Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

3D printing-as-a-service for collaborative engineering

3D printing or Additive Manufacturing (AM) are utilised as umbrella terms to denote a variety of technologies to manufacture or create a physical object based on a digital model. Commonly, these technologies create the objects by adding, fusing or melting a raw material in a layer-wise fashion. Apart from the 3D printer itself, no specialised tools are required to create almost any shape or form imaginable and designable. The possibilities of these technologies of these technologies are plentiful and cover the ability to manufacture every object, rapidly, locally and cost-efficiently without wasted resources and material. Objects can be created to specific forms to perform as perfectly fitting functions without consideration of the assembly process. To further the advance the availability and applicability of 3D printing, this thesis identifies the problems that currently exist and attempts to solve them. During the 3D printing process, data (i. e., files) must be converted from their original representation, e. g., CAD file, to the machine instructions for a specific 3D printer. During this process, information is lost, and other information is added. Traceability is lacking in 3D printing. The actual 3D printing can require a long period of time to complete, during which errors can occur. In 3D printing, these errors are often non-recoverable or reversible, which results in wasted material and time. In addition to the lack of closed-loop control systems for 3D printers, careful planning and preparation are required to avoid these costly misprints. 3D printers are usually located remotely from users, due to health and safety considerations, special placement requirements or out of comfort. Remotely placed equipment is impractical to monitor in person; however, such monitoring is essential. Especially considering the proneness of 3D printing to errors and the implications of this as described previously. Utilisation of 3D printers is an issue, especially with expensive 3D printers. As there are a number of differing 3D printing technologies available, having the required 3D printer, might be problematic. 3D printers are equipped with a variety of interfaces, depending on the make and model. These differing interfaces, both hard- and software, hinder the integration of different 3D printers into consistent systems. There exists no proper and complete ontology or resource description schema or mechanism that covers all the different 3D printing technologies. Such a resource description mechanism is essential for the automated scheduling in services or systems. In 3D printing services the selection and matching of appropriate and suitable 3D printers is essential, as not all 3D printing technologies are able to perform on all materials or are able to create certain object features, such as thin walls or hollow forms. The need for companies to sell digital models for AM will increase in scenarios where replacement or customised parts are 3D printed by consumers at home or in local manufacturing centres. Furthermore, requirements to safeguard these digital models will increase to avoid a repetition of the problems from the music industry, e. g., Napster. Replication and ‘theft’ of these models are uncontrollable in the current situation. In a service oriented deployment, or in scenarios where the utilisation is high, estimations of the 3D printing time are required to be available. Common 3D printing time estimations are inaccurate, which hinder the application of scheduling. The complete and comprehensive understanding of the complexity of an object is discordant, especially in the domain of AM. This understanding is required to both support the design of objects for AM and match appropriate manufacturing resources to certain objects. Quality in AM and FDM have been incompletely researched. The quality in general is increased with maturity of the technology; however, research on the quality achievable with consumer-grade 3D printers is lacking. Furthermore, cost-sensitive measurement methods for quality assessment are expandable. This thesis presents the structured design and implementation of a 3D printing service with associated contributions that provide solutions to particular problems present in the AM domain. The 3D printing service is the overarching component of this thesis and provides the platform for the other contributions with the intention to establish an online, cloud-based 3D printing service for use in end-user and professional settings with a focus on collaboration and cooperation

Classifiers and machine learning techniques for image processing and computer vision

Orientador: Siome Klein GoldensteinTese (doutorado) - Universidade Estadual de Campinas, Instituto da ComputaçãoResumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional. Os problemas de nosso interesse são: categorização de imagens em duas ou mais classes, detecçãao de mensagens escondidas, distinção entre imagens digitalmente adulteradas e imagens naturais, autenticação, multi-classificação, entre outros. Inicialmente, apresentamos uma revisão comparativa e crítica do estado da arte em análise forense de imagens e detecção de mensagens escondidas em imagens. Nosso objetivo é mostrar as potencialidades das técnicas existentes e, mais importante, apontar suas limitações. Com esse estudo, mostramos que boa parte dos problemas nessa área apontam para dois pontos em comum: a seleção de características e as técnicas de aprendizado a serem utilizadas. Nesse estudo, também discutimos questões legais associadas a análise forense de imagens como, por exemplo, o uso de fotografias digitais por criminosos. Em seguida, introduzimos uma técnica para análise forense de imagens testada no contexto de detecção de mensagens escondidas e de classificação geral de imagens em categorias como indoors, outdoors, geradas em computador e obras de arte. Ao estudarmos esse problema de multi-classificação, surgem algumas questões: como resolver um problema multi-classe de modo a poder combinar, por exemplo, caracteríisticas de classificação de imagens baseadas em cor, textura, forma e silhueta, sem nos preocuparmos demasiadamente em como normalizar o vetor-comum de caracteristicas gerado? Como utilizar diversos classificadores diferentes, cada um, especializado e melhor configurado para um conjunto de caracteristicas ou classes em confusão? Nesse sentido, apresentamos, uma tecnica para fusão de classificadores e caracteristicas no cenário multi-classe através da combinação de classificadores binários. Nós validamos nossa abordagem numa aplicação real para classificação automática de frutas e legumes. Finalmente, nos deparamos com mais um problema interessante: como tornar a utilização de poderosos classificadores binarios no contexto multi-classe mais eficiente e eficaz? Assim, introduzimos uma tecnica para combinação de classificadores binarios (chamados classificadores base) para a resolução de problemas no contexto geral de multi-classificação.Abstract: In this work, we propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are particularly interested in: two and multi-class image categorization, hidden messages detection, discrimination among natural and forged images, authentication, and multiclassification. To start with, we present a comparative survey of the state-of-the-art in digital image forensics as well as hidden messages detection. Our objective is to show the importance of the existing solutions and discuss their limitations. In this study, we show that most of these techniques strive to solve two common problems in Machine Learning: the feature selection and the classification techniques to be used. Furthermore, we discuss the legal and ethical aspects of image forensics analysis, such as, the use of digital images by criminals. We introduce a technique for image forensics analysis in the context of hidden messages detection and image classification in categories such as indoors, outdoors, computer generated, and art works. From this multi-class classification, we found some important questions: how to solve a multi-class problem in order to combine, for instance, several different features such as color, texture, shape, and silhouette without worrying about the pre-processing and normalization of the combined feature vector? How to take advantage of different classifiers, each one custom tailored to a specific set of classes in confusion? To cope with most of these problems, we present a feature and classifier fusion technique based on combinations of binary classifiers. We validate our solution with a real application for automatic produce classification. Finally, we address another interesting problem: how to combine powerful binary classifiers in the multi-class scenario more effectively? How to boost their efficiency? In this context, we present a solution that boosts the efficiency and effectiveness of multi-class from binary techniques.DoutoradoEngenharia de ComputaçãoDoutor em Ciência da Computaçã

Digital Image Processing

Newspapers and the popular scientific press today publish many examples of highly impressive images. These images range, for example, from those showing regions of star birth in the distant Universe to the extent of the stratospheric ozone depletion over Antarctica in springtime, and to those regions of the human brain affected by Alzheimer’s disease. Processed digitally to generate spectacular images, often in false colour, they all make an immediate and deep impact on the viewer’s imagination and understanding. Professor Jonathan Blackledge’s erudite but very useful new treatise Digital Image Processing: Mathematical and Computational Methods explains both the underlying theory and the techniques used to produce such images in considerable detail. It also provides many valuable example problems - and their solutions - so that the reader can test his/her grasp of the physical, mathematical and numerical aspects of the particular topics and methods discussed. As such, this magnum opus complements the author’s earlier work Digital Signal Processing. Both books are a wonderful resource for students who wish to make their careers in this fascinating and rapidly developing field which has an ever increasing number of areas of application. The strengths of this large book lie in: • excellent explanatory introduction to the subject; • thorough treatment of the theoretical foundations, dealing with both electromagnetic and acoustic wave scattering and allied techniques; • comprehensive discussion of all the basic principles, the mathematical transforms (e.g. the Fourier and Radon transforms), their interrelationships and, in particular, Born scattering theory and its application to imaging systems modelling; discussion in detail - including the assumptions and limitations - of optical imaging, seismic imaging, medical imaging (using ultrasound), X-ray computer aided tomography, tomography when the wavelength of the probing radiation is of the same order as the dimensions of the scatterer, Synthetic Aperture Radar (airborne or spaceborne), digital watermarking and holography; detail devoted to the methods of implementation of the analytical schemes in various case studies and also as numerical packages (especially in C/C++); • coverage of deconvolution, de-blurring (or sharpening) an image, maximum entropy techniques, Bayesian estimators, techniques for enhancing the dynamic range of an image, methods of filtering images and techniques for noise reduction; • discussion of thresholding, techniques for detecting edges in an image and for contrast stretching, stochastic scattering (random walk models) and models for characterizing an image statistically; • investigation of fractal images, fractal dimension segmentation, image texture, the coding and storing of large quantities of data, and image compression such as JPEG; • valuable summary of the important results obtained in each Chapter given at its end; • suggestions for further reading at the end of each Chapter. I warmly commend this text to all readers, and trust that they will find it to be invaluable. Professor Michael J Rycroft Visiting Professor at the International Space University, Strasbourg, France, and at Cranfield University, England

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

COSPO/CENDI Industry Day Conference

The conference's objective was to provide a forum where government information managers and industry information technology experts could have an open exchange and discuss their respective needs and compare them to the available, or soon to be available, solutions. Technical summaries and points of contact are provided for the following sessions: secure products, protocols, and encryption; information providers; electronic document management and publishing; information indexing, discovery, and retrieval (IIDR); automated language translators; IIDR - natural language capabilities; IIDR - advanced technologies; IIDR - distributed heterogeneous and large database support; and communications - speed, bandwidth, and wireless

Colour coded

This 300 word publication to be published by the Society of Dyers and Colourists (SDC) is a collection of the best papers from a 4-year European project that has considered colour from the perspective of both the arts and sciences.The notion of art and science and the crossovers between the two resulted in application and funding for cross disciplinary research to host a series of training events between 2006 and 2010 Marie Curie Conferences & Training Courses (SCF) Call Identifier: FP6-Mobility-4, Euros 532,363.80 CREATE – Colour Research for European Advanced Technology Employment. The research crossovers between the fields of art, science and technology was also a subject that was initiated through Bristol’s Festival if Ideas events in May 2009. The author coordinated and chaired an event during which the C.P Snow lecture “On Two Cultures’ (1959) was re-presented by Actor Simon Cook and then a lecture made by Raymond Tallis on the notion of the Polymath. The CREATE project has a worldwide impact for researchers, academics and scientists. Between January and October 2009, the site has received 221, 414 visits. The most popular route into the site is via the welcome page. The main groups of visitors originate in the UK (including Northern Ireland), Italy, France, Finland, Norway, Hungary, USA, Finland and Spain. A basic percentage breakdown of the traffic over ten months indicates: USA -15%; UK - 16%; Italy - 13%; France -12%; Hungary - 10%; Spain - 6%; Finland - 9%; Norway - 5%. The remaining approximate 14% of visitors are from other countries including Belgium, The Netherlands and Germany (approx 3%). A discussion group has been initiated by the author as part of the CREATE project to facilitate an ongoing dialogue between artists and scientists. http://createcolour.ning.com/group/artandscience www.create.uwe.ac.uk.Related papers to this research: A report on the CREATE Italian event: Colour in cultural heritage.C. Parraman, A. Rizzi, ‘Developing the CREATE network in Europe’, in Colour in Art, Design and Nature, Edinburgh, 24 October 2008.C. Parraman, “Mixing and describing colour”. CREATE (Training event 1), France, 2008

Harnessing Simulated Data with Graphs

Physically accurate simulations allow for unlimited exploration of arbitrarily crafted environments. From a scientific perspective, digital representations of the real world are useful because they make it easy validate ideas. Virtual sandboxes allow observations to be collected at-will, without intricate setting up for measurements or needing to wait on the manufacturing, shipping, and assembly of physical resources. Simulation techniques can also be utilized over and over again to test the problem without expending costly materials or producing any waste. Remarkably, this freedom to both experiment and generate data becomes even more powerful when considering the rising adoption of data-driven techniques across engineering disciplines. These are systems that aggregate over available samples to model behavior, and thus are better informed when exposed to more data. Naturally, the ability to synthesize limitless data promises to make approaches that benefit from datasets all the more robust and desirable. However, the ability to readily and endlessly produce synthetic examples also introduces several new challenges. Data must be collected in an adaptive format that can capture the complete diversity of states achievable in arbitrary simulated configurations while too remaining amenable to downstream applications. The quantity and zoology of observations must also straddle a range which prevents overfitting but is descriptive enough to produce a robust approach. Pipelines that naively measure virtual scenarios can easily be overwhelmed by trying to sample an infinite set of available configurations. Variations observed across multiple dimensions can quickly lead to a daunting expansion of states, all of which must be processed and solved. These and several other concerns must first be addressed in order to safely leverage the potential of boundless simulated data. In response to these challenges, this thesis proposes to wield graphs in order to instill structure over digitally captured data, and curb the growth of variables. The paradigm of pairing data with graphs introduced in this dissertation serves to enforce consistency, localize operators, and crucially factor out any combinatorial explosion of states. Results demonstrate the effectiveness of this methodology in three distinct areas, each individually offering unique challenges and practical constraints, and together showcasing the generality of the approach. Namely, studies observing state-of-the-art contributions in design for additive manufacturing, side-channel security threats, and large-scale physics based contact simulations are collectively achieved by harnessing simulated datasets with graph algorithms