The method was used to learn about registration algorithms that often lack benchmark tools. Crum et al.  have devised validation methods based on overlap measures of markup. These can measure the quality of registration, however they require ground-truth annotation.
Fig. 8 shows an appearance model which was built automatically using group-wise registration. Group-wise registration account for the entire set when registering, whereas pair-wise register with respect to a single reference. The initialisation algorithm is an information-theoretic group-wise algorithm that is described in .
Fig. 8. Appearance model which was built automatically by group-wise registration. First mode is shown, standard deviations.
It can be inferred from the results (Fig. 9) that group-wise registration outperforms pair-wise registration. This means that a group-wise registration leads to better model and is hence better representative of the group.
Fig. 9. Registration evaluation which compares 3 different registration algorithms. Specificity is shown on the left and generalisation ability on the right. Values are the mean over a wide range of modes in the model.
It was ensured that the comparison is not dependent upon the process where syntheses are extracted from the model. Since a finite number of modes is selected in synthesis, the range of 2-20 modes was investigated and the figure shows the average. Nonetheless, the properties of the bar charts did remain consistent throughout.