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New paper details algorithms for hierarchical watershed segmentation

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This paper presents a comprehensive pipeline for computing hierarchical watershed segmentations using edge-weighted graph algorithms. It reviews existing literature and details a step-by-step process from graph-based image representation to connected component computation. The work aims to consolidate various watershed notions and algorithms, including supervised and unsupervised versions, for easier implementation and application. AI

IMPACT Provides a consolidated reference for watershed segmentation algorithms, potentially aiding researchers in computer vision tasks.

RANK_REASON The cluster contains a single academic paper submission to arXiv. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New paper details algorithms for hierarchical watershed segmentation

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The cluster contains a single academic paper submission to arXiv. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jean Cousty (LIGM), Laurent Najman (KUSTAR, LIGM), Benjamin Perret (LIGM), Deise Santana Maia (CRIStAL) ·

    Playing with Kruskal: algorithms for flat and hierarchical watershed cuts

    arXiv:2610.10012v1 Announce Type: new Abstract: 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 seg…

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