论文标题
通用最短路径的超像素，用于精确分割球形图像
Generalized Shortest Path-based Superpixels for Accurate Segmentation of Spherical Images
论文作者
论文摘要
现有的大多数超级像素方法旨在将标准平面图像作为计算机视觉管道的预处理。然而，基于广角捕获设备（主要生成360°球形图像的广角捕获设备）的应用数量越来越多，已强制使用专用的超级像素方法。在本文中，我们引入了一种新的Superpixel方法，用于球形图像，称为SPHSP（用于球形最短的基于路径的Superpixels）。我们的方法尊重球形几何形状，并概括了3D球形采集空间上像素和超像素中心之间最短路径的概念。我们表明，该路径上的特征信息可以有效地集成到我们的聚类框架中，并共同提高对象轮廓和形状规则性的尊重。为了评估球形空间中的最后一个方面，我们还概括了平面全球规则度度量。最后，提出的SPHSP方法获得的性能明显优于平面和最新的球形超像素方法，参考360°球形全景分割数据集。
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.
