of SIFT and Random Sample Consensus (RANSAC) is used to produce panoramic image. In order to reject outliers and estimate the transformation model, affine and projective transformations are used to study the best geometrical transformations methods to be used. The results shows that the projective transformation has a better performance in terms of accuracy. © 2015 IEEE.
URI: http://dspace.uniten.edu.my/jspui/handle/123456789/8007
Appears in Collections:COE Scholarly Publication

Show full item record

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.