Geometry-based Superpixel Segmentation - Introduction of Planar Hypothesis for Superpixel Construction

Superpixel segmentation is widely used in the preprocessing step of many applications. Most of existing methods are based on a photometric criterion combined to the position of the pixels. In the same way as the Simple Linear Iterative Clustering (SLIC) method, based on k-means segmentation, a new algorithm is introduced. The main contribution lies on the definition of a new distance for the construction of the superpixels. This distance takes into account both the surface normals and a similarity measure between pixels that are located on the same planar surface. We show that our approach improves over-segmentation, like SLIC, i.e. the proposed method is able to segment properly planar surfaces