Towards computer vision based ancient coin recognition in the wild — automatic reliable image preprocessing and normalization

Brandon Conn, Ognjen Arandelovic

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

13 Citations (Scopus)

Abstract

As an attractive area of application in the sphere of cultural heritage, in recent years automatic analysis of ancient coins has been attracting an increasing amount of research attention from the computer vision community. Recent work has demonstrated that the existing state of the art performs extremely poorly when applied on images acquired in realistic conditions. One of the reasons behind this lies in the (often implicit) assumptions made by many of the proposed algorithms — a lack of background clutter, and a uniform scale, orientation, and translation of coins across different images. These assumptions are not satisfied by default and before any further progress in the realm of more complex analysis is made, a robust method capable of preprocessing and normalizing images of coins acquired ‘in the wild’ is needed. In this paper we introduce an algorithm capable of localizing and accurately segmenting out a coin from a cluttered image acquired by an amateur collector. Specifically, we propose a two stage approach which first uses a simple shape hypothesis to localize the coin roughly and then arrives at the final, accurate result by refining this initial estimate using a statistical model learnt from large amounts of data. Our results on data collected ‘in the wild’ demonstrate excellent accuracy even when the proposed algorithm is applied on highly challenging images.
Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
Pages1457-1464
Number of pages8
ISBN (Electronic)9781509061822
DOIs
Publication statusPublished - 14 May 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - William A. Egan Civic and Convention Center, Anchorage, United States
Duration: 14 May 201719 May 2017
http://www.ijcnn.org/

Conference

Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
Abbreviated titleIJCNN
Country/TerritoryUnited States
CityAnchorage
Period14/05/1719/05/17
Internet address

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