Publication: Affine versus projective transformation for SIFT and RANSAC image matching methods
Date
2016
Authors
Redzuwan R.
Radzi N.A.M.
Din N.M.
Mustafa I.S.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
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.
Description
Edge detection; Image matching; Mathematical transformations; Affine transformations; Projective transformation; RANSAC; SIFT; SURF; Image processing