Optimization and learning for registration of moving dynamic textures

We address the problem of registering a sequence of images in a moving dynamic texture video. This involves optimization with respect to camera motion, the average image, and the dynamic texture model. This problem is highly illposed and almost impossible to have good solutions without priors. In this paper, we introduce powerful priors for this problem, based on two simple observations: 1) registration should simplify the dynamic texture model while preserving all useful information. It motivates us to compute a prior for the dynamic texture by marginalizing over specific dynamics in the space of all stable auto-regressive sequences; 2) the statistics of derivative filter responses in the average image can be significantly changed by registration, and better registration should lead to a sharper average image. This offers us the prior of requiring the derivative distribution of the estimated average image to be close to that learned from the input image sequence. With these priors, a new registration approach is proposed by marginalizing over the "nuisance" variables under a Bayesian framework. And superior motion estimation results are obtained by jointly optimizing over the registration parameters, the average image, and the dynamic texture model. Experimental results on real video sequences of moving dynamic textures show convincing performance of the proposed approach.