Variational methods currently belong to the most accurate techniques for the
computation of the displacement field between the frames of an image sequence.
Accuracy and time performance improvements of these methods are achieved every
year. Most of the effort is directed towards finding better data and smoothness
terms for the energy functional. Usually people are working mainly with
black-and-white image sequences.
In this thesis we will consider only colour images, as we believe that the
carries much more information than the grey value and can help us for the
better estimation of the optic flow. So far most of the research done in optic
flow computation does not consider the presence of realistic illumination
changes in the image sequences.
One of the main goals of this thesis is to find new constancy assumptions for
the data term, which overcome the problems of severe illumination changes. So
far no research has been also done on combining variational methods with
statistical moments for the purpose of optic flow computation. The second goal
of this thesis is to investigate how and to what extend the optic flow methods
can benefit from the rotational invariant moments. We will introduce a new
variational methods framework that can combine all of the above mentioned new
assumptions into a successful optic flow computation technique.