With the increasing concerns on security breaches and transaction fraud, highly reliable and convenient personal verification and identification technologies are more and more requisite in our social activities and national services. Biometrics, used to recognize the identity of an individual, are gaining ever-growing popularity in an extensive array of governmental, military, forensic, and commercial security applications.""Advanced Biometric Recognition Technologies: Discriminant Criterion and Fusion Applications"" focuses on two kinds of advanced biometric recognition technologies, biometric data discrimination and multi-biometrics, while systematically introducing recent research in developing effective biometric recognition technologies. Organized into three main sections, this cutting-edge book explores advanced biometric data discrimination technologies, describes tensor-based biometric data discrimination technologies, and develops the fundamental conception and categories of multi-biometrics technologies.

In recent years, libraries and archives all around the world have increased their efforts to digitize historical manuscripts. To integrate the manuscripts into digital libraries, pattern recognition and machine learning methods are needed to extract and index the contents of the scanned images.The unique compendium describes the outcome of the HisDoc research project, a pioneering attempt to study the whole processing chain of layout analysis, handwriting recognition, and retrieval of historical manuscripts. This description is complemented with an overview of other related research projects, in order to convey the current state of the art in the field and outline future trends.This must-have volume is a relevant reference work for librarians, archivists and computer scientists.

This book presents a thorough and detailed guide to image registration, outlining the principles and reviewing state-of-the-art tools and methods. The book begins by identifying the components of a general image registration system, and then describes the design of each component using various image analysis tools. The text reviews a vast array of tools and methods, not only describing the principles behind each tool and method, but also measuring and comparing their performances using synthetic and real data. Features: discusses similarity/dissimilarity measures, point detectors, feature extraction/selection and homogeneous/heterogeneous descriptors; examines robust estimators, point pattern matching algorithms, transformation functions, and image resampling and blending; covers principal axes methods, hierarchical methods, optimization-based methods, edge-based methods, model-based methods, and adaptive methods; includes a glossary, an extensive list of references, and an appendix on PCA.

This practically-oriented textbook introduces the fundamentals of designing digital surveillance systems powered by intelligent computing techniques. The text offers comprehensive coverage of each aspect of the system, from camera calibration and data capture, to the secure transmission of surveillance data, in addition to the detection and recognition of individual biometric features and objects. The coverage concludes with the development of a complete system for the automated observation of the full lifecycle of a surveillance event, enhanced by the use of artificial intelligence and supercomputing technology. This updated third edition presents an expanded focus on human behavior analysis and privacy preservation, as well as deep learning methods. Topics and features: contains review questions and exercises in every chapter, together with a glossary; describes the essentials of implementing an intelligent surveillance system and analyzing surveillance data, including a range of biometric characteristics; examines the importance of network security and digital forensics in the communication of surveillance data, as well as issues of issues of privacy and ethics; discusses the Viola-Jones object detection method, and the HOG algorithm for pedestrian and human behavior recognition; reviews the use of artificial intelligence for automated monitoring of surveillance events, and decision-making approaches to determine the need for human intervention; presents a case study on a system that triggers an alarm when a vehicle fails to stop at a red light, and identifies the vehicle's license plate number; investigates the use of cutting-edge supercomputing technologies for digital surveillance, such as FPGA, GPU and parallel computing. This concise and accessible work serves as a classroom-tested textbook for graduate-level courses on intelligent surveillance. Researchers and engineers interested in entering this area will also find the book suitable as a helpful self-study reference.

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.

Patterns are becoming the focal point of many areas of scientific endeavour in recent years owing to the progress of computer science, laboratory experiments and observations, and analytical tools. This book brings together articles by the leading experts in this field. The following topics are discussed in this volume: current status of pattern research with emphasis on real phenomena and new theoretical concepts; interdisciplinary subjects involving Statistical Physics, Condensed Matter Physics, Fluid Mechanics, Nonequilibrium and Nonlinear Phenomena.

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.

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.

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.

Utilizing the ubiquity of social media in modern society, the emerging interdisciplinary field of social computing offers the promise of important human-centered applications.

"Human-Centered Social Media Analytics" provides a timely and unique survey of next-generation social computational methodologies. The text explains the fundamentals of this field, and describes state-of-the-art methods for inferring social status, relationships, preferences, intentions, personalities, needs, and lifestyles from human information in unconstrained visual data. The collected chapters present a range of different viewpoints examining the various possibilities and challenges to machine understanding of humans in a social context.

Topics and features: includes perspectives from an international and interdisciplinary selection of pre-eminent authorities; presents balanced coverage of both detailed theoretical analysis and real-world applications; examines social relationships in human-centered media for the development of socially-aware video, location-based, and multimedia applications; reviews techniques for recognizing the social roles played by people in an event, and for classifying human-object interaction activities; discusses the prediction and recognition of human attributes via social media analytics, including social relationships, facial age and beauty, and occupation; requires no prior background knowledge of the area.

This authoritative text/reference will be a valuable resource for researchers and graduate students interested in social media and networking, computer vision and biometrics, big data, and HCI. Practitioners in these fields, as well as in image processing and computer graphics, will also find the book of great interest.

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.

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.

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.

This book presents an interactive multimodal approach for efficient transcription of handwritten text images. This approach, rather than full automation, assists the expert in the recognition and transcription process.Until now, handwritten text recognition (HTR) systems are far from being perfect and heavy human intervention is often required to check and correct the results of such systems. The interactive scenario studied in this book combines the efficiency of automatic handwriting recognition systems with the accuracy of the experts, leading to a cost-effective perfect transcription of the handwritten text images.The interactive system here allows the user to repeatedly interact with the system. Hence, the quality and ergonomy of the interactive process is crucial for the success of the system. Moreover, more ergonomic multimodal interfaces are used to obtain an easier and more comfortable human-machine interaction.

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

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.

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

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.

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.

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.

This book describes the methods and algorithms for image pre-processing and recognition. These methods are based on a parallel shift technology of the imaging copy, as well as simple mathematical operations to allow the generation of a minimum set of features to describe and recognize the image. This book also describes the theoretical foundations of parallel shift technology and pattern recognition. Based on these methods and theories, this book is intended to help researchers with artificial intelligence systems design, robotics, and developing software and hardware applications.

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.

This book highlights the field of selfie biometrics, providing a clear overview and presenting recent advances and challenges. It also discusses numerous selfie authentication techniques on mobile devices. Biometric authentication using mobile devices is becoming a convenient and important means of verifying identity for secured access and services such as telebanking and electronic transactions. In this context, face and ocular biometrics in the visible spectrum has gained increased attention from the research community. However, device mobility and operation in uncontrolled environments mean that facial and ocular images captured with mobile devices exhibit substantial degradation as a result of adverse lighting conditions, specular reflections and motion and defocus blur. In addition, low spatial resolution and the small sensor of front-facing mobile cameras further degrade the sample quality, reducing the recognition accuracy of face and ocular recognition technology when integrated into smartphones. Presenting the state of the art in mobile biometric research and technology, and offering an overview of the potential problems in real-time integration of biometrics in mobile devices, this book is a valuable resource for final-year undergraduate students, postgraduate students, engineers, researchers and academics in various fields of computer engineering.

This book presents the state of the art in online visual tracking, including the motivations, practical algorithms, and experimental evaluations. Visual tracking remains a highly active area of research in Computer Vision and the performance under complex scenarios has substantially improved, driven by the high demand in connection with real-world applications and the recent advances in machine learning. A large variety of new algorithms have been proposed in the literature over the last two decades, with mixed success. Chapters 1 to 6 introduce readers to tracking methods based on online learning algorithms, including sparse representation, dictionary learning, hashing codes, local model, and model fusion. In Chapter 7, visual tracking is formulated as a foreground/background segmentation problem, and tracking methods based on superpixels and end-to-end deep networks are presented. In turn, Chapters 8 and 9 introduce the cutting-edge tracking methods based on correlation filter and deep learning. Chapter 10 summarizes the book and points out potential future research directions for visual tracking. The book is self-contained and suited for all researchers, professionals and postgraduate students working in the fields of computer vision, pattern recognition, and machine learning. It will help these readers grasp the insights provided by cutting-edge research, and benefit from the practical techniques available for designing effective visual tracking algorithms. Further, the source codes or results of most algorithms in the book are provided at an accompanying website.