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Face Detection and Recognition Theory and Practice

Face Detection and Recognition Theory and Practice

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

GBP 59.99
1

Bayesian Statistical Methods

Bayesian Statistical Methods

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods the book covers many general topics: Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures including sensitivity to priorsFrequentist properties of Bayesian methodsCase studies covering advanced topics illustrate the flexibility of the Bayesian approach:Semiparametric regression Handling of missing data using predictive distributionsPriors for high-dimensional regression modelsComputational techniques for large datasetsSpatial data analysisThe advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code motivating data sets and complete data analyses are available on the book’s website. Brian J. Reich Associate Professor of Statistics at North Carolina State University is currently the editor-in-chief of the Journal of Agricultural Biological and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh Professor of Statistics at North Carolina State University has over 22 years of research and teaching experience in conducting Bayesian analyses received the Cavell Brownie mentoring award and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.

GBP 39.99
1

Introduction to Linear Algebra

Introduction to Linear Algebra

Linear algebra provides the essential mathematical tools to tackle all the problems in Science. Introduction to Linear Algebra is primarily aimed at students in applied fields (e. g. Computer Science and Engineering) providing them with a concrete rigorous approach to face and solve various types of problems for the applications of their interest. This book offers a straightforward introduction to linear algebra that requires a minimal mathematical background to read and engage with. Features Presented in a brief informative and engaging style Suitable for a wide broad range of undergraduates Contains many worked examples and exercises

GBP 44.99
1

Exercises and Solutions in Biostatistical Theory

Exercises and Solutions in Biostatistical Theory

Drawn from nearly four decades of Lawrence L. Kupper‘s teaching experiences as a distinguished professor in the Department of Biostatistics at the University of North Carolina Exercises and Solutions in Biostatistical Theory presents theoretical statistical concepts numerous exercises and detailed solutions that span topics from basic probability to statistical inference. The text links theoretical biostatistical principles to real-world situations including some of the authors own biostatistical work that has addressed complicated design and analysis issues in the health sciences. This classroom-tested material is arranged sequentially starting with a chapter on basic probability theory followed by chapters on univariate distribution theory and multivariate distribution theory. The last two chapters on statistical inference cover estimation theory and hypothesis testing theory. Each chapter begins with an in-depth introduction that summarizes the biostatistical principles needed to help solve the exercises. Exercises range in level of difficulty from fairly basic to more challenging (identified with asterisks). By working through the exercises and detailed solutions in this book students will develop a deep understanding of the principles of biostatistical theory. The text shows how the biostatistical theory is effectively used to address important biostatistical issues in a variety of real-world settings. Mastering the theoretical biostatistical principles described in the book will prepare students for successful study of higher-level statistical theory and will help them become better biostatisticians.

GBP 175.00
1

The Biometric Computing Recognition and Registration

The Biometric Computing Recognition and Registration

The Biometric Computing: Recognition & Registration presents introduction of biometrics along with detailed analysis for identification and recognition methods. This book forms the required platform for understanding biometric computing and its implementation for securing target system. It also provides the comprehensive analysis on algorithms architectures and interdisciplinary connection of biometric computing along with detailed case-studies for newborns and resolution spaces. The strength of this book is its unique approach starting with how biometric computing works to research paradigms and gradually moves towards its advancement. This book is divided into three parts that comprises basic fundamentals and definitions algorithms and methodologies and futuristic research and case studies. Features: A clear view to the fundamentals of Biometric Computing Identification and recognition approach for different human characteristics Different methodologies and algorithms for human identification using biometrics traits such as face Iris fingerprint palm print voiceprint etc. Interdisciplinary connection of biometric computing with the fields like deep neural network artificial intelligence Internet of Biometric Things low resolution face recognition etc. This book is an edited volume by prominent invited researchers and practitioners around the globe in the field of biometrics describes the fundamental and recent advancement in biometric recognition and registration. This book is a perfect research handbook for young practitioners who are intending to carry out their research in the field of Biometric Computing and will be used by industry professionals graduate and researcher students in the field of computer science and engineering. | The Biometric Computing Recognition and Registration

GBP 140.00
1

Banach Limit and Applications

Banach Limit and Applications

Banach Limit and Applications provides all the results in the area of Banach Limit its extensions generalizations and applications to various fields in one go (as far as possible). All the results in this field after Banach introduced this concept in 1932 were scattered till now. Sublinear functionals generating and dominating Banach Limit unique Banach Limit (almost convergence) invariant means and invariant limits absolute and strong almost convergence applications to ergodicity law of large numbers Fourier series uniform distribution of sequences uniform density core theorems and functional Banach limits are discussed in this book. The discovery of functional analysis such as the Hahn-Banach Theorem and the Banach-Steinhaus Theorem helped the researchers to develop a modern rich and unified theory of sequence spaces by encompassing classical summability theory via matrix transformations and the topics related to sequence spaces which arose from the concept of Banach limits all of which are presented in this book. The unique features of this book are as follows: All the results in this area which were scattered till now are in one place. The book is the first of its kind in the sense that there is no other competitive book. The contents of this monograph did not appear in any book form before. The audience of this book are the researchers in this area and Ph. D. and advanced master’s students. The book is suitable for one- or two-semester course work for Ph. D. students M. S. students in North America and Europe and M. Phil. and master’s students in India.

GBP 130.00
1

Introduction to Math Olympiad Problems

Introduction to Math Olympiad Problems

Introduction to Math Olympiad Problems aims to introduce high school students to all the necessary topics that frequently emerge in international Math Olympiad competitions. In addition to introducing the topics the book will also provide several repetitive-type guided problems to help develop vital techniques in solving problems correctly and efficiently. The techniques employed in the book will help prepare students for the topics they will typically face in an Olympiad-style event but also for future college mathematics courses in Discrete Mathematics Graph Theory Differential Equations Number Theory and Abstract Algebra. Features: Numerous problems designed to embed good practice in readers and build underlying reasoning analysis and problem-solving skills Suitable for advanced high school students preparing for Math Olympiad competitions

GBP 24.99
1

Understanding Artificial Intelligence

Understanding Artificial Intelligence

Artificial Intelligence (AI) fascinates challenges and disturbs us. There are many voices in society that predict drastic changes that may come as a consequence of AI – a possible apocalypse or Eden on earth. However only a few people truly understand what AI is what it can do and what its limitations are. Understanding Artificial Intelligence explains through a straightforward narrative and amusing illustrations how AI works. It is written for a non-specialist reader adult or adolescent who is interested in AI but is missing the key to understanding how it works. The author demystifies the creation of the so-called intelligent machine and explains the different methods that are used in AI. It presents new possibilities offered by algorithms and the difficulties that researchers engineers and users face when building and using such algorithms. Each chapter allows the reader to discover a new aspect of AI and to become fully aware of the possibilities offered by this rich field.

GBP 31.99
1

Object Detection with Deep Learning Models Principles and Applications

Object Detection with Deep Learning Models Principles and Applications

Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision illustrating them using key topics including object detection face analysis 3D object recognition and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students researchers and practitioners interested in deep learning computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection | Object Detection with Deep Learning Models Principles and Applications

GBP 110.00
1

Fitting Statistical Distributions The Generalized Lambda Distribution and Generalized Bootstrap Methods

Fitting Statistical Distributions The Generalized Lambda Distribution and Generalized Bootstrap Methods

Throughout the physical and social sciences researchers face the challenge of fitting statistical distributions to their data. Although the study of statistical modelling has made great strides in recent years the number and variety of distributions to choose from-all with their own formulas tables diagrams and general properties-continue to create problems. For a specific application which of the dozens of distributions should one use? What if none of them fit well?Fitting Statistical Distributions helps answer those questions. Focusing on techniques used successfully across many fields the authors present all of the relevant results related to the Generalized Lambda Distribution (GLD) the Generalized Bootstrap (GB) and Monte Carlo simulation (MC). They provide the tables algorithms and computer programs needed for fitting continuous probability distributions to data in a wide variety of circumstances-covering bivariate as well as univariate distributions and including situations where moments do not exist. Regardless of your specific field-physical science social science or statistics practitioner or theorist-Fitting Statistical Distributions is required reading. It includes wide-ranging applications illustrating the methods in practice and offers proofs of key results for those involved in theoretical development. Without it you may be using obsolete methods wasting time and risking incorrect results. | Fitting Statistical Distributions The Generalized Lambda Distribution and Generalized Bootstrap Methods

GBP 59.99
1

Survival Analysis

Survival Analysis

Survival analysis generally deals with analysis of data arising from clinical trials. Censoring truncation and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties essentially asymptotic ones of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way. Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis. Features: Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model Aalen’s additive hazards model etc. Information criteria to facilitate model selection including Akaike Bayes and Focused Penalized methods Survival trees and ensemble techniques of bagging boosting and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book

GBP 99.99
1

Biometrics in a Data Driven World Trends Technologies and Challenges

Biometrics in a Data Driven World Trends Technologies and Challenges

Biometrics in a Data Driven World: Trends Technologies and Challenges aims to inform readers about the modern applications of biometrics in the context of a data-driven society to familiarize them with the rich history of biometrics and to provide them with a glimpse into the future of biometrics. The first section of the book discusses the fundamentals of biometrics and provides an overview of common biometric modalities namely face fingerprints iris and voice. It also discusses the history of the field and provides an overview of emerging trends and opportunities. The second section of the book introduces readers to a wide range of biometric applications. The next part of the book is dedicated to the discussion of case studies of biometric modalities currently used on mobile applications. As smartphones and tablet computers are rapidly becoming the dominant consumer computer platforms biometrics-based authentication is emerging as an integral part of protecting mobile devices against unauthorized access while enabling new and highly popular applications such as secure online payment authorization. The book concludes with a discussion of future trends and opportunities in the field of biometrics which will pave the way for advancing research in the area of biometrics and for the deployment of biometric technologies in real-world applications. The book is designed for individuals interested in exploring the contemporary applications of biometrics from students to researchers and practitioners working in this field. Both undergraduate and graduate students enrolled in college-level security courses will also find this book to be an especially useful companion. | Biometrics in a Data Driven World Trends Technologies and Challenges

GBP 44.99
1

Risks of Artificial Intelligence

Risks of Artificial Intelligence

If the intelligence of artificial systems were to surpass that of humans humanity would face significant risks. The time has come to consider these issues and this consideration must include progress in artificial intelligence (AI) as much as insights from AI theory. Featuring contributions from leading experts and thinkers in artificial intelligence Risks of Artificial Intelligence is the first volume of collected chapters dedicated to examining the risks of AI. The book evaluates predictions of the future of AI proposes ways to ensure that AI systems will be beneficial to humans and then critically evaluates such proposals. The book covers the latest research on the risks and future impacts of AI. It starts with an introduction to the problem of risk and the future of artificial intelligence followed by a discussion (Armstrong/Sokala/ÓhÉigeartaigh) on how predictions of its future have fared to date. Omohundro makes the point that even an innocuous artificial agent can easily turn into a serious threat for humans. T. Goertzel explains how to succeed in the design of artificial agents. But will these be a threat for humanity or a useful tool? Ways to assure beneficial outcomes through ‘machine ethics’ and ‘utility functions’ are discussed by Brundage and Yampolskiy. B. Goertzel and Potapov/Rodionov propose ‘learning’ and ‘empathy’ as paths towards safer AI while Kornai explains how the impact of AI may be bounded. Sandberg explains the implications of human-like AI via the technique of brain emulation. Dewey discusses strategies to deal with the ‘fast takeoff’ of artificial intelligence and finally Bishop explains why there is no need to worry because computers will remain in a state of ‘artificial stupidity’. Sharing insights from leading thinkers in artificial intelligence this book provides you with an expert-level perspective of what is on the horizon for AI whether it will be a threat for humanity and how we might counteract this threat.

GBP 44.99
1

Tree-Based Methods for Statistical Learning in R

Tree-Based Methods for Statistical Learning in R

Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit) and more. The core chapters also end with a detailed section on relevant software in both R and other opensource alternatives (e. g. Python Spark and Julia) and example usage on real data sets. While the book mostly uses R it is meant to be equally accessible and useful to non-R programmers. Consumers of this book will have gained a solid foundation (and appreciation) for tree-based methods and how they can be used to solve practical problems and challenges data scientists often face in applied work. Features: Thorough coverage from the ground up of tree-based methods (e. g. CART conditional inference trees bagging boosting and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package called treemisc which contains several data sets and functions used throughout the book (e. g. there’s an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations) or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining or even improving performance.

GBP 82.99
1