Abstract
Etruscan mirrors constitute a significant category within Etruscan art and, therefore, undergo systematic examinations to obtain insights into ancient times. A crucial aspect of their analysis involves the labor-intensive task of manually tracing engravings from the backside. Additionally, this task is inherently challenging due to the damage these mirrors have sustained, introducing subjectivity into the process. We address these challenges by automating the process through photometric-stereo scanning in conjunction with deep segmentation networks which, however, requires effective usage of the limited data at hand. We accomplish this by incorporating predictions on a per-patch level, and various data augmentations, as well as exploring self-supervised learning. Compared to our baseline, we improve predictive performance w.r.t. the pseudo-F-Measure by around 16%. When assessing performance on complete mirrors against a human baseline, our approach yields quantitative similar performance to a human annotator and significantly outperforms existing binarization methods. With our proposed methodology, we streamline the annotation process, enhance its objectivity, and reduce overall workload, offering a valuable contribution to the examination of these historical artifacts and other non-traditional documents.
Abstract (translated)
伊特鲁里亚镜子在伊特鲁里亚艺术中是一个重要的分类，因此它们会进行系统性的检查，以获得对古代时代的洞察。分析的关键部分涉及从背面手动绘制浮雕的劳动密集型任务。此外，由于这些镜子所承受的损害，这项任务本质上具有挑战性，并引入了主观因素。我们通过将光栅化与深度分割网络相结合来自动化这个过程，尽管如此，需要有效使用手头有限的数据。我们通过在每片区域上预测以及各种数据增强方法以及探索自监督学习来实现这一目标。与我们的基线相比，我们在关于伪-F-测量方面的预测性能提高了约16%。当评估整体镜子与人类基线上的表现时，我们的方法与人类注释者的量化类似，显著超过了现有的二值化方法。凭借我们提出的方法，我们简化了注释过程，提高了其客观性，并降低了整体工作量，为研究这些历史文物以及其他非传统文献提供了宝贵的贡献。
URL
https://arxiv.org/abs/2404.15903
