Generalized Shortest Path-based Superpixels for Accurate Segmentation of Spherical Images
This addresses the need for accurate segmentation in applications using 360° spherical images from wide-angle capture devices.
The paper tackles the problem of superpixel segmentation for spherical images by introducing SphSPS, a method that generalizes shortest paths to respect spherical geometry, resulting in significantly better performance than existing planar and spherical approaches on a reference dataset.
Most of existing superpixel methods are designed to segment standard planar images as pre-processing for computer vision pipelines. Nevertheless, the increasing number of applications based on wide angle capture devices, mainly generating 360° spherical images, have enforced the need for dedicated superpixel approaches. In this paper, we introduce a new superpixel method for spherical images called SphSPS (for Spherical Shortest Path-based Superpixels). Our approach respects the spherical geometry and generalizes the notion of shortest path between a pixel and a superpixel center on the 3D spherical acquisition space. We show that the feature information on such path can be efficiently integrated into our clustering framework and jointly improves the respect of object contours and the shape regularity. To relevantly evaluate this last aspect in the spherical space, we also generalize a planar global regularity metric. Finally, the proposed SphSPS method obtains significantly better performance than both planar and recent spherical superpixel approaches on the reference 360° spherical panorama segmentation dataset.