  We propose a novel method for large-scale image stitching that is robust
against repetitive patterns and featureless regions in the imagery. In such
cases, state-of-the-art image stitching methods easily produce image alignment
artifacts, since they may produce false pairwise image registrations that are
in conflict within the global connectivity graph. Our method augments the
current methods by collecting all the plausible pairwise image registration
candidates, among which globally consistent candidates are chosen. This enables
the stitching process to determine the correct pairwise registrations by
utilizing all the available information from the whole imagery, such as
unambiguous registrations outside the repeating pattern and featureless
regions. We formalize the method as a weighted multigraph whose nodes represent
the individual image transformations from the composite image, and whose sets
of multiple edges between two nodes represent all the plausible transformations
between the pixel coordinates of the two images. The edge weights represent the
plausibility of the transformations. The image transformations and the edge
weights are solved from a non-linear minimization problem with linear
constraints, for which a projection method is used. As an example, we apply the
method in a large-scale scanning application where the transformations are
primarily translations with only slight rotation and scaling component. Despite
these simplifications, the state-of-the-art methods do not produce adequate
results in such applications, since the image overlap is small, which can be
featureless or repetitive, and misalignment artifacts and their concealment are
unacceptable.
