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Finally, using the model above and the correlation recorded for that
model, it is possible to carry out the search as introduced in the
beginning of this section.
The search is basically reliant on error or similarity measures calculated
after each attempted parameterisation. Each such reparameterisation
is initially guided by the matrices (images) that express correlating
between the modes of appearance change and the intensity values.
The model, as shown in Figure 1, is placed within the image frame,
close enough to its target. How close it should be put to the target
is an issue that will not to be explained in any real detail, but
true convergence may never be reached if bad initialisation takes
place. The algorithm will most possibly then terminate when it reaches
a local minima.
The basic search algorithm, expressed in a simplified way, is as follows:
The technique of matching an appearance model to an image is described
in greater detail with some examples in [10]. It is also
worth mentioning that in practice, in order to decrease the total
run-time, varying increasing image resolutions are selected in the
search iterations. This technique is a very common one in computer
graphics and image interpretation tasks. A pyramid can be used to
describe the data available for choice. The figure below shows how
the size of the image quadruples (doubles in each of the two axes)
at each stage of the pyramid, where the base of this pyramid is level
0 (level numbers increase upwards). Finer resolution images are at
the bottom and low-resolution coarse ones at the top. The searching
process typically begins at the very top of the pyramid and declares
convergence only once it has reached the full resolution that is not
lossy, i.e. it captures the whole pre-existent data.
Figure 2: Resolution selection
Next: Applications of Active Appearance
Up: Learning Correlations in the
Previous: Learning Correlations in the
2004-07-19