Coarse-grained ancient coin classification using image-based reverse side motif recognition

Anwar, Hafeez; Zambanini, Sebastian; Kampel, Martin

doi:10.1007/s00138-015-0665-2

Cited by 23 publications

(18 citation statements)

References 11 publications

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“…Other works employed learning-based classifiers. Earlier attempts [1], [2] relied on Bag of Visual Words based representation of local image features where a visual dictionary is learned from a training set and classification is achieved with SVM in [1] and GMM in [2]. Recently, Schlag and Arandjelovic proposed to use a deep convolutional neural network for Roman coin classification in [18].…”

Section: Previous Workmentioning

confidence: 99%

Two sides of the same coin: Improved ancient coin classification using Graph Transduction Games

Aslan

Vascon

Pelillo

2020

Pattern Recognition Letters

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Section: Previous Workmentioning

confidence: 99%

Two sides of the same coin: Improved ancient coin classification using Graph Transduction Games

Aslan

Vascon

Pelillo

2020

Pattern Recognition Letters

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“…The reported performance of these methods has been rather disappointing and a major factor appears to be the loss of spatial, geometric relationship in the aforementioned representations [12,13]. In an effort to overcome this limitation, a number of approaches which divide a coin into segments have been described [14]. These methods implicitly assume that coins have perfect centring, are registered accurately, and are nearly circular in shape.…”

Section: Previous Workmentioning

confidence: 99%

Ancient Roman Coin Retrieval: A Systematic Examination of the Effects of Coin Grade

Fare

Arandjelović

2017

Lecture Notes in Computer Science

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Abstract. Ancient coins are historical artefacts of great significance which attract the interest of scholars, and a large and growing number of amateur collectors. Computer vision based analysis and retrieval of ancient coins holds much promise in this realm, and has been the subject of an increasing amount of research. The present work is in great part motivated by the lack of systematic evaluation of the existing methods in the context of coin grade which is one of the key challenges both to humans and automatic methods. We describe a series of methods -some being adopted from previous work and others as extensions thereof -and perform the first thorough analysis to date.

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“…The reported performance of these methods has been rather disappointing and a major factor appears to be the loss of spatial, geometric relationship in the aforementioned representations [15], [16]. In an effort to overcome this limitation, a number of approaches which divide a coin into segments have been described [17]. These methods implicitly assume that coins have perfect centring, are registered accurately, and are nearly circular in shape.…”

Section: B Previous Workmentioning

confidence: 99%

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

Conn

Arandjelović

2017

2017 International Joint Conference on Neural Networks (IJCNN)

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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 algorithmsa 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.

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Coarse-grained ancient coin classification using image-based reverse side motif recognition

Cited by 23 publications

References 11 publications

Two sides of the same coin: Improved ancient coin classification using Graph Transduction Games

Two sides of the same coin: Improved ancient coin classification using Graph Transduction Games

Ancient Roman Coin Retrieval: A Systematic Examination of the Effects of Coin Grade

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

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