发表机构
Khalifa University（哈利法大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文系统梳理流域分割与最小生成树的联系，提出端到端算法流水线，统一监督/无监督变体及种子计算，为复现提供参考。
AI 中文摘要
在边加权图的框架下，流域已被证明与众所周知的优化问题（如最小生成树）相关联，这使得设计用于计算（分层）流域分割的高效算法成为可能。在本文中，在回顾了与流域分割相关的文献之后，我们提出了一个详细的端到端算法流水线来计算（分层）流域分割，从基于图的图像表示的计算开始，直到最终（分层）分割的连通分量的计算。我们考虑了流域的几种变体，包括其监督和无监督版本，以及计算种子的各种方式，仅举几例。我们首次以紧凑且易于理解的方式将所有流域概念和算法汇集在一起。我们旨在为那些有兴趣在其任务中采用和重新实现流域分割框架的人提供参考。
英文摘要
In the framework of edge-weighted graphs, watersheds have proven to be linked to well-known optimization problems, as Minimum Spanning Tree, which allowed the design of efficient algorithms for computing (hierarchical) watershed segmentations. In the present article, after reviewing the literature related to watershed segmentation, we present a detailed end-to-end pipeline of algorithms to compute (hierarchical) watershed segmentations, starting from the computation of graph-based image representations, up to the computation of connected components of the final (hierarchical) segmentation. We consider the several variations of watersheds, including their supervised and unsupervised versions, and the various ways of computing seeds, to name a few. For the first time, we bring together all these watershed notions and algorithms in a compact and understandable way. We aim at providing a reference for those interested in employing and reimplementing the watershed segmentation framework for their task at hand.
Journal refJournal of Mathematical Imaging and Vision, 2026, 68 (5), pp.65
