Oil sand image segmentation using the inclusion filter

Oil sands may constitute two thirds of the world´s oil reserves. To efficiently harvest this important resource, image analysis is required to quantify production related performance in terms of particle size distribution. We utilize connected filters to simplify the oil sand images and to generate a robust segmentation. Specifically, a self-dual operator called the inclusion filter is applied to the difficult segmentation problem. The inclusion filter removes minor interior regions and clutter based on the connected component relationships defined by the adjacency forest. We show that the use of the inclusion filter significantly improves the edge fidelity and the insensitivity to initialization for the oil sand application.