| DC Field | Value | Language |
|---|
| dc.contributor.author | Jiarou Wang | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2026-05-22T08:27:36Z | - |
| dc.date.available | 2026-05-22T08:27:36Z | - |
| dc.date.issued | 2026 | - |
| dc.identifier.citation | Jiarou Wang. Deep learning-based skeleton extraction / Jiarou Wang // Информационная безопасность : сборник материалов 62-й научной конференции аспирантов, магистрантов и студентов БГУИР, Минск, 13–17 апреля 2026 г. / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: С. В. Дробот (гл. ред.) [и др.]. – Минск, 2026. – С. 197–198. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/63807 | - |
| dc.description.abstract | This paper presents BlumNet, a deep learning framework for object skeleton extraction based on graph component detection. It decomposes skeletons into curves, endpoints and junctions, uses a multi‑branch network and joint loss, then reconstructs topologically consistent skeletons. Experiments on the SK1491 dataset show that BlumNet avoids breaks and false branches, outperforms U‑Net and SkeletonNetV2, and achieves high accuracy and structural consistency. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | БГУИР | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | deep learning methods | en_US |
| dc.subject | skeleton curves | en_US |
| dc.subject | skeleton reconstruction module | en_US |
| dc.title | Deep learning-based skeleton extraction | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Информационная безопасность : материалы 62-й научной конференции аспирантов, магистрантов и студентов (2026)
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