|ABSTRACT: Since Principal Component Analysis
(PCA) technique has been applied by Sirovich and Kirby to parameterize
the face, many computer vision researches have used eigen-whatever techniques
to construct linear models of optical flow, shape, graylevel, etc.
One drawback of such a technique is the need to learn the model, since
it is required to gather aligned data, usually with the tedious and
inaccurate hand cropping process. This paper describes a robust algorithm
for automatically learning an appearance subspace of objects performing
rigid motion through an image sequence, given a manual initialization
of the regions of support (masks) in the first frame. The learning process
is posed as a continuous optimization problem and it is solved with a mixture
of stochastic and deterministic techniques achieving sub-pixel accuracy.
Additionally, we learn the dynamics of the motion and appearance parameters
for scene characterization and point out the benefits of working with modular
eigenspaces (ME). Preliminary results of automatic learning a modular eigenface
model with applications to real time video conferencing, human computer
interaction and actor animation are reported.