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Non-rigid registration (NRR) and model-based image analysis were previously believed to possess some commonality - a premise on which current research is still based. There is a growing belief that the best of both can be exploited to construct a unified framework. This modified framework might be more robust and offer higher utility and functionality when compared with the other two approaches working solely.
It is claimed that warping of the images, as is already done in medical imaging in particular, can be used to find correspondences that are optimal in some respects. Group-wise image registration using non-rigid transformations was the way in which previous research attempted to build good models of appearance. Furthermore, non-rigid registration made it possible to achieve better correspondence in images and appearance models could highlight desirable registration, i.e. ideal warping sequences. As a result, better active appearance models could be constructed and non-rigid registration was guided by appearance models rather than similarity measures.