Image completion using a diffusion driven mean curvature flow in a sub-riemannian space

In this paper we present an implementation of a perceptual completion model performed in the three dimensional space of position and orientation of level lines of an image. We show that the space is equipped with a natural subriemannian metric. This model allows to perform disocclusion representing both the occluding and occluded objects simultaneously in the space. The completion is accomplished by computing minimal surfaces with respect to the non Euclidean metric of the space. The minimality is achieved via diffusion driven mean curvature flow. Results are presented in a number of cognitive relevant cases.