Image matching is the first step in almost any 3D computer vision task, and hence has received extensive attention. In
this paper, the problem is addressed from a novel perspective, which is different from the classic stereo matching
paradigm. Two images with different resolutions, that is high resolution versus low resolution are matched. Since the
high resolution image only corresponds to a small region of the low resolution one, the matching task therefore consists
in finding a small region in the low resolution image that can be assigned to the whole high resolution image under the
plane similarity transformation, which can be defined by the local area correlation coefficient to match the interest points
and rectified by similarity transform. Experiment shows that our matching algorithm can be used for scale changing up
to a factor of 6. And it is successful to deal with the point matching between two images under large scale.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.