Combined Appearance Models
We can model the shape change of an object using a
Statistical Shape Model.. We can use a similar statistical model
to represent the intensity variation across a region. Given a set of
training images, labelled with landmark points, we can use image warping
to deform each image so that the object has the mean shape, then build
a statistical model of the grey-levels across the object.
We can then use the shape and grey-level parameters to synthise new
examples. Given a set of grey-level parameters, we generate the
grey-level image with a mean shape. We then warp that image to
match the shape defined by the shape parameters.
Of course, in many cases the shape and grey-level parameters will
be correllated, and we can build a model to represent this. For
instance, we have built a model of the appearance of my face. Here
is one of the modes of combined shape and grey-level appearance:
(The central image is the mean)
Here are a few example modes from a colour model generated by Gareth Edwards
Such models can be fit to new images using the
Active Appearance Model algorithm.
You can now download a set of tools to build and play with
Appearance Models and AAMs here. Enjoy.