Image Processing Reference
Fig. 8.4 The five steps of segmenting video data through image differencing
Step four of the algorithm is simply a matter of binarizing the difference image
Abs(g(x, y)) by comparing each pixel with a threshold value, T :
if Abs(g(x, y)) < T ;
The threshold in step four will, like any other threshold operation, produce noise due
to an imperfect camera sensor, small fluctuations in the lighting, the object being
similar to the background, etc. The noise will be in the form of missing pixels inside
the silhouette of the object (false negatives, see Appendix C) and silhouette-pixels
outside the actual silhouette (false positives). See Figs. 8.3 or 8.4 for examples.
The noise will in general have a negative influence on the quality of the results
and step five therefore removes the noise (if possible) using some kind of filtering.
Small isolated silhouette-pixels outside the actual silhouette can often be removed
using either a median filter or a morphologic opening operation. The holes inside
the silhouette can often be removed using a morphologic closing operation. Which
method to apply obviously depends on the concrete application.
In the following, image differencing and background subtraction are explained
in more detail.