Marginalized Maximum a Posteriori Hyper-parameter Estimation for Global Optical Flow Techniques

Global optical flow estimation methods contain a regularization parameter (or prior and likelihood hyper-parameters if we consider the statistical point of view) which control the tradeoff between the different constraints on the optical flow field. Although experiments (see e.g. Ng et al. [Ng and Solo(1997)]) indicate the importance of the optimal choice of the hyper-parameters, only little attention has been focused on the optimal choice of these parameters in global motion estimation techniques in literature so far (the authors are only aware of one contribution [Ng and Solo(1997)] which attempts to estimate only the prior hyper-parameter whereas the likelihood hyper-parameter needs to be known). We adapt the marginalized maximum a posteriori (MMAP) estimator proposed in [Mohammad-Djafari(1995)] to simultaneously estimating hyper-parameters and optical flow for global motion estimation techniques. Experiments demonstrate the performance of this optimization technique and show that the choice of the regularization parameter/hyperparameters is an essential key-point in order to obtain precise motion estimates.