Local Image Patterns for Counterfeit Coin Detection and Automatic Coin Grading

Abstract Local Image Patterns for Counterfeit Coin Detection and Automatic Coin Grading Coins are an essential part of our life, and we still use them for everyday transactions. We have always faced the issue of the counterfeiting of the coins, but it has become worse with time due to the innovation in the technology of counterfeiting, making it more difficult for detection. Through this thesis, we propose a counterfeit coin detection method that is robust and applicable to all types of coins, whether they have letters on them or just images or both of these characteristics. We use two different types of feature extraction methods. The first one is SIFT (Scale Invariant Feature transform) features, and the second one is RFR (Rotation and Flipping invariant Regional Binary Patterns) features to make our system complete in all aspects and very generic at the same time. The feature extraction methods used here are scale, rotation, illumination, and flipping invariant. We concatenate both our feature sets and use them to train our classifiers. Our feature sets highly complement each other in a way that SIFT provides us with most discriminative features that are scale and rotation invariant but do not consider the spatial value when we cluster them, and here our second set of features comes into play as it considers the spatial structure of each coin image. We train SVM classifiers with two different sets of features from each image. The method has an accuracy of 99.61% with both high and low-resolution images. We also took pictures of the coins at 90˚ and 45˚ angles using the mobile phone camera, to check the robustness of our proposed method, and we achieved promising results even with these low-resolution pictures. Also, we work on the problem of Coin Grading, which is another issue in the field of numismatic studies. Our algorithm proposed above is customized according to the coin grading problem and calculates the coin wear and assigns a grade to it. We can use this grade to remove low-quality coins from the system, which are otherwise sold to coin collectors online for a considerable price. Coin grading is currently done by coin experts manually and is a time consuming and expensive process. We use digital images and apply computer vision and machine learning algorithms to calculate the wear on the coin and then assign it a grade based on its quality level. Our method calculates the amount of wear on coins and assign them a label and achieve an accuracy of 98.5%

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

Invariant Image-Based Currency Denomination Recognition Using Local Entropy and Range Filters

We perform image-based denomination recognition of the Pakistani currency notes. There are a total of seven different denominations in the current series of Pakistani notes. Apart from color and texture, these notes differ from one another mainly due to their aspect ratios. Our aim is to exploit this single feature to attain an image-based recognition that is invariant to the most common image variations found in currency notes images. Among others, the most notable image variations are caused by the difference in positions and in-plane orientations of the currency notes in images. While most of the proposed methods for currency denomination recognition only focus on attaining higher recognition rates, our aim is more complex, i.e., attaining a high recognition rate in the presence of image variations. Since, the aspect ratio of a currency note is invariant to such differences, an image-based recognition of currency notes based on aspect ratio is more likely to be translation- and rotation-invariant. Therefore, we adapt a two step procedure that first extracts a currency note from the homogeneous image background via local entropy and range filters. Then, the aspect ratio of the extracted currency note is calculated to determine its denomination. To validate our proposed method, we gathered a new dataset with the largest and most diverse collection of Pakistani currency notes, where each image contains either a single or multiple notes at arbitrary positions and orientations. We attain an overall average recognition rate of 99% which is very encouraging for our method, which relies on a single feature and is suited for real-time applications. Consequently, the method may be extended to other international and historical currencies, which makes it suitable for business and digital humanities application

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%

Corinth Excavations Archaeological Manual

“The publication of the Corinth Excavation Manual offers us a unique view into real-life archaeological practice on one of the most important Classical sites in the Mediterranean.” Adam Rabinowitz, Associate Professor and Assistant Director, Institute of Classical Archaeology, University of Texas, Austin The Corinth Excavations Archaeological Manual is the first major field manual published from an American excavation in Greece and among a very small number of manuals published from the Eastern Mediterranean in the last generation. The appearance of this book is timely, however, as there is a growing interest in field methods and the history of excavation practices throughout the discipline of archaeology. Moreover, Corinth Excavations has long held a special place in American archaeology in Greece as the primary training excavation for graduate students associated with the American School of Classical Studies at Athens. As a result, the field manual has had a particular influence among American excavators and projects in Greece, among Mediterranean archaeologists, and in archaeology classrooms. Published as a technical field manual, an archival document, and a key statement of practice from a major excavation, the Corinth Excavations Archaeological Manual presents a guide for daily procedures at the Corinth Excavations, a complete record of documentation forms used in the field, and a practical glimpse into the functioning of a complex, major, project. The manual is a landmark text appropriate for the university student, the scholar of methodology, and the working field archaeologist. “The Corinth manual has grown over the years into a comprehensive and authoritative guide to open-area, stratigraphic excavation, covering everything from excavation of pits, wells, and robbing trenches to the removal of deposits to inventorying objects in the museum. ” David Pettegrew, Associate Professor, Messiah College and author of The Isthmus of Corinth (2016). All of the authors have worked on the excavations at Corinth in various capacities. This manual was developed under the directorship of Dr. Guy Sanders by former field directors Alicia Carter Johnson and Dr. Sarah James. Additional contributions come from past and present Corinth staff including assistant director Dr. Ioulia Tzonou-Herbst, architect James Herbst, conservator Nicol Anastasatou, and archaeologist Katerina Ragkou. The authors would also like to recognize the contributions of the many students from the American School of Classical Studies at Athens who offered valuable feedback on earlier versions of this manual over the past 10 years.https://commons.und.edu/press-books/1005/thumbnail.jp

Qualities, objects, sorts, and other treasures : gold digging in English and Arabic

In the present monograph, we will deal with questions of lexical typology in the nominal domain. By the term "lexical typology in the nominal domain", we refer to crosslinguistic regularities in the interaction between (a) those areas of the lexicon whose elements are capable of being used in the construction of "referring phrases" or "terms" and (b) the grammatical patterns in which these elements are involved. In the traditional analyses of a language such as English, such phrases are called "nominal phrases". In the study of the lexical aspects of the relevant domain, however, we will not confine ourselves to the investigation of "nouns" and "pronouns" but intend to take into consideration all those parts of speech which systematically alternate with nouns, either as heads or as modifiers of nominal phrases. In particular, this holds true for adjectives both in English and in other Standard European Languages. It is well known that adjectives are often difficult to distinguish from nouns, or that elements with an overt adjectival marker are used interchangeably with nouns, especially in particular semantic fields such as those denoting MATERIALS or NATlONALlTIES. That is, throughout this work the expression "lexical typology in the nominal domain" should not be interpreted as "a typology of nouns", but, rather, as the cross-linguistic investigation of lexical areas constitutive for "referring phrases" irrespective of how the parts-of-speech system in a specific language is defined

The Phoebe A. Hearst Expedition to Naga ed-Deir, Cemeteries N 2000 and N 2500

The Phoebe A. Hearst Expedition to Naga ed-Deir, Cemeteries N 2000 and N 2500 presents the results of excavations directed by George A. Reisner and led by Arthur C. Mace. The site of Naga ed-Deir, Egypt, is unusual for its continued use over a long period of time (c. 3500 BCE–650 CE). Burials in N 2000 and N 2500 date to the First Intermediate Period/Middle Kingdom and the Coptic era. In keeping with Reisner’s earlier publications of Naga ed-Deir, this volume presents artifacts in chapter-length studies devoted to a particular object type and includes a burial-by-burial description. The excavators’ original drawings, notes, and photographs are complemented by a contemporary analysis of the objects by experts in their subfields. Readership: Egyptologists and archaeologists