Invariance with Optic Flow

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 colour itself 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