This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed. Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others. Though the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development.>

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.

This text presents an overview of smart information systems for both the private and public sector, highlighting the research questions that can be studied by applying computational intelligence. The book demonstrates how to transform raw data into effective smart information services, covering the challenges and potential of this approach. Each chapter describes the algorithms, tools, measures and evaluations used to answer important questions. This is then further illustrated by a diverse selection of case studies reflecting genuine problems faced by SMEs, multinational manufacturers, service companies, and the public sector. Features: provides a state-of-the-art introduction to the field, integrating contributions from both academia and industry; reviews novel information aggregation services; discusses personalization and recommendation systems; examines sensor-based knowledge acquisition services, describing how the analysis of sensor data can be used to provide a clear picture of our world.

Although the field of texture processing is now well-established, research in this area remains predominantly restricted to texture analysis and simple and approximate static textures.

This comprehensive text/reference presents a survey of the state of the art in multidimensional, physically-correct visual texture modeling. Starting from basic principles and building upon the fundamentals to the latest advanced methods, the book brings together research from computer vision, pattern recognition, computer graphics, virtual and augmented reality. The text assumes a graduate-level understanding of statistics and probability theory, and a knowledge of basic computer graphics principles, but is accessible to newcomers to the field.

Topics and features: reviews the entire process of texture synthesis, including material appearance representation, measurement, analysis, compression, modeling, editing, visualization, and perceptual evaluation; explains the derivation of the most common representations of visual texture, discussing their properties, advantages, and limitations; describes a range of techniques for the measurement of visual texture, including BRDF, SVBRDF, BTF and BSSRDF; investigates the visualization of textural information, from texture mapping and mip-mapping to illumination- and view-dependent data interpolation; examines techniques for perceptual validation and analysis, covering both standard pixel-wise similarity measures and also methods of visual psychophysics; reviews the applications of visual textures, from visual scene analysis in image processing and medical applications, to high-quality visualizations for cultural heritage and the automotive industry.

Researchers, lecturers, students and practitioners will all find this book an invaluable reference on the rapidly developing new field of texture modeling.

The book presents a coherent understanding of computational intelligence from the perspective of what is known as "intelligent computing" with high-dimensional parameters. It critically discusses the central issue of high-dimensional neurocomputing, such as quantitative representation of signals, extending the dimensionality of neuron, supervised and unsupervised learning and design of higher order neurons. The strong point of the book is its clarity and ability of the underlying theory to unify our understanding of high-dimensional computing where conventional methods fail. The plenty of application oriented problems are presented for evaluating, monitoring and maintaining the stability of adaptive learning machine. Author has taken care to cover the breadth and depth of the subject, both in the qualitative as well as quantitative way. The book is intended to enlighten the scientific community, ranging from advanced undergraduates to engineers, scientists and seasoned researchers in computational intelligence.

Many important planning decisions in society and business depend on proper knowledge and a correct understanding of movement, be it in transportation, logistics, biology, or the life sciences. Today the widespread use of mobile phones and technologies like GPS and RFID provides an immense amount of data on location and movement. What is needed are new methods of visualization and algorithmic data analysis that are tightly integrated and complement each other to allow end-users and analysts to extract useful knowledge from these extremely large data volumes. This is exactly the topic of this book. As the authors show, modern visual analytics techniques are ready to tackle the enormous challenges brought about by movement data, and the technology and software needed to exploit them are available today. The authors start by illustrating the different kinds of data available to describe movement, from individual trajectories of single objects to multiple trajectories of many objects, and then proceed to detail a conceptual framework, which provides the basis for a fundamental understanding of movement data. With this basis, they move on to more practical and technical aspects, focusing on how to transform movement data to make it more useful, and on the infrastructure necessary for performing visual analytics in practice. In so doing they demonstrate that visual analytics of movement data can yield exciting insights into the behavior of moving persons and objects, but can also lead to an understanding of the events that transpire when things move. Throughout the book, they use sample applications from various domains and illustrate the examples with graphical depictions of both the interactive displays and the analysis results. In summary, readers will benefit from this detailed description of the state of the art in visual analytics in various ways. Researchers will appreciate the scientific precision involved, software technologists will find essential information on algorithms and systems, and practitioners will profit from readily accessible examples with detailed illustrations for practical purposes.

This volume offers a guide to the state of the art in the fast evolving field of biometric recognition to newcomers and experienced practitioners. It is focused on the emerging strategies to perform biometric recognition under uncontrolled data acquisition conditions. The mainstream research work in this field is presented in an organized manner, so the reader can easily follow the trends that best suits her/his interests in this growing field. The book chapters cover the recent advances in less controlled / covert data acquisition frameworks, segmentation of poor quality biometric data, biometric data quality assessment, normalization of poor quality biometric data. contactless biometric recognition strategies, biometric recognition robustness, data resolution, illumination, distance, pose, motion, occlusions, multispectral biometric recognition, multimodal biometrics, fusion at different levels, high confidence automatic surveillance.

In recent years, there have been significant progress in computational intelligence and image processing with machine learning and deep learning as important components of modern artificial intelligence. All these progresses face challenges in dealing with Covid-19 pandemic for detection and treatment.This comprehensive compendium provides not only updated advances of computational intelligence and image processing in the detection and treatment of Covid-19, but also other medical applications such as in cancer detection and cardiovascular diseases, etc. More traditional approaches such as 2D segmentation and 3D reconstruction are included.The useful reference text is an updated version of the edited title, Computer Vision in Medical Imaging (World Scientific, 2014) and its companion volume, Frontiers of Medical Imaging (World Scientific, 2015). The book is written for engineers, scientists and the medical community to meet the increased challenges in medical applications.

Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. "

Plan, Activity, and Intent Recognition" explains the crucial role of these techniques in a wide variety of applications including:

personal agent assistants computer and network security opponent modeling in games and simulation systems coordination in robots and software agents web e-commerce and collaborative filtering dialog modeling video surveillance smart homes In this book, follow the history of this research area and witness exciting new developments in the field made possible by improved sensors, increased computational power, and new application areas.
Combines basic theory on algorithms for plan/activity recognition along with results from recent workshops and seminarsExplains how to interpret and recognize plans and activities from sensor dataProvides valuable background knowledge and assembles key concepts into one guide for researchers or students studying these disciplines"

This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information...), principles (maximum entropy, minimax entropy...) and theories (rate distortion theory, method of types...).

This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.

The last decade has witnessed a rapid surge of interest in new sensing and monitoring devices for wellbeing and healthcare. One key development in this area is wireless, wearable and implantable "in vivo" monitoring and intervention. A myriad of platforms are now available from both academic institutions and commercial organisations. They permit the management of patients with both acute and chronic symptoms, including diabetes, cardiovascular diseases, treatment of epilepsy and other debilitating neurological disorders. Despite extensive developments in sensing technologies, there are significant research issues related to system integration, sensor miniaturisation, low-power sensor interface, wireless telemetry and signal processing.

In the 2nd edition of this popular and authoritative reference on Body Sensor Networks (BSN), major topics related to the latest technological developments and potential clinical applications are discussed, with contents covering.

Biosensor Design, Interfacing and Nanotechnology

Wireless Communication and Network Topologies

Communication Protocols and Standards

Energy Harvesting and Power Delivery

Ultra-low Power Bio-inspired Processing

Multi-sensor Fusion and Context Aware Sensing

Autonomic Sensing

Wearable, Ingestible Sensor Integration and Exemplar Applications

System Integration and Wireless Sensor Microsystems

The book also provides a comprehensive review of the current wireless sensor development platforms and a step-by-step guide to developing your own BSN applications through the use of BSN development kit.

This book addresses biometrics from a biomedical engineering point of view. Divided into five sections, it discusses topics including the influence of pathologies on various biometric modalities (e.g. face, iris, fingerprint), medical and security biometrics, behavioural biometrics, instrumentation, wearable technologies and imaging. The final chapters also present a number of case studies. The book is suitable for advanced graduate and postgraduate students, engineers and researchers, especially those in signal and image processing, biometrics, and biomedical engineering.

This book includes reviewed papers by international scholars from the 2020 International Conference on Pattern Recognition and Artificial Intelligence (held online). The papers have been expanded to provide more details specifically for the book. It is geared to promote ongoing interest and understanding about pattern recognition and artificial intelligence. Like the previous book in the series, this book covers a range of topics and illustrates potential areas where pattern recognition and artificial intelligence can be applied. It highlights, for example, how pattern recognition and artificial intelligence can be used to classify, predict, detect and help promote further discoveries related to credit scores, criminal news, national elections, license plates, gender, personality characteristics, health, and more.Chapters include works centred on medical and financial applications as well as topics related to handwriting analysis and text processing, internet security, image analysis, database creation, neural networks and deep learning. While the book is geared to promote interest from the general public, it may also be of interest to graduate students and researchers in the field.

This book addresses automated software fingerprinting in binary code, especially for cybersecurity applications. The reader will gain a thorough understanding of binary code analysis and several software fingerprinting techniques for cybersecurity applications, such as malware detection, vulnerability analysis, and digital forensics. More specifically, it starts with an overview of binary code analysis and its challenges, and then discusses the existing state-of-the-art approaches and their cybersecurity applications. Furthermore, it discusses and details a set of practical techniques for compiler provenance extraction, library function identification, function fingerprinting, code reuse detection, free open-source software identification, vulnerability search, and authorship attribution. It also illustrates several case studies to demonstrate the efficiency, scalability and accuracy of the above-mentioned proposed techniques and tools. This book also introduces several innovative quantitative and qualitative techniques that synergistically leverage machine learning, program analysis, and software engineering methods to solve binary code fingerprinting problems, which are highly relevant to cybersecurity and digital forensics applications. The above-mentioned techniques are cautiously designed to gain satisfactory levels of efficiency and accuracy. Researchers working in academia, industry and governmental agencies focusing on Cybersecurity will want to purchase this book. Software engineers and advanced-level students studying computer science, computer engineering and software engineering will also want to purchase this book.

Provides the basic concepts of process mining techniques for pattern recognition for readers to analyze, predict, forecast, and enhance the workflow of processes Covers the entire spectrum of process mining from process discovery to operational support Discusses several process mining techniques in the context of data science and big data Contains real-life applications and case studies related to process mining theories and practices Includes detailed examples, figures, and tables for easy understanding of concepts discussed

This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and interestingness.

Biometrics, the science of using physical traits to identify individuals, is playing an increasing role in our security-conscious society and across the globe. Biometric authentication, or bioauthentication, systems are being used to secure everything from amusement parks to bank accounts to military installations. Yet developments in this field have not been matched by an equivalent improvement in the statistical methods for evaluating these systems. Compensating for this need, this unique text/reference provides a basic statistical methodology for practitioners and testers of bioauthentication devices, supplying a set of rigorous statistical methods for evaluating biometric authentication systems. This framework of methods can be extended and generalized for a wide range of applications and tests. This is the first single resource on statistical methods for estimation and comparison of the performance of biometric authentication systems. The book focuses on six common performance metrics: for each metric, statistical methods are derived for a single system that incorporates confidence intervals, hypothesis tests, sample size calculations, power calculations and prediction intervals. These methods are also extended to allow for the statistical comparison and evaluation of multiple systems for both independent and paired data. Topics and features: * Provides a statistical methodology for the most common biometric performance metrics: failure to enroll (FTE), failure to acquire (FTA), false non-match rate (FNMR), false match rate (FMR), and receiver operating characteristic (ROC) curves * Presents methods for the comparison of two or more biometric performance metrics * Introduces a new bootstrap methodology for FMR and ROC curve estimation * Supplies more than 120 examples, using publicly available biometric data where possible * Discusses the addition of prediction intervals to the bioauthentication statistical toolset * Describes sample-size and power calculations for FTE, FTA, FNMR and FMR Researchers, managers and decisions makers needing to compare biometric systems across a variety of metrics will find within this reference an invaluable set of statistical tools. Written for an upper-level undergraduate or master's level audience with a quantitative background, readers are also expected to have an understanding of the topics in a typical undergraduate statistics course. Dr. Michael E. Schuckers is Associate Professor of Statistics at St. Lawrence University, Canton, NY, and a member of the Center for Identification Technology Research.

Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering focuses on fuzzy clustering methods which have proven to be very powerful in pattern recognition and considers the entire process of dynamic pattern recognition. This book sets a general framework for Dynamic Pattern Recognition, describing in detail the monitoring process using fuzzy tools and the adaptation process in which the classifiers have to be adapted, using the observations of the dynamic process. It then focuses on the problem of a changing cluster structure (new clusters, merging of clusters, splitting of clusters and the detection of gradual changes in the cluster structure). Finally, the book integrates these parts into a complete algorithm for dynamic fuzzy classifier design and classification.

This book examines the context, motivation and current status of biometric systems based on the palmprint, with a specific focus on touchless and less-constrained systems. It covers new technologies in this rapidly evolving field and is one of the first comprehensive books on palmprint recognition systems. It discusses the research literature and the most relevant industrial applications of palmprint biometrics, including the low-cost solutions based on webcams. The steps of biometric recognition are described in detail, including acquisition setups, algorithms, and evaluation procedures. Constraints and limitations of current palmprint recognition systems are analyzed and discussed. The authors also introduce innovative methods for touchless and less-constrained palmprint recognition, with the aim to make palmprint biometrics easier to use in practical, daily-life applications, and overcome the typical constraints and limitations described. Touchless Palmprint Recognition Systems targets professionals and researchers working in biometrics, image processing and three-dimensional reconstruction. Advanced-level students studying biometrics and computer science will also find this material valuable as a secondary text book or reference.

This book provides a framework for robust and novel biometric techniques, along with implementation and design strategies. The theory, principles, pragmatic and modern methods, and future directions of biometrics are presented, along with in-depth coverage of biometric applications in driverless cars, automated and AI-based systems, IoT, and wearable devices. Additional coverage includes computer vision and pattern recognition, cybersecurity, cognitive computing, soft biometrics, and the social impact of biometric technology. The book will be a valuable reference for researchers, faculty, and practicing professionals working in biometrics and related fields, such as image processing, computer vision, and artificial intelligence. Highlights robust and novel biometrics techniques Provides implementation strategies and future research directions in the field of biometrics Includes case studies and emerging applications

Presents original method of enhanced ant colony optimization in feature selection, based on mathematical experiments and modelling. Provides a theoretical concept in iris features searching and detection as part of feature extraction process. Demonstrates the iris features selection and detection using the proposed design methodology with enhanced ant colony optimization for iris recognition.

This unique text/reference presents a unified approach to the formulation of Gestalt laws for perceptual grouping, and the construction of nested hierarchies by aggregation utilizing these laws. The book also describes the extraction of such constructions from noisy images showing man-made objects and clutter. Each Gestalt operation is introduced in a separate, self-contained chapter, together with application examples and a brief literature review. These are then brought together in an algebraic closure chapter, followed by chapters that connect the method to the data - i.e., the extraction of primitives from images, cooperation with machine-readable knowledge, and cooperation with machine learning. Topics and features: offers the first unified approach to nested hierarchical perceptual grouping; presents a review of all relevant Gestalt laws in a single source; covers reflection symmetry, frieze symmetry, rotational symmetry, parallelism and rectangular settings, contour prolongation, and lattices; describes the problem from all theoretical viewpoints, including syntactic, probabilistic, and algebraic perspectives; discusses issues important to practical application, such as primitive extraction and any-time search; provides an appendix detailing a general adjustment model with constraints. This work offers new insights and proposes novel methods to advance the field of machine vision, which will be of great benefit to students, researchers, and engineers active in this area.

"This book guides you in the journey of 3D modeling from the theory with elegant mathematics to applications with beautiful 3D model pictures. Written in a simple, straightforward, and concise manner, readers will learn the state of the art of 3D reconstruction and modeling." -Professor Takeo Kanade, Carnegie Mellon University The computer vision and graphics communities use different terminologies for the same ideas. This book provides a translation, enabling graphics researchers to apply vision concepts, and vice-versa, independence of chapters allows readers to directly jump into a specific chapter of interest, compared to other texts, gives more succinct treatment overall, and focuses primarily on vision geometry. Image-Based Modeling is for graduate students, researchers, and engineers working in the areas of computer vision, computer graphics, image processing, robotics, virtual reality, and photogrammetry.

Optical Character Recognition (OCR) is a key technology enabling access to digital text data. This technique is especially valuable for Arabic scripts, for which there has been very little digital access.

Arabic script is widely used today. It is estimated that approximately 200 million people use Arabic as a first language, and the Arabic script is shared by an additional 13 languages, making it the second most widespread script in the world. However, Arabic scripts pose unique challenges for OCR systems that cannot be simply adapted from existing Latin character-based processing techniques.

This comprehensive "Guide to OCR for Arabic Scripts" is the first book of its kind, specifically devoted to this emerging field. Presenting state-of-the-art research from an international selection of pre-eminent authorities, the book reviews techniques and algorithms for the recognition of both handwritten and printed Arabic scripts. Many of these techniques can also be applied to other scripts, serving as an inspiration to all groups working in the area of OCR.

Topics and features: contains contributions from the leading researchers in the field; with a Foreword by Professor Bente Maegaard of the University of Copenhagen; presents a detailed overview of Arabic character recognition technology, covering a range of different aspects of pre-processing and feature extraction; reviews a broad selection of varying approaches, including HMM-based methods and a recognition system based on multidimensional recurrent neural networks; examines the evaluation of Arabic script recognition systems, discussing data collection and annotation, benchmarking strategies, and handwriting recognition competitions; describes numerous applications of Arabic script recognition technology, from historical Arabic manuscripts to online Arabic recognition.

This authoritative work is an essential reference for all researchers and graduate students interested in OCR technology and methodology in general, and in Arabic scripts in particular.