Coin Images Research Articles

Foreign Adoptions in Exterior of the Russian Change Money of 1718–1724

Introduction. The last phase of Peter’s coinage reform was marked, among others, by the alteration of all coin denomination appearances, specifically small ones. The reasons for this did not receive a clear explanation in the specialized literature, and the question of the origin of the forms of the new coin design was not raised at all. The article asks these questions, and it offers a historical reconstruction of novelties in the minor coins exterior of 1711–1724. The analysis of Russian and foreign numismatic material gives any possibility to conclude about the elaboration and introduction of a new design concept in this period, which was distinguished by the more simplicity of coin images. Comparing the exterior’s details of new Russian coins with foreign ones does point to the most probable sources and forms of the adoption. Conclusions. The change of the concept of coin appearance began in 1711 and finished in 1724. As a result, small denominations forfeited their long legends and bulky images. Each adoption was targeted to a concrete part of coin space; it was selected by Peter’s choice and taste. This explains both why the concept of the design was realized so long and why the sources of adoption were so numerous – from countries in Europe and Asia.

A framework for image-based counterfeit coin detection using pruned fuzzy associative classifier

Counterfeit coins pose a significant challenge in various real-world applications, from vending machines to currency exchange systems, making their reliable detection a matter of utmost importance. This research presents a novel framework designed to tackle this issue by harnessing the power of image-mining techniques. Our proposed framework is developed in two modules. In the first module, a method to detect the region of interest (ROIs) is applied that focuses on blob detection. In the second module, image mining is applied to find image patterns present in coin images using fuzzy association rules mining. The enhancement lies in utilizing Particle Swarm Optimization (PSO) within the image mining module. PSO refines the threshold parameters, thereby improving the efficiency of the fuzzy association rules mining process. This integration allows for the automatic determination of optimal values, contributing to the overall robustness of the counterfeit coin detection system. Comprising two modules, this framework offers a unique advantage as a compress, serving as a knowledge attainment tool. By harnessing the full power of fuzzy association rule mining, this paper introduces pruning methods to reduce redundant and insignificant rules. Moreover, we propose a novel algorithm for feature selection and a pruned-based fuzzy associative classifier to establish a robust counterfeit coin detection system. Comparative analysis with other methods using the same dataset showcases the superiority of our framework, exhibiting lower feature dimensions, smoother boundaries, and maintaining satisfactory accuracy. The generality of this study's problem formulation offers a common framework for addressing similar challenges across various domains.

In the Eye of the Lion: Iconological Observations on Frontal Depictions of Lions on Ancient Coins

This article provides an overview of the most important coin types depicting frontal lions from the Greek Archaic period to the first century BC. The spear-biting lions are not included in this paper as they form the subject of a separate study. Starting from the ancient written tradition, explanations are offered based on the effect of the lion’s gaze, which is repeatedly mentioned in ancient texts. With the addition of the respective reverses and modes of representation, it is possible to identify references to religious cults: Apollo, Artemis, Athena, Hera and Heracles. But rulers also used frontal lion images for specific purposes. To express the solar aspect of Apollo, frontality seemed the most appropriate. A particularly close connection between the lion and Loxías has been demonstrated for Rhegion. The example of Samos shows that an autocrat could choose the emblem of a “frontal lion’s head” – probably also to suggest solar qualities. Under Achaemenid influence however, the lion regularly became the victim of the ruler – albeit a particularly dangerous and fearsome one, which was emphasised through its frontality. The same goes for Lysimachus, whose choice of image is astonishingly close to Pliny’s reflex, which focuses entirely on the lion’s sight. It is also shown that lion masks are often associated with the deeds of Heracles, which also gave rise to other coin images in which the use of frontality brought special nuances of meaning, for example the illustration of special effort or a narrative connection between two coin images. In one case, a frontal lion leaping out of a rectangle was associated with the cult of Athena. Another group of frontal lions may have had an apotropaic-demonic character, mainly conveyed by the eyes fixed on the observer. Here we can see a connection with the Gorgoneion. As for the frequent image of devouring lions, a biblical source suggests that the animals were particularly irritable and dangerous during this process. Finally, it was recommended that lions and leopards should be carefully separated, with leopards being particularly suitable for depicting the ‘man-destroying’ god Dionysus in frontal perspective.

О некоторых образах архаических монет Ионии и Малой Азии на скифских зооморфных бронзовых бляхах

The article studies the influence of zoomorphic images on the archaic coinage of Ionia and Asia Minor on the Scythian animal style. The borrowing of ancient coin images and designs by Scythians has long been the subject of a separate study. However, as a rule, the attention of researchers was limited to a circle of prestigious things made of precious metals mainly in the second half of the 5 th—4 th centuries BC. As it will be shown in this article, the influence of the Ionian and Asia Minor coinage on the formation of images of the Scythian animal style can be traced at least from the turn of the 6 th—5 th centuries BC and manifests itself not only in prestigious items, but also in quite ordinary bronze plaques, mainly decorating horse bridles. It is proved that in a number of cases, within the framework of the Scythian animal style, there was a direct borrowing of coin images and motifs (the full face of a lion, the motif of a four-fingered clawed paw, composition consisting of three boar heads). In other cases, the influence of coinage on the formation of images of the Scythian animal style seems more than likely (profile images of a cat predator and a lion, the “bird on a fish” plot). It is suggested that the influence of archaic Greek coinage on the Scythian animal style could be associated with waves of Ionian and Asia Minor migrants to the Northern Black Sea region in the second half of the 6 th—early 5 th centuries BC, or with the contacts between the Scythians, Ionians and Greeks during the Scythian-Persian war and the period immediately following it.

Идеи, монеты и художники: об изображении монет и печатей в армянских рукописях XIII–XVI вв.

A systematic study of the Armenian manuscripts allowed us to identify in twelve Gospels created in the scriptoria of Cilician Armenia, Siwnik‘ and Vaspurakan from the mid-13th c. to 1592, images of gold coins and state seals. As prototypes for coin images, only gold coins from circulation were used — these were the Byzantine solidus of mid-10th — mid-11th cc., the dinar of Sultan Kay-Khusraw II, the Florentine florin, the dinars of Abu Sa‘id Ilkhanid and the Venetian ducat; but in Vaspurakan, lacking gold coins in monetary circulation during the 14th-15th cc., the images of gold coins were replaced by drawings of state Mongolian seals. The appearance of gold coins in the most important places of the Gospels is explained in Mkhit‘ar Gosh’s Lawcode (completed after 1184), in which gold coins, due to their pure nature, are interpreted in neoplatonical sense as a symbols of Christ’s divinity. A review of the discovered artistic tradition and its comparisons with synchronous traditions of illuminations allows us to indicate that it was in Cilician Armenia that real coins were first depicted, moreover, with such a degree of detail, which makes it possible to unambiguously determine their type. The identification of this artistic tradition significantly expands our understanding of the symbolic role of coins in medieval society.

The Youthful Heracles-Melqart in Tyre. Choosing a Visual Language for a Phoenician God in the Hellenistic Period

The Hellenistic silver coins of Tyre were widely used and accepted in the Southern Levant. They depicted the head of the youthful and beefy Heracles-Melqart on the obverse. In this paper, the image of Heracles is discussed and contextualized within the religious life of Tyre. It is argued that the image was influenced by Heracles’ depictions related to Alexander the Great and that it also recurred on ideas of Melqart being a young god. The coin image thus combined different aspects in its visual language and evoked different ideas for the viewers, contributing to the popularity of the image.

Медные монеты золотоордынского города Шехр ал- Джадида

The article describes the copper coins (puls) minted in the 1360s in the Golden Horde city of Shehr al-Jadida (New City), located in Old Orhei (Republic of Moldova). The article offers a comparative analysis of the results of studying these coins by other scientists. The catalog of the article contains data on 593 coins, including photographs of 164 of them. Coin images of identified varieties of original and imitative puls have been restored and their drawings are presented. The evolution of the use of the “islimi” ornamental substrate technique on the puls of Shehr al-Jadida is presented. Based on a study of stamping dies, a grid of correlations of dies was built and a classification of puls was created. The boundaries of the use of the former Juchid copper puls in re-minting Shehr al-Jadida coins at the early stages of their issue are determined. The criteria distinguishing original issues from imitative ones are revealed. These include the simplified, barbaric nature of the legend and images, as well as the absence of any connection between the stamping dies and the coins of the original issues. An analysis of the die characteristics of the coins of the New City, found in various regions of Moldova and Ukraine, allows us to conclude that the mint of Shehr al-Jadida could work only on the territory of the modern Old Orhei. The article is provided with a large number of illustrations, including previously unpublished varieties of copper puls.

DEEP LEARNING BASED MALAYSIAN COINS RECOGNITION FOR VISUAL IMPAIRED PERSON

Currency recognition has been widely developed using various types of techniques and able to assist people who have a visual impairment. Machine learning is one of the methods implemented where deep learning architecture is one of them. The deep learning approach is reliable and can be used in detection and recognition of objects based on images. As currency recognition has been developed for other currencies, thus in this project, currency recognition using Malaysian coins has been developed by modeling Convolutional Neural Network (CNN) in recognizing coin images. Malaysian coins dataset was developed consist of 2400 images of four classes of coins, 5 sen, 10 sen, 20 sen, and 50 sen. In this study, pretrained CNN which are AlexNet, GoogleNet, and MobileNetV2 were formulated in recognizing such coins. Performance of each trained model was evaluated using confusion matrix and GoogleNet obtained the best performance with 99.2% testing accuracy, 99.2% precision, 99.18% recall, and 99.19% F1 score. From the trained model, it can be further developed and implemented in assisting visually impaired persons by producing a prototype using Raspberry Pi and FPGA before it can be clinically tested on the subject.

Český kruhový peníz střížkem pro vídeňský fenik – záhada pokračuje?

The subject of this article is the previously presented round penny with a lion restruck with a Vienna pfennig. To identify the denominations, a computer-generated 3D model was used which showed the height of the relief in false colours. A detailed analysis of the individual parts of the coin image has identified a square flan (quadratum supercusum) coin, similar to the Castelin 37 type, and a Vienna pfennig of Albrecht V of Austria from the period of his guardianship by Leopold IV of Austria, type Koch CNA I Fa4. The original strike had been probably a square flan coin, which was later restruck by the pfennig of Albrecht V.

Pryč se zrádci: římské mince jako nástroj politické agitace

The considerable potential of the coin image as a means of political agitation during the time of the Roman Republic has been recognized long ago. This paper concerns with interpretation of mythological scenes presenting the punishment of high treason and examines their possible connection to particular historical events and real characters. Moreover, possible existence of two variants of the denarii of the RIC I2 Augustus 299 type is proposed.

Statistical edge-based feature selection for counterfeit coin detection

The number of counterfeit coins released into circulation is persistently increasing. According to official reports, the mass majority of these coins are circulated in the European Union member countries. This paper presents a robust method for counterfeit coin detection based on coin stamp differences between genuine and counterfeit coins. A set of measures based on edge differences are proposed in this paper. The proposed method compares the edge width, edge thickness, number of horizontal and vertical edges, and total number of edges between a test coin and a set of genuine reference coins. The method extends the measures to generate a defect map by subtracting the test coin image from the reference coins to count the number of pixels in small regions of the coin. Additionally, the Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), and Structural Similarity (SSIM) which are well-known measures to track the differences between two images are also applied to the coin image. The sets of features are then placed into index space where each vector represents the features of one test coin and a reference coin. The final feature vector represents the features set of one test coin and is computed by averaging the feature value of vectors in the index space. This feature vector is used to train a classifier to learn the edge feature differences between the two classes. The proposed method achieved precision and recall rates as high as 99.6% and 99.3% respectively, demonstrating the effectiveness and robustness of the selected edge features in authenticating coins. The method was evaluated on a real-life dataset of Danish coins as part of a collaborative effort.

An Image-Based Class Retrieval System for Roman Republican Coins.

We propose an image-based class retrieval system for ancient Roman Republican coins that can be instrumental in various archaeological applications such as museums, Numismatics study, and even online auctions websites. For such applications, the aim is not only classification of a given coin, but also the retrieval of its information from standard reference book. Such classification and information retrieval is performed by our proposed system via a user friendly graphical user interface (GUI). The query coin image gets matched with exemplar images of each coin class stored in the database. The retrieved coin classes are then displayed in the GUI along with their descriptions from a reference book. However, it is highly impractical to match a query image with each of the class exemplar images as there are 10 exemplar images for each of the 60 coin classes. Similarly, displaying all the retrieved coin classes and their respective information in the GUI will cause user inconvenience. Consequently, to avoid such brute-force matching, we incrementally vary the number of matches per class to find the least matches attaining the maximum classification accuracy. In a similar manner, we also extend the search space for coin class to find the minimal number of retrieved classes that achieve maximum classification accuracy. On the current dataset, our system successfully attains a classification accuracy of 99% for five matches per class such that the top ten retrieved classes are considered. As a result, the computational complexity is reduced by matching the query image with only half of the exemplar images per class. In addition, displaying the top 10 retrieved classes is far more convenient than displaying all 60 classes.

Combination of spatially enhanced bag-of-visual-words model and genuine difference subspace for fake coin detection

Fake coins are harmful for society, the detection of which is of paramount importance. Due to the large quantities of fake coins in the real world, it is impossible to examine them manually. To address this issue, we present an intelligent system to automatically detect fake coins based on their images. The intelligent system consists of two components: coin image representation and classifier learning. To represent the coin image, a new spatially enhanced bag-of-visual-words model, called SEBOVW model, is proposed. Afterwards, we improve the representation by building a genuine difference subspace. The coin is finally represented based on its projection onto this subspace. In order to discriminate between genuine and fake coins, we train a classifier using the subspace representations. A thorough evaluation of the proposed intelligent system has been conducted on four coin datasets, consisting of thousands of coins of different denominations and from two countries. Promising experimental results in excess of 98% accuracy demonstrate its effectiveness and validity.

Learning to Describe: A New Approach to Computer Vision Based Ancient Coin Analysis

In recent years, a range of problems under the broad umbrella of computer vision based analysis of ancient coins have been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. We explain that the approach of visual matching of coins, universally adopted in existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g., online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on understanding the semantic content of coins. Hence, we describe a novel approach—to first extract semantic concepts from real-world multimodal input and associate them with their corresponding coin images, and then to train a convolutional neural network to learn the appearance of these concepts. On a real-world data set, we demonstrate highly promising results, correctly identifying a range of visual elements on unseen coins with up to 84% accuracy.

Learning to Describe: A New Approach to Computer Vision Based Ancient Coin Analysis

In recent years, a range of problems under the broad umbrella of computer vision based analysis of ancient coins have been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. We explain that the approach of visual matching of coins, universally adopted in existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g., online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on understanding the semantic content of coins. Hence, we describe a novel approach—to first extract semantic concepts from real-world multimodal input and associate them with their corresponding coin images, and then to train a convolutional neural network to learn the appearance of these concepts. On a real-world data set, we demonstrate highly promising results, correctly identifying a range of visual elements on unseen coins with up to 84% accuracy.

Brazilian Coins Recognition Using Histogram of Oriented Gradients Features

This paper tends to the issue of coin recognition when dealing with shading and reflection variations under the same lighting conditions. In order to approach the problem, a database containing Brazilian coin images (both front and reverse side of the coin) consisting of five different denominations have been used which is provided by the kaggle-diverse and largest data community in the world. This work focuses on an automatic image classification process for Brazilian coins. The imagebased classification of coins primarily incorporates three stages where the initial step is Region of Interest (ROI) extraction; the subsequent advance is extraction of features and classification. The first step of ROI extraction is accomplished by segmenting the coin region using the proposed segmentation method. In the second step i.e., feature extraction; Histogram of Oriented Gradients (HOG) features are extracted from the image. The image is converted to a vector containing feature values. The third step is where the extracted features are mapped to the class and are known as classification. Three classification algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbour are compared for classification of five coin denominations. With the proposed segmentation methodology, the best classification accuracy of 92% is achieved in the case of ANN classifier.

An Enhanced Histogram of Oriented Gradient Descriptor for Numismatic Applications

The Histogram of Oriented Gradients (HOG) is one of the most widely used methods to extract the gradient features for object recognition and consistently shows high accuracy rates when compared to other descriptors. The major drawbacks of using the HOG method are the necessity of finding an optimal window size to fit the whole object; and the exhaustive search mechanism represented by a fixed window size sliding through the whole image to locate and recognize objects. This research proposes an efficient and robust Dynamic-HOG as an improvement to the traditional HOG method to locate and recognize structured objects in images. The proposed method works by locating and analyzing the structured objects in images in order to define a dynamic window size w.r.t. each object size. Moreover, the Dynamic-HOG method requires much less processing time by eliminating the exhaustive search mechanism. The method defines the height and width thresholds of objects and bounds each object with a window w.r.t. its size while ignoring non–object edges. It fits structured objects of a close range of heights and widths. This paper considers the characters that are minted on coins of different languages and sizes as the objects to recognize. There are several papers in the literature discussing coin recognition problem and proposing solutions based on various sets of features extracted from the entire coin image. This research also proposes a new method for coin recognition by focusing on recognize coins based on smaller part of the coin image which are the characters. Our method is evaluated on coins from diverse countries with different background complexity. The proposed method achieved precision and recall rates as high as 98.08 and 98.23%, respectively; which demonstrate the effectiveness and robustness of the proposed method.

Comparison of preprocessing techniques for coin recognition using image processing methods

Coin is very important role in human’s day life. For daily routine like shop, super market, banks etc the coins to be used. The coin is important part of economies and currency and it is used to pay for goods and also for our needs. Here the Indian coin has many number of count five rupee, ten rupee, two rupee, from this any one of the coin we are going to extract the texture feature for our Indian coin, first step is to preprocess the image is that method to enhance the image and remove the noise from enhanced image. For extracting clear information the image has to be preprocessed through some of the filtering techniques such as image size has to be resized, changing the contrast of the image, changing RGB to grayscale conversion for further operation such as segmentation and classification. At last the values to be compared by using PSNR, SNR, MSE of Filter noise removal with respective coin images.

Contributions to Anatolian History and Numismatics 13-14

Contributions to Anatolian History and Numismatics 13-14

An Image-Based Approach to Detection of Fake Coins

We propose a new approach to detect fake coins using their images in this paper. A coin image is represented in the dissimilarity space, which is a vector space constructed by comparing the image with a set of prototypes. Each dimension measures the dissimilarity between the image under consideration and a prototype. In order to obtain the dissimilarity between two coin images, the local keypoints on each image are detected and described. Based on the characteristics of the coin, the matched keypoints between the two images can be identified in an efficient manner. A post-processing procedure is further proposed to remove mismatched keypoints. Due to the limited number of fake coins in real life, one-class learning is conducted for fake coin detection, so only genuine coins are needed to train the classifier. Extensive experiments have been carried out to evaluate the proposed approach on different data sets. The impressive results have demonstrated its validity and effectiveness.