Globally optimal wavelet-based motion estimation using interscale edge and occlusion models

We propose a non-iterative, globally optimal dense motion field estimation technique based on a multiresolutional probability model. We consider the field to be estimated in terms of its wavelet coefficients and carry out the estimation in the field's wavelet transform domain. Our approach models interscale dependencies of the wavelet coefficients and allows for smooth. edge, and occluded regions in the field. We obtain segmentations of the field and our results show that the field estimates yield accurate depictions of scene motion. The globally optimal nature of our estimation framework allows it to be applicable in scenes exhibiting large motion and in settings of ill-posed motion. Hence, our algorithms can also be used to determine accurate initializations for optical flow type estimation techniques, which use more sophisticated models but can only obtain locally optimal solutions that are heavily dependent on initial conditions. The performance is illustrated on several examples.