To improve the search performance, good choice of parameters adjustments
is required. It is desirable to learn some correlations off-line and
use them along with the model above to form a robust and efficient
search. Observations are made to learn the correlation between the
change in parameter values (usually each mode independently considered)
and the pixel intensity difference that incurs. This means that for
each change in the parameter values or for a collective change of
several parameters, some change, in certain parts of the image in
particular, will be quite apparent. A matrix of pixels (where rows
represent horizontal scan lines in the image) is used to record the
difference that a re-parameterisation imposes. More mathematically,
for which are the parameters as described above, a change
is applied and the difference in intensities is calculated
as follows:
![]() |
(6) |
Usually sum-of-squares is used here to penalise more harshly for blunt
differences and ensure a summation of only positive values (
).
Taking this intensity difference into consideration, the main correlation
can now be expressed as:
![]() |
(7) |
which simply means that certain offsets to the parameters cause
a certain change in intensity. This correlation is recorded as follows:
![]() |
(8) |
where A is a matrix recording the change in intensities due to the
reparameterisation .
For each mode of variation and each pixel in the mean shape, weighting (negative or positive) is assigned to guide what the search will attempt to focus on. These ``maps'' of weights consume considerable amount of space, but are the only known paradigm for speeding-up through off-line computation. Wavelet compression can be used to reduce the space requirements and make active appearance models rather compact.