Das Rahmenthema der 25. Jahrestagung der Deutschen Gesellschaft fur Medizinische DOkumentation, Informatik und Statistik e. V. - Nachsorge und Krankheitsverlaufsanalyse - hat einen engen Bezug zu aktuellen Problemen des Gesundheitswesens. Insbesondere in Kliniken, in denen schwere Erkrankungen mit modernen erfolgversprechenden Massnahmen be- handelt werden, wird die Notwendigkeit einer systematischen weiteren Uberwachung dieser Patienten immer dringlicher erachtet. Die Beob- achtung des weiteren Schicksals, die arztliche Betreuung und die Bewertung der zeitlichen Verlaufe ergeben Ansatzpunkte fur die weitere Verbesserung der Therapie. Diese Aufgabe lasst sich nur be- waltigen, wenn die dabei auftretenden Probleme der planvollen Doku- mentation, der Informationsubermittlung, der Datenspeicherung und der statistischen Auswertung von den Vertretern unseres Faches aktiv in Angriff genommen werden. Nach 25 Jahren einer sturmischen techno- logischen und Methodenentwicklung ist unsere junge medizinische Dis- ziplin in der Lage - wenn die erforderliche apparative und perso- nelle Ausstattung zur Verfugung steht -, die Probleme der Nachsorge und Krankheitsverlaufsanalyse in Zusammenarbeit mit Klinikern und Allgemeinmedizinern wirksam zu bearbeiten und Ergebnisse zu zeitigen, die fur den Arzt relevant sind. Die Tagung soll dazu Anregungen ver- mitteln und Losungswege aufzeigen. Funf Workshops und apparative, organisatorische und methodische Pro- bleme runden den Bezug unserer Arbeit auf die Probleme der !1edizin von heute ab. Erlangen hat eine traditionelle Verbundenheit mit der technologischen Entwicklung in der Medizin. Auch heute besteht, sowohl in der Medi- zinischen wie in der Technischen Fakultat, auf deren Campus wir tragen, ein waches Interesse fur den humanen Einsatz technologischer Neuerungen, insbesondere auch der elektronischen Datenverarbeitung. L.

Der vorliegende Tagungsband enthalt die meisten Vortrage der gemeinsamen Tagung der Deutschen Gesellschaft fUr Angewandte Optik (DGaO) und der Deutschen Arbeits gemeinschaft fUr Musterkennung (DAr, M). Die Beziehungen der beiden Organisationen sind folgendermaBen: Wahrend die DGaO eine traditionsreiche wissenschaftliche Gesellschaft ist, stellt die DAGM einen problemorientierten Dachverband folgender wissenschaftlicher Gesellschaften dar: - Deutsche Gesellschaft fUr angewandte Optik (DGaO) - Deutsche Gesellschaft fUr Nuklearmedizin (DGNM) - Deutsche Gesellschaft fUr Ortung und Navigation (DGON) - Deutsche Gesellschaft fUr Medizinische Dokumentation, Informatik und Statistik (GMDS) - Deutsche Gesellschaft fUr Angewandte Datenverarbeitung und Automation in der Medizin (GADAM) - Gesellschaft fUr Informatik (GI) - Nachrichtentechnische Gesellschaft (NTG). Die DAGM fordert den Erfahrungsaustausch auf dem Gebiet der Mustererkennung und ist Mitglied der International Association for Pattern Recognition (IAPR). Die verantwortungsvolle Arbeit des Programmausschusses wurde unter Vorsitz von Prof. Hertel, Berlin, von folgenden Herren geleistet: Dr. Dreher, Bonn; Prof. Fercher, Essen; Dr. Kossel, Wetzlar; Prof. Lanzl, Oberpfaffenhofen; Dipl.lng. Platzer, MUnchen; Dr.-Ing. Poppl, MUnchen; Dr. Pretschner, Hannover; Prof. Schmidt, Oberkochen; Dr. Triendl, Oberpfaffenhofen; Dr. Walter, MUnchen; Prof. Winkler, Berlin. Unser ganz besonderer Dank gilt dem SchriftfUhrer der Deutschen Gesellschaft fUr Angewandte Optik, Herrn PreuB, Wetzlar, insbesondere fUr seine Mitarbeit im ProgrammausschuB und fUr die Drucklegung des gemeinsamen Tagungsprogramms. Wir wUnschen allen Teilnehmern, daB diese gemeinsame Tagung fUr sie ein interessanter und fruchtbarer Erfahrungsaustausch wird."

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

In dem Buch werden Methoden vorgestellt, mit denen ubersehenes IT-Potenzial in Organisation genutzt werden kann. Dabei geht die Autorin davon aus, dass das Wissen bereits vorhanden ist und nur gehoben werden muss. Mit Checklisten und Tipps fur die Umsetzung."

An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.

Die automatische Auswertung von Signalen spielt in der modernen Informationstechnik eine grosse Rolle. Dieses Lehrbuch bietet, ausgehend von der Reprasentation des Signals im Merkmalraum, die Beschreibung wichtiger Klassifikationsverfahren. Dazu zahlen Linear- und Bayes Klassifikatoren, Supportvektormaschinen, Klassifikatoren auf der Basis von Gaussian-Mixture-Modellen und Hidden-Markov-Modellen sowie Klassenfolgenklassifikatoren.Weiterhin werden wichtige Grundlagen der Automatentheorie (Finite State Machines) sowie ausgewahlte maschinelle Lernverfahren dargestellt.Die Darstellung setzt die Verfahren zur Merkmalgewinnung voraus, die im ersten Band vermittelt wurden, so dass das Gesamtwerk eine umfassende Beschreibung der Kette darstellt, die in modernen Systemen der Informationsverarbeitung von der Signalerfassung bis hin zum Klassifikationsergebnis fuhrt.

For machine intelligence applications to work successfully, machines must perform reliably under variations of data and must be able to keep up with data streams. Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds unveils computational models that address performance and scalability to achieve higher levels of reliability. It explores different ways of implementing pattern recognition using machine intelligence. Based on the authors' research from the past 10 years, the text draws on concepts from pattern recognition, parallel processing, distributed systems, and data networks. It describes fundamental research on the scalability and performance of pattern recognition, addressing issues with existing pattern recognition schemes for Internet-scale data deployment. The authors review numerous approaches and introduce possible solutions to the scalability problem. By presenting the concise body of knowledge required for reliable and scalable pattern recognition, this book shortens the learning curve and gives you valuable insight to make further innovations. It offers an extendable template for Internet-scale pattern recognition applications as well as guidance on the programming of large networks of devices.

Stereoscopic processes are increasingly used in virtual reality and entertainment. This technology is interesting because it allows for a quick immersion of the user, especially in terms of depth perception and relief clues. However, these processes tend to cause stress on the visual system if used over a prolonged period of time, leading some to question the cause of side effects that these systems generate in their users, such as eye fatigue. This book explores the mechanisms of depth perception with and without stereoscopy and discusses the indices which are involved in the depth perception. The author describes the techniques used to capture and retransmit stereoscopic images. The causes of eyestrain related to these images are then presented along with their consequences in the long and short term. The study of the causes of eyestrain forms the basis for an improvement in these processes in the hopes of developing mechanisms for easier virtual viewing.

This book constitutes the proceedings of the 16th Chinese Conference on Biometric Recognition, CCBR 2022, which took place in Beijing, China, in November 2022. The 70 papers presented in this volume were carefully reviewed and selected from 115 submissions. The papers cover a wide range of topics such as Fingerprint, Palmprint and Vein Recognition; Face Detection, Recognition and Tracking; Gesture and Action Recognition; Affective Computing and Human-Computer Interface; Speaker and Speech Recognition; Gait, Iris and Other Biometrics; Multi-modal Biometric Recognition and Fusion; Quality Evaluation and Enhancement of Biometric Signals; Animal Biometrics; Trustworthy, Privacy and Personal Data Security; Medical and Other Applications.

Nel 1925, Economo e Koskinas pubblicarono l'atlante piu accurato e completo sulla citoarchitettonica della corteccia cerebrale umana mai realizzato. Una sintesi del contenuto dell'atlante venne in seguito resa disponibile in tedesco e tradotta in italiano, inglese e francese. Il valore scientifico di quest'opera e divenuto piu significativo negli ultimi vent'anni con l'avvento delle tecniche di neuroimaging funzionale, le quali consentono persino di rilevare specifici focolai di attivazione nella corteccia cerebrale umana durante l'esecuzione di compiti cognitivi. Questa riedizione in italiano e stata ampliata rispetto all'originale con l'aggiunta della mappa dei solchi e dei giri del cervello. Inoltre, e stata inclusa la tabella delle corrispondenze tra le aree individuate da Economo e Koskinas e quelle descritte da Brodmann. Il volume sara di grande interesse per tutti coloro che desiderano approfondire la relazione tra la struttura del cervello e le sue funzioni; rappresentera inoltre un utile strumento di lavoro per i professionisti che utilizzano le neuroimmagini nella loro pratica quotidiana, quali neurofisiologi, neuropsicologi, neuroradiologi, neurologi e neurochirurghi.

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.

The proceedings set LNCS 13231, 13232, and 13233 constitutes the refereed proceedings of the 21st International Conference on Image Analysis and Processing, ICIAP 2022, which was held during May 23-27, 2022, in Lecce, Italy,The 168 papers included in the proceedings were carefully reviewed and selected from 307 submissions. They deal with video analysis and understanding; pattern recognition and machine learning; deep learning; multi-view geometry and 3D computer vision; image analysis, detection and recognition; multimedia; biomedical and assistive technology; digital forensics and biometrics; image processing for cultural heritage; robot vision; etc.

Edited by a panel of experts, this book fills a gap in the existing literature by comprehensively covering system, processing, and application aspects of biometrics, based on a wide variety of biometric traits. The book provides an extensive survey of biometrics theory, methods,and applications, making it an indispensable source of information for researchers, security experts, policy makers, engineers, practitioners, and graduate students. The book's wide and in-depth coverage of biometrics enables readers to build a strong, fundamental understanding of theory and methods, and provides a foundation for solutions to many of today's most interesting and challenging biometric problems. Biometric traits covered: Face, Fingerprint, Iris, Gait, Hand Geometry, Signature, Electrocardiogram (ECG), Electroencephalogram (EEG), physiological biometrics. Theory, Methods and Applications covered: Multilinear Discriminant Analysis, Neural Networks for biometrics, classifier design, biometric fusion, Event-Related Potentials, person-specific characteristic feature selection, image and video-based face, recognition/verification, near-infrared face recognition, elastic graph matching, super-resolution of facial images, multimodal solutions, 3D approaches to biometrics, facial aging models for recognition, information theory approaches to biometrics, biologically-inspired methods, biometric encryption, decision-making support in biometric systems, privacy in biometrics

Der vorliegende Band beschaftigt sich mit Mediennetzen. Vorgestellt werden die Techniken, die beim Transport von Informationen durch unterschiedliche Netze zum Einsatz kommen. Schwerpunkte bilden also die "Verpackung" der Information z.B. durch Kompressionsverfahren und der Transport digitalisierter Information. Die nachste Frage, die sich beim Transport von Information stellt, ist die nach der Mediensicherheit. Verfahren wie Digitale Wasserzeichen und Digital-Rights-Management-Systeme werden erlautert. Schliesslich werden verschiedene Multimediennetze und insbesondere Mobile Netze prasentiert."

This book constitutes refereed proceedings of the Second International Conference on Smart Technologies, Systems and Applications, held in Quito, Ecuador, in December 2021. Due to the COVID-19 pandemic the conference was held in a hybrid format. The 29 full papers along with 1 short paper presented were carefully reviewed and selected from 104 submissions. The papers of this volume are organized in topical sections on smart technologies; smart systems; smart trends and applications.

This book is open access. This book undertakes a multifaceted and integrated examination of biometric identification, including the current state of the technology, how it is being used, the key ethical issues, and the implications for law and regulation. The five chapters examine the main forms of contemporary biometrics-fingerprint recognition, facial recognition and DNA identification- as well the integration of biometric data with other forms of personal data, analyses key ethical concepts in play, including privacy, individual autonomy, collective responsibility, and joint ownership rights, and proposes a raft of principles to guide the regulation of biometrics in liberal democracies. Biometric identification technology is developing rapidly and being implemented more widely, along with other forms of information technology. As products, services and communication moves online, digital identity and security is becoming more important. Biometric identification facilitates this transition. Citizens now use biometrics to access a smartphone or obtain a passport; law enforcement agencies use biometrics in association with CCTV to identify a terrorist in a crowd, or identify a suspect via their fingerprints or DNA; and companies use biometrics to identify their customers and employees. In some cases the use of biometrics is governed by law, in others the technology has developed and been implemented so quickly that, perhaps because it has been viewed as a valuable security enhancement, laws regulating its use have often not been updated to reflect new applications. However, the technology associated with biometrics raises significant ethical problems, including in relation to individual privacy, ownership of biometric data, dual use and, more generally, as is illustrated by the increasing use of biometrics in authoritarian states such as China, the potential for unregulated biometrics to undermine fundamental principles of liberal democracy. Resolving these ethical problems is a vital step towards more effective regulation.

A new approach to the issue of data quality in pattern recognition Detailing foundational concepts before introducing more complex methodologies and algorithms, this book is a self-contained manual for advanced data analysis and data mining. Top-down organization presents detailed applications only after methodological issues have been mastered, and step-by-step instructions help ensure successful implementation of new processes. By positioning data quality as a factor to be dealt with rather than overcome, the framework provided serves as a valuable, versatile tool in the analysis arsenal. For decades, practical need has inspired intense theoretical and applied research into pattern recognition for numerous and diverse applications. Throughout, the limiting factor and perpetual problem has been data--its sheer diversity, abundance, and variable quality presents the central challenge to pattern recognition innovation. Pattern Recognition: A Quality of Data Perspective repositions that challenge from a hurdle to a given, and presents a new framework for comprehensive data analysis that is designed specifically to accommodate problem data. Designed as both a practical manual and a discussion about the most useful elements of pattern recognition innovation, this book: Details fundamental pattern recognition concepts, including feature space construction, classifiers, rejection, and evaluation Provides a systematic examination of the concepts, design methodology, and algorithms involved in pattern recognition Includes numerous experiments, detailed schemes, and more advanced problems that reinforce complex concepts Acts as a self-contained primer toward advanced solutions, with detailed background and step-by-step processes Introduces the concept of granules and provides a framework for granular computing Pattern recognition plays a pivotal role in data analysis and data mining, fields which are themselves being applied in an expanding sphere of utility. By facing the data quality issue head-on, this book provides students, practitioners, and researchers with a clear way forward amidst the ever-expanding data supply.

This book constitutes the refereed proceedings of the 14th National Conference on Man-Machine Speech Communication, NCMMSC 2017, held in Lianyungang, China, in October 2017. The 13 revised full papers presented were carefully reviewed and selected from 39 submissions. The papers address issues such as challenging issues in speech recognition and enhancement, speaker and language recognition, speech synthesis, corpus and phonetic in speech technology, speech generation, speech analyzing and modelling, speech processing of ethnic minorities, speech emotion recognition and audio signal processing.

Face detection and recognition are the nonintrusive biometrics of choice in many security applications. Examples of their use include border control, driver's license issuance, law enforcement investigations, and physical access control. Face Detection and Recognition: Theory and Practice elaborates on andexplains the theory and practice of face detection and recognition systems currently in vogue. The book begins with an introduction to the state of the art, offering a general review of the available methods and an indication of future research using cognitive neurophysiology. The text then: Explores subspace methods for dimensionality reduction in face image processing, statistical methods applied to face detection, and intelligent face detection methods dominated by the use of artificial neural networks Covers face detection with colour and infrared face images, face detection in real time, face detection and recognition using set estimation theory, face recognition using evolutionary algorithms, and face recognition in frequency domain Discusses methods for the localization of face landmarks helpful in face recognition, methods of generating synthetic face images using set estimation theory, and databases of face images available for testing and training systems Features pictorial descriptions of every algorithm as well as downloadable source code (in MATLAB (R)/PYTHON) and hardware implementation strategies with code examples Demonstrates how frequency domain correlation techniques can be used supplying exhaustive test results Face Detection and Recognition: Theory and Practice provides students, researchers, and practitioners with a single source for cutting-edge information on the major approaches, algorithms, and technologies used in automated face detection and recognition.

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques.

This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples.

"Statistical Pattern Recognition," 3rd Edition: Provides a self-contained introduction to statistical pattern recognition.Includes new material presenting the analysis of complex networks.Introduces readers to methods for Bayesian density estimation.Presents descriptions of new applications in biometrics, security, finance and condition monitoring.Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applicationsDescribes mathematically the range of statistical pattern recognition techniques.Presents a variety of exercises including more extensive computer projects.

The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. "Statistical Pattern Recognition" is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields.

www.wiley.com/go/statistical_pattern_recognition

This book features high-quality research papers presented at the 3rd International Conference on Computational Intelligence in Pattern Recognition (CIPR 2021), held at the Institute of Engineering and Management, Kolkata, West Bengal, India, on 24 - 25 April 2021. It includes practical development experiences in various areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.

Sacred Geometry exists all around us in the natural world, from the unfurling of a rose bud to the pattern of a tortoise shell, the sub-atomic to the galactic. A pure expression of number and form, it is the language of creation and navigates the unseen dimensions beyond our three-dimensional reality. Since its discovery, humans have found many ways - stone circles, mandalas, labyrinths, temples- to call upon this universal law as a way of raising consciousness and communicating with a divine source. By becoming aware of the dots and lines that build the world around you, Sacred Geometry will teach you how to bring this mystical knowledge into your daily practice.

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms, and deep dives into several applications, make the book equally useful for researchers, students and practitioners.

Der vorliegende Band des Kompendiums Medieninformatik beschaftigt sich mit der Medienpraxis. Er behandelt Aspekte der praktischen Informatik und ihre Anwendung in der Medientechnik wie die Entwicklung von Multimedia-Anwendungen, Grundlagen der Computergrafik sowie Theorie und Praxis von Mediendatenbanken. Hinzu kommen ausgewahlte Anwendungen der Medieninformatik: Mit Content-Related-Technologien konnen mediale Informationen in besonders effizienter Weise organisiert, strukturiert und an die richtigen Empfanger verteilt werden.

Zusammen mit dem Band "Mediennetze" beschreibt das Kompendium Medieninformatik die komplette Wertschopfungskette von digitalen Mediendaten: Erzeugung, Kodierung, Transport durch drahtgebundene oder drahtlose Netze bis hin zum Endnutzer."