Understanding ancient coin images

In recent years, a range of problems within the broad umbrella of automatic, computer vision based analysis of ancient coins has been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in the 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. Firstly, we explain that the approach of visual matching of coins, universally adopted in all 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 the understanding of the semantic content of coins. Hence, we describe a novel method which uses real-world multimodal input to extract and associate semantic concepts with the correct coin images and then using a novel convolutional neural network learn the appearance of these concepts. Empirical evidence on a real-world and by far the largest data set of ancient coins, we demonstrate highly promising results.Postprin

Using the Optical Mouse Sensor as a Two-Euro Counterfeit Coin Detector

In this paper, the sensor of an optical mouse is presented as a counterfeit coin detector applied to the two-Euro case. The detection process is based on the short distance image acquisition capabilities of the optical mouse sensor where partial images of the coin under analysis are compared with some partial reference coin images for matching. Results show that, using only the vision sense, the counterfeit acceptance and rejection rates are very similar to those of a trained user and better than those of an untrained user

Ancient Roman coin retrieval : a systematic examination of the effects of coin grade

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.Postprin

Towards computer vision based ancient coin recognition in the wild — automatic reliable image preprocessing and normalization

As an attractive area of application in the sphere of cultural heritage, in recent years automatic analysis of ancient coins has been attracting an increasing amount of research attention from the computer vision community. Recent work has demonstrated that the existing state of the art performs extremely poorly when applied on images acquired in realistic conditions. One of the reasons behind this lies in the (often implicit) assumptions made by many of the proposed algorithms — a lack of background clutter, and a uniform scale, orientation, and translation of coins across different images. These assumptions are not satisfied by default and before any further progress in the realm of more complex analysis is made, a robust method capable of preprocessing and normalizing images of coins acquired ‘in the wild’ is needed. In this paper we introduce an algorithm capable of localizing and accurately segmenting out a coin from a cluttered image acquired by an amateur collector. Specifically, we propose a two stage approach which first uses a simple shape hypothesis to localize the coin roughly and then arrives at the final, accurate result by refining this initial estimate using a statistical model learnt from large amounts of data. Our results on data collected ‘in the wild’ demonstrate excellent accuracy even when the proposed algorithm is applied on highly challenging images.Postprin

Images of Roman Imperial denarii : a curated data set for the evaluation of computer vision algorithms applied to ancient numismatics, and an overview of challenges in the field

Automatic ancient Roman coin analysis only recently emerged as a topic of computer science research. Nevertheless, owing to its ever-increasing popularity, the field is already reaching a certain degree of maturity, as witnessed by a substantial publication output in the last decade. At the same time, it is becoming evident that research progress is being limited by a somewhat veering direction of effort and the lack of a coherent framework which facilitates the acquisition and dissemination of robust, repeatable, and rigorous evidence. Thus, in the present article, we seek to address several associated challenges. To start with, (i) we provide a first overview and discussion of different challenges in the field, some of which have been scarcely investigated to date, and others which have hitherto been unrecognized and unaddressed. Secondly, (ii) we introduce the first data set, carefully curated and collected for the purpose of facilitating methodological evaluation of algorithms and, specifically, the effects of coin preservation grades on the performance of automatic methods. Indeed, until now, only one published work at all recognized the need for this kind of analysis, which, to any numismatist, would be a trivially obvious fact. We also discuss a wide range of considerations which had to be taken into account in collecting this corpus, explain our decisions, and describe its content in detail. Briefly, the data set comprises 100 different coin issues, all with multiple examples in Fine, Very Fine, and Extremely Fine conditions, giving a total of over 650 different specimens. These correspond to 44 issuing authorities and span the time period of approximately 300 years (from 27 BC until 244 AD). In summary, the present article should be an invaluable resource to researchers in the field, and we encourage the community to adopt the collected corpus, freely available for research purposes, as a standard evaluation benchmark.Publisher PDFPeer reviewe

A Siamese transformer network for zero-shot ancient coin classification

Ancient numismatics, the study of ancient coins, has in recent years become an attractive domain for the application of computer vision and machine learning. Though rich in research problems, the predominant focus in this area to date has been on the task of attributing a coin from an image, that is of identifying its issue. This may be considered the cardinal problem in the field and it continues to challenge automatic methods. In the present paper, we address a number of limitations of previous work. Firstly, the existing methods approach the problem as a classification task. As such, they are unable to deal with classes with no or few exemplars (which would be most, given over 50,000 issues of Roman Imperial coins alone), and require retraining when exemplars of a new class become available. Hence, rather than seeking to learn a representation that distinguishes a particular class from all the others, herein we seek a representation that is overall best at distinguishing classes from one another, thus relinquishing the demand for exemplars of any specific class. This leads to our adoption of the paradigm of pairwise coin matching by issue, rather than the usual classification paradigm, and the specific solution we propose in the form of a Siamese neural network. Furthermore, while adopting deep learning, motivated by its successes in the field and its unchallenged superiority over classical computer vision approaches, we also seek to leverage the advantages that transformers have over the previously employed convolutional neural networks, and in particular their non-local attention mechanisms, which ought to be particularly useful in ancient coin analysis by associating semantically but not visually related distal elements of a coin’s design. Evaluated on a large data corpus of 14,820 images and 7605 issues, using transfer learning and only a small training set of 542 images of 24 issues, our Double Siamese ViT model is shown to surpass the state of the art by a large margin, achieving an overall accuracy of 81%. Moreover, our further investigation of the results shows that the majority of the method’s errors are unrelated to the intrinsic aspects of the algorithm itself, but are rather a consequence of unclean data, which is a problem that can be easily addressed in practice by simple pre-processing and quality checking.Publisher PDFPeer reviewe

An Autoencoding Method for Detecting Counterfeit Coins

In our daily lives, we use coins to pay for goods and services. However, the market for antique and historical coins is another place where coin quality and genuinity are important. Since counterfeiting has become more common as a result of technological advancements, dealing with fake coins is unavoidable. As a result, researchers have considered various methods in coin detection studies. In recent years, image-based coin detection has made extensive use of 2-D and 3-D image processing approaches. We propose a method for detecting counterfeit coins based on image content in this paper. We used SIFT, SURF, and MSER to determine the degree of similarity between our datasets. Then, using statistical analysis, we determine which descriptor is the most effective criterion for counterfeit coin detection. SIFT was chosen as the most reliable algorithm for the Danish and Canadian coin image dataset according to the Experiments' results. The autoencoder is then trained to detect anomalies in the coin images. A coin image is fed to the trained autoencoder as input and outputs a new image. Using the chosen criterion, the output image is compared to a baseline image. If the similarity between these two images is greater than a certain threshold, the coin is genuine. For training, most counterfeit coin detection methods require fake data. Our autoencoding-based anomaly method can eliminate this. Our proposed method for distinguishing genuine coins from counterfeit coins yielded promising results. In addition, we present a method for increasing the speed of counterfeit coin detection. We conducted our research on the Mint Mark of Canadian toonies coin images and we were able to achieve acceptable results by combining the edge detection technique with GAN and autoencoder

Coin Wear Estimation and Automatic Coin Grading

In numismatic studies, coin grading is referred to as the set of detailed experiments on a coin in order to estimate its quality, which is the most important factor to estimate the coin's value. Usually, the task is done by three expert numismatists to minimize personal biases. Each numismatist tests the coin's wear, coloration, and toning under different lighting conditions. Coin grading is a sensitive task to be done by humans. There are different parameters that can define the coin's value, however, dependent on the numismatist expert conducting the test, some parameters are neglected and some are given a heavier weight, which makes the procedure very subjective. A computer-aided algorithm for coin grading is considered an asset to help conduct more objective coin grading experiments. We propose a coin wear estimation algorithm, which is fully based on features extracted from the digital images of coins. Apart from coin grading, the proposed algorithm is useful to find and dismiss the heavily worn out currency from the market. As online trading is getting more and more popular among coin collectors, it has become easier for individuals to sell a low-quality coin instead of a high-quality one or foist fake copies instead of real coins. This study is concentrated on the feasibility of having a computer-aided program to conduct coin grading. The required specifications for the dataset are fully investigated and the final dataset is collected after lots of experiments. In our proposed method, SIFT key points are used to distinguish the amount of wear on the coins. These key points are known for their high accuracy in shape detection problems. Our approach in using these descriptors to estimate the amount of wear on the coins attains a high accuracy of 93%