Abstract
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 types, 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.
Highlights
It is no longer an exaggeration to say that computer vision is pervasive in everyday life: face detection [1,2] has been a standard feature of digital cameras and smartphones for well over a decade, online image depositories are increasingly successful at categorizing images by their semantic content [3,4,5], automatic diagnosis and prognosis of diseases has even surpassed the performance of human experts in some domains [6,7,8], etc
Recalling our aim of collecting a data corpus curated by the condition of coins it contains, there are several reasons for why we decided to focus on denarii in particular
We addressed a number of important issues in the increasingly active research domain of application of computer vision and machine-learning-based analysis of ancient coins, which has received insufficient attention to date
Summary
It is no longer an exaggeration to say that computer vision is pervasive in everyday life: face detection [1,2] has been a standard feature of digital cameras and smartphones for well over a decade, online image depositories are increasingly successful at categorizing images by their semantic content (scene: beach, city, countryside, etc.; objects: cars, buildings, dogs, churches, statues, etc.) [3,4,5], automatic diagnosis and prognosis of diseases has even surpassed the performance of human experts in some domains [6,7,8], etc This success, coupled with the increasing pervasiveness of powerful computing devices and the dramatic improvement in the user-friendliness of technology in general, is having a positive impact on inter-disciplinary research, with a growing interest in the application of modern computer science in other scientific fields, as well as in the arts and humanities [9,10,11]. We begin this section with an explanation of the relevant numismatic terminology necessary for the understanding of the present article and the related literature, categorize and describe in detail the most important (practically and technically) challenges in the field, and summarize the progress to date in addressing these
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