The following chooses to focus on the incorporation of MDL in shape models. The process aims to choose good landmarks along some curve without any human intervention.

At the start, correspondences across a set of examples are places
quite arbitrarily. Usually, a path-length spread of the points is
a sensible enough choice which means landmarks are equally-spaced.
This allows maximum freedom of movements for all landmarks mutually.
A model is then created for the whole set and its parameters which
can solely characterise it are used to evaluate its compactness. For
good choice of correspondences across the set we expect *low*
values of as well as ones with low variance, that is, a small
range of acceptable values. The correspondences are then shifted along
the contour of the shape iteratively in a process known as *continuous
reparameterisation*. It is not just mathematically continuous (as
it is functionally defined so that it fits any scale), but it also
potentially affects all landmarks along the curve in a ripple-like/cascading
behaviour. The reparameterisation is kept diffeomorphic so that no
landmarks move beyond the position of their successor (or more generally,
one of their two neighbours), a step that could result in tearing
and/or folding^{5}.

Experience has discovered that examples within the training sets should
be dealt with one at the time^{6}, evaluating the *whole* set and the model at each stage. Reparameterisation
is usually defined by some transformation rules that are vital to
get good and fast results. The reparameterisation is usually applied
to a number of adjacent landmarks at a time and different scales are
chosen at random as well as the location being affected. At the later
stages of the reparameterisation process, it is usually expected that
no real improvements will be made for large scale alternation attempts
and these will therefore be discarded fully in favour of small scale
alternations that make the final fine adjustments. Experience has
also shown that ultimately good choice of landmark can be made *automatic*
mainly due to the ability of evaluating the model from information
theoretic point-of-view, namely MDL.

MDL is well described by Rissanen in [16,17] and the world wide web at: http://www.mdl-research.org/.