Figure 2

Schematic illustrating a Gauss-Newton scheme for maximizing the posterior probability of the parameters required to spatially normalize an image. This scheme is iterative. At each step the conditional estimate of the parameters is obtained by jointly minimizing the likelihood and the prior potentials. The former is the difference between a resampled (i.e. warped) version y of the image f and the best linear combination of some templates g. These parameters are used to mix the templates and resample the image to progressively reduce both the spatial and intensity differences. After convergence the resampled image can be considered normalized.