The past 20 years has seen a progressive evolution of computer vision algorithms for unsupervised 2D image segmentation. While earlier efforts relied on Markov random fields and efficient optimization (graph cuts, etc.), the next wave of methods beginning in the early part of this century were, in the main, stovepiped. Of these 2D segmentation efforts, one of the most popular and, indeed, one that comes close to being a state of the art method is the ultrametric contour map (UCM). The pipelined methodology consists of (i) computing local, oriented responses, (ii) graph creation, (iii) eigenvector computation (globalization), (iv) integration of local and global information, (v) contour extraction, and (vi) superpixel hierarchy construction. UCM performs well on a range of 2D tasks. Consequently, it is somewhat surprising that no 3D version of UCM exists at the present time. To address that lack, we present a novel 3D supervoxel segmentation method, dubbed 3D UCM, which closely follows its 2D counterpart while adding 3D relevant features. The methodology, driven by supervoxel extraction, combines local and global gradient-based features together to first produce a low-level supervoxel graph. Subsequently, an agglomerative approach is used to group supervoxel structures into a segmentation hierarchy with explicitly imposed containment of lower-level supervoxels in higher-level supervoxels. Comparisons are conducted against state of the art 3D segmentation algorithms. The considered applications are 3D spatial and 2D spatiotemporal segmentation scenarios. For the latter comparisons, we present results of 3D UCM with and without optical flow video pre-processing. As expected, when motion correction beyond a certain range is required, we demonstrate that 3D UCM in conjunction with optical flow is a very useful addition to the pantheon of video segmentation methods.

中文翻译：

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基于时空处理的基于Supervoxel的3D图像分割与光流集成

在过去的20年中，用于无监督2D图像分割的计算机视觉算法得到了逐步发展。尽管早期的工作依赖于马尔可夫随机场和有效的优化（图形切割等），但从本世纪初开始的下一波方法主要是瘦腿的。在这些2D分割工作中，最流行的，而且实际上最接近一种最先进方法的工作之一就是超轮廓线图（UCM）。流水线方法包括（i）计算局部定向响应，（ii）图形创建，（iii）特征向量计算（全球化），（iv）局部和全局信息的集成，（v）轮廓提取和（vi）超像素层次结构。UCM在一系列2D任务中表现出色。所以，令人惊讶的是，目前不存在UCM的3D版本。为了解决这一不足，我们提出了一种新颖的3D超体素分割方法，称为3D UCM，该方法紧跟其2D对应对象，同时添加了3D相关功能。该方法由超体素提取驱动，将基于局部和全局梯度的特征组合在一起，以首先生成低级超体素图。随后，使用聚集方法将超体素结构分组为分段层次结构，并在高级别超体素中明确施加了对低级超体素的约束。针对最先进的3D分割算法进行了比较。所考虑的应用是3D空间和2D时空分割方案。对于后面的比较，我们介绍了带有或不带有光流视频预处理的3D UCM的结果。不出所料，当需要超出一定范围的运动校正时，我们证明3D UCM与光流相结合是万能视频分割方法的一个非常有用的补充。