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Classification, Parameter Estimation and State Estimation, 2nd Edition

Book Description

A practical introduction to intelligent computer vision theory, design, implementation, and technology

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 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.

Table of Contents

  1. Preface
    1. Note
  2. Acknowledgements
  3. About the Companion Website
  4. 1 Introduction
    1. 1.1 The Scope of the Book
    2. 1.2 Engineering
    3. 1.3 The Organization of the Book
    4. 1.4 Changes from First Edition
    5. 1.5 References
    6. Note
  5. 2 PRTools Introduction
    1. 2.1 Motivation
    2. 2.2 Essential Concepts
    3. 2.3 PRTools Organization Structure and Implementation
    4. 2.4 Some Details about PRTools
    5. 2.5 Selected Bibliography
  6. 3 Detection and Classification
    1. 3.1 Bayesian Classification
    2. 3.2 Rejection
    3. 3.3 Detection: The Two-Class Case
    4. 3.4 Selected Bibliography
    5. Exercises
  7. 4 Parameter Estimation
    1. 4.1 Bayesian Estimation
    2. 4.2 Performance Estimators
    3. 4.3 Data Fitting
    4. 4.4 Overview of the Family of Estimators
    5. 4.5 Selected Bibliography
    6. Exercises
    7. Notes
  8. 5 State Estimation
    1. 5.1 A General Framework for Online Estimation
    2. 5.2 Infinite Discrete-Time State Variables
    3. 5.3 Finite Discrete-Time State Variables
    4. 5.4 Mixed States and the Particle Filter
    5. 5.5 Genetic State Estimation
    6. 5.6 State Estimation in Practice
    7. 5.7 Selected Bibliography
    8. Exercises
  9. 6 Supervised Learning
    1. 6.1 Training Sets
    2. 6.2 Parametric Learning
    3. 6.3 Non-parametric Learning
    4. 6.4 Adaptive Boosting – Adaboost
    5. 6.5 Convolutional Neural Networks (CNNs)
    6. 6.6 Empirical Evaluation
    7. 6.7 Selected Bibliography
    8. Exercises
    9. Note
  10. 7 Feature Extraction and Selection
    1. 7.1 Criteria for Selection and Extraction
    2. 7.2 Feature Selection
    3. 7.3 Linear Feature Extraction
    4. 7.4 References
    5. Exercises
  11. 8 Unsupervised Learning
    1. 8.1 Feature Reduction
    2. 8.2 Clustering
    3. 8.3 References
    4. Exercises
    5. Note
  12. 9 Worked Out Examples
    1. 9.1 Example on Image Classification with PRTools
    2. 9.2 Boston Housing Classification Problem
    3. 9.3 Time-of-Flight Estimation of an Acoustic Tone Burst
    4. 9.4 Online Level Estimation in a Hydraulic System
    5. 9.5 References
  13. A Topics Selected from Functional Analysis
    1. A.1 Linear Spaces
    2. A.2 Metric Spaces
    3. A.3 Orthonormal Systems and Fourier Series
    4. A.4 Linear Operators
    5. A.5 Selected Bibliography
    6. Notes
  14. B Topics Selected from Linear Algebra and Matrix Theory
    1. B.1 Vectors and Matrices
    2. B.2 Convolution
    3. B.3 Trace and Determinant
    4. B.4 Differentiation of Vector and Matrix Functions
    5. B.5 Diagonalization of Self-Adjoint Matrices
    6. B.6 Singular Value Decomposition (SVD)
    7. B.7 Selected Bibliography
    8. Note
  15. C Probability Theory
    1. C.1 Probability Theory and Random Variables
    2. C.2 Bivariate Random Variables
    3. C.3 Random Vectors
    4. C.4 Selected Bibliography
    5. Notes
  16. D Discrete-Time Dynamic Systems
    1. D.1 Discrete-Time Dynamic Systems
    2. D.2 Linear Systems
    3. D.3 Linear Time-Invariant Systems
    4. Selected Bibliography
  17. Index
  18. EULA