This book describes breath signal processing technologies and their applications in medical sample classification and diagnosis. First, it provides a comprehensive introduction to breath signal acquisition methods, based on different kinds of chemical sensors, together with the optimized selection and fusion acquisition scheme. It then presents preprocessing techniques, such as drift removing and feature extraction methods, and uses case studies to explore the classification methods. Lastly it discusses promising research directions and potential medical applications of computerized breath diagnosis. It is a valuable interdisciplinary resource for researchers, professionals and postgraduate students working in various fields, including breath diagnosis, signal processing, pattern recognition, and biometrics.

This book integrates the research being carried out in the field of lexical semantics in linguistics with the work on knowledge representation and lexicon design in computational linguistics. It provides a stimulating and unique discussion between the computational perspective of lexical meaning and the concerns of the linguist for the semantic description of lexical items in the context of syntactic descriptions.

Biometric recognition, or simply Biometrics, is a rapidly evolving field with applications ranging from accessing one's computer to gaining entry into a country. Biometric systems rely on the use of physical or behavioral traits, such as fingerprints, face, voice and hand geometry, to establish the identity of an individual. The deployment of large-scale biometric systems in both commercial (e.g., grocery stores, amusement parks, airports) and government (e.g., US-VISIT) applications has served to increase the public's awareness of this technology. This rapid growth has also highlighted the challenges associated with designing and deploying biometric systems. Indeed, the problem of biometric recognition is a Grand Challenge in its own right. The past five years has seen a significant growth in biometric research resulting in the development of innovative sensors, robust and efficient algorithms for feature extraction and matching, enhanced test methodologies and novel applications. These advances have resulted in robust, accurate, secure and cost effective biometric systems. The Handbook of Biometrics -- an edited volume contributed by prominent invited researchers in Biometrics -- describes the fundamentals as well as the latest advancements in the burgeoning field of biometrics. It is designed for professionals composed of practitioners and researchers in Biometrics, Pattern Recognition and Computer Security. The Handbook of Biometrics can be used as a primary textbook for an undergraduate biometrics class. This book is also suitable as a secondary textbook or reference for advanced-level students in computer science.

Biometrics-based recognition systems offer many benefits over traditional authentication approaches. However, such systems raise new challenges related to personal data protection.

This important text/reference presents the latest secure and privacy-compliant techniques in automatic human recognition. Featuring viewpoints from an international selection of experts in the field, the comprehensive coverage spans both theory and practical implementations, taking into consideration all ethical and legal issues.

Topics and features: presents a unique focus on novel approaches and new architectures for unimodal and multimodal template protection; examines signal processing techniques in the encrypted domain, security and privacy leakage assessment, and aspects of standardization; describes real-world applications, from face and fingerprint-based user recognition, to biometrics-based electronic documents, and biometric systems employing smart cards; reviews the ethical implications of the ubiquity of biometrics in everyday life, and its impact on human dignity; provides guidance on best practices for the processing of biometric data within a legal framework.

This timely and authoritative volume is essential reading for all practitioners and researchers involved in biometrics-based automatic human recognition. Graduate students of computer science and electrical engineering will also find the text to be an invaluable practical reference.

Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications captures the latest research in this area, providing a learning theorists with a mathematically sound framework within which evaluate their models. The significance of this book lies in its theoretical advances, which are grounded in an understanding of computational and biological learning. The approach taken moves the entire field closer to a watershed moment of learning models, through the interaction of computer science, psychology and neurobiology.

Rapid development of computer hardware has enabled usage of automatic object recognition in an increasing number of applications, ranging from industrial image processing to medical applications, as well as tasks triggered by the widespread use of the internet. Each area of application has its specific requirements, and consequently these cannot all be tackled appropriately by a single, general-purpose algorithm. This easy-to-read text/reference provides a comprehensive introduction to the field of object recognition (OR). The book presents an overview of the diverse applications for OR and highlights important algorithm classes, presenting representative example algorithms for each class. The presentation of each algorithm describes the basic algorithm flow in detail, complete with graphical illustrations. Pseudocode implementations are also included for many of the methods, and definitions are supplied for terms which may be unfamiliar to the novice reader. Supporting a clear and intuitive tutorial style, the usage of mathematics is kept to a minimum. Topics and features: presents example algorithms covering global approaches, transformation-search-based methods, geometrical model driven methods, 3D object recognition schemes, flexible contour fitting algorithms, and descriptor-based methods; explores each method in its entirety, rather than focusing on individual steps in isolation, with a detailed description of the flow of each algorithm, including graphical illustrations; explains the important concepts at length in a simple-to-understand style, with a minimum usage of mathematics; discusses a broad spectrum of applications, including some examples from commercial products; contains appendices discussing topics related to OR and widely used in the algorithms, (but not at the core of the methods described in the chapters). Practitioners of industrial image processing will find this simple introduction and overview to OR a valuable reference, as will graduate students in computer vision courses. Marco Treiber is a software developer at Siemens Electronics Assembly Systems, Munich, Germany, where he is Technical Lead in Image Processing for the Vision System of SiPlace placement machines, used in SMT assembly.

The pattern recognition and machine learning communities have, until recently, focused mainly on feature-vector representations, typically considering objects in isolation. However, this paradigm is being increasingly challenged by similarity-based approaches, which recognize the importance of relational and similarity information.

This accessible text/reference presents a coherent overview of the emerging field of non-Euclidean similarity learning. The book presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely theoretical challenges to practical, real-world applications. The coverage includes both supervised and unsupervised learning paradigms, as well as generative and discriminative models.

Topics and features: explores the origination and causes of non-Euclidean (dis)similarity measures, and how they influence the performance of traditional classification algorithms; reviews similarity measures for non-vectorial data, considering both a kernel tailoring approach and a strategy for learning similarities directly from training data; describes various methods for structure-preserving embeddings of structured data; formulates classical pattern recognition problems from a purely game-theoretic perspective; examines two large-scale biomedical imaging applications that provide assistance in the diagnosis of physical and mental illnesses from tissue microarray images and MRI images.

This pioneering work is essential reading for graduate students and researchers seeking an introduction to this important and diverse subject."

Decision forests (also known as random forests) are an indispensable tool for automatic image analysis.

This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. A number of exercises encourage the reader to practice their skills with the aid of the provided free software library. An international selection of leading researchers from both academia and industry then contribute their own perspectives on the use of decision forests in real-world applications such as pedestrian tracking, human body pose estimation, pixel-wise semantic segmentation of images and videos, automatic parsing of medical 3D scans, and detection of tumors. The book concludes with a detailed discussion on the efficient implementation of decision forests.

Topics and features: with a foreword by Prof. Yali Amit and Prof. Donald Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests in a hands-on manner.

With its clear, tutorial structure and supporting exercises, this text will be of great value to students wishing to learn the basics of decision forests, researchers wanting to become more familiar with forest-based learning, and practitioners interested in exploring modern and efficient image analysis techniques.

This book focuses on use of voice as a biometric measure for personal authentication. In particular, "Speaker Recognition" covers two approaches in speaker authentication: speaker verification (SV) and verbal information verification (VIV). The SV approach attempts to verify a speaker 's identity based on his/her voice characteristics while the VIV approach validates a speaker 's identity through verification of the content of his/her utterance(s). SV and VIV can be combined for new applications. This is still a new research topic with significant potential applications.The book provides with a broad overview of the recent advances in speaker authentication while giving enough attention to advanced and useful algorithms and techniques. It also provides a step by step introduction to the current state of the speaker authentication technology, from the fundamental concepts to advanced algorithms. We will also present major design methodologies and share our experience in developing real and successful speaker authentication systems. Advanced and useful topics and algorithms are selected with real design examples and evaluation results. Special attention is given to the topics related to improving overall system robustness and performances, such as robust endpoint detection, fast discriminative training theory and algorithms, detection-based decoding, sequential authentication, etc. For example, the sequential authentication was developed based on statistical sequential testing theory. By adding enough subtests, a speaker authentication system can achieve any accuracy requirement. The procedure of designing the sequential authentication will be presented. For any presented technique, we will provide experimental results to validate the usefulness. We will also highlight the important developments in academia, government, and industry, and outline a few open issues.As the methodologies developed in speaker authentication span several diverse fields, the tutorial book provides an introductory forum for a broad spectrum of researchers and developers from different areas to acquire the knowledge and skills to engage in the interdisciplinary fields of user authentication, biometrics, speech and speaker recognition, multimedia, and dynamic pattern recognition.

Micromechanical manufacturing based on microequipment creates new possibi- ties in goods production. If microequipment sizes are comparable to the sizes of the microdevices to be produced, it is possible to decrease the cost of production drastically. The main components of the production cost - material, energy, space consumption, equipment, and maintenance - decrease with the scaling down of equipment sizes. To obtain really inexpensive production, labor costs must be reduced to almost zero. For this purpose, fully automated microfactories will be developed. To create fully automated microfactories, we propose using arti?cial neural networks having different structures. The simplest perceptron-like neural network can be used at the lowest levels of microfactory control systems. Adaptive Critic Design, based on neural network models of the microfactory objects, can be used for manufacturing process optimization, while associative-projective neural n- works and networks like ART could be used for the highest levels of control systems. We have examined the performance of different neural networks in traditional image recognition tasks and in problems that appear in micromechanical manufacturing. We and our colleagues also have developed an approach to mic- equipment creation in the form of sequential generations. Each subsequent gene- tion must be of a smaller size than the previous ones and must be made by previous generations. Prototypes of ?rst-generation microequipment have been developed and assessed.

This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems. After a thorough introductory chapter, each of the following chapters focus on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Features: fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems; provides comprehensive coverage of face detection, tracking, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications; contains numerous step-by-step algorithms; describes a broad range of applications; presents contributions from an international selection of experts; integrates numerous supporting graphs, tables, charts, and performance data.

In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.

Spatial trajectories have been bringing the unprecedented wealth to a variety of research communities. A spatial trajectory records the paths of a variety of moving objects, such as people who log their travel routes with GPS trajectories. The field of moving objects related research has become extremely active within the last few years, especially with all major database and data mining conferences and journals.

"Computing with Spatial Trajectories" introduces the algorithms, technologies, and systems used to process, manage and understand existing spatial trajectories for different applications. This book also presents an overview on both fundamentals and the state-of-the-art research inspired by spatial trajectory data, as well as a special focus on trajectory pattern mining, spatio-temporal data mining and location-based social networks. Each chapter provides readers with a tutorial-style introduction to one important aspect of location trajectory computing, case studies and many valuable references to other relevant research work.

"Computing with Spatial Trajectories" is designed as a reference or secondary text book for advanced-level students and researchers mainly focused on computer science and geography. Professionals working on spatial trajectory computing will also find this book very useful.

Biometrics is becoming increasingly common in establishments that require high security such as state security and financial sectors. The increased threat to national security by terrorists has led to the explosive popularity of biometrics. A number of biometric devices are now available to capture biometric measurements such as fingerprints, palm, retinal scans, keystroke, voice recognition and facial scanning. However, the accuracy of these measurements varies, which has a direct relevance on the levels of security they offer. With the need to combat the problems related to identify theft and other security issues, society will have to compromise between security and personal freedoms. Securing Biometrics Applications investigates and identifies key impacts of biometric security applications, while discovering opportunities and challenges presented by the biometric technologies available.

Theoriginalmotivationsfordevelopingopticalcharacterrecognitiontechnologies weremodesttoconvertprintedtexton?atphysicalmediatodigitalform,prod- ingmachine-readabledigitalcontent. Bydoingthis,wordsthathadbeeninertand bound to physical material would be brought into the digital realm and thus gain newandpowerfulfunctionalitiesandanalyticalpossibilities. First-generation digital OCR researchers in the 1970s quickly realized that by limiting their ambitions primarily to contemporary documents printed in st- dard font type from the modern Roman alphabet (and of these, mostly English language materials), they were constraining the possibilities for future research andtechnologiesconsiderably. Domainresearchersalsosawthatthetrajectoryof OCR technologies if left unchanged would exclude a large portion of the human record. Digitalconversionofdocumentsandmanuscriptsinotheralphabets,scripts, and cursive styles was of critical importance. Embedded in non-Roman alp- bet source documents, including ancient manuscripts, papyri scrolls, clay tablets, and other inscribed artifacts was not only a wealth of scholarly information but alsonewopportunitiesandchallengesforadvancingOCR,imagingsciences,and othercomputationalresearchareas. Thelimitingcircumstancesatthetimeincluded the rudimentary capability (and high cost) of computational resources and lack of network-accessible digital content. Since then computational technology has advancedataveryrapidpaceandnetworkinginfrastructurehasproliferated. Over time, thisexponential decrease inthecost of computation, memory, and com- nicationsbandwidthcombinedwiththeexponentialincreaseinInternet-accessible digitalcontenthastransformededucation,scholarship,andresearch. Largenumbers ofresearchers,scholars,andstudentsuseanddependuponInternet-basedcontent andcomputationalresources. Thechaptersinthisbookdescribeacriticallyimportantareaofinvestigation- addressingconversionofIndicscriptintomachine-readableform. Roughestimates haveitthatcurrentlymorethanabillionpeopleuseIndicscripts. Collectively,Indic historic and cultural documents contain a vast richness of human knowledge and experience. The state-of-the-art research described in this book demonstrates the multiple values associated with these activities. Technically, the problems associated with Indicscriptrecognitionareverydif?cultandwillcontributetoandinformrelated v vi Foreword scriptrecognitionefforts. Theworkalsohasenormousconsequenceforenriching andenablingthestudyofIndicculturalheritagematerialsandthehistoricrecord of its people. This in turn broadens the intellectual context for domain scholars focusingonothersocieties,ancientandmodern. Digital character recognition has brought about another milestone in coll- tivecommunicationbybringinginert,?xed-in-place,textintoaninteractivedi- talrealm. Indoingso,theinformationhasgainedadditionalfunctionalitieswhich expandourabilitiestoconnect,combine,contextualize,share,andcollaboratively pursue knowledge making. High-quality Internet content continues to grow in an explosivefashion. Inthenewglobalcyberenvironment,thefunctionalitiesandapp- cationsofdigitalinformationcontinuetotransformknowledgeintonewundersta- ingsofhumanexperienceandtheworldinwhichwelive. Thepossibilitiesforthe futurearelimitedonlybyavailableresearchresourcesandcapabilitiesandtheim- inationandcreativityofthosewhousethem. Arlington,Virginia StephenM.

This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition. Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks. This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.

Soft computing includes several intelligent computing paradigms, like fuzzy logic, neural networks, and bio-inspired optimization algorithms. This book describes the application of soft computing techniques to intelligent control, pattern recognition, and optimization problems. The book is organized in four main parts. The first part deals with nature-inspired optimization methods and their applications. Papers included in this part propose new models for achieving intelligent optimization in different application areas. The second part discusses hybrid intelligent systems for achieving control. Papers included in this part make use of nature-inspired techniques, like evolutionary algorithms, fuzzy logic and neural networks, for the optimal design of intelligent controllers for different kind of applications. Papers in the third part focus on intelligent techniques for pattern recognition and propose new methods to solve complex pattern recognition problems. The fourth part discusses new theoretical concepts and methods for the application of soft computing to many different areas, such as natural language processing, clustering and optimization.

Biometrics deals with recognition of individuals based on their physiological or behavioral characteristics. The human ear is a new feature in biometrics that has several merits over the more common face, fingerprint and iris biometrics. Unlike the fingerprint and iris, it can be easily captured from a distance without a fully cooperative subject, although sometimes it may be hidden with hair, scarf and jewellery. Also, unlike a face, the ear is a relatively stable structure that does not change much with the age and facial expressions.

Human Ear Recognition by Computer is the first book on the automatic recognition of human ears. It presents an entire range of computational algorithms for recognition of humans by their ears. These algorithms have been tested and validated on the largest databases that are available today. Specific algorithms addressed include:

a [ Ear helix/anti-helix based representation

a [ Global-to-local registration

a [ Ear recognition using helix/anti-helix representation

a [ Ear recognition using a new local surface patch representation

a [ Efficient ear indexing and recognition

a [ Performance prediction for 3D ear recognition

a [ Generality and applications in computer vision and pattern recognition

This state-of-the-art research reference explores all aspects of 3D ear recognition, including representation, detection, recognition, indexing and performance prediction. It has been written for a professional audience of both researchers and practitioners within industry, and is also ideal as aninformative text for graduate students in computer science and engineering.

Professor Bir Bhanu has been director of the Visualization and Intelligent Systems Laboratory (at the University of California at Riverside) since 1991 and serves as the founding Director for the Center for Research in Intelligent Systems. He also has considerable experience working within industry and is the successful author of several books. He is a Fellow of IEEE, AAAS, IAPR, SPIE and was a Senior Fellow at Honeywell Inc.

Dr. Hui Chen works alongside Professor Bhanu and has worked for Siemens Medical solutions and the Chinese Academy of Sciences.

Many formal approaches for pattern specification are emerging as a means to cope with the inherent shortcomings of informal description. Design Pattern Formalization Techniques presents multiple mathematical, formal approaches for pattern specification, emphasizing on software development processes for engineering disciplines. Design Pattern Formalization Techniques focuses on formalizing the solution element of patterns, providing tangible benefits to pattern users, researchers, scholars, academicians, practitioners and students working in the field of design patterns and software reuse.Design Pattern Formalization Techniques explains details on several specification languages, allowing readers to choose the most suitable formal technique to solve their specific inquiries.

Human and animal vision systems have been driven by the pressures of evolution to become capable of perceiving and reacting to their environments as close to instantaneously as possible. Casting such a goal of reactive vision into the framework of existing technology necessitates an artificial system capable of operating continuously, selecting and integrating information from an environment within stringent time delays. The YAP (Vision As Process) project embarked upon the study and development of techniques with this aim in mind. Since its conception in 1989, the project has successfully moved into its second phase, YAP II, using the integrated system developed in its predecessor as a basis. During the first phase of the work the "vision as a process paradigm" was realised through the construction of flexible stereo heads and controllable stereo mounts integrated in a skeleton system (SA V A) demonstrating continuous real-time operation. It is the work of this fundamental period in the V AP story that this book aptly documents. Through its achievements, the consortium has contributed to building a strong scientific base for the future development of continuously operating machine vision systems, and has always underlined the importance of not just solving problems of purely theoretical interest but of tackling real-world scenarios. Indeed the project members should now be well poised to contribute (and take advantage of) industrial applications such as navigation and process control, and already the commercialisation of controllable heads is underway.

Terahertz biomedical imaging has become an area of interest due to its ability to simultaneously acquire both image and spectral information. Terahertz imaging systems are being commercialized, with increasing trials performed in a biomedical setting. As a result, advanced digital image processing algorithms are needed to assist screening, diagnosis, and treatment. "Pattern Recognition and Tomographic Reconstruction" presents these necessary algorithms, which will play a critical role in the accurate detection of abnormalities present in biomedical imaging. Terhazertz tomographic imaging and detection technology contributes to the ability to identify opaque objects with clear boundaries, and would be useful to both in vivo and ex vivo environments, making this book a must-read for anyone in the field of biomedical engineering and digital imaging.

This practically-focused text presents a hands-on guide to making biometric technology work in real-life scenarios. Extensively revised and updated, this new edition takes a fresh look at what it takes to integrate biometrics into wider applications. An emphasis is placed on the importance of a complete understanding of the broader scenario, covering technical, human and implementation factors. This understanding may then be exercised through interactive chapters dealing with educational software utilities and the BANTAM Program Manager. Features: provides a concise introduction to biometrics; examines both technical issues and human factors; highlights the importance of a broad understanding of biometric technology implementation from both a technical and operational perspective; reviews a selection of freely available utilities including the BANTAM Program Manager; considers the logical next steps on the path from aspiration to implementation, and looks towards the future use of biometrics in context.

Since research on face recognition began in the 1960's, the field has rapidly widened to automated face analysis including face detection, facial gesture recognition, and facial expression recognition. ""Automated Face Analysis: Emerging Technologies and Research"" provides theoretical background to understand the overall configuration and challenging problem of automated face analysis systems, featuring a comprehensive review of recent research for the practical implementation of the analysis system. A must-read for practitioners and students in the field, this book provides understanding by systematically dividing the subject into several subproblems such as detection, modeling, and tracking of the face.

This book offers researchers an understanding of the fundamental issues and a good starting point to work on this rapidly expanding field. It provides a comprehensive survey of current developments of heterogeneous information network. It also presents the newest research in applications of heterogeneous information networks to similarity search, ranking, clustering, recommendation. This information will help researchers to understand how to analyze networked data with heterogeneous information networks. Common data mining tasks are explored, including similarity search, ranking, and recommendation. The book illustrates some prototypes which analyze networked data. Professionals and academics working in data analytics, networks, machine learning, and data mining will find this content valuable. It is also suitable for advanced-level students in computer science who are interested in networking or pattern recognition.

This book contains the proceedings of the International Symposium on Mathematical Morphology and its Applications to Image and Signal Processing IV, held June 3-5, 1998, in Amsterdam, The Netherlands. The purpose of the work is to provide the image analysis community with a sampling of recent developments in theoretical and practical aspects of mathematical morphology and its applications to image and signal processing. Among the areas covered are: digitization and connectivity, skeletonization, multivariate morphology, morphological segmentation, color image processing, filter design, gray-scale morphology, fuzzy morphology, decomposition of morphological operators, random sets and statistical inference, differential morphology and scale-space, morphological algorithms and applications. Audience: This volume will be of interest to research mathematicians and computer scientists whose work involves mathematical morphology, image and signal processing.