{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T10:15:43Z","timestamp":1783073743842,"version":"3.54.6"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031915710","type":"print"},{"value":"9783031915727","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-91572-7_15","type":"book-chapter","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T04:14:33Z","timestamp":1747973673000},"page":"246-262","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Die Studies for\u00a0Ancient Numismatics"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5829-9741","authenticated-orcid":false,"given":"Cl\u00e9ment","family":"Cornet","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5850-5087","authenticated-orcid":false,"given":"H\u00e9lo\u00efse","family":"Auma\u00eetre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1331-5768","authenticated-orcid":false,"given":"Romaric","family":"Besan\u00e7on","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1579-1311","authenticated-orcid":false,"given":"Julien","family":"Olivier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1016-3340","authenticated-orcid":false,"given":"Thomas","family":"Faucher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0520-8436","authenticated-orcid":false,"given":"Herv\u00e9","family":"Le Borgne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Allaoui, M., Kherfi, M.L., Cheriet, A.: Considerably improving clustering algorithms using UMAP dimensionality reduction technique: a comparative study. In: International Conference on Image and Signal Processing, pp. 317\u2013325. Springer (2020)","DOI":"10.1007\/978-3-030-51935-3_34"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Anwar, H., Anwar, S., Zambanini, S., Porikli, F.: Deep ancient roman republican coin classification via feature fusion and attention. Pattern Recognit. 114(107871) (2021)","DOI":"10.1016\/j.patcog.2021.107871"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Barath, D., Noskova, J., Ivashechkin, M., Matas, J.: MAGSAC++, a fast, reliable and accurate robust estimator. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1304\u20131312 (2020)","DOI":"10.1109\/CVPR42600.2020.00138"},{"key":"15_CR4","unstructured":"Bland, R.: Quantifying the size of a coinage: die studies or coin finds. In: Elkins, N., DeRose\u00a0Evans, J. (eds.) Concordia Disciplinarum. Essays on Ancient Coinage, History, and Archaeology in Honor of William E. Metcalf, pp. 223\u2013234. Numismatic Studies 38, New York (2018)"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"15_CR6","first-page":"195","volume":"28","author":"GF Carter","year":"1983","unstructured":"Carter, G.F.: A simplified method for calculating original number of dies from die link statistics. Museum Notes (American Numismatic Society) 28, 195\u2013206 (1983)","journal-title":"Museum Notes (American Numismatic Society)"},{"issue":"2","key":"15_CR7","doi-asserted-by":"publisher","first-page":"27","DOI":"10.3390\/sci2020027","volume":"2","author":"J Cooper","year":"2020","unstructured":"Cooper, J., Arandjelovi\u0107, O.: Learning to describe: a new approach to computer vision based ancient coin analysis. Science 2(2), 27 (2020)","journal-title":"Science"},{"key":"15_CR8","doi-asserted-by":"publisher","unstructured":"Deligio, C., Tolle, K., Wigg-Wolf, D.: Supporting the analysis of a large coin hoard with AI-based methods. In: CAA Conference, Amsterdam (2023). https:\/\/doi.org\/10.5281\/zenodo.11187474","DOI":"10.5281\/zenodo.11187474"},{"key":"15_CR9","unstructured":"Edstedt, J., Sun, Q., B\u00f6kman, G., Wadenb\u00e4ck, M., Felsberg, M.: RoMa: robust dense feature matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19790\u201319800 (2024)"},{"key":"15_CR10","unstructured":"Esty, W.: The geometric model for estimating the number of dies. In: de\u00a0Callata\u00ff, F. (ed.) Quantifying Monetary Supplies in Greco-Roman Times, pp. 43\u201358. Pragmateiai 19, Bari (2011)"},{"key":"15_CR11","unstructured":"Faucher, T., Olivier, J., Brissaud, P., Desbordes, C.: EH 208. Tr\u00e9sor de Tanis, 1986. In: Faucher, T., Meadows, A., Lorber, C. (eds.) Egyptian Hoards I, The Ptolemies, pp. 203\u2013222. No.\u00a0168 in Biblioth\u00e8que d\u2019\u00e9tude (2017). https:\/\/hal.science\/hal-02510823"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Fonov, N., Ksenia, U.: Development an accurate neural network for coin recognition. In: IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), St. Petersburg, Moscow, Russia, pp. 337\u2013341 (2021)","DOI":"10.1109\/ElConRus51938.2021.9396592"},{"issue":"383","key":"15_CR13","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1080\/01621459.1983.10478008","volume":"78","author":"EB Fowlkes","year":"1983","unstructured":"Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78(383), 553\u2013569 (1983)","journal-title":"J. Am. Stat. Assoc."},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp.\u00a01\u20136 (1988)","DOI":"10.5244\/C.2.23"},{"key":"15_CR15","unstructured":"Heinecke, A., Mayer, E., Natarajan, A., Jung, Y.: Unsupervised statistical learning for die analysis in ancient numismatics. arXiv preprint arXiv:2112.00290 (2021)"},{"key":"15_CR16","unstructured":"Horache, S., Deschaud, J.E., Goulette, F., Gruel, K., Lejars, T., Masson, O.: Riedones3D: a celtic coin dataset for registration and fine-grained clustering. arXiv preprint arXiv:2109.15033 (2021)"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Jiang, H., Karpur, A., Cao, B., Huang, Q., Araujo, A.: OmniGlue: generalizable feature matching with foundation model guidance. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19865\u201319875 (2024)","DOI":"10.1109\/CVPR52733.2024.01878"},{"key":"15_CR18","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s10845-018-1438-3","volume":"31","author":"K Joshi","year":"2020","unstructured":"Joshi, K., Chauhan, V., Surgenor, B.: A flexible machine vision system for small part inspection based on a hybrid SVM\/ANN approach. J. Intell. Manuf. 31, 103\u2013125 (2020)","journal-title":"J. Intell. Manuf."},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Kaufman, L., Rousseeuw, P.: Agglomerative Nesting (program Agnes) in Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken (1990)","DOI":"10.1002\/9780470316801"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Li, Z., Snavely, N.: MegaDepth: learning single-view depth prediction from internet photos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2041\u20132050 (2018)","DOI":"10.1109\/CVPR.2018.00218"},{"key":"15_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Lindenberger, P., Sarlin, P.E., Pollefeys, M.: LightGlue: local feature matching at light speed. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 17627\u201317638 (2023)","DOI":"10.1109\/ICCV51070.2023.01616"},{"key":"15_CR23","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vision"},{"key":"15_CR24","unstructured":"Manzoor, S., Ali, N., Raees, M., Khan, K.A., Ayub, M.U., Ahmed, A.: Ancient coin classification based on recent trends of deep learning. In: VIPERC (2022)"},{"issue":"11","key":"15_CR25","doi-asserted-by":"publisher","first-page":"205","DOI":"10.21105\/joss.00205","volume":"2","author":"L McInnes","year":"2017","unstructured":"McInnes, L., Healy, J., Astels, S., et al.: hdbscan: Hierarchical density based clustering. J. Open Source Softw. 2(11), 205 (2017)","journal-title":"J. Open Source Softw."},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., Melville, J.: UMAP: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)","DOI":"10.21105\/joss.00861"},{"issue":"5","key":"15_CR27","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1016\/j.jmva.2006.11.013","volume":"98","author":"M Meil\u0103","year":"2007","unstructured":"Meil\u0103, M.: Comparing clusterings-an information based distance. J. Multivar. Anal. 98(5), 873\u2013895 (2007)","journal-title":"J. Multivar. Anal."},{"key":"15_CR28","unstructured":"Mishkin, D., Matas, J., Perdoch, M., Lenc, K.: WxBS: wide baseline stereo generalizations. In: British Machine Vision Conference (2015)"},{"issue":"546","key":"15_CR29","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1080\/01621459.2023.2191821","volume":"119","author":"A Natarajan","year":"2024","unstructured":"Natarajan, A., De Iorio, M., Heinecke, A., Mayer, E., Glenn, S.: Cohesion and repulsion in bayesian distance clustering. J. Am. Stat. Assoc. 119(546), 1374\u20131384 (2024)","journal-title":"J. Am. Stat. Assoc."},{"key":"15_CR30","unstructured":"Olivier, J., Faucher, T.: Egyptian Hoards I: The Ptolemies, chap. EH 69. Le tr\u00e9sor de Paphos (IGCH 147; CH 2.106; 4.68), pp. 258\u2013478. Institut Fran\u00e7ais d\u2019Arch\u00e9ologie orientale (2017)"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Potje, G., Cadar, F., Araujo, A., Martins, R., Nascimento, E.R.: XFeat: accelerated features for lightweight image matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2682\u20132691 (2024)","DOI":"10.1109\/CVPR52733.2024.00259"},{"issue":"3","key":"15_CR32","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.76.036106","volume":"76","author":"UN Raghavan","year":"2007","unstructured":"Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)","journal-title":"Phys. Rev. E"},{"issue":"134","key":"15_CR33","first-page":"1","volume":"17","author":"S Romano","year":"2016","unstructured":"Romano, S., Vinh, N.X., Bailey, J., Verspoor, K.: Adjusting for chance clustering comparison measures. J. Mach. Learn. Res. 17(134), 1\u201332 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR34","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987)","journal-title":"J. Comput. Appl. Math."},{"key":"15_CR35","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 International Conference on Computer Vision, pp. 2564\u20132571. IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"15_CR36","doi-asserted-by":"publisher","DOI":"10.1109\/IPTA.2014.7001960","volume-title":"Segmentation System and its Evaluation for Gray Scale Coin Documents","author":"V Say","year":"2014","unstructured":"Say, V., Coustaty, M., Chazalon, J., Ogier, J.M.: Segmentation System and its Evaluation for Gray Scale Coin Documents. Image Processing Theory, Tools and Applications (2014)"},{"key":"15_CR37","unstructured":"Taylor, Z.M.: The computer-aided die study (CADS): a tool for conducting numismatic die studies with computer vision and hierarchical clustering. Computer Science Honors Theses, 54 (2020)"},{"key":"15_CR38","doi-asserted-by":"crossref","unstructured":"Traag, V., Waltman, L., van Eck, N.: From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9 (2019)","DOI":"10.1038\/s41598-019-41695-z"},{"key":"15_CR39","first-page":"14254","volume":"33","author":"M Tyszkiewicz","year":"2020","unstructured":"Tyszkiewicz, M., Fua, P., Trulls, E.: DISK: learning local features with policy gradient. Adv. Neural. Inf. Process. Syst. 33, 14254\u201314265 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"15_CR40","doi-asserted-by":"crossref","unstructured":"Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: is a correction for chance necessary? In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1073\u20131080 (2009)","DOI":"10.1145\/1553374.1553511"},{"key":"15_CR41","unstructured":"Zambanini, S., Kampel, M.: Segmentation of ancient coins based on local entropy and gray value range. In: Proceedings of the 13th Computer Vision Winter Workshop, pp. 9\u201316 (2008)"},{"key":"15_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhao, X.: MESA: matching everything by segmenting anything. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20217\u201320226 (2024)","DOI":"10.1109\/CVPR52733.2024.01911"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91572-7_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T09:20:03Z","timestamp":1783070403000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91572-7_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031915710","9783031915727"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91572-7_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}