This review volume provides from both theoretical and application points of views, recent developments and state-of-the-art reviews in various areas of pattern recognition, image processing, machine learning, soft computing, data mining and web intelligence.

Machine Interpretation of Patterns: Image Analysis and Data Mining is an essential and invaluable resource for professionals and advanced graduates in computer science, mathematics and life sciences. It can also be considered as an integrated volume to researchers interested in doing interdisciplinary research where computer science is a component.

In recent years, there has been a growing interest in the fields of pattern recognition and machine vision in academia and industries. New theories have been developed, with new design of technology and systems in both hardware and software. They are widely applied to our daily life to solve real problems in such diverse areas as science, engineering, agriculture, e-commerce, education, robotics, government, medicine, games and animation, medical imaging analysis and diagnosis, military, and national security. The foundation of all this field can be traced back to the late Prof. King-Sun Fu, one of the founding fathers of pattern recognition, who, with visionary insight founded the International Association for Pattern Recognition around 1980. In the almost 30 years since then, the world has witnessed the rapid growth and development of this field. It is probably true to say that most people are affected by, or use applications of pattern recognition in daily life. Today, on the eve of 25th anniversary of the unfortunate and untimely passing of Prof. Fu, we are proud to produce this volume of collected works from world renowned professionals and experts in pattern recognition and machine vision, in honor and memory of the late Prof. King-Sun Fu. We hope this book will help promote further the course, not only of fundamental principles, systems and technologies, but also its vast range of applications to help in solving problems in daily life. Contents Basic Foundations of Pattern Recognition and Artificial Intelligence, Methodologies of Machine Vision and Image Processing, Intelligent Pattern Recognition Systems, 3-D Object Pattern Analysis, Modelling and Simulation, Analysis of DNA Microarray Gene Expression Data based on Pattern Recognition Methods, PRMV Applications.

This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.

This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and related areas for a long time.

Countless facial images are generated everyday through digital and cell phone cameras, surveillance video systems, webcams, and traditional film and broadcast video. As a result, law enforcement and intelligence agencies have numerous opportunities to acquire and analyze images that depict persons of interest. Computer-Aided Forensic Facial Comparison is a comprehensive exploration of the scientific, technical, and statistical challenges facing researchers investigating courtroom identification from facial images.

Supported by considerable background material, research data, and prototypic statistical and applications software, this volume brings together contributions from anthropologists, computer scientists, forensic scientists, and statisticians. Topics discussed include:

• Face database collection in 3D
• Error and distinguishing power associated with craniofacial landmarks
• Statistical analysis of face shape variation
• Comparison of instrumentation
• Court admissibility issues
• Missing data
• Computer applications development

Based on the quantification and analysis of more than 3000 facial images, this seminal work lays the foundation for future forensic facial comparison, computer applications development, and research in face shape variation and analysis. Using experimental and real case data, it demonstrates the influence of illumination, image resolution, perspective, and pose angle on landmark visibility. Two DVDs are included which contain the raw 3D landmark datasets for 3000 faces, additional datasets used in 2D analysis, and computer programs and spreadsheets used in analysis and in the development of prototypic applications software.

Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications.

The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.

Both pattern recognition and computer vision have experienced rapid progress in the last twenty-five years. This book provides the latest advances on pattern recognition and computer vision along with their many applications. It features articles written by renowned leaders in the field while topics are presented in readable form to a wide range of readers. The book is divided into five parts: basic methods in pattern recognition, basic methods in computer vision and image processing, recognition applications, life science and human identification, and systems and technology. There are eight new chapters on the latest developments in life sciences using pattern recognition as well as two new chapters on pattern recognition in remote sensing.

The 2nd edition is an update of the book Wavelet Theory and its Application to Pattern Recognition published in 2000. Three new chapters, which are research results conducted during 2001-2008, are added. The book consists of three parts - the first presents a brief survey of the status of pattern recognition with wavelet theory; the second contains the basic theory of wavelet analysis; the third includes applications of wavelet theory to pattern recognition. The new book provides a bibliography of 170 references including the current state-of-the-art theory and applications of wavelet analysis to pattern recognition.

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.

This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback.

. Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques

. Many more diagrams included--now in two color--to provide greater insight through visual presentation

. Matlab code of the most common methods are given at the end of each chapter.

. More Matlab code is available, together with an accompanying manual, via this site

. Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms.

. An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869).
Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques Many more diagrams included--now in two color--to provide greater insight through visual presentation Matlab code of the most common methods are given at the end of each chapter An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913) Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor. "

This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.

Rules - the clearest, most explored and best understood form of knowledge representation - are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning.

The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.

The computational methods of bioinformatics are being used more and more to process the large volume of current biological data. Promoting an understanding of the underlying biology that produces this data, Pattern Discovery in Bioinformatics: Theory and Algorithms provides the tools to study regularities in biological data. Taking a systematic approach to pattern discovery, the book supplies sound mathematical definitions and efficient algorithms to explain vital information about biological data. It explores various data patterns, including strings, clusters, permutations, topology, partial orders, and boolean expressions. Each of these classes captures a different form of regularity in the data, providing possible answers to a wide range of questions. The book also reviews basic statistics, including probability, information theory, and the central limit theorem. This self-contained book provides a solid foundation in computational methods, enabling the solution of difficult biological questions.

The authors provide an in-depth, comprehensive examination of hierarchical parallel systems within a comparative and taxonomical framework. They include a general introduction to hierarchical structures, possible topologies, and possible designs; an in-depth discussion of all proposed or built hierarchical parallel systems; and language supports and programming strategies. Their work will serve as both a teacher and reference to programmers and students in computer sciences and electrical engineering.

This book constitutes the refereed proceedings of the 5th International Conference on Intelligence Science, ICIS 2022, held in Xi'an, China, in August 2022. The 41 full and 5 short papers presented in this book were carefully reviewed and selected from 85 submissions. They were organized in topical sections as follows: Brain cognition; machine learning; data intelligence; language cognition; remote sensing images; perceptual intelligence; wireless sensor; and medical artificial intelligence.

This book presents a thorough analysis of gestural data extracted from raw images and/or range data with an aim to recognize the gestures conveyed by the data. It covers image morphological analysis, type-2 fuzzy logic, neural networks and evolutionary computation for classification of gestural data. The application areas include the recognition of primitive postures in ballet/classical Indian dances, detection of pathological disorders from gestural data of elderly people, controlling motion of cars in gesture-driven gaming and gesture-commanded robot control for people with neuro-motor disability. The book is unique in terms of its content, originality and lucid writing style. Primarily intended for graduate students and researchers in the field of electrical/computer engineering, the book will prove equally useful to computer hobbyists and professionals engaged in building firmware for human-computer interfaces. A prerequisite of high school level mathematics is sufficient to understand most of the chapters in the book. A basic background in image processing, although not mandatory, would be an added advantage for certain sections.

Recent advances in biometrics include new developments in sensors, modalities and algorithms. As new sensors are designed, newer challenges emerge in the algorithms for accurate recognition. Written for researchers, advanced students and practitioners to use as a handbook, this volume captures the very latest state-of-the-art research contributions from leading international researchers. It offers coverage of the entire gamut of topics in the field, including sensors, data acquisition, pattern-matching algorithms, and issues that impact at the system level, such as standards, security, networks, and databases

Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography, pattern recognition for satellite images from remote sensing, management of agriculture and forests, assessment of climate change, and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics are covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms, and deep learning, as well as the recent merging of machine learning and physics. End-of-chapter exercises allow readers to develop their problem-solving skills and online data sets allow readers to practise analysis of real data.

Reflecting the increasingly critical importance of speech for the ubiquitous technologies of digital speech processing, this thorough reference/text encompasses fundamental and advanced techniques, the latest applications, and technological breakthroughs in human-machine communications. Completely revised and updated and incorporating the latest advances, Digital Speech Processing, Synthesis, and Recognition, Second Edition contains newly added sections on the international standardization of robust and flexible speech coding techniques, especially CELP and its use in cellular phones waveform unit concatenation-based speech synthesis large-vocabulary continuous-speech recognitionbased on statistical pattern recognition paradigms such as hidden Markov models (HMMs) and stochastic language models increased robustness of speech recognition systems against speech variation, including speaker-independent and speaker-adaptive recognition robust algorithms against noise and channel distortions and more With over 750 references, equations, drawings, photographs, and tables, Digital Speech Processing, Synthesis, and Recognition, Second Edition is a necessary reference for electrical and electronics, acoustical, computer science, system, multimedia, and communication engineers; analysts and scientists involved in digital speech, design, and signal processing, and artificial intelligence; and a superb text for upper-level undergraduate and graduate students in these disciplines.

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images.
This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition.
In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume.

The human face is perhaps the most familiar and easily recognized object in the world, yet both its three-dimensional shape and its two-dimensional images are complex and hard to characterize. This book develops the vocabulary of ridges and parabolic curves, of illumination eigenfaces and elastic warpings for describing the perceptually salient features of a face and its images. The book also explores the underlying mathematics and applies these mathematical techniques to the computer vision problem of face recognition, using both optical and range images.

Knowledge-Based Intelligent Techniques in Character Recognition presents research results on intelligent character recognition techniques, reflecting the tremendous worldwide interest in the applications of knowledge-based techniques in this challenging field.
This resource will interest anyone involved in computer science, computer engineering, applied mathematics, or related fields. It will also be of use to researchers, application engineers and students who wish to develop successful character recognition systems such as those used in reading addresses in a postal routing system or processing bank checks.

Features

As optical character recognition (OCR) begins to find applications ranging from store checkout scanners to money-changing machines and postal system automation, it has become one of the most dynamic areas in information science today. Yet few volumes explore this data-oriented process without relying heavily on mathematical background reading.

Now, Shunji Mori, Hirobumi Nishida, and Hiromitsu Yamada, among the field's most respected researchers since its inception, present this self-contained, clearly written guidebook to OCR—the first comprehensive treatment of the preprocessing, feature-extraction, and systematic description-matching stages of the OCR process. Including a wealth of original research material available here for the first time, this book is both an ideal professional reference source and an excellent entry point for course work in the subject.

Key features of Optical Character Recognition:

• Theoretical framework based on functional analysis—not previously available in a detailed, English-language version
• Extensive explanation of preprocessing theory, including blurring and sampling, normalization, thinning, and binary and gray-scale morphology
• Intensive section on feature extraction, exploring linear methods, structure analysis, and algebraic description
• Original work on systematic shape description as a prerequisite to matching
• Original material on elastic matching, including image recognition of characters and objects
• Requires only the standard undergraduate requisites of algebra, linear algebra, and advanced calculus

Contents:
Introduction. The Nature of Caricature. Caricatures by Computer. Peacock's Tails and other Natural Caricatures. The Power of Extremes. The Psychology of Caricatures. Caricatures and Face Recognition. The View from Here.

A seminal collection of research methodology themes, this two-volume work provides a set of key scholarly developments related to robustness, allowing scholars to advance their knowledge of research methods used outside of their own immediate fields. With a focus on emerging methodologies within management, key areas of importance are dissected with chapters covering statistical modelling, new measurements, digital research, biometrics and neuroscience, the philosophy of research, computer modelling approaches and new mathematical theories, among others. A genuinely pioneering contribution to the advancement of research methods in business studies, Innovative Research Methodologies in Management presents an analytical and engaging discussion on each topic. By introducing new research agendas it aims to pave the way for increased application of innovative techniques, allowing the exploration of future research perspectives. Volume II explores a range of research methodologies including the Spatial Delphi and Spatial Shang, Virtual Reality, the Futures Polygon and Neuroscience research.

The addition of artificial neural network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical guide to the application of artificial neural networks.
Geared toward the practitioner, Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the same framework. Through the book's presentation of underlying theory and numerous practical examples, readers gain an understanding that will allow them to make judicious design choices rendering neural application predictable and effective. The book provides an intuitive explanation of each method for each network paradigm. This discussion is supported by a rigorous mathematical approach where necessary.
C++ has emerged as a rich and descriptive means by which concepts, models, or algorithms can be precisely described. For many of the neural network models discussed, C++ programs are presented for the actual implementation. Pictorial diagrams and in-depth discussions explain each topic. Necessary derivative steps for the mathematical models are included so that readers can incorporate new ideas into their programs as the field advances with new developments. For each approach, the authors clearly state the known theoretical results, the known tendencies of the approach, and their recommendations for getting the best results from the method.
The material covered in the book is accessible to working engineers with little or no explicit background in neural networks. However, the material is presented in sufficient depth so that those with prior knowledge will find this book beneficial. Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate or graduate level. This book is valuable for academic as well as practical research.