2020
DOI: 10.3390/sci2020037
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Classification of Ancient Roman Coins by Denomination Using Colour, a Forgotten Feature in Automatic Ancient Coin Analysis

Abstract: Ancient numismatics, that is, the study of ancient currencies (predominantly coins), is an interesting domain for the application of computer vision and machine learning, and has been receiving an increasing amount of attention in recent years. Notwithstanding the number of articles published on the topic, the variety of different methodological approaches described, and the mounting realisation that the relevant problems in the field are most challenging indeed, all research to date has entirely ignored one s… Show more

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Cited by 8 publications
(7 citation statements)
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“…The investigation of colour of ancient coinage presents as an analytical approach with high potential, as cases of the application of colour to classify ancient coins has been reported elsewhere 30 , demonstrating its usefulness in the field. Thus, the methodological developments and validation presented here propose a new smartphone-based approach, with corrected CIELAB descriptors that accurately represent the colour of the samples.…”
Section: Resultsmentioning
confidence: 93%
“…The investigation of colour of ancient coinage presents as an analytical approach with high potential, as cases of the application of colour to classify ancient coins has been reported elsewhere 30 , demonstrating its usefulness in the field. Thus, the methodological developments and validation presented here propose a new smartphone-based approach, with corrected CIELAB descriptors that accurately represent the colour of the samples.…”
Section: Resultsmentioning
confidence: 93%
“…In this study, a 98% classification success rate was obtained by using the reverse side of the coins [6]. Ma and Arandjelović reached an average classification performance of 74.25% with a tone-based random forest classifier in a study using 400 Roman coins used for four different denominations during the reign of the Roman emperor Dominican [7]. Anwar et al developed a special neural network model called CoinNet for the data set consisting of 228 object classes.…”
Section: Introductionmentioning
confidence: 80%
“…The image was sourced from [4] To date, a significant amount of research had been done on the robust detection, description and matching of invariant features related to motif and pattern classification. Features extraction algorithm and classification methods were applied to batik motif and batik making using convolutional neural network (CNN) model architectures [6]- [10], using multiwindow and multiscale extended center symmetric local binary patterns (MU2ECS-LBP) [11] and [12], using scale invariant feature transform (SIFT) [13], [14] using gray level co-occurrence matrices (GLCM) [15], [16], fine arts [17]- [19], for license plate recognition [20], [21], and tattoo recognition [22]- [24]. Even though the reported performance was quite high but these methods still suffer from false positives due to similar features that contain more than one pattern and noisy background.…”
Section: Introductionmentioning
confidence: 99%