Optic Flow from Dynamic Anchor Point Attributes

Optic flow describes the apparent motion that is present in an image sequence. We show the feasibility of obtaining optic flow from dynamic properties of a sparse set of so called anchor points. Singular points of a Gaussian scale space image are identified as feasible anchor point candidates and analytical expressions describing their dynamic properties are presented. The advantage of approaching the optic flow estimation problem using these anchor points is that in these points the notorious aperture problem does not manifest itself. The proposed optic flow estimation algorithm heavily depends on stationary reconstruction, which aims for a reconstruction of a static image from a set of features that is visually close to the image from which the features are extracted. Degrees of freedom that are not fixed by the constraints are disambiguated with the help of a so-called prior (i.e. a user defined model). A linear reconstruction framework is proposed that generalises a previously proposed scheme. As an example we propose a specific prior and apply it to the reconstruction from singular points. The reconstruction is visually more attractive and has a smaller L2-error than the previously proposed linear methods. The proposed optic flow estimation method succeeds in finding a dense vector field that approaches the optic flow field from a sparse set of inherent multi scale anchor points. As opposed to classical optic flow estimation schemes the proposed method accounts for an explicit scale component of the vector field, which could encode a hitherto unknown dynamic property.