Die studies are fundamental to quantifying ancient monetary production, providing insights into the relationship between coinage, politics, and history. The process requires tedious manual work, which limits the size of the corpora that can be studied. Few works have attempted to automate this task, and none have been properly released and evaluated from a computer vision perspective. We propose a fully automatic approach that introduces several innovations compared to previous methods. We rely on fast and robust local descriptors matching that is set automatically. Second, the core of our proposal is a clustering-based approach that uses an intrinsic metric (that does not need the ground truth labels) to determine its critical hyper-parameters. We validate the approach on two corpora of Greek coins, propose an automatic implementation and evaluation of previous baselines, and show that our approach significantly outperforms them.

翻译：模具研究是量化古代货币生产的基础，为理解铸币、政治与历史之间的关系提供关键洞见。该过程需要繁琐的手工操作，限制了可研究语料库的规模。此前少有研究尝试自动化此任务，且从计算机视觉角度均未得到正式发布与系统评估。本文提出一种全自动方法，相比既有方法引入多项创新：首先采用可自动设置的快速鲁棒局部描述符匹配；其次，本方法核心是基于聚类的方案，利用无需真实标签的内在度量确定关键超参数。我们在两套希腊钱币语料库上验证了该方法，实现了对现有基线的自动化实施与评估，结果表明本方法性能显著优于既有基线。