  Image registration, especially the quantification of image similarity, is an
important task in image processing. Various approaches for the comparison of
two images are discussed in the literature. However, although most of these
approaches perform very well in a two image scenario, an extension to a
multiple images scenario deserves attention. In this article, we discuss and
compare registration methods for multiple images. Our key assumption is, that
information about the singular values of a feature matrix of images can be used
for alignment. We introduce, discuss and relate three recent approaches from
the literature: the Schatten q-norm based SqN distance measure, a rank based
approach, and a feature volume based approach. We also present results for
typical applications such as dynamic image sequences or stacks of histological
sections. Our results indicate that the SqN approach is in fact a suitable
distance measure for image registration. Moreover, our examples also indicate
that the results obtained by SqN are superior to those obtained by its
competitors.
