Szczegóły publikacji
Opis bibliograficzny
Optimizing image segmentation for microstructure analysis of high-strength steel: histogram-based recognition of martensite and bainite / Filip HALLO, Tomasz JAŻDŻEWSKI, Piotr BAŁA, Grzegorz Korpała, Krzysztof REGULSKI // Materials [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1944 . — 2026 — vol. 19 iss. 2 art. no. 429, s. 1-17. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 16-17, Abstr. — Publikacja dostępna online od: 2026-01-22
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165610 |
|---|---|
| Data dodania do BaDAP | 2026-02-05 |
| Tekst źródłowy | URL |
| DOI | 10.3390/ma19020429 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Materials |
Abstract
This study systematically compares three unsupervised segmentation algorithms (Simple Linear Iterative Clustering (SLIC), Felzenszwalb’s graph-based method, and the Watershed algorithm) in combination with two classification approaches: Random Forest using histogram-based features and Convolutional Neural Networks (CNNs). The study employs Bayesian optimization to jointly tune segmentation parameters and model hyperparameters, investigating how segmentation quality impacts downstream classification performance. The methodology is validated using light optical microscopy images of a high-strength steel sample, with performance evaluated through stratified cross-validation and independent test sets. The findings demonstrate the critical importance of segmentation algorithm selection and provide insights into the trade-offs between feature-engineered and end-to-end learning approaches for microstructure analysis.
