@inproceedings{c00bfe71f0514d578a7426ef575441da,
title = "Reading ancient coins: automatically identifying denarii using obverse legend seeded retrieval",
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.",
keywords = "Image, Inscription, Motif, Recognition, Reverse, Text",
author = "Oggie Arandelovic",
year = "2012",
doi = "10.1007/978-3-642-33765-9_23",
language = "English",
isbn = "9783642337642",
volume = "7575 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 4",
pages = "317--330",
editor = "A Fitzgibbon and S Lazebnik and P Perona and Y Sato and C Schmid",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 4",
note = "12th European Conference on Computer Vision, ECCV 2012 ; Conference date: 07-10-2012 Through 13-10-2012",
}