The Second Wave

Renaissance antiquarians relied heavily upon coins to reconnect with the Classical world. Popes and princes became avid collectors, stocking their Kunstkammern (cabinets of curiosities) with thousands of numismatic treasures. Collecting led to cataloguing and research, but also to the production of forgeries and fantasy coins to feed the antiquities market and to fill the gaps of history. Books showcased imagined coin portraits of every notable figure beginning with Adam and Eve. This preoccupation with portraiture abetted the use of physiognomy by numismatists, who sought psychological insights from the images of Cleopatra, Alexander the Great, Nero, and others depicted on coins. The efforts of numismatists such as Joseph Eckhel to collect and classify all known ancient coin-types, numbering in the tens of thousands, eventually made it impossible for a single individual to comprehend all of numismatics.

A Tunable “Ancient Coin”-Type Perfect Absorber with High Refractive Index Sensitivity and Good Angular Polarization Tolerance

In this paper, we design and present a graphene-based “ancient coin”-type dual-band perfect metamaterial absorber, which is composed of a silver layer, silicon dioxide layer, and a top “ancient coin” graphene layer. The absorption performance of the absorber is affected by the hollowed-out square in the center of the graphene layer and geometric parameters of the remaining nano disk. The optical properties of graphene can be changed by adjusting the voltage, to control the absorption performance of the absorber dynamically. In addition, the centrally symmetric pattern structure greatly eliminates the polarization angle dependence of our proposed absorber, and it exhibits good angular polarization tolerance. Furthermore, the proposed “ancient coin”-type absorber shows great application potential as a sensor or detector in biopharmaceutical, optical imaging, and other fields due to its strong tunability and high refractive index sensitivity.

Modeling of Hong Seok-gu’s Seals

In May 1968, 83 seals were excavated from the tomb of Hong Seok-gu, one of the literati in the mid-Joseon dynasty. Hong was a literator who designed and engraved the seal by himself in the 17th century. The lifetime of Hong Seok-gu was reorganized through the Buried Epitaph(墓誌銘) by Lee Dan-ha(李端夏) and the Tombstone Inscription(墓碣銘) by Jeong Ho(鄭澔). which revealed that he was often dismissed from the public office and assigned to the local government. Therefore, he repeatedly resigned and retired from his office. It is assumed that his personal misfortune allowed him to devote his time and space in engraving the seals. The style of his seals was influenced from the famous Andong Kim clan(安東 金門). He carved high-quality seals using various ancient seal script(古篆體) introduced in the calligraphy circles(書壇) with 38 seal script(篆書) including small seal script(小篆) and jade chopsticks seal script(玉筯篆). In addition, he applied composition techniques(章法) which were creatively transformed from the method of putting both depressed and embossed engraving into one seal(朱白相間法), a popular way in the 17th century. He also held his experimental point of view in the composition techniques of the seals shaped of an ancient coin(孔方錢). Regarding the poetry seal(詞句印), his brilliant sense of modeling is noticeable in combining literature with figuration. Shapes such as a small table for a incense burner(香佐兒), a liquor bottle(酒甁), and an arrow throwing pot(投壺) implied literature, while seals carved with <Jeilgangsan(第一江山)>, <Gyosang-noin(橋上老人)> and <Daimyoung-cheonha-ilseosaeng(大明天下一書生)> were employed to attempt shaping with literature. The characteristic of this style is recognized as an artistic accomplishment of the seals in the Joseon dynasty, which was different from the seals of Ming dynasty following the style of the Han dynasty.

Egyptian Shabtis Identification by Means of Deep Neural Networks and Semantic Integration with Europeana

Ancient Egyptians had a complex religion, which was active for longer than the time that has passed since Cleopatra until our days. One amazing belief was to be buried with funerary statuettes to help the deceased carry out his/her tasks in the underworld. These funerary statuettes, mainly known as shabtis, were produced in different materials and were usually inscribed in hieroglyphs with formulas including the name of the deceased. Shabtis are important archaeological objects which can help to identify the owners, their jobs, ranks or their families. They are also used for tomb dating because, depending on different elements: color, formula, tools, wig, hand positions, etc., it is possible to associate them to a concrete type or period of time. Shabtis are spread all over the world, in excavations, museums or private collections, and many of them have not been studied and identified because this process requires a deep study and reading of the hieroglyphs. Our system is able to solve this problem using two different YOLO v3 networks for detecting the figure itself and the hieroglyphic names, which provide identification and cataloguing. Until now, there has been no other work on the detection and identification of shabtis. In addition, a semantic approach has been followed, creating an ontology to connect our system with the semantic metadata aggregator, Europeana, linking our results with known shabtis in different museums. A complete dataset has been created, a comparison with previous technologies for similar problems has been provided, such as SIFT in the ancient coin classification, and the results of identification and cataloguing are shown. These results are over similar problems and have led us to create a web application that shows our system and is available on line.

Visual Reconstruction of Ancient Coins Using Cycle-Consistent Generative Adversarial Networks

In this paper, our goal is to perform a virtual restoration of an ancient coin from its image. The present work is the first one to propose this problem, and it is motivated by two key promising applications. The first of these emerges from the recently recognised dependence of automatic image based coin type matching on the condition of the imaged coins; the algorithm introduced herein could be used as a pre-processing step, aimed at overcoming the aforementioned weakness. The second application concerns the utility both to professional and hobby numismatists of being able to visualise and study an ancient coin in a state closer to its original (minted) appearance. To address the conceptual problem at hand, we introduce a framework which comprises a deep learning based method using Generative Adversarial Networks, capable of learning the range of appearance variation of different semantic elements artistically depicted on coins, and a complementary algorithm used to collect, correctly label, and prepare for processing a large numbers of images (here 100,000) of ancient coins needed to facilitate the training of the aforementioned learning method. Empirical evaluation performed on a withheld subset of the data demonstrates extremely promising performance of the proposed methodology and shows that our algorithm correctly learns the spectra of appearance variation across different semantic elements, and despite the enormous variability present reconstructs the missing (damaged) detail while matching the surrounding semantic content and artistic style.

Classification of Ancient Roman Coins by Denomination Using Colour, a Forgotten Feature in Automatic Ancient Coin Analysis

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 specific, readily accessible modality: colour. Invariably, colour is discarded and images of coins treated as being greyscale. The present article is the first one to question this decision (and indeed, it is a decision). We discuss the reasons behind the said choice, present a case why it ought to be reexamined, and in turn investigate the issue for the first time in the published literature. Specifically, we propose two new colour-based representations specifically designed with the aim of being applied to ancient coin analysis, and argue why it is sensible to employ them in the first stages of the classification process as a means of drastically reducing the initially enormous number of classes involved in type matching ancient coins (tens of thousands, just for Ancient Roman Imperial coins). Furthermore, we introduce a new data set collected with the specific aim of denomination-based categorisation of ancient coins, where we hypothesised colour could be of potential use, and evaluate the proposed representations. Lastly, we report surprisingly successful performances which goes further than confirming our hypothesis—rather, they convincingly demonstrate a much higher relevant information content carried by colour than even we expected. Thus we trust that our findings will be noted by others in the field and that more attention and further research will be devoted to the use of colour in automatic ancient coin analysis.

Chinese Ancient Coin - Uang kepeng: A Strengthener of Bali and China Relationship

Relations between Bali and China have been established since the Han Dynasty around 100 BC. This opinion was strengthened by the discovery of nekara, a bronze kettle drum, in Pejeng, Gianyar. The relationship between Bali and China is a trade relation in which Bali was written in the notes of Chinese traders who came to Southeast Asia in the 5th century. One of the legacies of Chinese trade in Bali is the uang kepeng (ancient Chinese coin) which was used as a means of a transaction at that time. The existence of uang kepeng in Bali is very interesting to discuss because of the transformation of its function. Previously uang kepeng functioned as a tool for trading transactions transformed into a means of transactions in religious rituals. This research used the method of observation, literature study, and in-depth interviews to reveals the existence and role of uang kepeng or pis bolong in Bali. At present, uang kepeng has also become a craft commodity that has a role in tourism in Bali.

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.

Classification of Ancient Roman Coins by Denomination Using Colour, a Forgotten Feature in Automatic Ancient Coin Analysis

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 specific, readily accessible modality: colour. Invariably, colour is discarded and images of coins treated as being greyscale. The present article is the first one to question this decision (and indeed, it is a decision). We discuss the reasons behind the said choice, present a case why it ought to be reexamined, and in turn investigate the issue for the first time in the published literature. Specifically, we propose two new colour-based representations specifically designed with the aim of being applied to ancient coin analysis, and argue why it is sensible to employ them in the first stages of the classification process as a means of drastically reducing the initially enormous number of classes involved in type matching ancient coins (tens of thousands, just for Ancient Roman Imperial coins). Furthermore, we introduce a new data set collected with the specific aim of denomination-based categorisation of ancient coins, where we hypothesised colour could be of potential use, and evaluate the proposed representations. Lastly, we report surprisingly successful performances which goes further than confirming our hypothesis—rather, they convincingly demonstrate a much higher relevant information content carried by colour than even we expected. Thus we trust that our findings will be noted by others in the field and that more attention and further research will be devoted to the use of colour in automatic ancient coin analysis.