Details

<p>Preface xi</p> <p>About the Companion Website xv</p> <p><b>Introduction 1</b></p> <p>1.1 The Scope of the Book 2</p> <p>1.2 Engineering 10</p> <p>1.3 The Organization of the Book 12</p> <p>1.4 Changes from First Edition 14</p> <p>1.5 References 15</p> <p><b>PRTools Introduction 17</b></p> <p>2.1 Motivation 17</p> <p>2.2 Essential Concepts 18</p> <p>2.3 PRTools Organization Structure and Implementation 22</p> <p>2.4 Some Details about PRTools 26</p> <p>2.5 Selected Bibliography 42</p> <p><b>Detection and Classification 43</b></p> <p>3.1 Bayesian Classification 46</p> <p>3.2 Rejection 62</p> <p>3.3 Detection:The Two-Class Case 66</p> <p>3.4 Selected Bibliography 74</p> <p>Exercises 74</p> <p><b>Parameter Estimation 77</b></p> <p>4.1 Bayesian Estimation 79</p> <p>4.2 Performance Estimators 94</p> <p>4.3 Data Fitting 100</p> <p>4.4 Overview of the Family of Estimators 110</p> <p>4.5 Selected Bibliography 111</p> <p>Exercises 112</p> <p><b>State Estimation 115</b></p> <p>5.1 A General Framework for Online Estimation 117</p> <p>5.2 Infinite Discrete-Time State Variables 125</p> <p>5.3 Finite Discrete-Time State Variables 147</p> <p>5.4 Mixed States and the Particle Filter 163</p> <p>5.5 Genetic State Estimation 170</p> <p>5.6 State Estimation in Practice 183</p> <p>5.7 Selected Bibliography 201</p> <p>Exercises 204</p> <p><b>Supervised Learning 207</b></p> <p>6.1 Training Sets 208</p> <p>6.2 Parametric Learning 210</p> <p>6.3 Non-parametric Learning 217</p> <p>6.4 Adaptive Boosting – Adaboost 245</p> <p>6.5 Convolutional Neural Networks (CNNs) 249</p> <p>6.6 Empirical Evaluation 252</p> <p>6.7 Selected Bibliography 257</p> <p>Exercises 257</p> <p><b>Feature Extraction and Selection 259</b></p> <p>7.1 Criteria for Selection and Extraction 261</p> <p>7.2 Feature Selection 272</p> <p>7.3 Linear Feature Extraction 288</p> <p>7.4 References 300</p> <p>Exercises 300</p> <p><b>Unsupervised Learning 303</b></p> <p>8.1 Feature Reduction 304</p> <p>8.2 Clustering 320</p> <p>8.3 References 345</p> <p>Exercises 346</p> <p><b>Worked Out Examples 349</b></p> <p>9.1 Example on Image Classification with PRTools 349</p> <p>9.2 Boston Housing Classification Problem 361</p> <p>9.3 Time-of-Flight Estimation of an Acoustic Tone Burst 372</p> <p>9.4 Online Level Estimation in a Hydraulic System 392</p> <p>9.5 References 406</p> <p>Appendix A: Topics Selected from Functional Analysis 407</p> <p>Appendix B: Topics Selected from Linear Algebra and Matrix Theory 421</p> <p>Appendix C: Probability Theory 437</p> <p>Appendix D: Discrete-Time Dynamic Systems 453</p> <p>Index 459</p>

<p><b><b>Professor  Bangjun Lei, Dr. Guangzhu Xu,  and Dr. Ming Feng </b></b>are with The Institute of Intelligent Vision and Image Information, China Three Gorges University, China.<br /><b><b><br />Professor Yaobin Zou is</b></b> an associate professor at China Three Gorges University.<b><br /><br />Dr. Ferdinand van der Heijden, Ph.D., </b>is on the faculty of theDepartment of Signals and Systems, University of Twente, Netherlands. <br /><br /><b>Professor Dick de Ridder</b> is Professor at the Bioinformatics lab at Wageningen University, Netherlands. <br /><b><br />Professor David M. J. Tax, </b>is a researcher with the Pattern Recognition laboratory, Delft University of Technology.</p> <p> </p> <p> </p> <p> </p>

<p><b>A practical introduction to intelligent computer vision theory, design, implementation, and technology</b></p> <p>The 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 <i>Classification, Parameter Estimation and State Estimation</i> 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. <p>The authors walk the reader through the basics of computer vision, past and present, and explore the more subtle intricacies of intelligent computer vision, with an emphasis on intelligent measurement systems. Using many timely, real-world examples, the authors explain and vividly demonstrate the latest developments in image and video processing techniques and technologies for machine learning in computer vision systems, including: <ul><li>PRTools5 software for MATLAB®—especially the latest representation and generalization software toolbox for PRTools5</li> <li>Machine learning applications for computer vision, with detailed discussions of contemporary state estimation techniques vs older content of particle filter methods</li> <li>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</li> <li>All new coverage of the Adaboost and its implementation in PRTools5</li></ul> <p>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 practical examples can be found on a companion website.

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