  In this paper we address the problem of matching two images with two
different resolutions: a high-resolution image and a low-resolution one. The
difference in resolution between the two images is not known and without loss
of generality one of the images is assumed to be the high-resolution one. On
the premise that changes in resolution act as a smoothing equivalent to changes
in scale, a scale-space representation of the high-resolution image is
produced. Hence the one-to-one classical image matching paradigm becomes
one-to-many because the low-resolution image is compared with all the
scale-space representations of the high-resolution one. Key to the success of
such a process is the proper representation of the features to be matched in
scale-space. We show how to represent and extract interest points at variable
scales and we devise a method allowing the comparison of two images at two
different resolutions. The method comprises the use of photometric- and
rotation-invariant descriptors, a geometric model mapping the high-resolution
image onto a low-resolution image region, and an image matching strategy based
on local constraints and on the robust estimation of this geometric model.
Extensive experiments show that our matching method can be used for scale
changes up to a factor of 6.
