Image differencing is used for many applications involving
change detection. Although it is usually followed by a
thresholding operation to isolate regions of change there
are few methods available in the literature specific to (and
appropriate for) change detection. We describe four different
methods for selecting thresholds that work on very
different principles. Either the noise or the signal is modelled,
and the model covers either the spatial or intensity
distribution characteristics. The methods are: 1/ a Normal
model is used for the noise intensity distribution, 2/ signal
intensities are tested by making local intensity distribution
comparisons in the two image frames (i.e. the difference
map is not used), 3/ the spatial properties of the noise are
modelled by a Poisson distribution, and 4/ the spatial properties
of the signal are modelled as a stable number of regions
(or stable Euler number).
