Abstract—Image registration is necessary when images from
multiple viewpoints need be brought into common coordinate
system. Image registration techniques can be classified as
area-based methods and feature-based methods. In area-based
methods, no features are selected and regularly tessellated areas
are usually used for matching. In feature-based methods,
features such as regions, lines, and prominent points are
detected and used for matching. When image contains rich
features, feature-based methods are preferred and when it does
not, area-based methods are usually adopted. There are
occasions where richness of features varies locally in the image.
In this case, either area-based methods or feature-based
methods alone may not generate successful results. In this
paper, we propose a mixture of two methods termed as featured
area-based method. In the proposed, we first tessellate the
image into equal-sized areas, estimate richness of features of
each area utilizing the edge direction histogram, choose only
those areas with a certain level of richness, and use them for
matching. We compared the proposed with well-known
conventional methods such as Kanade-Lucas-Tomasi(KLT)
method, speeded up robust features (SURF), and
scale-invariant feature transform (SIFT), and showed that the
proposed performs better than others.
Index Terms—Image registration, KLT, SIFT, SURF.
The authors are with the Myongji University, Yongin, 449-728, Korea
(e-mail: soh@mju.ac.kr, mudasar.kalwar@gmail.com,
aamergcu@hotmail.com; wise_sunys@nate.com, nothan111@gmail.com,
kit@mju.ac.kr).
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Cite:Youngsung Soh, Mudasar Qadir, Aamer Mehmood, Yongsuk Hae, Hadi Ashraf, and Intaek Kim, "A Featured Area-Based Image Registration," International Journal of Computer Theory and Engineering vol. 6, no. 5, pp. 407-411, 2014.
