Supporting Ancient Coin Classification by Image-Based Reverse Side Symbol Recognition

Anwar, Hafeez; Zambanini, Sebastian; Kampel, Martin

doi:10.1007/978-3-642-40246-3_3

Cited by 18 publications

(27 citation statements)

References 11 publications

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“…Given the approximately circular shape of coins, a natural way of segmenting a coin is into radial segments or wedges, constructing a histogram for each segment, and concatenating these into a single, higher dimensional vector used to represent the entirety of a coin [20]. We refer to this method as Wedge SIFT.…”

Section: Wedge Siftmentioning

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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Section: Wedge Siftmentioning

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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“…This visual similarity is then utilized for a coarse-to-fine search to retrieve the most similar coin images in a database. We recently proposed a novel approach by using symbol recognition for image-based ancient coin classification [18]. Symbol recognition for 8 symbols is performed by using the dense SIFT based BoVWs model.…”

Section: Fig 1: Variations In Symbolsmentioning

confidence: 99%

“…As a pre-processing step, the backgrounds of coin images are suppressed [19]. Based on our previous work [18], we use symbol recognition for image-based ancient coin classification. Due to degradations in the ancient coins caused by the aforementioned reasons, features are densely extracted from images to capture the underlying structures of symbols.…”

Section: Fig 1: Variations In Symbolsmentioning

confidence: 99%

“…Due to degradations in the ancient coins caused by the aforementioned reasons, features are densely extracted from images to capture the underlying structures of symbols. We use circular tilings [18] to add spatial information to the BoVWs model in a rotation invariant way. Furthermore, we model the geometric relationships of identical visual words by using the angles histogram proposed by [20].…”

Section: Fig 1: Variations In Symbolsmentioning

confidence: 99%

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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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“…However, it can serve as a preselection step, which prunes the set of classes. The proposed algorithm (Anwar et al, 2013) is based on a Bag of Visual Words (BoVW) algorithm. For an input image, SIFT features are densely extracted at a constant pixel stride.…”

Section: Coin Image Recognitionmentioning

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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Supporting Ancient Coin Classification by Image-Based Reverse Side Symbol Recognition

Cited by 18 publications

References 11 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

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

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

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