Ahmad Aal-Yhia, Paul Malcolm, Otar Akanyeti, Reyer Zwiggelaar, and
Bernard Tiddeman
Groupwise image alignment automatically provides non-rigid registration
across a set of images. It has found applications in facial image
analysis and medical image analysis by automatically generating
statistical models of shape and appearance. The main approaches used
previously include iterative and graph-based approaches. In iterative
approaches, the registration of each image is iteratively updated to
minimise an error measure across the set. Various metrics and
optimisation strategies have been proposed to achieve this. Graph-based
methods perform registration of each pair of images in the set, to form a
weighted graph of the ''distance'' between all the images, and then finds
the optimal paths between the most central image and every other image.
In this paper, we use a graph-based approach to perform initialisation,
which is then refined with an iterative approach. Pairwise registration
is performed using demons registration, then shortest paths identified in
the resulting graph are used to provide an initial warp for each image by
concatenating warps along the path. The warps are refined using an
iterative Levenberg-Marquardt minimisation to the mean, based on updating
the locations of a small number of points and incorporating a stiffness
constraint. This optimisation approach is efficient, has very few free
parameters to tune and we show how to tune the few remaining parameters.
We compare the combined approach to both the iterative and graph-based
approaches used independently. Results demonstrate that the combined
method improves the alignment of various datasets, including two face
datasets and a difficult medical dataset of prostate MRI images.
CCS Concepts: Computing methodologies --> Computer vision;
Matching; Image processing
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