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

Reconhecimento automático de moedas medievais usando visão por computador

Dissertação de mestrado em Engenharia InformáticaThe use of computer vision for identification and recognition of coins is well studied and of renowned interest. However the focus of research has consistently been on modern coins and the used algorithms present quite disappointing results when applied to ancient coins. This discrepancy is explained by the nature of ancient coins that are manually minted, having plenty variances, failures, ripples and centuries of degradation which further deform the characteristic patterns, making their identification a hard task even for humans. Another noteworthy factor in almost all similar studies is the controlled environments and uniform illumination of all images of the datasets. Though it makes sense to focus on the more problematic variables, this is an impossible premise to find outside the researchers’ laboratory, therefore a problematic that must be approached. This dissertation focuses on medieval and ancient coin recognition in uncontrolled “real world” images, thus trying to pave way to the use of vast repositories of coin images all over the internet that could be used to make our algorithms more robust. The first part of the dissertation proposes a fast and automatic method to segment ancient coins over complex backgrounds using a Histogram Backprojection approach combined with edge detection methods. Results are compared against an automation of GrabCut algorithm. The proposed method achieves a Good or Acceptable rate on 76% of the images, taking an average of 0.29s per image, against 49% in 19.58s for GrabCut. Although this work is oriented to ancient coin segmentation, the method can also be used in other contexts presenting thin objects with uniform colors. In the second part, several state of the art machine learning algorithms are compared in the search for the most promising approach to classify these challenging coins. The best results are achieved using dense SIFT descriptors organized into Bags of Visual Words, and using Support Vector Machine or Naïve Bayes as machine learning strategies.O uso de visão por computador para identificação e reconhecimento de moedas é bastante estudado e de reconhecido interesse. No entanto o foco da investigação tem sido sistematicamente sobre as moedas modernas e os algoritmos usados apresentam resultados bastante desapontantes quando aplicados a moedas antigas. Esta discrepância é justificada pela natureza das moedas antigas que, sendo cunhadas à mão, apresentam bastantes variações, falhas e séculos de degradação que deformam os padrões característicos, tornando a sua identificação dificil mesmo para o ser humano. Adicionalmente, a quase totalidade dos estudos usa ambientes controlados e iluminação uniformizada entre todas as imagens dos datasets. Embora faça sentido focar-se nas variáveis mais problemáticas, esta é uma premissa impossível de encontrar fora do laboratório do investigador e portanto uma problemática que tem que ser estudada. Esta dissertação foca-se no reconhecimento de moedas medievais e clássicas em imagens não controladas, tentando assim abrir caminho ao uso de vastos repositórios de imagens de moedas disponíveis na internet, que poderiam ser usados para tornar os nossos algoritmos mais robustos. Na primeira parte é proposto um método rápido e automático para segmentar moedas antigas sobre fundos complexos, numa abordagem que envolve Histogram Backprojection combinado com deteção de arestas. Os resultados são comparados com uma automação do algoritmo GrabCut. O método proposto obtém uma classificação de Bom ou Aceitável em 76% das imagens, demorando uma média de 0.29s por imagem, contra 49% em 19,58s do GrabCut. Não obstante o foco em segmentação de moedas antigas, este método pode ser usado noutros contextos que incluam objetos planos de cor uniforme. Na segunda parte, o estado da arte de Machine Learning é testado e comparado em busca da abordagem mais promissora para classificar estas moedas. Os melhores resultados são alcançados usando descritores dense SIFT, organizados em Bags of Visual Words e usando Support Vector Machine ou Naive Bayes como estratégias de machine learning

Digital Techniques for Documenting and Preserving Cultural Heritage

In this unique collection the authors present a wide range of interdisciplinary methods to study, document, and conserve material cultural heritage. The methods used serve as exemplars of best practice with a wide variety of cultural heritage objects having been recorded, examined, and visualised. The objects range in date, scale, materials, and state of preservation and so pose different research questions and challenges for digitization, conservation, and ontological representation of knowledge. Heritage science and specialist digital technologies are presented in a way approachable to non-scientists, while a separate technical section provides details of methods and techniques, alongside examples of notable applications of spatial and spectral documentation of material cultural heritage, with selected literature and identification of future research. This book is an outcome of interdisciplinary research and debates conducted by the participants of the COST Action TD1201, Colour and Space in Cultural Heritage, 2012–16 and is an Open Access publication available under a CC BY-NC-ND licence.https://scholarworks.wmich.edu/mip_arc_cdh/1000/thumbnail.jp

Digital Techniques for Documenting and Preserving Cultural Heritage

This book presents interdisciplinary approaches to the examination and documentation of material cultural heritage, using non-invasive spatial and spectral optical technologies

Investigating Linked Data Usability for Ancient World Research

Linked Data technologies are used to describe and connect entities, based on features they have in common. Rich semantic descriptions, disambiguation capabilities, and interoperability allow investigation of new research questions and reveal previously undiscovered relationships. However, previous studies have shown that uptake of Linked Data among Humanities researchers has, thus far, been low, partly due to usability issues with the resulting tools and resources. I therefore set out to investigate how their usability might be improved, and how Linked Data technologies might most effectively be integrated with existing research methods. My study focused on the Ancient World, where Linked Data implementation seems to be higher than in other Humanities disciplines, and involved a survey and interviews to elicit user and producer needs from researchers in this subject area. I start this thesis by introducing and contextualising my research topic in Chapter 1. In Chapter 2, I consult existing literature and datasets to discuss Linked Humanities Data implementation, its advantages, and current barriers. Chapter 3 provides an outline of my survey and interview methodologies, while Chapter 4 presents initial survey analysis and identifies themes for discussion in the subsequent chapters. Chapter 5 focuses on five research methods already embedded in the practices of Ancient World researchers, where Linked Data could effectively be integrated: Discovering, Gathering, Data Recognition, Annotating, and Visualization. In Chapter 6, I explore the user experience more broadly, including aspects such as interface design, reliability, and data quality. Chapter 7 then discusses areas of the production process that affect Linked Data usability: training, collaboration, user-centred design, documentation, access, and sustainability. My findings form the basis of a series of recommendations in Chapter 8, which focus on teamwork, openness and transparency, extensibility, user consultation, discoverability, sustainability, and communities, culminating in a Five-Star Model for Linked Humanities Data Usability.</i

Semantic enrichment for enhancing LAM data and supporting digital humanities. Review article

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest in the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data’s quality and discoverability while enabling a self-sustaining ecosystem, “semantic enrichment” becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data’s discoverability, use- and reuse-ability, and their value in the mainstream of DH and Semantic Web

Cultural Contact in Early Roman Spain through Linked Open Data

The study of the Roman colonisation of the western provinces has produced much literature, especially about the processes of assimilation of Roman culture by indigenous communities and the cultural changes experienced by these under Roman influence. In Spain, traditional scholarship has looked mainly at the literary evidence for these processes, and therefore, the ‘Roman’ perspective of the conquest; current schools of thought argue for a new reading of the cultural processes rooted in theory and a contextualised analysis of archaeological data. Traditional methods lacked the tools capable of making effective relationships within large amounts of data. Linked Open Data (hereafter LOD) technologies provide the means to resolve this deadlock. In the last decade, a number of projects have made available large amounts of data leading to a burgeoning of resources that rely on LOD technologies. The number of databases collecting information from Hispania is also continuously increasing. While these resources provide a vast amount of material, most of them do not meet open-access requirements, becoming information silos that hinder information accessibility and interoperability. This research applies LOD technologies to align and connect web-exposed datasets (that follow or can be integrated to follow LOD standards) together with enhanced and aggregated information to investigate the dynamics of cultural interaction in the southern area of Spain between the 4th century BCE and the 1st century CE on the basis of epigraphic, monetary and sculptural evidence. Ultimately, this thesis examines the extent to which the application of LOD technologies can improve the way archaeological information is accessed, retrieved and analysed by means of a LOD dataset (ERUB) and the Cultural Contact Ontology (CuCoO)

Semantic enrichment for enhancing LAM data and supporting digital humanities. Review article

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data’s quality and discoverability while enabling a self-sustaining ecosystem, “semantic enrichment” becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data’s discoverability, use- and reuse-ability, and their value in the mainstream of DH and Semantic Web

Surface Appearance Estimation from Video Sequences

The realistic virtual reproduction of real world objects using Computer Graphics techniques requires the accurate acquisition and reconstruction of both 3D geometry and surface appearance. Unfortunately, in several application contexts, such as Cultural Heritage (CH), the reflectance acquisition can be very challenging due to the type of object to acquire and the digitization conditions. Although several methods have been proposed for the acquisition of object reflectance, some intrinsic limitations still make its acquisition a complex task for CH artworks: the use of specialized instruments (dome, special setup for camera and light source, etc.); the need of highly controlled acquisition environments, such as a dark room; the difficulty to extend to objects of arbitrary shape and size; the high level of expertise required to assess the quality of the acquisition. The Ph.D. thesis proposes novel solutions for the acquisition and the estimation of the surface appearance in fixed and uncontrolled lighting conditions with several degree of approximations (from a perceived near diffuse color to a SVBRDF), taking advantage of the main features that differentiate a video sequences from an unordered photos collections: the temporal coherence; the data redundancy; the easy of the acquisition, which allows acquisition of many views of the object in a short time. Finally, Reflectance Transformation Imaging (RTI) is an example of widely used technology for the acquisition of the surface appearance in the CH field, even if limited to single view Reflectance Fields of nearly flat objects. In this context, the thesis addresses also two important issues in RTI usage: how to provide better and more flexible virtual inspection capabilities with a set of operators that improve the perception of details, features and overall shape of the artwork; how to increase the possibility to disseminate this data and to support remote visual inspection of both scholar and ordinary public