Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXI International Conference on Neuroinformatics, held on October 7-11, 2019, in Dolgoprudny, a town in Moscow region, Russia.

This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis. Incorporating multiple views to improve the generalization performance, multiview learning is also known as data fusion or data integration from multiple feature sets. This self-contained book is applicable for multi-modal learning research, and requires minimal prior knowledge of the basic concepts in the field. It is also a valuable reference resource for researchers working in the field of machine learning and also those in various application domains.

This is the second volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This second book focuses on recent developments in response to the demands of new digital technologies. It is divided into two parts: the first part includes four chapters on the decomposition and recovery of signals, with special emphasis on images. In turn, the second part includes three chapters and addresses important data-based actions, such as adaptive filtering, experimental modeling, and classification.

This book presents an overview of the state of the art in video coding technology. Specifically, it introduces the tools of the AVS2 standard, describing how AVS2 can help to achieve a significant improvement in coding efficiency for future video networks and applications by incorporating smarter coding tools such as scene video coding. Features: introduces the basic concepts in video coding, and presents a short history of video coding technology and standards; reviews the coding framework, main coding tools, and syntax structure of AVS2; describes the key technologies used in the AVS2 standard, including prediction coding, transform coding, entropy coding, and loop-filters; examines efficient tools for scene video coding and surveillance video, and the details of a promising intelligent video coding system; discusses optimization technologies in video coding systems; provides a review of image, video, and 3D content quality assessment algorithms; surveys the hot research topics in video compression.

This book proceedings collects a number of papers presented at the International Conference on Sensing and Imaging, which was held at Guangxi University of Science and Technology from October 15-18, 2018. Sensing and imaging is an interdisciplinary field covering a variety of sciences and techniques such as optics, electricity, magnetism, heat, sound, and computing technologies. The field has diverse applications of interest such as image processing techniques.The results in the book bridge the gap between theory and applications, translating techniques into better products. The text will appeal to students, professionals and researchers alike.

This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

Remote Sensing Image Fusion: A Practical Guide gives an introduction to remote sensing image fusion providing an overview on the sensors and applications. It describes data selection, application requirements and the choice of a suitable image fusion technique. It comprises a diverse selection of successful image fusion cases that are relevant to other users and other areas of interest around the world. The book helps newcomers to obtain a quick start into the practical value and benefits of multi-sensor image fusion. Experts will find this book useful to obtain an overview on the state of the art and understand current constraints that need to be solved in future research efforts. For industry professionals the book can be a great introduction and basis to understand multisensor remote sensing image exploitation and the development of commercialized image fusion software from a practical perspective. The book concludes with a chapter on current trends and future developments in remote sensing image fusion. Along with the book, RSIF website provides additional up-to-date information in the field.

The book familiarizes readers with fundamental concepts and issues related to computer vision and major approaches that address them. The focus of the book is on image acquisition and image formation models, radiometric models of image formation, image formation in the camera, image processing concepts, concept of feature extraction and feature selection for pattern classification/recognition, and advanced concepts like object classification, object tracking, image-based rendering, and image registration. Intended to be a companion to a typical teaching course on computer vision, the book takes a problem-solving approach.

Taking a novel, more appealing approach than current texts, An Integrated Introduction to Computer Graphics and Geometric Modeling focuses on graphics, modeling, and mathematical methods, including ray tracing, polygon shading, radiosity, fractals, freeform curves and surfaces, vector methods, and transformation techniques. The author begins with fractals, rather than the typical line-drawing algorithms found in many standard texts. He also brings the turtle back from obscurity to introduce several major concepts in computer graphics. Supplying the mathematical foundations, the book covers linear algebra topics, such as vector geometry and algebra, affine and projective spaces, affine maps, projective transformations, matrices, and quaternions. The main graphics areas explored include reflection and refraction, recursive ray tracing, radiosity, illumination models, polygon shading, and hidden surface procedures. The book also discusses geometric modeling, including planes, polygons, spheres, quadrics, algebraic and parametric curves and surfaces, constructive solid geometry, boundary files, octrees, interpolation, approximation, Bezier and B-spline methods, fractal algorithms, and subdivision techniques. Making the material accessible and relevant for years to come, the text avoids descriptions of current graphics hardware and special programming languages. Instead, it presents graphics algorithms based on well-established physical models of light and cogent mathematical methods.

Image and video signals require large transmission bandwidth and storage, leading to high costs. The data must be compressed without a loss or with a small loss of quality. Thus, efficient image and video compression algorithms play a significant role in the storage and transmission of data. Image and Video Compression: Fundamentals, Techniques, and Applications explains the major techniques for image and video compression and demonstrates their practical implementation using MATLAB (R) programs. Designed for students, researchers, and practicing engineers, the book presents both basic principles and real practical applications. In an accessible way, the book covers basic schemes for image and video compression, including lossless techniques and wavelet- and vector quantization-based image compression and digital video compression. The MATLAB programs enable readers to gain hands-on experience with the techniques. The authors provide quality metrics used to evaluate the performance of the compression algorithms. They also introduce the modern technique of compressed sensing, which retains the most important part of the signal while it is being sensed.

This is the first volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed. The book is divided into three parts, the first of which introduces readers to periodic and non-periodic signals. The second part is devoted to filtering, which is an important and commonly used application. The third part addresses more advanced topics, including the analysis of real-world non-stationary signals and data, e.g. structural fatigue, earthquakes, electro-encephalograms, birdsong, etc. The book's last chapter focuses on modulation, an example of the intentional use of non-stationary signals.

This book constitutes the refereed proceedings of the 10th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2018, held in Nanning, China, in October 2018. The 37 full papers and 8 short papers presented were carefully reviewed and selected from 80 submissions. They are organized in topical sections on machine learning, deep learning, multi-agent systems, neural computing and swarm intelligence, natural language processing, recommendation systems, social computing, business intelligence and security, pattern recognition, and image understanding.

This book is a collection of research papers selected for presentation at the International Conference on Smart Computational Methods in Continuum Mechanics 2021, organized by Moscow Institute of Physics and Technology and the Institute for Computer Aided Design of Russian Academy of Sciences. The work is presented in two volumes. The primary objective of the book is to report the state-of-the-art on smart computational paradigms in continuum mechanics and explore the use of artificial intelligence paradigms such as neural nets and machine learning for improving the performance of the designed engineering systems. The book includes up-to-date smart computational methods which are used to solve problems in continuum mechanics, engineering, seismic prospecting, non-destructive testing, and so on. The main features of the book are the research papers on the application of novel smart methods including neural nets and machine learning, computational algorithms, smart software systems, and high-performance computer systems for solving complex engineering problems. The case studies pertaining to the real-world applications in the above fields are included. The book presents a collection of best research papers in English language from some of the world leaders in the field of smart system modelling and design of engineering systems.

This book presents essential algorithms for the image processing pipeline of photo-printers and accompanying software tools, offering an exposition of multiple image enhancement algorithms, smart aspect-ratio changing techniques for borderless printing and approaches for non-standard printing modes. All the techniques described are content-adaptive and operate in an automatic mode thanks to machine learning reasoning or ingenious heuristics. The first part includes algorithms, for example, red-eye correction and compression artefacts reduction, that can be applied in any photo processing application, while the second part focuses specifically on printing devices, e.g. eco-friendly and anaglyph printing. The majority of the techniques presented have a low computational complexity because they were initially designed for integration in system-on-chip. The book reflects the authors' practical experience in algorithm development for industrial R&D.

¿..Take advantage of open-source software tools for practical 2-D and 3-D programming¿. "Handbook of Geometric Programming Using Open Geometry GL" is a comprehensive reference on the practice of geometrics and graphics programming. It is based upon and utilizes the industry standard open-source graphics API, Open GL. Readers will find a complete compendium of all methods and classes for Open Geometry GL, using any standard computer operating system (Windows 2000, Windows NT, Unix, and Linux). They will learn how to use these methods and classes and apply them in theoretical and practical applications for 2-D and 3-D graphics and animation, and data exchange, as well as create their own easily programmed extensions using C++ language. Topics and features: *Reinforced with more than 100 extensive documented examples *Flexibility and extendibility with open-source code and API library, for custom solutions to any geometric or graphics problems *Object-oriented viewpoint for all methods and programming in C++ *Based upon Open GL API and standards *More than 200 sample programs and executables on CD-ROM, covering wide areas of application, and with full source code *A comprehensive ¿toolkit¿ that is easy-to-use for beginning and advanced users of Open Geometry GL *Includes new coverage on projective geometry, fractals or Bézier surfaces, and B-spline surfaces With its accessibility, breadth, and well-devised organization, this handbook is a superb resource for those looking to develop a solid grounding and expertise in geometric programming with Open Geometry GL. It is an indispensable reference work ideally suited for practitioners, professionals, and researchers.

This book presents works detailing the application of processing and visualization techniques for analyzing the Earth's subsurface. The topic of the book is interactive data processing and interactive 3D visualization techniques used on subsurface data. Interactive processing of data together with interactive visualization is a powerful combination which has in the recent years become possible due to hardware and algorithm advances in. The combination enables the user to perform interactive exploration and filtering of datasets while simultaneously visualizing the results so that insights can be made immediately. This makes it possible to quickly form hypotheses and draw conclusions. Case studies from the geosciences are not as often presented in the scientific visualization and computer graphics community as e.g., studies on medical, biological or chemical data. This book will give researchers in the field of visualization and computer graphics valuable insight into the open visualization challenges in the geosciences, and how certain problems are currently solved using domain specific processing and visualization techniques. Conversely, readers from the geosciences will gain valuable insight into relevant visualization and interactive processing techniques. Subsurface data has interesting characteristics such as its solid nature, large range of scales and high degree of uncertainty, which makes it challenging to visualize with standard methods. It is also noteworthy that parallel fields of research have taken place in geosciences and in computer graphics, with different terminology when it comes to representing geometry, describing terrains, interpolating data and (example-based) synthesis of data. The domains covered in this book are geology, digital terrains, seismic data, reservoir visualization and CO2 storage. The technologies covered are 3D visualization, visualization of large datasets, 3D modelling, machine learning, virtual reality, seismic interpretation and multidisciplinary collaboration. People within any of these domains and technologies are potential readers of the book.

This book encompasses a collection of topics covering recent advances that are important to the Arabic language in areas of natural language processing, speech and image analysis. This book presents state-of-the-art reviews and fundamentals as well as applications and recent innovations.The book chapters by top researchers present basic concepts and challenges for the Arabic language in linguistic processing, handwritten recognition, document analysis, text classification and speech processing. In addition, it reports on selected applications in sentiment analysis, annotation, text summarization, speech and font analysis, word recognition and spotting and question answering.Moreover, it highlights and introduces some novel applications in vital areas for the Arabic language. The book is therefore a useful resource for young researchers who are interested in the Arabic language and are still developing their fundamentals and skills in this area. It is also interesting for scientists who wish to keep track of the most recent research directions and advances in this area.

The purpose of this volume is to present current work of the Intelligent Computer Graphics community, a community growing up year after year. This volume is a kind of continuation of the previously published Springer volume "Artificial Int- ligence Techniques for Computer Graphics". Nowadays, intelligent techniques are more and more used in Computer Graphics in order, not only to optimise the pr- essing time, but also to find more accurate solutions for a lot of Computer Gra- ics problems, than with traditional methods. What are intelligent techniques for Computer Graphics? Mainly, they are te- niques based on Artificial Intelligence. So, problem resolution (especially constraint satisfaction) techniques, as well as evolutionary techniques, are used in Declarative scene Modelling; heuristic search techniques, as well as strategy games techniques, are currently used in scene understanding and in virtual world exploration; multi-agent techniques and evolutionary algorithms are used in behavioural animation; and so on. However, even if in most cases the used intelligent techniques are due to Artificial - telligence, sometimes, simple human intelligence can find interesting solutions in cases where traditional Computer Graphics techniques, even combined with Artificial Intelligence ones, cannot propose any satisfactory solution. A good example of such a case is the one of scene understanding, in the case where several parts of the scene are impossible to access.

This book deals with two fundamental issues in the semiotics of the image. The first is the relationship between image and observer: how does one look at an image? To answer this question, this book sets out to transpose the theory of enunciation formulated in linguistics over to the visual field. It also aims to clarify the gains made in contemporary visual semiotics relative to the semiology of Roland Barthes and Emile Benveniste. The second issue addressed is the relation between the forces, forms and materiality of the images. How do different physical mediums (pictorial, photographic and digital) influence visual forms? How does materiality affect the generativity of forms? On the forces within the images, the book addresses the philosophical thought of Gilles Deleuze and Rene Thom as well as the experiment of Aby Warburg's Atlas Mnemosyne. The theories discussed in the book are tested on a variety of corpora for analysis, including both paintings and photographs, taken from traditional as well as contemporary sources in a variety of social sectors (arts and sciences). Finally, semiotic methodology is contrasted with the computational analysis of large collections of images (Big Data), such as the "Media Visualization" analyses proposed by Lev Manovich and Cultural Analytics in the field of Computer Science to evaluate the impact of automatic analysis of visual forms on Digital Art History and more generally on the image sciences.

The Book presents an overview of newly developed watermarking techniques in various independent and hybrid domains Covers the basics of digital watermarking, its types, domain in which it is implemented and the application of machine learning algorithms onto digital watermarking Reviews hardware implementation of watermarking Discusses optimization problems and solutions in watermarking with a special focus on bio-inspired algorithms Includes a case study along with its MATLAB code and simulation results

Marking a distinct departure from the perspectives of frame theory and discrete transforms, this book provides a comprehensive mathematical and algorithmic introduction to wavelet theory. As such, it can be used as either a textbook or reference guide. As a textbook for graduate mathematics students and beginning researchers, it offers detailed information on the basic theory of framelets and wavelets, complemented by self-contained elementary proofs, illustrative examples/figures, and supplementary exercises. Further, as an advanced reference guide for experienced researchers and practitioners in mathematics, physics, and engineering, the book addresses in detail a wide range of basic and advanced topics (such as multiwavelets/multiframelets in Sobolev spaces and directional framelets) in wavelet theory, together with systematic mathematical analysis, concrete algorithms, and recent developments in and applications of framelets and wavelets. Lastly, the book can also be used to teach on or study selected special topics in approximation theory, Fourier analysis, applied harmonic analysis, functional analysis, and wavelet-based signal/image processing.

"Blind Signal Processing: Theory and Practice" not only introduces related fundamental mathematics, but also reflects the numerous advances in the field, such as probability density estimation-based processing algorithms, underdetermined models, complex value methods, uncertainty of order in the separation of convolutive mixtures in frequency domains, and feature extraction using Independent Component Analysis (ICA). At the end of the book, results from a study conducted at Shanghai Jiao Tong University in the areas of speech signal processing, underwater signals, image feature extraction, data compression, and the like are discussed.

This book will be of particular interest to advanced undergraduate students, graduate students, university instructors and research scientists in related disciplines. Xizhi Shi is a Professor at Shanghai Jiao Tong University.

Inspection is crucial to the management of ageing infrastructure. Visual information on structures is regularly collected but very little work exists on its organised and quantitative analysis, even though image processing can significantly enhance these inspection processes and transfer real financial and safety benefits to the managers, owners and users. Additionally, new opportunities exist in the fast evolving sectors of wind and wave energy to add value to image-based inspection techniques. This book is a first for structural engineers and inspectors who wish to harness the full potential of cameras as an inspection tool. It is particularly directed to the inspection of offshore and marine structures and the application of image-based methods in underwater inspections. It outlines a set of best practice guidelines for obtaining imagery, then the fundamentals of image processing are covered along with several image processing techniques which can be used to assess multiple damage forms: crack detection, corrosion detection, and depth analysis of marine growth on offshore structures. The book provides benchmark performance measures for these techniques under various visibility conditions using an image repository which will help inspectors to envisage the effectiveness of the techniques when applied. MATLAB (R) scripts and access to the underwater image repository are included so readers can run these techniques themselves. Practising engineers and managers of infrastructure assets are guided in image processing based inspection. Researchers can use this book as a primer, and it also suits advanced graduate courses in infrastructure management or on applied image processing.

Edited by a renowned international expert in the field, Nuclear Medicine Physics offers an up-to-date, state-of-the-art account of the physics behind the theoretical foundation and applications of nuclear medicine. It covers important physical aspects of the methods and instruments involved in modern nuclear medicine, along with related biological topics. The book first discusses the physics of and machines for producing radioisotopes suitable for use in conventional nuclear medicine and PET. After focusing on positron physics and the applications of positrons in medicine and biology, it describes the use of radiopharmaceuticals in molecular imaging, clinical, and research studies. The text then covers modern radiation detectors and measuring methods, including those used in nuclear imaging, as well as numerous imaging methodologies and models, such as two- and three-dimensional image reconstruction algorithms, data processing sequences, new nuclear oncology techniques, and physiological models of the central nervous system. It also introduces biological systems theory, nuclear medicine methods as systems theory procedures, and aspects of kinetic modeling. The final chapter explores dosimetry and the biological effects of ionizing radiation. With many new developments occurring in nuclear medicine, it is important to understand how advanced approaches are being used in emerging applications. Offering invaluable insight into this growth, Nuclear Medicine Physics provides in-depth descriptions of new radiolabeled biological drugs, new cell labeling techniques, new technical concepts in radiation detection, improvements in instrumentation, and much more.