We contend that the task of registration should seek a globally correct answer, rather than applying transformations that are reliant on the choice of a single reference image. The intrinsic power of appearance models, as well as Minimum Description Length considerations, allow us to define a fully automatic group-wise non-rigid registration scheme.
Various observations motivated us to investigate the innate bonds between non-rigid image registration and appearance models. Unification of the two will be mutually beneficiary and can entail a novel image analysis and alignment framework.
A complex and highly flexible application we constructed and called AART (Autonomous Appearance-based Registration Test-bed) provides proof of the premise above. It benchmarks genuine registration algorithms against other well-performing pair-wise algorithms and newly-conceived methods to illustrate the advantages gained by unifying registration and statistical modeling.