Automatic Die Studies for Ancient Numismatics
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 42 references that are in the map. First posted on arXiv 30 Jul 2024.
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References · 16
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2018MegaDepth: Learning Single-View Depth Prediction from Internet Photoscited by 731
- 2023LightGlue: Local Feature Matching at Light Speedcited by 583
- 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 379
- 2020DISK: Learning local features with policy gradientcited by 310
- 2023RoMa: Robust Dense Feature Matchingcited by 296
- 2019MAGSAC++, a fast, reliable and accurate robust estimatorcited by 160
- 2024XFeat: Accelerated Features for Lightweight Image Matchingcited by 113
- 2024OmniGlue: Generalizable Feature Matching with Foundation Model Guidancecited by 55
- 2015Adjusting for Chance Clustering Comparison Measurescited by 27
- 2015WxBS: Wide Baseline Stereo Generalizationscited by 23
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- 2024MESA: Matching Everything by Segmenting Anythingcited by 16
- 2021Riedones3D: a celtic coin dataset for registration and fine-grained clusteringcited by 3
- 2021Cohesion and Repulsion in Bayesian Distance Clusteringcited by 2
- 2021Unsupervised Statistical Learning for Die Analysis in Ancient Numismaticscited by 2
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