143 research outputs found

    Quantitative Ink Analysis: Estimating the Number of Inks in Documents through Hyperspectral Imaging

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    In the field of document forensics, ink analysis plays a crucial role in determining the authenticity of legal and historic documents and detecting forgery. Visual examination alone is insufficient for distinguishing visually similar inks, necessitating the use of advanced scientific techniques. This paper proposes an ink analysis technique based on hyperspectral imaging, which enables the examination of documents in hundreds of narrowly spaced spectral bands, revealing hidden details. The main objective of this study is to identify the number of distinct inks used in a document. Three clustering algorithms, namely k-means, Agglomerative, and c-means, are employed to estimate the number of inks present. The methodology involves data extraction, ink pixel segmentation, and ink number determination. The results demonstrate the effectiveness of the proposed technique in identifying ink clusters and distinguishing between different inks. The analysis of a hyperspectral cube dataset reveals variations in spectral reflectance across different bands and distinct spectral responses among the 12 lines, indicating the presence of multiple inks. The clustering algorithms successfully identify ink clusters, with k-means clustering showing superior classification performance. These findings contribute to the development of reliable methodologies for ink analysis using hyperspectral imaging, enhancing th

    Profiling and imaging of forensic evidence – a pan-European forensic round robin study part 1: document forgery

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    The forensic scenario, on which the round robin study was based, simulated a suspected intentional manipulation of a real estate rental agreement consisting of a total of three pages. The aims of this study were to (i) establish the amount and reliability of information extractable from a single type of evidence and to (ii) provide suggestions on the most suitable combination of compatible techniques for a multi-modal imaging approach to forgery detection. To address these aims, seventeen laboratories from sixteen countries were invited to answer the following tasks questions: (i) which printing technique was used? (ii) were the three pages printed with the same printer? (iii) were the three pages made from the same paper? (iv) were the three pages originally stapled? (v) were the headings and signatures written with the same ink? and (vi) were headings and signatures of the same age on all pages? The methods used were classified into the following categories: Optical spectroscopy, including multispectral imaging, smartphone mapping, UV-luminescence and LIBS; Infrared spectroscopy, including Raman and FTIR (micro-)spectroscopy; X-ray spectroscopy, including SEM-EDX, PIXE and XPS; Mass spectrometry, including ICPMS, SIMS, MALDI and LDIMS; Electrostatic imaging, as well as non-imaging methods, such as non-multimodal visual inspection, (micro-)spectroscopy, physical testing and thin layer chromatography. The performance of the techniques was evaluated as the proportion of discriminated sample pairs to all possible sample pairs. For the undiscriminated sample pairs, a distinction was made between undecidability and false positive claims. It was found that none of the methods used were able to solve all tasks completely and/or correctly and that certain methods were a priori judged unsuitable by the laboratories for some tasks. Correct results were generally achieved for the discrimination of printer toners, whereas incorrect results in the discrimination of inks. For the discrimination of paper, solid state analytical methods proved to be superior to mass spectrometric methods. None of the participating laboratories deemed addressing ink age feasible. It was concluded that correct forensic statements can only be achieved by the complementary application of different methods and that the classical approach of round robin studies to send standardised subsamples to the participants is not feasible for a true multimodal approach if the techniques are not available at one location

    Development of new methodologies for dating in the forensic field, combining analytical techniques with multivariate regression treatments

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    318 p.Whenever a crime is committed, there is always a unity of time, place and action that forensic experts will aim to demonstrate throughout the investigation. Determining the succession, simultaneity, frequency or duration of criminal activities as well as the age of objects, persons and traces is therefore one of the most important goals of forensics in reconstructing the crime scene or in finding and understanding the connections between the evidence and the suspects involved therein. However, time has been largely unexplored due to the complexity of the overall challenge at hand, not yet successfully overcome by single cutting-edge techniques. The coupling of such techniques with chemometrics, more specifically with multivariate regression methods, could turn this situation upside down thanks to the development of age quantitation methodologies based on the modelling of the modifications experienced by the evidence in its properties with respect to time. The potential applicability of these chemometric tools, however, remains poorly understood and underexploited due to their recent introduction into forensic dating research and the statistical background required for their optimal application. That is why this thesis focuses on highlighting the usefulness of multivariate regression methods in several forensicfields, such as questioned documents, art forgery and medico-legal death investigation, through the development and validation of dating methodologies in which non-destructive and micro-destructive techniques are applied together with the (orthogonal) partial least squares regression ((O)PLSR) method

    Face recognition by means of advanced contributions in machine learning

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    Face recognition (FR) has been extensively studied, due to both scientific fundamental challenges and current and potential applications where human identification is needed. FR systems have the benefits of their non intrusiveness, low cost of equipments and no useragreement requirements when doing acquisition, among the most important ones. Nevertheless, despite the progress made in last years and the different solutions proposed, FR performance is not yet satisfactory when more demanding conditions are required (different viewpoints, blocked effects, illumination changes, strong lighting states, etc). Particularly, the effect of such non-controlled lighting conditions on face images leads to one of the strongest distortions in facial appearance. This dissertation addresses the problem of FR when dealing with less constrained illumination situations. In order to approach the problem, a new multi-session and multi-spectral face database has been acquired in visible, Near-infrared (NIR) and Thermal infrared (TIR) spectra, under different lighting conditions. A theoretical analysis using information theory to demonstrate the complementarities between different spectral bands have been firstly carried out. The optimal exploitation of the information provided by the set of multispectral images has been subsequently addressed by using multimodal matching score fusion techniques that efficiently synthesize complementary meaningful information among different spectra. Due to peculiarities in thermal images, a specific face segmentation algorithm has been required and developed. In the final proposed system, the Discrete Cosine Transform as dimensionality reduction tool and a fractional distance for matching were used, so that the cost in processing time and memory was significantly reduced. Prior to this classification task, a selection of the relevant frequency bands is proposed in order to optimize the overall system, based on identifying and maximizing independence relations by means of discriminability criteria. The system has been extensively evaluated on the multispectral face database specifically performed for our purpose. On this regard, a new visualization procedure has been suggested in order to combine different bands for establishing valid comparisons and giving statistical information about the significance of the results. This experimental framework has more easily enabled the improvement of robustness against training and testing illumination mismatch. Additionally, focusing problem in thermal spectrum has been also addressed, firstly, for the more general case of the thermal images (or thermograms), and then for the case of facialthermograms from both theoretical and practical point of view. In order to analyze the quality of such facial thermograms degraded by blurring, an appropriate algorithm has been successfully developed. Experimental results strongly support the proposed multispectral facial image fusion, achieving very high performance in several conditions. These results represent a new advance in providing a robust matching across changes in illumination, further inspiring highly accurate FR approaches in practical scenarios.El reconeixement facial (FR) ha estat àmpliament estudiat, degut tant als reptes fonamentals científics que suposa com a les aplicacions actuals i futures on requereix la identificació de les persones. Els sistemes de reconeixement facial tenen els avantatges de ser no intrusius,presentar un baix cost dels equips d’adquisició i no la no necessitat d’autorització per part de l’individu a l’hora de realitzar l'adquisició, entre les més importants. De totes maneres i malgrat els avenços aconseguits en els darrers anys i les diferents solucions proposades, el rendiment del FR encara no resulta satisfactori quan es requereixen condicions més exigents (diferents punts de vista, efectes de bloqueig, canvis en la il·luminació, condicions de llum extremes, etc.). Concretament, l'efecte d'aquestes variacions no controlades en les condicions d'il·luminació sobre les imatges facials condueix a una de les distorsions més accentuades sobre l'aparença facial. Aquesta tesi aborda el problema del FR en condicions d'il·luminació menys restringides. Per tal d'abordar el problema, hem adquirit una nova base de dades de cara multisessió i multiespectral en l'espectre infraroig visible, infraroig proper (NIR) i tèrmic (TIR), sota diferents condicions d'il·luminació. En primer lloc s'ha dut a terme una anàlisi teòrica utilitzant la teoria de la informació per demostrar la complementarietat entre les diferents bandes espectrals objecte d’estudi. L'òptim aprofitament de la informació proporcionada pel conjunt d'imatges multiespectrals s'ha abordat posteriorment mitjançant l'ús de tècniques de fusió de puntuació multimodals, capaces de sintetitzar de manera eficient el conjunt d’informació significativa complementària entre els diferents espectres. A causa de les característiques particulars de les imatges tèrmiques, s’ha requerit del desenvolupament d’un algorisme específic per la segmentació de les mateixes. En el sistema proposat final, s’ha utilitzat com a eina de reducció de la dimensionalitat de les imatges, la Transformada del Cosinus Discreta i una distància fraccional per realitzar les tasques de classificació de manera que el cost en temps de processament i de memòria es va reduir de forma significa. Prèviament a aquesta tasca de classificació, es proposa una selecció de les bandes de freqüències més rellevants, basat en la identificació i la maximització de les relacions d'independència per mitjà de criteris discriminabilitat, per tal d'optimitzar el conjunt del sistema. El sistema ha estat àmpliament avaluat sobre la base de dades de cara multiespectral, desenvolupada pel nostre propòsit. En aquest sentit s'ha suggerit l’ús d’un nou procediment de visualització per combinar diferents bandes per poder establir comparacions vàlides i donar informació estadística sobre el significat dels resultats. Aquest marc experimental ha permès més fàcilment la millora de la robustesa quan les condicions d’il·luminació eren diferents entre els processos d’entrament i test. De forma complementària, s’ha tractat la problemàtica de l’enfocament de les imatges en l'espectre tèrmic, en primer lloc, pel cas general de les imatges tèrmiques (o termogrames) i posteriorment pel cas concret dels termogrames facials, des dels punt de vista tant teòric com pràctic. En aquest sentit i per tal d'analitzar la qualitat d’aquests termogrames facials degradats per efectes de desenfocament, s'ha desenvolupat un últim algorisme. Els resultats experimentals recolzen fermament que la fusió d'imatges facials multiespectrals proposada assoleix un rendiment molt alt en diverses condicions d’il·luminació. Aquests resultats representen un nou avenç en l’aportació de solucions robustes quan es contemplen canvis en la il·luminació, i esperen poder inspirar a futures implementacions de sistemes de reconeixement facial precisos en escenaris no controlats.Postprint (published version

    Digital Techniques for Documenting and Preserving Cultural Heritage

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    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

    Authentication of Amadeo de Souza-Cardoso Paintings and Drawings With Deep Learning

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    Art forgery has a long-standing history that can be traced back to the Roman period and has become more rampant as the art market continues prospering. Reports disclosed that uncountable artworks circulating on the art market could be fake. Even some principal art museums and galleries could be exhibiting a good percentage of fake artworks. It is therefore substantially important to conserve cultural heritage, safeguard the interest of both the art market and the artists, as well as the integrity of artists’ legacies. As a result, art authentication has been one of the most researched and well-documented fields due to the ever-growing commercial art market in the past decades. Over the past years, the employment of computer science in the art world has flourished as it continues to stimulate interest in both the art world and the artificial intelligence arena. In particular, the implementation of Artificial Intelligence, namely Deep Learning algorithms and Neural Networks, has proved to be of significance for specialised image analysis. This research encompassed multidisciplinary studies on chemistry, physics, art and computer science. More specifically, the work presents a solution to the problem of authentication of heritage artwork by Amadeo de Souza-Cardoso, namely paintings, through the use of artificial intelligence algorithms. First, an authenticity estimation is obtained based on processing of images through a deep learning model that analyses the brushstroke features of a painting. Iterative, multi-scale analysis of the images is used to cover the entire painting and produce an overall indication of authenticity. Second, a mixed input, deep learning model is proposed to analyse pigments in a painting. This solves the image colour segmentation and pigment classification problem using hyperspectral imagery. The result is used to provide an indication of authenticity based on pigment classification and correlation with chemical data obtained via XRF analysis. Further algorithms developed include a deep learning model that tackles the pigment unmixing problem based on hyperspectral data. Another algorithm is a deep learning model that estimates hyperspectral images from sRGB images. Based on the established algorithms and results obtained, two applications were developed. First, an Augmented Reality mobile application specifically for the visualisation of pigments in the artworks by Amadeo. The mobile application targets the general public, i.e., art enthusiasts, museum visitors, art lovers or art experts. And second, a desktop application with multiple purposes, such as the visualisation of pigments and hyperspectral data. This application is designed for art specialists, i.e., conservators and restorers. Due to the special circumstances of the pandemic, trials on the usage of these applications were only performed within the Department of Conservation and Restoration at NOVA University Lisbon, where both applications received positive feedback.A falsificação de arte tem uma história de longa data que remonta ao período romano e tornou-se mais desenfreada à medida que o mercado de arte continua a prosperar. Relatórios revelaram que inúmeras obras de arte que circulam no mercado de arte podem ser falsas. Mesmo alguns dos principais museus e galerias de arte poderiam estar exibindo uma boa porcentagem de obras de arte falsas. Por conseguinte, é extremamente importante conservar o património cultural, salvaguardar os interesses do mercado da arte e dos artis- tas, bem como a integridade dos legados dos artistas. Como resultado, a autenticação de arte tem sido um dos campos mais pesquisados e bem documentados devido ao crescente mercado de arte comercial nas últimas décadas.Nos últimos anos, o emprego da ciência da computação no mundo da arte floresceu à medida que continua a estimular o interesse no mundo da arte e na arena da inteligência artificial. Em particular, a implementação da Inteligência Artificial, nomeadamente algoritmos de aprendizagem profunda (ou Deep Learning) e Redes Neuronais, tem-se revelado importante para a análise especializada de imagens.Esta investigação abrangeu estudos multidisciplinares em química, física, arte e informática. Mais especificamente, o trabalho apresenta uma solução para o problema da autenticação de obras de arte patrimoniais de Amadeo de Souza-Cardoso, nomeadamente pinturas, através da utilização de algoritmos de inteligência artificial. Primeiro, uma esti- mativa de autenticidade é obtida com base no processamento de imagens através de um modelo de aprendizagem profunda que analisa as características de pincelada de uma pintura. A análise iterativa e multiescala das imagens é usada para cobrir toda a pintura e produzir uma indicação geral de autenticidade. Em segundo lugar, um modelo misto de entrada e aprendizagem profunda é proposto para analisar pigmentos em uma pintura. Isso resolve o problema de segmentação de cores de imagem e classificação de pigmentos usando imagens hiperespectrais. O resultado é usado para fornecer uma indicação de autenticidade com base na classificação do pigmento e correlação com dados químicos obtidos através da análise XRF. Outros algoritmos desenvolvidos incluem um modelo de aprendizagem profunda que aborda o problema da desmistura de pigmentos com base em dados hiperespectrais. Outro algoritmo é um modelo de aprendizagem profunda estabelecidos e nos resultados obtidos, foram desenvolvidas duas aplicações. Primeiro, uma aplicação móvel de Realidade Aumentada especificamente para a visualização de pigmentos nas obras de Amadeo. A aplicação móvel destina-se ao público em geral, ou seja, entusiastas da arte, visitantes de museus, amantes da arte ou especialistas em arte. E, em segundo lugar, uma aplicação de ambiente de trabalho com múltiplas finalidades, como a visualização de pigmentos e dados hiperespectrais. Esta aplicação é projetada para especialistas em arte, ou seja, conservadores e restauradores. Devido às circunstâncias especiais da pandemia, os ensaios sobre a utilização destas aplicações só foram realizados no âmbito do Departamento de Conservação e Restauro da Universidade NOVA de Lisboa, onde ambas as candidaturas receberam feedback positivo

    Drawing, Handwriting Processing Analysis: New Advances and Challenges

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    International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline

    Digital Techniques for Documenting and Preserving Cultural Heritage

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

    Going hyperspectral: the 'unseen' captured?

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    All objects, name them soil, water, trees, vegetation, structures, metals, paints or fabrics, create a unique spectral fingerprint. A sensor determines these fingerprints by measuring reflected light, most of which registers in wavelengths, or bands, invisible to humans. This is what the crime scene investigation (CSI) television programs have popularized how DNA or fingerprints can be used to solve crimes. Similarly, forest CSI of “seeing” the trees in the deep high mountain tropical forest is now a major focus in the air and spaceborne hyperspectral sensing technology and in other different applications such as agriculture, environment, geology, transportation, security, and several others. The availability of sub-meter resolution colour imagery from satellites coupled with internet based services like Google Earth and Microsoft Virtual Earth have resulted in an enormous interest in remote sensing among the general public. The ability to see one’s home or familiar landmarks in an image taken from hundreds of kilometers above the earth elicits wonder and awe. Deciding where, when, what and how to sense or measure the DNA of individual trees from the air or space is a crucial question in the sustainable development and management of our Malaysian tropical forest ecosystems. However, to monitor, quantify, map and understand the content and nature of our forest, one would ideally like to monitor it everywhere and all the time too. This is impossible, and consequently, forest engineers must select relatively very high to high near to real time resolution sensors with the ability to transcend boundaries, capabilities, features and interfacing realms for such measurement. The dynamic interplay of these elements is precisely coordinated by signaling networks that orchestrate their interactions. High-throughput experimental and analytical techniques now provide forest engineers with incredibly rich and potentially revealing datasets from both air and spaceborne hyperspectral sensors (also known as imaging spectrometers). However, it is impossible to exhaustively explore the full experimental and operational hyperspectral sensors available in the market out there and so forest engineers must judiciously choose which one is the best to perform and fulfill their project objectives and missions. The complexity and high-dimensionality of these systems makes it incredibly difficult for forest engineers and other users alone to manage and optimize sensing processes. In order to add or derive value from a hyperspectral remotely sensed image several factors such as resolution, swath, and signal to noise ratio, amongst others need to be considered. A grand challenge for the forest engineer’s scientific discovery in the 21st Century is therefore, to devise very high real-time ultra-spatial and spectral air and space borne sensors that automatically measure and adapt sensing operations in large-scale and economical systems with the unseen captured. This lecture therefore focuses on the emerging theory, origin of the hyperspectral sensors, research, practice, limitations and identifies future challenge and outlook of hyperspectral sensing systems in the quest towards a sustainable Malaysian forestry context and other different applications to capture the “unseen”. It is quite certain that advances in hyperspectral remote sensing and more sophisticated analytical methods will resolve any “unseen” issues in time with the best approach of transcending boundaries and interfacing remote sensing data with precise information from the field plots. Unfortunately, as a relatively new analytical technique, the full potential of air and spaceborne hyperspectral imaging has not yet been realized in Malaysi
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