A practical introduction to intelligent computer vision theory, design, implementation, and technologyThe past decade has witnessed epic growth in image processing and intelligent computer vision technology. Advancements in machine learning methods--especially among adaboost varieties and particle filtering methods--have made machine learning in intelligent computer vision more accurate and reliable than ever before. The need for expert coverage of the state of the art in this burgeoning field has never been greater, and this book satisfies that need. Fully updated and extensively revised, this 2nd Edition of the popular guide provides designers, data analysts, researchers and advanced post-graduates with a fundamental yet wholly practical introduction to intelligent computer vision. The authors walk you through the basics of computer vision, past and present, and they explore the more subtle intricacies of intelligent computer vision, with an emphasis on intelligent measurement systems. Using many timely, real-world examples, they explain and vividly demonstrate the latest developments in image and video processing techniques and technologies for machine learning in computer vision systems, including:* PRTools5 software for MATLAB--especially the latest representation and generalization software toolbox for PRTools5* Machine learning applications for computer vision, with detailed discussions of contemporary state estimation techniques vs older content of particle filter methods* The latest techniques for classification and supervised learning, with an emphasis on Neural Network, Genetic State Estimation and other particle filter and AI state estimation methods* All new coverage of the Adaboost and its implementation in PRTools5.A valuable working resource for professionals and an excellent introduction for advanced-level students, this 2nd Edition features a wealth of illustrative examples, ranging from basic techniques to advanced intelligent computer vision system implementations. Additional examples and tutorials, as well as a question and solution forum, can be found on a companion website.

Preface xiAbout the Companion Website xvIntroduction 11.1 The Scope of the Book 21.2 Engineering 101.3 The Organization of the Book 121.4 Changes from First Edition 141.5 References 15PRTools Introduction 172.1 Motivation 172.2 Essential Concepts 182.3 PRTools Organization Structure and Implementation 222.4 Some Details about PRTools 262.5 Selected Bibliography 42Detection and Classification 433.1 Bayesian Classification 463.2 Rejection 623.3 Detection:The Two-Class Case 663.4 Selected Bibliography 74Exercises 74Parameter Estimation 774.1 Bayesian Estimation 794.2 Performance Estimators 944.3 Data Fitting 1004.4 Overview of the Family of Estimators 1104.5 Selected Bibliography 111Exercises 112State Estimation 1155.1 A General Framework for Online Estimation 1175.2 Infinite Discrete-Time State Variables 1255.3 Finite Discrete-Time State Variables 1475.4 Mixed States and the Particle Filter 1635.5 Genetic State Estimation 1705.6 State Estimation in Practice 1835.7 Selected Bibliography 201Exercises 204Supervised Learning 2076.1 Training Sets 2086.2 Parametric Learning 2106.3 Non-parametric Learning 2176.4 Adaptive Boosting - Adaboost 2456.5 Convolutional Neural Networks (CNNs) 2496.6 Empirical Evaluation 2526.7 Selected Bibliography 257Exercises 257Feature Extraction and Selection 2597.1 Criteria for Selection and Extraction 2617.2 Feature Selection 2727.3 Linear Feature Extraction 2887.4 References 300Exercises 300Unsupervised Learning 3038.1 Feature Reduction 3048.2 Clustering 3208.3 References 345Exercises 346Worked Out Examples 3499.1 Example on Image Classification with PRTools 3499.2 Boston Housing Classification Problem 3619.3 Time-of-Flight Estimation of an Acoustic Tone Burst 3729.4 Online Level Estimation in a Hydraulic System 3929.5 References 406Appendix A: Topics Selected from Functional Analysis 407Appendix B: Topics Selected from Linear Algebra and Matrix Theory 421Appendix C: Probability Theory 437Appendix D: Discrete-Time Dynamic Systems 453Index 459