Recognizing Ancient Coins Based on Local Features

Kampel, Martin; Zaharieva, Maia

doi:10.1007/978-3-540-89639-5_2

Cited by 48 publications

(48 citation statements)

References 24 publications

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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%

“…In particular, all work to date has been highly unstructured and ad hoc in its evaluation methodology. Some authors use data sets with coins in different conditions and unstated distributions thereof [9], and others very small data sets with coins in extremely rare, museum grade [8]. Hence the current understanding of different methods' behaviour is not very well understood at all.…”

Section: Previous Workmentioning

confidence: 99%

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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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Section: Previous Workmentioning

confidence: 99%

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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“…One of the first end-to-end coin recognition techniques for ancient coins was presented by Kampel and Zaharieva (Kampel and Zaharieva, 2008). In their work, various interest point detectors, local image descriptors and their combinations are tested for their applicability to image-based coin recognition.…”

Section: Related Workmentioning

confidence: 99%

“…The central contribution of his work is a novel feature type called locally-biased directional histogram that captures geometric relationships between interest points determined with the Difference-of-Gaussian detector (Lowe, 2004). Huber-Mörk et al (Huber-Mörk et al, 2011) presented an extension to the coin identification of Kampel and Zaharieva (Kampel and Zaharieva, 2008). A preselection step which analyzes the contour of the coin is introduced and allows for an efficient pruning of mismatching coins.…”

Section: Related Workmentioning

confidence: 99%

The Ilac-Project: Supporting Ancient Coin Classification by Means of Image Analysis

Kavelar

Zambanini

Kampel

et al. 2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents the ILAC project, which aims at the development of an automated image-based classification system for ancient Roman Republican coins. The benefits of such a system are manifold: operating at the suture between computer vision and numismatics, ILAC can reduce the day-to-day workload of numismatists by assisting them in classification tasks and providing a preselection of suitable coin classes. This is especially helpful for large coin hoard findings comprising several thousands of coins. Furthermore, this system could be implemented in an online platform for hobby numismatists, allowing them to access background information about their coin collection by simply uploading a photo of obverse and reverse for the coin of interest. ILAC explores different computer vision techniques and their combinations for the use of image-based coin recognition. Some of these methods, such as image matching, use the entire coin image in the classification process, while symbol or legend recognition exploit certain characteristics of the coin imagery. An overview of the methods explored so far and the respective experiments is given as well as an outlook on the next steps of the project.

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“…As image-based coin recognition has become an active research topic for recent decade, various algorithms based on training global features (Fukumi et al, 1992;Huber et al, 2005;Van Der Maaten and Poon, 2006;Reisert et al, 2007) or matching local features (Kampel and Zaharieva, 2008;Arandjelovic, 2010;Pan et al, 2014) have been proposed. At the beginning, studies were limited in classification of modern coins where legends were treated no differently as other patterns in relief.…”

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confidence: 99%

A New Database of Digits Extracted from Coins with Hard-to-Segment Foreground for Optical Character Recognition Evaluation

Pan

Tougne

2017

Front. ICT

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Since the release date struck on a coin is important information of its monetary type, recognition of extracted digits may assist in identification of monetary types. However, digit images extracted from coins are challenging for conventional optical character recognition methods because the foreground of such digits has very often the same color as their background. In addition, other noises, including the wear of coin metal, make it more difficult to obtain a correct segmentation of the character shape. To address those challenges, this article presents the CoinNUMS database for automatic digit recognition. The database CoinNUMS, containing 3,006 digit images, is divided into three subsets. The first subset CoinNUMS_geni consists of 606 digit images manually cropped from high-resolution photographs of well-conserved coins from GENI coin photographs; the second subset CoinNUMS_pcgs_a consists of 1,200 digit images automatically extracted from a subset of the USA_Grading numismatic database containing coins in different quality; the last subset CoinNUMS_pcgs_m consists of 1,200 digit images manually extracted from the same coin photographs as CoinNUMS_pcgs_a. In CoinNUMS_pcgs_a and CoinNUMS_pcgs_m, the digit images are extracted from the release date. In CoinNUMS_geni, the digit images can come from the cropped date, the face value, or any other legends containing digits in the coin. To show the difficulty of these databases, we have tested recognition algorithms of the literature. The database and the results of the tested algorithms will be freely available on a dedicated website.

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Recognizing Ancient Coins Based on Local Features

Cited by 48 publications

References 24 publications

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

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

The Ilac-Project: Supporting Ancient Coin Classification by Means of Image Analysis

A New Database of Digits Extracted from Coins with Hard-to-Segment Foreground for Optical Character Recognition Evaluation

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