Image Based Recognition of Ancient Coins

Zaharieva, Maia; Kampel, Martin; Zambanini, Sebastian

doi:10.1007/978-3-540-74272-2_68

Cited by 48 publications

(44 citation statements)

References 5 publications

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“…Moreover, unlike ancient coins, modern coins do not suffer from corrosion and absence of visually vital parts. Therefore approaches for modern coins [10][11][12] are not sufficient for ancient coin classification as shown by [13]. In [14], one of the first frameworks developed for ancient coins recognition is presented.Various interest point detectors, local image descriptors and their combinations are evaluated for ancient coins recognition.…”

Section: Fig 1: Variations In Symbolsmentioning

confidence: 99%

A rotation-invariant bag of visual words model for symbols based ancient coin classification

Anwar

Zambanini

Kampel

2014

2014 IEEE International Conference on Image Processing (ICIP)

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We propose to perform image-based ancient coin classification by recognizing symbols minted on the reverse side of coins. Dense sampling based bag-of-visual-words model is used for symbol recognition. The lack of spatial information in the bag-of-visual-words model degrades symbol recognition rate as the symbols have specific geometric structures. Furthermore, coins can be imaged under various rotations resulting in severely rotated symbols. Therefore we propose a novel bag-of-visual-words model for symbol-based coin classification which accounts for the spatial arrangement of the visual words in a rotation invariant manner. We perform our experiments on images collected from three different sources thus making our dataset more challenging. To evaluate our proposed model for robustness to rotations, we synthetically generated severely rotated coin images. In the presence of rotation differences between coins, our model outperforms the conventional bag-of-visual-words model as well as recently proposed angles histograms of pair-wise identical visual words model.

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Section: Fig 1: Variations In Symbolsmentioning

confidence: 99%

A rotation-invariant bag of visual words model for symbols based ancient coin classification

Anwar

Zambanini

Kampel

2014

2014 IEEE International Conference on Image Processing (ICIP)

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“…This problem is of limited practical interest, its use being limited to such tasks as the identification of stolen coins or the detection of repeated entries in digital collections. Other works focus on coin type recognition, which is a far more difficult problem [7][8][9]. Most of these methods are local feature based, employing local feature descriptors such as SIFT [10] or SURF [11].…”

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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“…This problem is of limited practical interest, its use being limited to such tasks as the identification of stolen coins or the detection of repeated entries in digital collections. Other works focus on coin type recognition, which is a far more difficult problem [10], [11], [12]. Most of these methods are local feature based, employing local feature descriptors such as SIFT [13] or SURF [14].…”

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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Image Based Recognition of Ancient Coins

Cited by 48 publications

References 5 publications

A rotation-invariant bag of visual words model for symbols based ancient coin classification

A rotation-invariant bag of visual words model for symbols based ancient coin classification

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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