Scholarlink

<p>This paper introduces a computational framework for clustering and visualizing textile fragment relationships using Reflectance Transformation Imaging (RTI). Our approach leverages deep learning models and utilizes the Hemispherical Harmonics (HSH) to extract discriminative features. The RTI data is first modeled using HSH before feature extraction by the deep feature extractor. The feature vectors are visualized through dimensionality reduction techniques, which help reveal the relationship between fragments by clustering them. We tested the proposed framework on the Oseberg textile collection (an open archeological artifact assembly problem) and a control dataset of Polish Dragoons textiles, demonstrating that the algorithm achieves good intra-class similarity and inter-class separation, distinguishing different textiles. RTI-based framework achieves higher clustering and dimensionality reduction evaluation scores between related fragments than RGB photography. The results confirm RTI’s potential as a data-rich, non-destructive imaging technique for supporting archeological reconstruction.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Beyond relighting: RTI for clustering fragmented heritage textiles using deep learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月12日

查看全文
  • Muhammad Arsalan Khawaja,
  • Davit Gigilashvili,
  • Tomasz Łojewski,
  • Sony George,
  • Franck Marzani,
  • Jon Yngve Hardeberg,
  • Alamin Mansouri

摘要

This paper introduces a computational framework for clustering and visualizing textile fragment relationships using Reflectance Transformation Imaging (RTI). Our approach leverages deep learning models and utilizes the Hemispherical Harmonics (HSH) to extract discriminative features. The RTI data is first modeled using HSH before feature extraction by the deep feature extractor. The feature vectors are visualized through dimensionality reduction techniques, which help reveal the relationship between fragments by clustering them. We tested the proposed framework on the Oseberg textile collection (an open archeological artifact assembly problem) and a control dataset of Polish Dragoons textiles, demonstrating that the algorithm achieves good intra-class similarity and inter-class separation, distinguishing different textiles. RTI-based framework achieves higher clustering and dimensionality reduction evaluation scores between related fragments than RGB photography. The results confirm RTI’s potential as a data-rich, non-destructive imaging technique for supporting archeological reconstruction.