Pattern recognition Books
Pearson Education (US) Design Patterns
Book SynopsisDr. Erich Gamma is technical director at the Software Technology Center of Object Technology International in Zurich, Switzerland. Dr. Richard Helm is a member of the Object Technology Practice Group in the IBM Consulting Group in Sydney, Australia. Dr. Ralph Johnson is a faculty member at the University of Illinois at Urbana-Champaign's Computer Science Department. John Vlissides is a member of the research staff at the IBM T. J. Watson Research Center in Hawthorne, New York. He has practiced object-oriented technology for more than a decade as a designer, implementer, researcher, lecturer, and consultant. In addition to co-authoring Design Patterns: Elements of Reusable Object-Oriented Software, he is co-editor of the book Pattern Languages of Program Design 2 (both from Addison-Wesley). He and the other co-authors of Design Patterns are recipients of the 1998 Dr. Dobb's Journal Excellence in Programming Award. 020163Table of Contents 1. Introduction. 2. A Case Study: Designing a Document Editor. 3. Creational Patterns. 4. Structural Pattern. 5. Behavioral Patterns. 6. Conclusion. Appendix A: Glossary. Appendix B: Guide to Notation. Appendix C: Foundation Classes. Bibliography. Index.
£44.09
Wellesley-Cambridge Press,U.S. Linear Algebra and Learning from Data
Book SynopsisLinear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
£59.84
Springer Pattern Recognition and Machine Learning
Book SynopsisProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.Trade ReviewFrom the reviews: "This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software "In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007) "This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study." (W. R. Howard, Kybernetes, Vol. 36 (2), 2007) "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Summing Up: Highly recommended. Upper-division undergraduates through professionals." (C. Tappert, CHOICE, Vol. 44 (9), May, 2007) "The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … ." (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007) "This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value." (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007) "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008) "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … ." (L. State, ACM Computing Reviews, October, 2008) "Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts." (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008)Table of ContentsProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
£67.49
Cambridge University Press Mathematics for Machine Learning
Book SynopsisThis self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.Trade Review'This book provides great coverage of all the basic mathematical concepts for machine learning. I'm looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.' Joelle Pineau, McGill University, Montreal'The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory. It will prove valuable both as a tutorial for newcomers to the field, and as a reference text for machine learning researchers and engineers.' Christopher Bishop, Microsoft Research Cambridge'This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended for anyone wanting a one-stop-shop to acquire a deep understanding of machine learning foundations.' Pieter Abbeel, University of California, Berkeley'Really successful are the numerous explanatory illustrations, which help to explain even difficult concepts in a catchy way. Each chapter concludes with many instructive exercises. An outstanding feature of this book is the additional material presented on the website …' Volker H. Schulz, SIAM ReviewTable of Contents1. Introduction and motivation; 2. Linear algebra; 3. Analytic geometry; 4. Matrix decompositions; 5. Vector calculus; 6. Probability and distribution; 7. Optimization; 8. When models meet data; 9. Linear regression; 10. Dimensionality reduction with principal component analysis; 11. Density estimation with Gaussian mixture models; 12. Classification with support vector machines.
£37.99
Cambridge University Press Mining of Massive Datasets
Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
£61.74
Cambridge University Press Foundations of Data Science
Book SynopsisThis 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 noTrade Review'This beautifully written text is a scholarly journey through the mathematical and algorithmic foundations of data science. Rigorous but accessible, and with many exercises, it will be a valuable resource for advanced undergraduate and graduate classes.' Peter Bartlett, University of California, Berkeley'The rise of the Internet, digital media, and social networks has brought us to the world of data, with vast sources from every corner of society. Data Science - aiming to understand and discover the essences that underlie the complex, multifaceted, and high-dimensional data - has truly become a 'universal discipline', with its multidisciplinary roots, interdisciplinary presence, and societal relevance. This timely and comprehensive book presents - by bringing together from diverse fields of computing - a full spectrum of mathematical, statistical, and algorithmic materials fundamental to data analysis, machine learning, and network modeling. Foundations of Data Science offers an effective roadmap to approach this fascinating discipline and engages more advanced readers with rigorous mathematical/algorithmic theory.' Shang-Hua Teng, University of Southern California'A lucid account of mathematical ideas that underlie today's data analysis and machine learning methods. I learnt a lot from it, and I am sure it will become an invaluable reference for many students, researchers and faculty around the world.' Sanjeev Arora, Princeton University, New Jersey'It provides a very broad overview of the foundations of data science that should be accessible to well-prepared students with backgrounds in computer science, linear algebra, and probability theory … These are all important topics in the theory of machine learning and it is refreshing to see them introduced together in a textbook at this level.' Brian Borchers, MAA Reviews'One plausible measure of [Foundations of Data Science's] impact is the book's own citation metrics. Semantic Scholar (https://www.semanticscholar.org) reports 81 citations with 42 citations related to background or methods; [Foundations of Data Science] appears to be on course to becoming influential.' M. Mounts, ChoiceTable of Contents1. Introduction; 2. High-dimensional space; 3. Best-fit subspaces and Singular Value Decomposition (SVD); 4. Random walks and Markov chains; 5. Machine learning; 6. Algorithms for massive data problems: streaming, sketching, and sampling; 7. Clustering; 8. Random graphs; 9. Topic models, non-negative matrix factorization, hidden Markov models, and graphical models; 10. Other topics; 11. Wavelets; 12. Appendix.
£42.74
Pearson Education (US) Quick Start Guide to Large Language Models
Book SynopsisSinan Ozdemir is currently the founder and CTO of Shiba Technologies. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master's degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, CA.Trade Review"Ozdemir's book cuts through the noise to help readers understand where the LLM revolution has come from--and where it is going. Ozdemir breaks down complex topics into practical explanations and easy to follow code examples."--Shelia Gulati, former GM at Microsoft and current Managing Director of Tola Capital "When it comes to building Large Language Models (LLMs), it can be a daunting task to find comprehensive resources that cover all the essential aspects. However, my search for such a resource recently came to an end when I discovered this book. "One of the stand-out features of Sinan is his ability to present complex concepts in a straightforward manner. The author has done an outstanding job of breaking down intricate ideas and algorithms, ensuring that readers can grasp them without feeling overwhelmed. Each topic is carefully explained, building upon examples that serve as steppingstones for better understanding. This approach greatly enhances the learning experience, making even the most intricate aspects of LLM development accessible to readers of varying skill levels. "Another strength of this book is the abundance of code resources. The inclusion of practical examples and code snippets is a game-changer for anyone who wants to experiment and apply the concepts they learn. These code resources provide readers with hands-on experience, allowing them to test and refine their understanding. This is an invaluable asset, as it fosters a deeper comprehension of the material and enables readers to truly engage with the content. "In conclusion, this book is a rare find for anyone interested in building LLMs. Its exceptional quality of explanation, clear and concise writing style, abundant code resources, and comprehensive coverage of all essential aspects make it an indispensable resource. Whether you are a beginner or an experienced practitioner, this book will undoubtedly elevate your understanding and practical skills in LLM development. I highly recommend Quick Start Guide to Large Language Models to anyone looking to embark on the exciting journey of building LLM applications."--Pedro Marcelino, Machine Learning Engineer, Co-Founder and CEO @overfit.studyTable of ContentsForeword xvPreface xviiAcknowledgments xxiAbout the Author xxiii Part I: Introduction to Large Language Models 1 Chapter 1: Overview of Large Language Models 3What Are Large Language Models? 4Popular Modern LLMs 20Domain-Specific LLMs 22Applications of LLMs 23Summary 29 Chapter 2: Semantic Search with LLMs 31Introduction 31The Task 32Solution Overview 34The Components 35Putting It All Together 51The Cost of Closed-Source Components 54Summary 55 Chapter 3: First Steps with Prompt Engineering 57Introduction 57Prompt Engineering 57Working with Prompts Across Models 65Building a Q/A Bot with ChatGPT 69Summary 74 Part II: Getting the Most Out of LLMs 75 Chapter 4: Optimizing LLMs with Customized Fine-Tuning 77Introduction 77Transfer Learning and Fine-Tuning: A Primer 78A Look at the OpenAI Fine-Tuning API 82Preparing Custom Examples with the OpenAI CLI 84Setting Up the OpenAI CLI 87Our First Fine-Tuned LLM 88Case Study: Amazon Review Category Classification 93Summary 95 Chapter 5: Advanced Prompt Engineering 97Introduction 97Prompt Injection Attacks 97Input/Output Validation 99Batch Prompting 103Prompt Chaining 104Chain-of-Thought Prompting 111Revisiting Few-Shot Learning 113Testing and Iterative Prompt Development 123Summary 124 Chapter 6: Customizing Embeddings and Model Architectures 125Introduction 125Case Study: Building a Recommendation System 126Summary 144 Part III: Advanced LLM Usage 145 Chapter 7: Moving Beyond Foundation Models 147Introduction 147Case Study: Visual Q/A 147Case Study: Reinforcement Learning from Feedback 163Summary 173 Chapter 8: Advanced Open-Source LLM Fine-Tuning 175Introduction 175Example: Anime Genre Multilabel Classification with BERT 176Example: LaTeX Generation with GPT2 189Sinan's Attempt at Wise Yet Engaging Responses: SAWYER 193The Ever-Changing World of Fine-Tuning 206Summary 207 Chapter 9: Moving LLMs into Production 209Introduction 209Deploying Closed-Source LLMs to Production 209Deploying Open-Source LLMs to Production 210Summary 225 Part IV: Appendices 227 Appendix A: LLM FAQs 229Appendix B: LLM Glossary 233Appendix C: LLM Application Archetypes 239 Index 243
£34.19
Elsevier Science Computer Vision
Book SynopsisTable of Contents1. Vision, the Challenge2. Images and Imaging Operations3. Image Filtering and Morphology4. The Role of Thresholding5. Edge Detection6. Corner, Interest Point and Invariant Feature Detection7. Texture Analysis8. Binary Shape Analysis9. Boundary Pattern Analysis10. Line, Circle and Ellipse Detection11. The Generalised Hough Transform12. Object Segmentation and Shape Models13. Basic Classification Concepts14. Machine Learning: Probabilistic Methods15. Deep Learning Networks16. The Three-Dimensional World17. Tackling the Perspective n-point Problem18. Invariants and perspective19. Image transformations and camera calibration20. Motion21. Face Detection and Recognition: the Impact of Deep Learning22. Surveillance23. In-Vehicle Vision Systems24. Epilogue—Perspectives in VisionAppendix A: Robust statisticsAppendix B: The Sampling TheoremAppendix C: The representation of colourAppendix D: Sampling from distributions
£77.39
Elsevier Science Machine Learning for Biomedical Applications
Book SynopsisTable of Contents1. Programming in Python 2. Machine Learning Basics 3. Regression 4. Classification 5. Dimensionality reduction 6. Clustering 7. Ensemble methods 8. Feature extraction and selection 9. Introduction to Deep Learning 10. Neural Networks 11. Convolutional Neural Networks
£55.05
Oxford University Press Biometrics
Book SynopsisWe live in a society which is increasingly interconnected, in which communication between individuals is mostly mediated via some electronic platform, and transactions are often carried out remotely. In such a world, traditional notions of trust and confidence in the identity of those with whom we are interacting, taken for granted in the past, can be much less reliable. Biometrics - the scientific discipline of identifying individuals by means of the measurement of unique personal attributes - provides a reliable means of establishing or confirming an individual''s identity. These attributes include facial appearance, fingerprints, iris patterning, the voice, the way we write, or even the way we walk. The new technologies of biometrics have a wide range of practical applications, from securing mobile phones and laptops to establishing identity in bank transactions, travel documents, and national identity cards. This Very Short Introduction considers the capabilities of biometrics-based identity checking, from first principles to the practicalities of using different types of identification data. Michael Fairhurst looks at the basic techniques in use today, ongoing developments in system design, and emerging technologies, all aimed at improving precision in identification, and providing solutions to an increasingly wide range of practical problems. Considering how they may continue to develop in the future, Fairhurst explores the benefits and limitations of these pervasive and powerful technologies, and how they can effectively support our increasingly interconnected society.ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.Table of Contents1: Are you who you say you are?2: Biometrics: where should I start?3: Making biometrics work4: Enhancing biometric processing5: An introduction to predictive biometrics6: Where are we going?Further readingIndex
£9.49
Springer Signal Processing Methods for Music Transcription
Book SynopsisFoundations.- to Music Transcription.- An Introduction to Statistical Signal Processing and Spectrum Estimation.- Sparse Adaptive Representations for Musical Signals.- Rhythm and Timbre Analysis.- Beat Tracking and Musical Metre Analysis.- Unpitched Percussion Transcription.- Automatic Classification of Pitched Musical Instrument Sounds.- Multiple Fundamental Frequency Analysis.- Multiple Fundamental Frequency Estimation Based on Generative Models.- Auditory Model-Based Methods for Multiple Fundamental Frequency Estimation.- Unsupervised Learning Methods for Source Separation in Monaural Music Signals.- Entire Systems, Acoustic and Musicological Modelling.- Auditory Scene Analysis in Music Signals.- Music Scene Description.- Singing Transcription.Table of ContentsFoundations.- to Music Transcription.- An Introduction to Statistical Signal Processing and Spectrum Estimation.- Sparse Adaptive Representations for Musical Signals.- Rhythm and Timbre Analysis.- Beat Tracking and Musical Metre Analysis.- Unpitched Percussion Transcription.- Automatic Classification of Pitched Musical Instrument Sounds.- Multiple Fundamental Frequency Analysis.- Multiple Fundamental Frequency Estimation Based on Generative Models.- Auditory Model-Based Methods for Multiple Fundamental Frequency Estimation.- Unsupervised Learning Methods for Source Separation in Monaural Music Signals.- Entire Systems, Acoustic and Musicological Modelling.- Auditory Scene Analysis in Music Signals.- Music Scene Description.- Singing Transcription.
£123.49
Springer-Verlag New York Inc. Introduction to Biometrics
Book SynopsisIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.Table of ContentsIntroduction.- Fingerprint Recognition.- Face Recognition.- Iris Recognition.- Additional Biometric Traits.- Multibiometrics.- Security of Biometric Systems.
£59.99
Cambridge University Press Flexible Pattern Matching in Strings
Book SynopsisPresents recently developed algorithms for searching for simple, multiple and extended strings, regular expressions, exact and approximate matches.Trade Review'If you need efficient pattern matching for any kind of string then this is the only book I know that comes even close to providing you [with] the tools for the job.' The Journal of the ACCU'I really enjoyed reading and studying this book. I am convinced it is a must-read, especially chapters 4 through 6, for anyone who is involved in the task of designing algorithms for modern string or sequence matching.' Computing ReviewsTable of Contents1. Introduction; 2. String matching; 3. Multiple string matching; 4. Extended string matching; 5. Regular expression matching; 6. Approximate matching; 7. Conclusion; Bibliography; Index.
£53.99
Cambridge University Press Kernel Methods for Pattern Analysis
Book SynopsisThe kernel functions methodology described here provides a powerful and unified framework for disciplines ranging from neural networks and pattern recognition to machine learning and data mining. This book provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, suitable for standard pattern discovery problems.Trade Review'Kernel methods form an important aspect of modern pattern analysis, and this book gives a lively and timely account of such methods. … if you want to get a good idea of the current research in this field, this book cannot be ignored.' SIAM Review'… the book provides an excellent overview of this growing field. I highly recommend it to those who are interested in pattern analysis and machine learning, and especailly to those who want to apply kernel-based methods to text analysis and bioinformatics problems.' Computing Reviews' … I enjoyed reading this book and am happy about is addition to my library as it is a valuable practitioner's reference. I especially liked the presentation of kernel-based pattern analysis algorithms in terse mathematical steps clearly identifying input data, output data, and steps of the process. The accompanying Matlab code or pseudocode is al extremely useful.' IAPR NewsletterTable of ContentsPreface; Part I. Basic Concepts: 1. Pattern analysis; 2. Kernel methods: an overview; 3. Properties of kernels; 4. Detecting stable patterns; Part II. Pattern Analysis Algorithms: 5. Elementary algorithms in feature space; 6. Pattern analysis using eigen-decompositions; 7. Pattern analysis using convex optimisation; 8. Ranking, clustering and data visualisation; Part III. Constructing Kernels: 9. Basic kernels and kernel types; 10. Kernels for text; 11. Kernels for structured data: strings, trees, etc.; 12. Kernels from generative models; Appendix A: proofs omitted from the main text; Appendix B: notational conventions; Appendix C: list of pattern analysis methods; Appendix D: list of kernels; References; Index.
£82.64
Cambridge University Press Mathematical Analysis of Machine Learning
Book SynopsisThis self-contained textbook introduces students and researchers of AI to the key mathematical concepts and techniques necessary to learn and analyze machine learning algorithms. Readers will gain the technical knowledge needed to understand research papers in theoretical machine learning, without much difficulty.Trade Review'This graduate-level text gives a thorough, rigorous and up-to-date treatment of the main mathematical tools that have been developed for the analysis and design of machine learning methods. It is ideal for a graduate class, and the exercises at the end of each chapter make it suitable for self-study. An excellent addition to the literature from one of the leading researchers in this area, it is sure to become a classic.' Peter Bartlett, University of California, Berkeley'This book showcases the breadth and depth of mathematical ideas in learning theory. The author has masterfully synthesized techniques from the many disciplines that have contributed to this subject, and presented them in an accessible format that will be appreciated by both newcomers and experts alike. Readers will learn the tools-of-the-trade needed to make sense of the research literature and to express new ideas with clarity and precision.' Daniel Hsu, Columbia University'Tong Zhang shares in this book his deep and broad knowledge of machine learning, writing an impressively comprehensive and up-to-date reference text, providing a rigorous and rather advanced treatment of the most important topics and approaches in the mathematical study of machine learning. As an authoritative reference and introduction, his book will be a great asset to the field.' Robert Schapire, Microsoft Research'This book gives a systematic treatment of the modern mathematical techniques that are commonly used in the design and analysis of machine learning algorithms. Written by a key contributor to the field, it is a unique resource for graduate students and researchers seeking to gain a deep understanding of the theory of machine learning.' Shai Shalev-Shwartz, Hebrew University of JerusalemTable of Contents1. Introduction; 2. Basic probability inequalities for sums of independent random variables; 3. Uniform convergence and generalization analysis; 4. Empirical covering number analysis and symmetrization; 5. Covering number estimates; 6. Rademacher complexity and concentration inequalities; 7. Algorithmic stability analysis; 8. Model selection; 9. Analysis of kernel methods; 10. Additive and sparse models; 11. Analysis of neural networks; 12. Lower bounds and minimax analysis; 13. Probability inequalities for sequential random variables; 14. Basic concepts of online learning; 15. Online aggregation and second order algorithms; 16. Multi-armed bandits; 17. Contextual bandits; 18. Reinforcement learning; A. Basics of convex analysis; B. f-Divergence of probability measures; References; Author index; Subject index.
£42.74
Cambridge University Press HandsOn Network Machine Learning with Python
£47.49
O'Reilly Media AI at the Edge
Book SynopsisThis practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI.
£47.99
Cambridge University Press Understanding Machine Learning From Theory to
Book SynopsisMachine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the 'hows' and 'whys' of machine-learning algorithms, making the field accessible to both students and practitioners.Trade Review'This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data.' Bernhard Schölkopf, Max Planck Institute for Intelligent Systems, Germany'This is a timely text on the mathematical foundations of machine learning, providing a treatment that is both deep and broad, not only rigorous but also with intuition and insight. It presents a wide range of classic, fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This is a great book for anyone interested in the mathematical and computational underpinnings of this important and fascinating field.' Avrim Blum, Carnegie Mellon University'This text gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by two key contributors to the theoretical foundations in this area, it covers the range from theoretical foundations to algorithms, at a level appropriate for an advanced undergraduate course.' Peter L. Bartlett, University of California, BerkeleyTable of Contents1. Introduction; Part I. Foundations: 2. A gentle start; 3. A formal learning model; 4. Learning via uniform convergence; 5. The bias-complexity trade-off; 6. The VC-dimension; 7. Non-uniform learnability; 8. The runtime of learning; Part II. From Theory to Algorithms: 9. Linear predictors; 10. Boosting; 11. Model selection and validation; 12. Convex learning problems; 13. Regularization and stability; 14. Stochastic gradient descent; 15. Support vector machines; 16. Kernel methods; 17. Multiclass, ranking, and complex prediction problems; 18. Decision trees; 19. Nearest neighbor; 20. Neural networks; Part III. Additional Learning Models: 21. Online learning; 22. Clustering; 23. Dimensionality reduction; 24. Generative models; 25. Feature selection and generation; Part IV. Advanced Theory: 26. Rademacher complexities; 27. Covering numbers; 28. Proof of the fundamental theorem of learning theory; 29. Multiclass learnability; 30. Compression bounds; 31. PAC-Bayes; Appendix A. Technical lemmas; Appendix B. Measure concentration; Appendix C. Linear algebra.
£48.44
Cambridge University Press The Cambridge Handbook of Cognitive Linguistics
Book SynopsisThe best survey of cognitive linguistics available, this Handbook provides a thorough explanation of its rich methodology, key results, and interdisciplinary context. With in-depth coverage of the research questions, basic concepts, and various theoretical approaches, the Handbook addresses newly emerging subfields and shows their contribution to the discipline. The Handbook introduces fields of study that have become central to cognitive linguistics, such as conceptual mappings and construction grammar. It explains all the main areas of linguistic analysis traditionally expected in a full linguistics framework, and includes fields of study such as language acquisition, sociolinguistics, diachronic studies, and corpus linguistics. Setting linguistic facts within the context of many other disciplines, the Handbook will be welcomed by researchers and students in a broad range of disciplines, including linguistics, cognitive science, neuroscience, gesture studies, computational linguisticTrade ReviewAdvance praise: 'This is the definitive introduction to cognitive linguistics that the mature field deserves, written by the leading practitioners in cognitive approaches to grammar, semantics, conceptual structure, phonology, and everything in-between (and all around). I can't imagine a better introduction for students of language.' Benjamin K. Bergen, University of California, San DiegoTable of ContentsIntroduction Barbara Dancygier; Part I. Language in Cognition and Culture: 1. Opening commentary: language in cognition and culture N. J. Enfield; 2. Relationships between language and cognition Daniel Casasanto; 3. The study of indigenous languages Sally Rice; 4. First language acquisition Laura E. De Ruiter and Anna L. Theakston; 5. Second language acquisition Andrea Tyler; Part II. Language, Body, and Multimodal Communication: 6. Opening commentary: polytropos and communication in the wild Mark Turner; 7. Signed languages Sherman Wilcox and Corinne Occhino; 8. Gesture, language, and cognition Kensy Cooperrider and Susan Goldin-Meadow; 9. Multimodality in interaction Kurt Feyaerts, Geert Brône and Bert Oben; 10. Viewpoint Lieven Vandelanotte; 11. Embodied intersubjectivity Jordan Zlatev; 12. Intersubjectivity and grammar Ronny Boogaart and Alex Reuneker; Part III. Aspects of Linguistic Analysis: 13. Opening commentary: linguistic analysis John Newman; 14. Phonology Geoffrey S. Nathan; 15. The construction of words Geert Booij; 16. Lexical semantics John R. Taylor; 17. Cognitive grammar Ronald W. Langacker; 18. From constructions to construction grammars Thomas Hoffmann; 19. Construction grammars Thomas Hoffmann; 20. Cognitive linguistics and pragmatics Kerstin Fischer; 21. Fictive interaction Esther Pascual and Todd Oakley; 22. Diachronic approaches Alexander Bergs; Part IV. Conceptual Mappings: 23. Opening commentary: conceptual mappings Eve Sweetser; 24. Conceptual metaphor Karen Sullivan; 25. Metonymy Jeannette Littlemore; 26. Conceptual blending theory Todd Oakley and Esther Pascual; 27. Embodiment Raymond W. Gibbs, Jr; 28. Corpus linguistics and metaphor Elena Semino; 29. Metaphor, simulation, and fictive motion Teenie Matlock; Part V. Methodological Approaches: 30. Opening commentary: getting the measure of meaning Chris Sinha; 31. The quantitative turn Laura A. Janda; 32. Language and the brain Seana Coulson; 33. Cognitive sociolinguistics Willem B. Hollmann; 34. Computational resources: framenet and constructicon Hans C. Boas; 35. Computational approaches to metaphor: the case of MetaNet Oana A. David; 36. Corpus approaches Stefan Gries; 37. Cognitive linguistics and the study of textual meaning Barbara Dancygier; Part VI. Concepts and Approaches: Space and Time: 38. Linguistic patterns of space and time vocabulary Eve Sweetser and Alice Gaby; 39. Space-time mappings beyond language Alice Gaby and Eve Sweetser; 40. Conceptualizing time in terms of space: experimental evidence Tom Gijssels and Daniel Casasanto; 41. Discovering spatiotemporal concepts in discourse Thora Tenbrink.
£47.99
Cambridge University Press Natural Language Processing
Book SynopsisWith 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 onliTrade Review'An amazingly compact, and at the same time comprehensive, introduction and reference to natural language processing (NLP). It describes the NLP basics, then employs this knowledge to solve typical NLP problems. It achieves very high coverage of NLP through a clever abstraction to typical high-level tasks, such as sequence labelling. Finally, it explains the topics in deep learning. The book captivates through its simple elegance, depth, and accessibility to a wide range of readers from undergrads to experienced researchers.' Iryna Gurevych, Technical University of Darmstadt, Germany'An excellent introduction to the field of natural language processing including recent advances in deep learning. By organising the material in terms of machine learning techniques - instead of the more traditional division by linguistic levels or applications - the authors are able to discuss different topics within a single coherent framework, with a gradual progression from basic notions to more complex material.' Joakim Nivre, Uppsala University'The book is a valuable tool for both beginning and advanced researchers in the field.' Catalin Stoean, zbMATHTable of ContentsPart I. Basics: 1. Introduction; 2. Counting relative frequencies; 3. Feature vectors; 4. Discriminative linear classifiers; 5. A perspective from information theory; 6. Hidden variables; Part II. Structures: 7. Generative sequence labelling; 8. Discriminative sequence labelling; 9. Sequence segmentation; 10. Predicting tree structures; 11. Transition-based methods for structured prediction; 12. Bayesian models; Part III. Deep Learning: 13. Neural network; 14. Representation learning; 15. Neural structured prediction; 16. Working with two texts; 17. Pre-training and transfer learning; 18. Deep latent variable models; Index.
£55.09
Cambridge University Press Introduction to Graph Signal Processing
Book SynopsisAn 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.Table of Contents1. Introduction; 2. Node domain processing; 3. Graph signal frequency-Spectral graph theory; 4. Sampling; 5. Graph signal representations; 6. How to choose a graph; 7. Applications; Appendix A. Linear algebra and signal representations; Appendix B. GSP with Matlab: the GraSP toolbox; References; Index.
£69.99
Cambridge University Press Scaling Up Machine Learning
Book SynopsisIn many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too large or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a variety of machine learning methods developed specifically for parallel or distributed systems, covering algorithms, platforms and applications.Trade Review'One of the landmark achievements of our time is the ability to extract value from large volumes of data. Engineering and algorithmic developments on this front have gelled substantially in recent years, and are quickly being reduced to practice in widely available, reusable forms. This book provides a broad and timely snapshot of the state of developments in scalable machine learning, which should be of interest to anyone who wishes to understand and extend the state of the art in analyzing data.' Joseph M. Hellerstein, University of California, Berkeley'This is a book that every machine learning practitioner should keep in their library.' Yoram Singer, Google Inc.'The contributions in this book run the gamut from frameworks for large-scale learning to parallel algorithms to applications, and contributors include many of the top people in this burgeoning subfield. Overall this book is an invaluable resource for anyone interested in the problem of learning from and working with big datasets.' William W. Cohen, Carnegie Mellon University, Pennsylvania'This unique, timely book provides a 360 degrees view and understanding of both conceptual and practical issues that arise when implementing leading machine learning algorithms on a wide range of parallel and high-performance computing platforms. It will serve as an indispensable handbook for the practitioner of large-scale data analytics and a guide to dealing with BIG data and making sound choices for efficient applying learning algorithms to them. It can also serve as the basis for an attractive graduate course on parallel/distributed machine learning and data mining.' Joydeep Ghosh, University of TexasTable of Contents1. Scaling up machine learning: introduction Ron Bekkerman, Mikhail Bilenko and John Langford; Part I. Frameworks for Scaling Up Machine Learning: 2. Mapreduce and its application to massively parallel learning of decision tree ensembles Biswanath Panda, Joshua S. Herbach, Sugato Basu and Roberto J. Bayardo; 3. Large-scale machine learning using DryadLINQ Mihai Budiu, Dennis Fetterly, Michael Isard, Frank McSherry and Yuan Yu; 4. IBM parallel machine learning toolbox Edwin Pednault, Elad Yom-Tov and Amol Ghoting; 5. Uniformly fine-grained data parallel computing for machine learning algorithms Meichun Hsu, Ren Wu and Bin Zhang; Part II. Supervised and Unsupervised Learning Algorithms: 6. PSVM: parallel support vector machines with incomplete Cholesky Factorization Edward Chang, Hongjie Bai, Kaihua Zhu, Hao Wang, Jian Li and Zhihuan Qiu; 7. Massive SVM parallelization using hardware accelerators Igor Durdanovic, Eric Cosatto, Hans Peter Graf, Srihari Cadambi, Venkata Jakkula, Srimat Chakradhar and Abhinandan Majumdar; 8. Large-scale learning to rank using boosted decision trees Krysta M. Svore and Christopher J. C. Burges; 9. The transform regression algorithm Ramesh Natarajan and Edwin Pednault; 10. Parallel belief propagation in factor graphs Joseph Gonzalez, Yucheng Low and Carlos Guestrin; 11. Distributed Gibbs sampling for latent variable models Arthur Asuncion, Padhraic Smyth, Max Welling, David Newman, Ian Porteous and Scott Triglia; 12. Large-scale spectral clustering with Mapreduce and MPI Wen-Yen Chen, Yangqiu Song, Hongjie Bai, Chih-Jen Lin and Edward Y. Chang; 13. Parallelizing information-theoretic clustering methods Ron Bekkerman and Martin Scholz; Part III. Alternative Learning Settings: 14. Parallel online learning Daniel Hsu, Nikos Karampatziakis, John Langford and Alex J. Smola; 15. Parallel graph-based semi-supervised learning Jeff Bilmes and Amarnag Subramanya; 16. Distributed transfer learning via cooperative matrix factorization Evan Xiang, Nathan Liu and Qiang Yang; 17. Parallel large-scale feature selection Jeremy Kubica, Sameer Singh and Daria Sorokina; Part IV. Applications: 18. Large-scale learning for vision with GPUS Adam Coates, Rajat Raina and Andrew Y. Ng; 19. Large-scale FPGA-based convolutional networks Clement Farabet, Yann LeCun, Koray Kavukcuoglu, Berin Martini, Polina Akselrod, Selcuk Talay and Eugenio Culurciello; 20. Mining tree structured data on multicore systems Shirish Tatikonda and Srinivasan Parthasarathy; 21. Scalable parallelization of automatic speech recognition Jike Chong, Ekaterina Gonina, Kisun You and Kurt Keutzer.
£42.74
Cambridge University Press Machine Learning Refined
Book SynopsisWith its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for gradTrade Review'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego'Some machine learning books cover only programming aspects, often relying on outdated software tools; some focus exclusively on neural networks; others, solely on theoretical foundations; and yet more books detail advanced topics for the specialist. This fully revised and expanded text provides a broad and accessible introduction to machine learning for engineering and computer science students. The presentation builds on first principles and geometric intuition, while offering real-world examples, commented implementations in Python, and computational exercises. I expect this book to become a key resource for students and researchers.' Osvaldo Simeone, Kings College London'This book is great for getting started in machine learning. It builds up the tools of the trade from first principles, provides lots of examples, and explains one thing at a time at a steady pace. The level of detail and runnable code show what's really going when we run a learning algorithm.' David Duvenaud, University of Toronto'This book covers various essential machine learning methods (e.g., regression, classification, clustering, dimensionality reduction, and deep learning) from a unified mathematical perspective of seeking the optimal model parameters that minimize a cost function. Every method is explained in a comprehensive, intuitive way, and mathematical understanding is aided and enhanced with many geometric illustrations and elegant Python implementations.' Kimiaki Sihrahama, Kindai University, Japan'Books featuring machine learning are many, but those which are simple, intuitive, and yet theoretical are extraordinary 'outliers'. This book is a fantastic and easy way to launch yourself into the exciting world of machine learning, grasp its core concepts, and code them up in Python or Matlab. It was my inspiring guide in preparing my 'Machine Learning Blinks' on my BASIRA YouTube channel for both undergraduate and graduate levels.' Islem Rekik, Director of the Brain And SIgnal Research and Analysis (BASIRA) Laboratory'With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.' politcommerce.com'This is a comprehensive textbook on the fundamental concepts of machine learning. In the second edition, the authors provide a very accessible introduction to the main ideas behind machine learning models.' Helena Mihaljević, zbMATHTable of Contents1. Introduction to machine learning; Part I. Mathematical Optimization: 2. Zero order optimization techniques; 3. First order methods; 4. Second order optimization techniques; Part II. Linear Learning: 5. Linear regression; 6. Linear two-class classification; 7. Linear multi-class classification; 8. Linear unsupervised learning; 9. Feature engineering and selection; Part III. Nonlinear Learning: 10. Principles of nonlinear feature engineering; 11. Principles of feature learning; 12. Kernel methods; 13. Fully-connected neural networks; 14. Tree-based learners; Part IV. Appendices: Appendix A. Advanced first and second order optimization methods; Appendix B. Derivatives and automatic differentiation; Appendix C. Linear algebra.
£55.09
Cambridge University Press The Science of Deep Learning
Book SynopsisThe Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notatioTrade Review'In the avalanche of books on Deep Learning, this one stands out. Iddo Drori has mastered reinforcement learning - in its technical meaning and in his successful, commonsense approach to teaching and understanding.' Gilbert Strang, Massachusetts Institute of Technology'This book covers an impressive breadth of foundational concepts and algorithms behind modern deep learning. By reading this book, readers will quickly but thoroughly learn and appreciate foundations and advances of modern deep learning.' Kyunghyun Cho, New York University'This book offers a fascinating tour of the field of deep learning, which in only ten years has come to revolutionize almost every area of computing. Drori provides concise descriptions of many of the most important developments, combining unified mathematical notation and ample figures to form an essential resource for students and practitioners alike.' Jonathan Ventura, Cal Poly'Drori's textbook goes under the hood of deep learning, covering a broad swath of modern techniques in optimization that are useful for efficiently training neural networks. The book also covers regularization methods to avoid overfitting, a common issue when working with deep learning models. Overall, this is an excellent textbook for students and practitioners who want to gain a deeper understanding of deep learning.' Madeleine Udell, Stanford University'This textbook provides an excellent introduction to contemporary methods and models in deep learning. I expect this book to become a key resource in data science education for students and researchers.' Nakul Verma, Columbia University'This new book by Professor Drori brings fresh insights from his experience teaching thousands of students at Columbia, MIT, and NYU during the past several years. The book is a unique resource and opportunity for educators and researchers worldwide to build on his highly successful deep learning course.' Claudio Silva, New York University'Drori's book covers deep learning, from fundamentals to applications. The fundamentals are covered with clear figures and examples, making the underlying algorithms easy to understand for non-specialists. The multidisciplinary applications are thoughtfully selected to illustrate the broad applications of deep neural networks to specialized domains while highlighting the common themes and architectures between them.' Tonio Buonassisi, Professor of Mechanical Engineering, Massachusetts Institute of Technology'Drori's textbook makes the learning curve for deep learning a whole lot easier to climb. It follows a rigid scientific narrative, accompanied by a trove of code examples and visualizations. These enable a truly multi-modal approach to learning that will allow many students to understand the material better and sets them on a path of exploration.' Joaquin Vanschoren, Assistant Professor of Machine Learning, Eindhoven University of TechnologyTable of ContentsPreface; Notation; Part I. Foundations: 1. Introduction; 2. Forward and backpropagation; 3. Optimization; 4. Regularization; Part II. Architectures: 5. Convolutional neural networks; 6. Sequence models; 7. Graph neural networks; 8. Transformers; Part III. Generative Models: 9. Generative adversarial networks; 10. Variational autoencoders; Part IV. Reinforcement Learning: 11. Reinforcement learning; 12. Deep reinforcement learning; Part V. Applications: 13. Applications; Appendices; References; Index.
£42.74
Cambridge University Press Algorithmic HighDimensional Robust Statistics
Book SynopsisThis reference text offers a clear unified treatment for graduate students, academic researchers, and professionals interested in understanding and developing statistical procedures for high-dimensional data that are robust to idealized modeling assumptions, including robustness to model misspecification and to adversarial outliers in the dataset.Trade Review'This is a timely book on efficient algorithms for computing robust statistics from noisy data. It presents lucid intuitive descriptions of the algorithms as well as precise statements of results with rigorous proofs - a nice combination indeed. The topic has seen fundamental breakthroughs over the last few years and the authors are among the leading contributors. The reader will get a ringside view of the developments.' Ravi Kannan, Visiting Professor, Indian Institute of ScienceTable of Contents1. Introduction to robust statistics; 2. Efficient high-dimensional robust mean estimation; 3. Algorithmic refinements in robust mean estimation; 4. Robust covariance estimation; 5. List-decodable learning; 6. Robust estimation via higher moments; 7. Robust supervised learning; 8. Information-computation tradeoffs in high-dimensional robust statistics; A. Mathematical background; References; Index.
£42.74
Cambridge University Press Machine Learning Evaluation
Book SynopsisThis accessible, comprehensive guide is aimed at students, practitioners, engineers, and users. The emphasis is on building robust, responsible machine learning products incorporating meaningful metrics, rigorous statistical analysis, fair training sets, and explainability. Implementations in Python and sklearn are available on the book's website.
£56.99
Apress Pro Processing for Images and Computer Vision
Book SynopsisTagline: Teaching your computer to seeTable of Contents1. Getting Started with Processing and OpenCV2. Image Sources and Representations3. Pixel-Based Manipulation4. Geometry and Transformation5. Identification of Structure6. Understanding Motion7. Feature Detection and Matching8. Application Deployment and Conclusion
£47.10
APress Practical Machine Learning and Image Processing
Book Synopsis Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You''ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You''ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you''ll explore how models are made in real time and then deployed using various DevOps tools. All the concepTable of ContentsChapter 1: Installation and Environment Setup Chapter Goal: Making System Ready for Image Processing and Analysis No of pages 20 Sub -Topics (Top 2) 1. Installing Jupyter Notebook 2. Installing OpenCV and other Image Analysis dependencies 3. Installing Neural Network Dependencies Chapter 2: Introduction to Python and Image Processing Chapter Goal: Introduction to different concepts of Python and Image processing Application on it. No of pages: 50 Sub - Topics (Top 2) 1. Essentials of Python 2. Terminologies related to Image Analysis Chapter 3: Advanced Image Processing using OpenCV Chapter Goal: Understanding Algorithms and their applications using Python No of pages: 100 Sub - Topics (Top 2): 1. Operations on Images 2. Image Transformations Chapter 4: Machine Learning Approaches in Image Processing Chapter Goal: Basic Implementation of Machine and Deep Learning Models, which takes care of Image Processing, before applications in real-time scenario No of pages: 100 Sub - Topics (Top 2): 1. Image Classification and Segmentation 2. Applying Supervised and Unsupervised Learning approaches on Images using Python Chapter 5: Real Time Use Cases Chapter Goal: Working on 5 projects using Python, applying all the concepts learned in this book No of pages: 100 Sub - Topics (Top 5): 1. Facial Detection 2. Facial Recognition 3. Hand Gesture Movement Recognition 4. Self-Driving Cars Conceptualization: Advanced Lane Finding 5. Self-Driving Cars Conceptualization: Traffic Signs Detection Chapter 6: Appendix A Chapter Goal: Advanced concepts Introduction No of pages: 50 Sub - Topics (Top 2): 1. AdaBoost and XGBoost 2. Pulse Coupled Neural Networks
£46.74
Springer-Verlag New York Inc. Pattern Recognition and Machine Learning
Book SynopsisProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.Trade ReviewFrom the reviews: "This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software "In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007) "This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study." (W. R. Howard, Kybernetes, Vol. 36 (2), 2007) "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Summing Up: Highly recommended. Upper-division undergraduates through professionals." (C. Tappert, CHOICE, Vol. 44 (9), May, 2007) "The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … ." (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007) "This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value." (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007) "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008) "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … ." (L. State, ACM Computing Reviews, October, 2008) "Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts." (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008)Table of ContentsProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
£58.49
PublicAffairs We See It All: Liberty and Justice in an Age of
Book Synopsis
£21.00
Nova Science Publishers Inc Face Recognition: Methods, Applications &
Book Synopsis
£149.99
Springer London Ltd Computational Methods in Biometric Authentication: Statistical Methods for Performance Evaluation
Book SynopsisBiometrics, the science of using physical traits to identify individuals, is playing an increasing role in our security-conscious society and across the globe. Biometric authentication, or bioauthentication, systems are being used to secure everything from amusement parks to bank accounts to military installations. Yet developments in this field have not been matched by an equivalent improvement in the statistical methods for evaluating these systems. Compensating for this need, this unique text/reference provides a basic statistical methodology for practitioners and testers of bioauthentication devices, supplying a set of rigorous statistical methods for evaluating biometric authentication systems. This framework of methods can be extended and generalized for a wide range of applications and tests. This is the first single resource on statistical methods for estimation and comparison of the performance of biometric authentication systems. The book focuses on six common performance metrics: for each metric, statistical methods are derived for a single system that incorporates confidence intervals, hypothesis tests, sample size calculations, power calculations and prediction intervals. These methods are also extended to allow for the statistical comparison and evaluation of multiple systems for both independent and paired data. Topics and features: * Provides a statistical methodology for the most common biometric performance metrics: failure to enroll (FTE), failure to acquire (FTA), false non-match rate (FNMR), false match rate (FMR), and receiver operating characteristic (ROC) curves * Presents methods for the comparison of two or more biometric performance metrics * Introduces a new bootstrap methodology for FMR and ROC curve estimation * Supplies more than 120 examples, using publicly available biometric data where possible * Discusses the addition of prediction intervals to the bioauthentication statistical toolset * Describes sample-size and power calculations for FTE, FTA, FNMR and FMR Researchers, managers and decisions makers needing to compare biometric systems across a variety of metrics will find within this reference an invaluable set of statistical tools. Written for an upper-level undergraduate or master’s level audience with a quantitative background, readers are also expected to have an understanding of the topics in a typical undergraduate statistics course. Dr. Michael E. Schuckers is Associate Professor of Statistics at St. Lawrence University, Canton, NY, and a member of the Center for Identification Technology Research.Table of ContentsPart I: Introduction Introduction Statistical Background Part II: Primary Matching and Classification Measures False Non-Match Rate False Match Rate Receiver Operating Characteristic Curve and Equal Error Rate Part III: Biometric Specific Measures Failure to Enrol Failure to Acquire Part IV: Additional Topics and Appendices Additional Topics and Discussion Tables
£123.49
Springer London Ltd Advanced Algorithmic Approaches to Medical Image
Book SynopsisMedical imaging is an important topic and plays a key role in robust diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound. This book focuses primarily on model-based segmentation techniques, which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors working in industry and academia, and presents new material.Table of Contents1. Principles of Image Generation.- 1.1 Introduction.- 1.2 Ultrasound Image Generation.- 1.2.1 The Principle of Pulse-Echo Ultrasound Imaging.- 1.2.2 B-Scan Quality and the Ultimate Limits.- 1.2.3 Propagation-Related Artifacts and Resolution Limits.- 1.2.3 Attenuation-Related Artifacts.- 1.3 X-Ray Cardiac Image Generation.- 1.3.1 LV Data Acquisition System Using X-Rays.- 1.3.2 Drawbacks of Cardiac Catheterization.- 1.4 Magnetic Resonance Image Generation.- 1.4.1 Physical Principles of Nuclear Magnetic Resonance.- 1.4.2 Basics of Magnetic Resonance Imaging.- 1.4.3 Gradient-Echo (GRE).- 1.4.4 The Latest Techniques for MR Image Generation.- 1.4.5 3-D Turbo FLASH (MP-RAGE) Technique.- 1.4.6 Non-Rectilinear k-Space Trajectory: Spiral.- 1.4.7 Fat Suppression.- 1.4.8 High Speed MRI: Perfusion-Weighted.- 1.4.9 Time of Flight (TOF) MR Angiography.- 1.4.10 Fast Spectroscopic Imaging.- 1.4.11 Recent MR Imaging Techniques.- 1.5 Computer Tomography Image Generation.- 1.5.1 Fourier Reconstruction Method.- 1.6 Positron-Emission Tomography Image Generation.- 1.6.1 Underlying Principles of.- 1.6.2 Usage of PET in Diagnosis.- 1.6.3 Fourier Slice Theorem.- 1.6.4 The Reconstruction Algorithm in PET.- 1.6.5 Image Reconstruction Using Filtered Back-Projection.- 1.7 Comparison of Imaging Modalities: A Summary.- 1.7.1 Acknowledgements.- 2. Segmentation in Echocardiographic Images.- 2.1 Introduction.- 2.2 Heart Physiology and Anatomy.- 2.2.1 Cardiac Function.- 2.2.2 Standard LV Views in 2-DEs.- 2.2.3 LV Function Assessment Using 2-DEs.- 2.3 Review of LV Boundary Extraction Techniques Applied to Echocardiographic Data.- 2.3.1 Acoustic Quantification Techniques.- 2.3.2 Image-Based Techniques.- 2.3.3 2-DE Image Processing Techniques.- 2.4Automatic Fuzzy Reasoning-Based Left Ventricular Center Point Extraction.- 2.4.1 LVCP Extraction System Overview.- 2.4.2 Stage 1: Pre-Processing.- 2.4.3 Stage 2: LVCP Features Fuzzification.- 2.4.4 Template Matching.- 2.4.5 Experimental Results.- 2.4.6 Conclusion.- 2.5 A New Edge Detection in the Wavelet Transform Domain.- 2.5.1 Multiscale Edge Detection and the Wavelet Transform.- 2.5.2 Edge Detection Based on the Global Maximum of Wavelet Transform (GMWT).- 2.5.3 GMWT Performance Analysis and Comparison.- 2.6 LV Segmentation System.- 2.6.1 Overall Reference.- 2.6.2 3D Non-Uniform Radial Intensity Sampling.- 2.6.3 LV Boundary Edge Detection on 3D Radial Intensity Matrix.- 2.6.4 Post-Processing of the Edges and Closed LVE Approximation.- 2.6.5 Automatic LV Volume Assessment.- 2.7 Conclusions.- 2.8 Acknowledgments.- 3. Cardiac Boundary Segmentation.- 3.1 Introduction.- 3.2 Cardiac Anatomy and Data Acquisitions for MR, CT, Ul-trasound and X-Rays.- 3.2.1 Cardiac Anatomy.- 3.2.2 Cardiac MR, CT, Ultrasound and X-Ray Acquisitions.- 3.3 Low- and Medium-Level LV Segmentation Techniques.- 3.3.1 Smoothing Image Data.- 3.3.2 Manual and Semi-Automatic LV Thresholding.- 3.3.3 LV Dynamic Thresholding.- 3.3.4 Edge-Based Techniques.- 3.3.5 Mathematical Morphology-Based Techniques.- 3.3.6 Drawbacks of Low-Level LV Segmentation Techniques.- 3.4 Model-Based Pattern Recognition Methods for LV Modeling.- 3.4.1 LV Active Contour Models in the Spatial and Temporal Domains.- 3.4.2 Model-Based Pattern Recognition Learning Methods.- 3.4.3 Polyline Distance Measure and Performance Terms.- 3.4.4 Data Analysis Using IdCM, InCM and the Greedy Method.- 3.5 Left Ventricle Apex Modeling: A Model-Based Approach.- 3.5.1 Longitudinal Axis and Apex Modeling.- 3.5.2 Ruled Surface Model.- 3.5.3 Ruled Surface sr and its Coefficients.- 3.5.4 Estimation of Robust Coefficients and Coordinates of the Ruled Surface.- 3.5.5 Experiment Design.- 3.5.6 Analytical Error Measure, AQin for Inlier Data.- 3.5.7 Experiments, Results and Discussions.- 3.5.8 Conclusions on LV Apex Modeling.- 3.6 Integration of Low-Level Features in LV Model-Based Cardiac Imaging: Fusion of Two Computer Vision Systems.- 3.7 General Purpose LV Validation Technique.- 3.8 LV Convex Hulling: Quadratic Training-Based Point Modeling.- 3.8.1 Quadratic Vs. Linear Optimization for Convex Hulling.- 3.9 LV Eigen Shape Modeling.- 3.9.1 Procrustes Superposition.- 3.9.2 Dimensionality Reduction Using Constraints for Joint.- 3.10 LV Neural Network Models.- 3.11 Comparative Study and Summary of the Characteristics of Model-Based Techniques.- 3.11.1 Characteristics of Model-Based LV Imaging.- 3.12 LV Quantification: Wall Motion and Tracking.- 3.12.1 LV Wall Motion Measurements.- 3.12.2 LV Volume Measurements.- 3.12.3 LV Wall Motion Tracking.- 3.13 Conclusions.- 3.13.1 Cardiac Hardware.- 3.13.2 Cardiac Software.- 3.13.3 Summary.- 3.13.4 Acknowledgments.- 4. Brain Segmentation Techniques.- 4.1 Introduction.- 4.1.1 Human Brain Anatomy and the MRI System.- 4.1.2 Applications of Brain Segmentation.- 4.2 Brain Scanning and its Clinical Significance.- 4.3 Region-Based 2-D and 3-D Cortical Segmentation Techniques.- 4.3.1 Atlas-Based and Threshold-Based Techniques.- 4.3.2 Cortical Segmentation Using Probability-Based Techniques.- 4.3.3 Clustering-Based Cortical Segmentation Techniques.- 4.3.4 Mathematical Morphology-Based Cortical Segmentation Techniques.- 4.3.5 Prior Knowledge-Based Techniques.- 4.3.6 Texture-Based Techniques.- 4.3.7 Neural Network-Based Techniques.- 4.3.8 Regional Hyperstack: Fusion of Edge-Diffusion with Region-Linking.- 4.3.9 Fusion of Probability-Based with Edge Detectors, Connectivity and Region-Growing.- 4.3.10 Summary of Region-Based Techniques: Pros and Cons.- 4.4 Boundary/Surface-Based 2-D and 3-D Cortical Segmentation Techniques: Edge, Reconstruction, Parametric and Geometric Snakes/Surfaces.- 4.4.1 Edge-Based Cortical-Boundary Estimation Techniques.- 4.4.2 3-D Cortical Reconstruction From 2-D Serial Cross-Sections (Bourke/Victoria).- 4.4.3 2-D and 3-D Parametric Deformable Models for Cortical Boundary Estimation: Snakes, Fitting, Constrained, Ribbon, T-Surface, Connectedness.- 4.4.4 2-D and 3-D Geometric Deformable Models.- 4.4.5 A Note on Isosurface Extraction (Lorensen/GE).- 4.4.6 Summary of Boundary/Surface-Based Techniques: Pros and Cons.- 4.5 Fusion of Boundary/Surface with Region-Based 2-D and 3-D Cortical Segmentation Techniques.- 4.5.1 2-D/3-D Regional Parametric Boundary: Fusion of Boundary with Classification (Kapur/MIT).- 4.5.2 Regional Parametric Surfaces: Fusion of Surface with Clustering (Xu/JHU).- 4.5.3 2-D Regional Geometric Boundary: Fusion of Boundary with Clustering for Cortical Boundary Estimation (Suri/Marconi).- 4.5.34 3-D Regional Geometric Surfaces: Fusion of Geometric Surface with Probability-Based Voxel Classification (Zeng/Yale).- 4.5.5 2-D/3-D Regional Geometric Surface: Fusion of Geometric Boundary/Surface with Global Shape Information (Leventon/MIT).- 4.5.6 2-D/3-D Regional Geometric Surface: Fusion of Boundary/Surface with Bayesian-Based Pixel Classification (Barillot/IRISA).- 4.5.7 Similarities/Differences Between Different Cortical Segmentation Techniques.- 4.6 3-D Visualization Using Volume Rendering and Texture Mapping.- 4.6.1 Volume Rendering Algorithm for Brain Segmentation.- 4.6.2 Texture Mapping Algorithm for Segmented Brain Visualization.- 4.7 A Note on fMRI: Algorithmic Approach for Establishing the Relationship Between Cognitive Functions and Brain Cortical Anatomy.- 4.7.1 Superiority of fMRI over PET/SPECT Imaging.- 4.7.2 Applications of fMRI.- 4.7.3 Algorithm for Superimposition of Functional and Anatomical Cortex.- 4.7.4 A Short Note on fMRI Time Course Data Analysis.- 4.7.5 Measure of Cortex Geometry.- 4.8 Discussions: Advantages, Validation and New Challenges i 2-D.- 4.8.1 Advantages of Regional Geometric Boundary/Surfaces.- 4.8.2 Validation of 2-D and 3-D Cortical Segmentation Algorithms.- 4.8.3 Challenges in 2-D and 3-D Cortical Segmentation Algorithms.- 4.8.4 Challenges in fMRI.- 4.9 Conclusions and the Future.- 4.9.1 Acknowledgements.- 5. Segmentation for Multiple Sclerosis Lesion.- 5.1 Introduction.- 5.2 Segmentation Techniques.- 5.2.1 Multi-Spectral Techniques.- 5.2.2 Feature Space Classification.- 5.2.3 Supervised Segmentation.- 5.2.4 Unsupervised Segmentation.- 5.2.5 Automatic Segmentation.- 5.3 AFFIRMATIVE Images.- 5.4 Image Pre-Processing.- 5.4.1 RF Inhomogeneity Correction.- 5.4.2 Image Stripping.- 5.4.3 Three Dimensional MR Image Registration.- 5.4.4 Segmentation.- 5.4.5 Flow Correction.- 5.4.6 Evaluation and Validation.- 5.5 Quantification of Enhancing Multiple Sclerosis Lesions.- 5.6 Quadruple Contrast Imaging.- 5.7 Discussion.- 5.7.1 Acknowledgements.- 6. Finite Mixture Models.- 6.1 Introduction.- 6.2 Pixel Labeling Using the Classical Mixture Model.- 6.3 Pixel Labeling Using the Spatially Variant Mixture Model.- 6.4 Comparison of CMM and SVMM for Pixel Labeling.- 6.5 Bayesian Pixel Labeling Using the SVMM.- 6.6 Segmentation Results.- 6.6.1 Computer Simulations.- 6.6.2 Application to Magnetic Resonance Images.- 6.7 Practical Aspects.- 6.8 Summary.- 6.9 Acknowledgements.- 7. MR Spectroscopy.- 7.1 Introduction.- 7.2 A Short History of Neurospectroscopic Imaging and Segmentation in Alzheimer’s Disease and Multiple Sclerosis.- 7.2.1 Alzheimer’s Disease.- 7.2.2 Multiple Sclerosis.- 7.3 Data Acquisition and Image Segmentation.- 7.3.1 Image Pre-Processing for Segmentation.- 7.3.2 Image Post-Processing for Segmentation.- 7.4 Proton Magnetic Resonance Spectroscopic Imaging and Segmentation in Multiple Sclerosis.- 7.4.1 Automatic MRSI Segmentation and Image Processing Algorithm.- 7.4.2 Relative Metabolite Concentrations and Contribution of Gray Matter and White Matter in the Normal Human Brain.- 7.4.3 MRSI and Gadolinium-Enhanced (Gd).- 7.4.4 Lesion Load and Metabolite Concentrations by Segmentation and MRSI.- 7.4.5 MR Spectroscopic Imaging and Localization for Segmentation.- 7.4.6 Lesion Segmentation and Quantification.- 7.4.7 Magnetic Resonance Spectroscopic Imaging and Segmentation Data Processing.- 7.4.8 Statistical Analysis.- 7.5 Proton Magnetic Resonance Spectroscopic Imaging and Segmentation of Alzheimer’s Disease.- 7.5.1 MRSI Data Acquisition Methods.- 7.5.2 H-1 MR Spectra Analysis.- 7.6 Applications of Magnetic Resonance Spectroscopic Imaging and Segmentation.- 7.6.1 Multiple Sclerosis Lesion Metabolite Characteristics and Serial Changes.- 7.6.2 zheimer’s Disease Plaque Metabolite Characteristics.- 7.7 Discussion.- 7.8 Conclusion.- 7.8.1 Acknowledgements.- 8. Fast WM/GM Boundary Estimation.- 8.1 Introduction.- 8.2 Derivation of the Regional Geometric Active Contour Model from the Classical Parametric Deformable Model.- 8.3 Numerical Implementation of the Three Speed Functions in the Level Set Framework for Geometric Snake Propagation.- 8.3.1 Regional Speed Term Expressed in Terms of the Level Set Function (ø).- 8.3.2 Gradient Speed Term Expressed in Terms of the Level Set Function (ø).- 8.3.3 Curvature Speed Term Expressed in Terms of the Level Set Function (ø).- 8.4 Fast Brain Segmentation System Based on Regional Level Sets.- 8.4.1 Overall System and Its Components.- 8.4.2 Fuzzy Membership Computation/Pixel Classification.- 8.4.3 Eikonal Equation and its Mathematical Solution.- 8.4.4 Fast Marching Method for Solving the Eikonal Equation.- 8.4.5 A Note on the Heap Sorting Algorithm.- 8.4.6 Segmentation Engine: Running the Level Set Method in the Narrow Band.- 8.5 MR Segmentation Results on Synthetic and Real Data.- 8.5.1 Input Data Set and Input Level Set Parameters.- 8.5.2 Results: Synthetic and Real.- 8.5.3 Numerical Stability, Signed Distance Transformation Computation, Sensitivity of Parameters and Speed Issues.- 8.6 Advantages of the Regional Level Set Technique.- 8.7 Discussions: Comparison with Previous Techniques.- 8.8 Conclusions and Further Directions.- 8.8.1 Acknowledgements.- 9. Digital Mammography Segmentation.- 9.1 Introduction.- 9.2 Image Segmentation in Mammography.- 9.3 Anatomy of the Breast.- 9.4 Image Acquisition and Formats.- 9.4.1 Digitization of X-Ray Mammograms.- 9.4.2 Image Formats.- 9.4.3 Image Quantization and Tree-Pyramids.- 9.5 Mammogram Enhancement Methods.- 9.6 Quantifying Mammogram Enhancement.- 9.7 Segmentation of Breast Profile.- 9.8 Segmentation of Microcalcifications.- 9.9 Segmentation of Masses.- 9.9.1 Global Methods.- 9.9.2 Edge-Based Methods.- 9.9.3 Region-Based Segmentation.- 9.9.4 ROI Detection Techniques Using a Single Breast.- 9.9.5 ROI Detection Techniques Using Breast Symmetry.- 9.9.6 Detection of Spicules.- 9.9.7 Breast Alignment for Segmentation.- 9.10 Measures of Segmentation and Abnormality Detection.- 9.11 Feature Extraction From Segmented Regions.- 9.11.1 Morphological Features.- 9.11.2 Texture Features.- 9.11.3 Other Features.- 9.12 Public Domain Databases in Mammography.- 9.12.1 The Digital Database for Screening Mammography (DDSM).- 9.12.2 LLNL/UCSF Database.- 9.12.3 Washington University Digital Mammography Database.- 9.12.4 The Mammographic Image Analysis Society (MIAS) Database.- 9.13 Classification and Measures of Performance.- 9.13.1 Classification Techniques.- 9.13.2 The Receiver Operating Characteristic Curve.- 9.14 Conclusions.- 9.15 Acknowledgements.- 10. Cell Image Segmentation for Diagnostic Pathology.- 10.1 Introduction.- 10.2 Segmentation.- 10.2.1 Feature Space Analysis.- 10.2.2 Mean Shift Procedure.- 10.2.3 Cell Segmentation.- 10.2.4 Segmentation Examples.- 10.3 Decision Support System for Pathology.- 10.3.1 Problem Domain.- 10.3.2 System Overview.- 10.3.3 Current Database.- 10.3.4 Analysis of Visual Attributes.- 10.3.5 Overall Dissimilarity Metric.- 10.3.6 Performance Evaluation and Comparisons.- 10.4 Conclusion.- 11. The Future in Segmentation.- 11.1 Future Research in Medical Image Segmentation.- 11.1.1 The Future of MR Image Generation and Physical Principles.- 11.1.2 The Future of Cardiac Imaging.- 11.2.3 The Future of Neurological Segmentation.- 11.2.4 The Future in Digital Mammography.- 11.2.5 The Future of Pathology Image Segmentation.
£179.99
Springer Nature Switzerland AG Smart Assisted Living: Toward An Open Smart-Home Infrastructure
Book SynopsisSmart Homes (SH) offer a promising approach to assisted living for the ageing population. Yet the main obstacle to the rapid development and deployment of Smart Home (SH) solutions essentially arises from the nature of the SH field, which is multidisciplinary and involves diverse applications and various stakeholders. Accordingly, an alternative to a one-size-fits-all approach is needed in order to advance the state of the art towards an open SH infrastructure.This book makes a valuable and critical contribution to smart assisted living research through the development of new effective, integrated, and interoperable SH solutions. It focuses on four underlying aspects: (1) Sensing and Monitoring Technologies; (2) Context Interference and Behaviour Analysis; (3) Personalisation and Adaptive Interaction, and (4) Open Smart Home and Service Infrastructures, demonstrating how fundamental theories, models and algorithms can be exploited to solve real-world problems.This comprehensive and timely book offers a unique and essential reference guide for policymakers, funding bodies, researchers, technology developers and managers, end users, carers, clinicians, healthcare service providers, educators and students, helping them adopt and implement smart assisted living systems.Table of ContentsPart I: Sensing and Activity Monitoring Multi-Resident Activity Monitoring in Smart Homes Through Non-Wearable Non-Intrusive SensorsSon N. Tran and Qing Zhang and Vanessa Smallbon and Mohan Karunanithi Where Am I? Comparing CNN and LSTM for Location Classification in Egocentric VideosGeorgios Kapidis, Ronald W. Poppe, Elsbeth A. van Dam, Remco C. Veltkamp, and Lucas P. J. J. Noldus A Privacy-Preserving Wearable Camera Setup for Dietary Event Spotting in Free-LivingGiovanni Schiboni, Fabio Wasner, and Oliver AmftSaving Energy on EMG-Monitoring Eyeglasses for Free-Living Eating Event Spotting Using Adaptive Duty-CyclingGiovanni Schiboni and Oliver Amft Indoor Localisation with WiFi Fingerprinting Based on a Convolutional Neural NetworkZumin Wang Unobtrusive Sensing to Assist with Post-Stroke RehabilitationChris Nugent Part II: Activity Recognition and Behaviour Analysis Energy-Based Decision Engine for Household Human Activity RecognitionAnastasios Vafeiadis, Thanasis Vafeiadis, Stelios Zikos, Stelios Krinidis, Konstantinos Votis, Dimitrios Giakoumis, Dimosthenis Ioannidis, Dimitrios Tzovaras, Liming Chen, and Raouf Hamzaoui Distributed Context Recognition, a Systematic ReviewUmar Ahmad and Luis Lopera Exercise Type Recognition Using Transfer LearningHossein Malekmohamadi Meta-Intelligence for Behaviour RecognitionXiaodong Liu and Qi Liu Part III: User Needs and Personalisation A Conceptual Framework for Adaptive User Interfaces for Older AdultsEduardo Machado, Deepika Singhy, Federico Cruciani, Liming Chen, Sten Hankey, Fernando Salvago, Johannes Kropf, and Andreas HolzingerStudying the Technological Barriers and Needs of People with Dementia: A Quantitative StudyNikolaos Liappas, Rebeca Isabel García-Betances, José Gabriel Teriús-Padrón, and María Fernanda Cabrera-Umpiérrez Adaptive Service Robot Behaviours Based on User Mood: Towards Better Personalized Support of MCI Patients at HomeDimitrios Giakoumis, Georgia Peleka, Manolis Vasileiadis, Ioannis Kostavelis, and Dimitrios Tzovaras Part IV: Ambient Assisted Living Solutions Towards Cognitive Assisted LivingClaudia Steinberger and Judith Michael Towards Self-Management of Chronic Diseases in Smart HomesJosé G. Teriús-Padrón, Georgios Kapidis, Sarah Fallmann, Erinc Merdivan, Sten Hanke, Rebeca I. García-Betances, and María Fernanda Cabrera-UmpiérrezA Deep Learning Approach for Privacy Preservation in Assisted LivingIsmini Psychoula, Erinc Merdivany, Deepika Singhy, Liming Chen, Feng Chen, Sten Hankey, Johannes Kropfy, Andreas Holzingerx, and Matthieu GeistTowards Socially Assistive Robots for the Elderly: An End-to-End Object Search FrameworkMohammad Reza Loghmani, Timothy Patten and Markus VinczeModelling Activities of Daily Living with Petri NetsMatias Garcia-Constantino, Alexandros Konios and Chris Nugent Calculus of Context-Aware Ambients for Assisted Living System ModellingFrancois Siewe
£75.99
Springer Nature Switzerland AG An Intuitive Exploration of Artificial
Book SynopsisThis book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future.An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential.The book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI, practitioners and researchers will equally benefit from the book.Table of ContentsPart I, Foundations.- AI Sculpture.- Make Me Learn.- Images and Sequences.- Why AI Works.- Learning to Sculpt.- Unleashing the Power of Generation.- The Road Most Rewarded.- The Classical World.- Part II, Applications.- To See is to Believe.- Read, Read, Read.- Lend Me Your Ear.- Create Your Shire and Rivendell.- Math to Code to Petaflops.- AI and Business.- Part III, Road Ahead.- Keep Marching on.- Benevolent AI for All.- Am I Looking at Myself?.- App. A, Solutions.- Further Reading.- Acronyms.- Glossary.- References.- Index.
£49.49
Springer Nature Switzerland AG Virtual and Augmented Reality (VR/AR):
Book SynopsisThis comprehensive textbook offers a scientifically sound and at the same time practical introduction to Virtual and Augmented Reality (VR/AR). Readers will gain the theoretical foundation needed to design, implement or enhance VR/AR systems, evaluate and improve user interfaces and applications using VR/AR methods, assess and enrich user experiences, and develop a deeper understanding of how to apply VR/AR techniques. Whether utilizing the book for a principal course of study or reference reading, students of computer science, education, media, natural sciences, engineering and other subject areas can benefit from its in-depth content and vivid explanation. The modular structure allows selective sequencing of topics to the requirements of each teaching unit and provides an easy-to-use format from which to choose specific themes for individual self-study. Instructors are provided with extensive materials for creating courses as well as a foundational text upon which to build their advanced topics. The book enables users from both research and industry to deal with the subject in detail so they can properly assess the extent and benefits of VR/AR deployment and determine required resources. Technology enthusiasts and professionals can learn about the current status quo in the field of VR/AR and interested newcomers can gain insight into this fascinating world. Grounded on a solid scientific foundation, this textbook, addresses topics such as perceptual aspects of VR/AR, input and output devices including tracking, interactions in virtual worlds, real-time aspects of VR/AR systems and the authoring of VR/AR applications in addition to providing a broad collection of case studies.Table of Contents1 R. Doerner et al., Introduction to Virtual and Augmented Reality.- 2 R. Doerner and F. Steinicke, Perceptual Aspects of VR.- 3 B. Jung and A. Vitzhum, Virtual Worlds.- 4 P. Grimm at al., VR/AR Input Devices and Tracking.- 5 W. Broll et al., VR/AR Output Devices.- 6 R. Doerner et al., Interaction in Virtual Worlds.- 7 M. Buhr et al., Real-time Aspects of VR Systems.- 8 W. Broll, Augmented Reality.- 9 R. Doerner et al., VR/AR Case Studies.- 10 W. Broll et al., Authoring of VR/AR Applications.- 11 R. Doerner, Mathematical Foundations of VR/AR.
£49.49
Springer Nature Switzerland AG Handbook of Fingerprint Recognition
Book SynopsisA major new professional reference work on fingerprint security systems and technology from leading international researchers in the field. Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems. This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.Table of ContentsIntroduction.- Fingerprint sensing.- Fingerprint analysis and representation.- Fingerprint matching.- Fingerprint classification and indexing.- Latent fingerprint recognition.- Fingerprint synthesis.- Fingerprint individuality.- Securing fingerprint systems.
£104.49
Springer Nature Switzerland AG Handbook of Digital Face Manipulation and
Book SynopsisThis open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area.Table of ContentsPart I - Introduction: 1. Digital Face Manipulation: An Introduction.- 2. Face Manipulation in Biometric Systems.- 3. Face Manipulation in Media Forensics.- Part II - Face Manipulation Detection Methods: 4. DeepFakes Manipulation.- 5. DeepFakes Detection.- 6. Attacking Face Recognition Systems with DeepFakes.- 7. Vulnerability of Face Recognition Systems to Morphing Attacks.- 8. Face Morphing Attack Detection.- 9. Face Synthesis Detection.- 10. Expression Swap Detection.- 11. Audio- and Text-to-Video Detection.- 12. Detection of Facial Retouching.- 13. Face De-Identification Detection.- Part III - Further Topics: 14. All-in-One Face Manipulation Detection: Generalization Analysis.- 15. Reversion of Face Manipulation.- 16. 3D Face Manipulation Detection.- 17. Improving Face Recognition with Face Image Manipulation.- 18. Impact of Post-Processing on Face Manipulation Detection.- 19. Societal and Legal Aspects of Face Manipulation.- 20. Face Manipulation for Privacy Protection.- 21. Privacy-preserving Face Manipulation Detection.- 22. Face Manipulation in Operational Systems.- Part IV - Open Issues, Trends, and Challenges: 23. All: Future trends in face Manipulation and Fake Detection.
£31.49
Springer Nature Switzerland AG Biometric Identification, Law and Ethics
Book SynopsisThis 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.Table of ContentsAcknowledgment1. The Rise of Biometric Identification, Fingerprints and Applied Ethics2. Facial Recognition and Privacy Rights3. DNA Identification, Joint Rights and Collective Responsibility4. Biometric and Non-Biometric Integration: Dual Use Dilemmas5. The Future of Biometrics and Liberal DemocracyIndex
£23.74
Springer International Publishing AG Pattern Recognition and Image Analysis: 10th
Book SynopsisThis book constitutes the refereed proceedings of the 10th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2022, held in Aveiro, Portugal, in May 2022. The 54 papers accepted for these proceedings were carefully reviewed and selected from 72 submissions. They deal with document analysis; medical image processing; biometrics; pattern recognition and machine learning; computer vision; and other applications. Table of ContentsDOCUMENT ANALYSIS.- Test Sample Selection for Handwriting Recognition through Language Modeling.- Classification of Untranscribed Handwritten Notarial Documents by Textual Contents.- Incremental Vocabularies in Machine Translation through Aligned Embedding Projections.- An Interactive Machine Translation Framework for Modernizing the Language of Historical Documents.- From Captions to Explanations: A Multimodal Transformer-based Architecture for Natural Language Explanation Generation.- MEDICAL IMAGE PROCESSING.- Diagnosis of Skin Cancer Using Hierarchical Neural Networks and Metadata.- Lesion-Based Chest Radiography Image Retrieval for Explainability in Pathology Detection.- Deep Learning for Diagnosis of Alzheimer’s Disease with FDG-PET Neuroimaging.- Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes.- Increased Robustness in Chest X-ray Classification through Clinical Report-driven regularization.- MEDICAL APPLICATIONS.- Deep Detection Models for Measuring Epidermal Bladder Cells.- On the performance of deep learning models for respiratory sound classification trained on unbalanced data.- Automated Adequacy Assessment of Cervical Cytology Samples using Deep Learning.- Exploring Alterations in Electrocardiogram during the Postoperative Pain.- Differential Gene Expression Analysis of the Most Relevant Genes for Lung Cancer Prediction and Sub-type Classification.- Detection of epilepsy in EEGs using Deep Sequence models - A Comparative Study.- BIOMETRICS.- Facial Emotion Recognition for Sentiment Analysis of Social Media Data.- Heartbeat selection based on outlier removal.- Characterization of emotions through facial Electromyogram signals.- Feature selection for emotional well-being monitorization.- Temporal Convolutional Networks for Robust Face Liveness Detection.- PATTERN RECOGNITION & MACHINE LEARNING.- MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks.- Transparent management of adjacencies in the cubic grid.- Abbreviating Labelling Cost for Sentinel-2 Image Scene Classification through Active Learning.- Feature-based classification of archaeal sequences using compression-based methods.- A first approach to Image Transformation Sequence Retrieval.- Discriminative Learning of Two-Dimensional Probabilistic Context-Free Grammars for Mathematical Expression Recognition and Retrieval.- COMPUTER VISION.- Golf Swing Sequencing using Computer Vision.- Domain Adaptation in Robotics: A Study Case on Kitchen Utensil Recognition.- An Innovative Vision System for Floor-Cleaning Robots based on YOLOv5.- LIDAR Signature based Node Detection and Classification in graph topological maps for indoor navigation.- Event Vision in Egocentric Human Action Recognition.- An edge-based computer vision approach for determination of sulfonamides in water.- IMAGE PROCESSING.- Visual Semantic Context Encoding for Aerial Data Introspection and Domain Prediction.- An End-to-End Approach for Seam Carving Detection using Deep Neural Networks.- Proposal of a comparative framework for face super-resolution algorithms in forensics.- On the use of Transformers for end-to-end Optical Music Recognition.- Retrieval of Music-Notation Primitives via Image-to-Sequence.- Digital image conspicuous features classification using TLCNN model with SVM classifier.- Contribution of low, mid and high-level image features in predicting human similarity judgements.- On the Topological Disparity Characterization of Square-pixel Binary Image Data by a Labeled Bipartite Graph.- Learning Sparse Masks for Diffusion-based Image Inpainting.- Extracting Descriptive Words from Untranscribed Handwritten Images.- OTHER APPLICATIONS.- GMM-aided DNN Bearing Fault Diagnosis using Sparse Autoencoder Feature Extraction.- Identification of External Defects on FruitsUsing Deep Learning.- Improving Action Quality Assessment using Weighted Aggregation.- Improving Licence Plate Detection using Generative Adversarial Networks.- Film shot type classification based on camera movement styles.- The CleanSea Set: A Benchmark Corpus for Underwater Debris Detection and Recognition.- A case of study on traffic cone detection for autonomous racing on a Jetson platform.- Energy savings in residential buildings based on adaptive thermal comfort models.- Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition.- Using bus tracking data to detect potential hazard driving zones.- Dynamic PCA based statistical monitoring of air pollutant concentrations in wildfire scenarios.
£80.99
Springer International Publishing AG Pattern Recognition: 14th Mexican Conference, MCPR 2022, Ciudad Juárez, Mexico, June 22–25, 2022, Proceedings
Book SynopsisThis book constitutes the proceedings of the 14th Mexican Conference on Pattern Recognition, MCPR 2022, which was held in planned to be held Ciudad Juárez, Mexico, in June 2022. The 33 papers presented in this volume were carefully reviewed and selected from 66 submissions. They are organized in the following topical sections: pattern recognition techniques; neural networks and deep learning; image and signal processing and analysis; natural language processing and recognition; robotics and remote sensing applications of pattern recognition; medical applications of pattern recognition.Table of ContentsPattern Recognition Techniques.- Hot Spots & Hot Regions Detection using Classification Algorithms in BMPs Complexes at the Protein-protein Interface with the Ground-state Energy Feature.- Clustering of Twitter Networks based on Users’ Structural Profile.- Changing Model from NGSIM Dataset.- A Robust Fault Diagnosis Method in Presence of Noise and Missing Information for Industrial Plants.- A Preliminary Study of SMOTE on Imbalanced Big Datasets when Dealing with Sparse and Dense High Dimensionality.- A Novel Survival Analysis-based Approach for Predicting Behavioral Probability of Mining Mixed Data Bases using Machine Learning Algorithms.- Networks and Deep Learning A CNN-based Driver’s Drowsiness and Distraction Detection System.- 3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain.- Learning Dendrite Morphological Neurons Using Linkage Trees for Pattern Classification.- Deep Variational Method with Attention for High-Definition Face Generation.- Indoor Air Pollution Forecasting using Deep Neural Networks.- Extreme Machine Learning Architectures based on Correlation.- Image & Signal Processing and Analysis Evaluating New Set of Acoustical Features for Cry Signal Classification.- Motor Imagery Classification Using Riemannian Geometry in Multiple Frequency Bands with a Weighted Nearest Neighbors Approach.- Virtualizing 3D Real Environments Using 2D Pictures Based on Photogrammetry.- Factorized U-net for Retinal Vessel Segmentation.- Multi-view Learning for EEG Signal Classification of Imagined Speech.- Escalante Emotion Recognition using Time-frequency Distribution and GLCM Features from EEG Signals.- Natural Language Processing and Recognition Leveraging Multiple Characterizations of Social Media Users for Depression Detection Using Data Fusion.- A Wide & Deep Learning Approach for Covid-19 Tweet Classification.- Does this Tweet Report an Adverse Drug Reaction? An Enhanced BERT-based Method to Identify Drugs Side Effects in Twitter.- We Will Know Them by Their Style: Fake News Detection based on Masked n-grams.- Multi-Document Text Summarization based on Genetic Algorithm and the Relevance of Sentence Features.- ´ Robotics & Remote Sensing Applications of Pattern Recognition On Labelling Pointclouds with the Nearest Facet of Triangulated Building Models.- Dust Deposition Classification on the Receiver Tube of the Parabolic Trough Collector: A Deep Learning-based Approach.- Detection of Pain Caused By A Thermal Stimulus Using EEG and Machine Learning.- Data Mining.- Natural Language Processing and Recognition.- Document Processing and Recognition.- Fuzzy and Hybrid Techniques in Pattern Recognition.- Image Coding, Processing and Analysis.- Industrial and Medical Applications of Pattern Recognition.- Bioinformatics.- Logical Combinatorial Pattern Recognition.- Mathematical Morphology.- Artificial Intelligence Techniques and Recognition.- Pattern Recognition Principles.- Robotics & Remote Sensing Applications of Pattern Recognition.- Shape and Texture Analysis.- Signal Processing and Analysis.
£58.49
Springer International Publishing AG Biometric Recognition: 16th Chinese Conference,
Book SynopsisThis 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.Table of ContentsFingerprint, Palmprint and Vein Recognition.- A Finger BiModal Fusion Algorithm based on Improved DenseNet.- A lightweight segmentation network based on extraction.- A novel multi-layered minutiae extractor based on OCT fingerprints.- An overview and forecast of biometric recognition technology used in forensic science.- Combining Band-Limited OTSDF Filter and Directional Representation for Palmprint Recognition.- Cross-Dataset Image Matching Network for Heterogeneous Palmprint Recognition.- DUAL MODE NEAR-INFRARED SCANNER FOR IMAGING DORSAL HAND VEINS.- Multi-Stream Convolutional Neural Networks Fusion for Palmprint Recognition.- Multi-view Finger Vein Recognition using Attention-based MVCNN.- SELECTIVE DETAIL ENHANCEMENT ALGORITHM FOR FINGER VEIN IMAGES.- SP-FVR: SuperPoint-based Finger Vein Recognition.- TransFinger: Transformer based Finger Tri-modal Biometrics.- Face Detection, Recognition and Tracking.- A Survey of Domain Generalization-based Face Anti-spoofing.- An Empirical Comparative Analysis of Africans with Asians using DCNN Facial Biometric Models.- Disentanglement of Deep Features for Adversarial Face Detection.- Estimation of Gaze-Following Based on Transformer and the Guiding Offset.- Learning Optimal Transport Mapping of Joint Distribution for Cross-Scenario Face Anti-Spooffing.- MLFW: A Database for Face Recognition on Masked Faces.- Multi-scale object detection algorithm based on adaptive feature fusion.- Sparsity-Regularized Geometric Mean Metric Learning for Kinship Verification.- YoloMask: An Enhanced YOLO Model for Detection of Face Mask Wearing Normality, Irregularity and Spoofing.- Gesture and Action Recognition.- Adaptive Joint Interdependency Learning for 2D Occluded Hand Pose Estimation.- Contrastive and Consistent Learning for Unsupervised Human Parsing.- Dynamic Hand Gesture Authentication Based on Improved Two-stream CNN.- Efficient Video Understanding-based Random Hand Gesture Authentication.- Multidimension Joint Networks for Action Recognition.- Multi-Level Temporal-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition.- Research on Gesture Recognition of Surface EMG Based on Machine Learning.- Affective Computing and Human-Computer Interface.- Adaptive Enhanced Micro-expression Spotting Network based on Multi-stage Features Extraction.- Augmented Feature Representation with Parallel Convolution for Cross-domain Facial Expression Recognition.- Hemispheric Asymmetry Measurement Network for Emotion Classification.- Human Action Recognition Algorithm of Non-Local Two-Stream Convolution Network Based on Image Depth Flow.- Synthetic Feature Generative Adversarial Network for Motor Imagery Classification: Create Feature from Sampled Data.- Speaker and Speech Recognition.- An End-to-end Conformer-based Speech Recognition Model for Mandarin Radiotelephony Communications in Civil Aviation.- ATRemix: An Auto-Tune Remix Dataset for Singer Recognition.- Low-resource speech keyword search based on residual neural network.- Online Neural Speaker Diarization with Core Samples.- Pose-unconstrainted 3D Lip Behaviometrics via Unsupervised Symmetry Correction.- Virtual Fully-Connected Layer for a Large-Scale Speaker Verification Dataset.- Gait, Iris and Other Biometrics.- A Simple Convolutional Neural Network for Small Sample Multi-lingual Offline Handwritten Signature Recognition.- Attention Skip Connection Dense Network for Accurate Iris Segmentation.- Gait Recognition with Various Data Modalities: A Review.- INCREMENTAL EEG BIOMETRIC RECOGNITION BASED ON EEG RELATION NETWORK.- Salient Foreground-Aware Network for Person Search.- Shoe print retrieval algorithm based on improved ecientnetV2.- Multi-modal Biometric Recognition and Fusion.- A novel dual-modal biometric recognition method based on weighted joint group sparse representation classification.- FINGER TRIMODAL FEATURES CODING FUSION METHOD.- Fusion of Gait and Face for Human Identification at the Feature Level.- Gait Recognition in Sensing Insoles: a study based on a Hybrid CNN-Attention-LSTM Network.- Identity Authentication Using a Multimodal Sensing Insole a Feasibility Study.- MDF-Net: Multimodal Deep Fusion for Large-scale Product Recognition.- Survey on Deep Learning based Fusion Recognition of Multimodal Biometrics.- Synthesizing Talking Face Videos with a Spatial Attention Mechanism.- Quality Evaluation and Enhancement of Biometric Signals.- Blind Perceptual Quality Assessment for Single Image Motion Deblurring.- Low-illumination Palmprint Image Enhancement Method Based On U-Net Neural Network.- Texture-guided multiscale feature learning network for palmprint image quality assessment.- Animal Biometrics.- An Adaptive Weight Joint Loss Optimization For Dog Face Recognition.- Improved YOLOv5 for Dense Wildlife Object Detection.- Self-Attention based Cross-level Fusion Network for Camou aged Object Detection.- Trustyworth, Privacy and Persondal Data Security.- Face Forgery Detection by Multi-dimensional Image Decomposition.- IrisGuard: Image Forgery Detection for Iris Anti-spooffing.- Multi-branch network with circle loss using voice conversion and channel robust data augmentation for synthetic speech detection.- Spoof Speech Detection Based on Raw Cross-dimension Interaction Attention Network.- Medical and Other Applications.- A Deformable Convolution Encoder with Multi-Scale Attention Fusion Mechanism for Classification of Brain Tumor MRI Images.- GI Tract Lesion Classification Using Multi-task Capsule Networks with Hierarchical Convolutional Layers.- Grading Diagnosis of Sacroiliitis in CT Scans Based on Radiomics and Deep Learning.- Noninvasive blood pressure waveform measurement method based on CNN-LSTM.- Recurrence Quantification Analysis of Cardiovascular System During Cardiopulmonary Resuscitation.- UAV AERIAL PHOTOGRAPHY TRAFFIC OBJECT DETECTION BASED ON LIGHTWEIGHT DESIGN AND FEATURE FUSION.- UMixer: A novel U-shaped convolutional mixer for multi-scale feature fusion in Medical Image Segmentation.
£75.99
Springer International Publishing AG Artificial Neural Networks in Pattern Recognition: 10th IAPR TC3 Workshop, ANNPR 2022, Dubai, United Arab Emirates, November 24–26, 2022, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 10th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2022, held in Dubai, UAE, in November 2022. The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The conference presents papers on subject such as pattern recognition and machine learning based on artificial neural networks. Table of ContentsTransformer-Encoder generated context-aware embeddings for spell correction.- Graph Augmentation for Neural Networks Using Matching-Graphs.- Wavelet Scattering Transform Depth Benefit, An Application for Speaker Identification.- Assessment of Pharmaceutical Patent Novelty using Siamese Neural Network.- A Review of Capsule Networks in Medical Image Analysis.- Multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisions.- Introducing an Atypical Loss: A Perceptual Metric Learning for Image Pairing.- A Study on the Autonomous Detection of Impact Craters.- Minimizing Cross Intersections in Graph Drawing via Linear Splines.- Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech.- Do Minimal Complexity Least Squares Support Vector Machines Work?.- A Novel Representation of Graphical Patterns for Graph Convolution Networks.- Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi Utilization of Vision Transformer for Classification and Ranking of Video Distortions.- White Blood Cell Classification of Porcine Blood Smear Images.- Medical Deepfake Detection using 3-Dimensional Neural Learning.
£47.49
Springer International Publishing AG Recent Trends in Image Processing and Pattern
Book SynopsisThis book constitutes the refereed proceedings of the 5th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2022, held in Kingsville, TX, USA, in collaboration with the Applied AI Research Laboratory of the University of South Dakota, during December 01-02, 2022.The 31 full papers included in this book were carefully reviewed and selected from 69 submissions. They were organized in topical sections as follows: healthcare: medical imaging and informatics; computer vision and pattern recognition; internet of things and security; and signal processing and machine learning.Table of ContentsHealthcare: medical imaging and informatics.- Data Characterization for Reliable AI in Medicine.- Alzheimer’s Disease Detection using Ensemble Learning and Artificial Neural Networks.- Semi-supervised Multi-domain Learning for Medical Image Classification.- Significant CC400 functional brain parcellations based LeNet5 Convolutional Neural Network for Autism Spectrum Disorder detection.- 2D respiratory sound analysis to detect lung abnormalities.- Analyzing Chest X-Ray to Detect the Evidence of Lung Abnormality due to Infectious Disease.- Chest X-ray Image Super-resolution via Deep Contrast Consistent Feature Network.- A Novel Approach to Enhance Effectiveness of Image Segmentation Techniques on Extremely Noisy Medical Images.- Federated Learning for Lung Sound Analysis.- Performance Analysis of CNN and Quantized CNN Model for Rheumatoid Arthritis Identification using Thermal Image.- Image Processing and Pattern Recognition of Micropores of Polysulfone Membrane for the Bio-separation of Viruses from Whole Blood.- An Extreme Learning Machine-basedAutoEncoder (ELM-AE)for denoising knee X-ray images and grading knee osteoarthritis severity.- Computer Vision and Pattern Recognition.- Motor Imagery Classification CombiningRiemannian Geometry and Artificial Neural Networks.- Autism Spectrum Disorder Detection using Transfer Learning with VGG 19, Inception V3 and DenseNet 201.- Shrimp Shape Analysis by a Chord LengthFunction Based Methodology.- Supervised Neural Networks for Fruit Identification.- Targeted Clean-Label Poisoning Attacks On Federated Learning.- Building Marathi SentiWordNet.- A computational study on calibrated VGG19 formultimodal learning and representation insurveillance.- Automated Deep Learning based approach for Albinism Detection.- A Deep learning-based regression scheme for angle estimation in image dataset.- The classification of Native and Invasive Speciesin North America: A Transfer Learning and Random Forest Pipeline.- Internet of Things and Security.- Towards a Digital Twin Integrated DLT and IoT-based Automated Healthcare Ecosystem.- Enabling Edge Devices using Federated Learning and Big Data for Proactive Decisions.- IoT and Blockchain oriented gender determination of Bangladeshi populations.- Federated Learning based secured computational offloading in cyber-physical IoST systems.- A Hybrid Campus Security System Combined ofFace, Number-plate, and Voice Recognition.- Signal Processing and Machine.- Single-trial detection of event-related potentials with artificial examples based on coloring transformation.- Identifying the relationship between hypothesis and premise.- Data Poisoning Attack by Label Flipping onSplitFed Learning.- A Deep Learning-powered voice-enabled mathtutor for kids.
£71.24
Springer International Publishing AG Computer Vision – ECCV 2022 Workshops: Tel Aviv,
Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.
£80.74
Springer International Publishing AG Computer Vision – ECCV 2022 Workshops: Tel Aviv,
Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.
£80.74
Springer International Publishing AG Computer Vision – ECCV 2022 Workshops: Tel Aviv,
Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.
£80.74
Springer International Publishing AG Computer Vision – ECCV 2022 Workshops: Tel Aviv,
Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.
£61.74
