{
  "abstract": "Superpixel segmentation provides an important mid-level representation for computer vision, but accurate boundary adherence remains difficult in images with weak contours, cluttered backgrounds, and complex textures. We propose a boundary-complexity-guided adaptive weighting framework for single-image unsupervised superpixel segmentation, termed AW-Super. The proposed Enhanced Boundary Feature Extraction Module (EBFEM) fuses semantic edge cues with gradient-based local structural information to produce a continuous complexity map, which is then used to reweight the optimization objective spatially. As a result, the method imposes stronger boundary-sensitive constraints in structurally complex regions while encouraging shape regularity in relatively homogeneous areas. We further introduce an adaptive stopping criterion into the Deep Image Prior optimization process, replacing a fixed iteration schedule with an image-dependent termination rule. Experiments on BSDS500 and SBD under matched superpixel budgets ( $$N=50$$ –500) demonstrate high boundary recall together with competitive overall segmentation performance, without requiring dataset-level training or ground-truth superpixel annotations. An additional cross-domain evaluation on DRIVE retinal images against SLIC and SEEDS further supports the applicability of AW-Super beyond natural-image benchmarks.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Nannan Wang"
    },
    {
      "name": "Qianyi Zhang"
    },
    {
      "name": "Yongxia Zhang"
    },
    {
      "name": "Wei Wei"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6c6vfbjay8rm3.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-74191-2",
  "fulltext_url": null,
  "id": "6c6vfbjay8rm3",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-74191-2",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7220685025",
  "pdf_url": "https://www.nature.com/articles/s41598-026-74191-2.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/6c6vfbjay8rm3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.1038/s41598-026-74191-2",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-74191-2"
    }
  ],
  "subjects": [],
  "title": "Boundary-complexity-guided adaptive weighting for single-image unsupervised superpixel segmentation",
  "title_text": "Boundary-complexity-guided adaptive weighting for single-image unsupervised superpixel segmentation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/6c6vfbjay8rm3",
  "venue": "Scientific Reports",
  "versions": []
}