Reading ancient coins: automatically identifying denarii using obverse legend seeded retrieval

Oggie Arandelovic*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The aim of this paper is to automatically identify a Roman Imperial denarius from a single query photograph of its obverse and reverse. Such functionality has the potential to contribute greatly to various national schemes which encourage laymen to report their finds to local museums. Our work introduces a series of novelties: (i) this is the first paper which describes a method for extracting the legend of an ancient coin from a photograph; (ii) we are also the first to suggest the idea and propose a method for identifying a coin using a series of carefully engineered retrievals, each harnessed for further information using visual or meta-data processing; (iii) we show how in addition to a unique standard reference number for a query coin, the proposed system can be used to extract salient coin information (issuing authority, obverse and reverse descriptions, mint date) and retrieve images of other coins of the same type.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsA Fitzgibbon, S Lazebnik, P Perona, Y Sato, C Schmid
Pages317-330
Number of pages14
Volume7575 LNCS
EditionPART 4
DOIs
Publication statusPublished - 2012
Event12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy
Duration: 7 Oct 201213 Oct 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume7575 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference12th European Conference on Computer Vision, ECCV 2012
Country/TerritoryItaly
CityFlorence
Period7/10/1213/10/12

Keywords

  • Image
  • Inscription
  • Motif
  • Recognition
  • Reverse
  • Text

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