Classification, parameter estimation, and state estimation : an engineering approach using MATLAB / Bangjun Lei [and six others].

Format:

Book

Author/Creator:

Heijden, Ferdinand van der.

Lei, Bangjun, 1973- author.

Language:

English

Subjects (All):

Engineering mathematics--Data processing.

Engineering mathematics.

Measurement--Data processing.

Measurement.

Estimation theory--Data processing.

Estimation theory.

MATLAB.

Physical Description:

1 online resource (431 pages) : illustrations

Edition:

Second edition.

Place of Publication:

Hoboken, New Jersey : Wiley, 2017.

Summary:

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.

Contents:

Intro

Classification, Parameter Estimation and State Estimation

Contents

Preface

Acknowledgements

About the Companion Website

1 Introduction

1.1 The Scope of the Book

1.1.1 Classification

1.1.2 Parameter Estimation

1.1.3 State Estimation

1.1.4 Relations between the Subjects

1.2 Engineering

1.3 The Organization of the Book

1.4 Changes from First Edition

1.5 References

2 PRTools Introduction

2.1 Motivation

2.2 Essential Concepts

2.3 PRTools Organization Structure and Implementation

2.4 Some Details about PRTools

2.4.1 Datasets

2.4.2 Datafiles

2.4.3 Datafiles Help Information

2.4.4 Classifiers and Mappings

2.4.5 Mappings Help Information

2.4.6 How to Write Your Own Mapping

2.5 Selected Bibliography

3 Detection and Classification

3.1 Bayesian Classification

3.1.1 Uniform Cost Function and Minimum Error Rate

3.1.2 Normal Distributed Measurements

Linear and Quadratic Classifiers

3.2 Rejection

3.2.1 Minimum Error Rate Classification with Reject Option

3.3 Detection: The Two-Class Case

3.4 Selected Bibliography

4 Parameter Estimation

4.1 Bayesian Estimation

4.1.1 MMSE Estimation

4.1.2 MAP Estimation

4.1.3 The Gaussian Case with Linear Sensors

4.1.4 Maximum Likelihood Estimation

4.1.5 Unbiased Linear MMSE Estimation

4.2 Performance Estimators

4.2.1 Bias and Covariance

4.2.2 The Error Covariance of the Unbiased Linear MMSE Estimator

4.3 Data Fitting

4.3.1 Least Squares Fitting

4.3.2 Fitting Using a Robust Error Norm

4.3.3 Regression

4.4 Overview of the Family of Estimators

4.5 Selected Bibliography

5 State Estimation

5.1 A General Framework for Online Estimation

5.1.1 Models

5.1.2 Optimal Online Estimation

5.2 Infinite Discrete-Time State Variables.

5.2.1 Optimal Online Estimation in Linear-Gaussian Systems

5.2.2 Suboptimal Solutions for Non-linear Systems

5.3 Finite Discrete-Time State Variables

5.3.1 Hidden Markov Models

5.3.2 Online State Estimation

5.3.3 Offline State Estimation

5.4 Mixed States and the Particle Filter

5.4.1 Importance Sampling

5.4.2 Resampling by Selection

5.4.3 The Condensation Algorithm

5.5 Genetic State Estimation

5.5.1 The Genetic Algorithm

5.5.2 Genetic State Estimation

5.5.3 Computational Issues

5.6 State Estimation in Practice

5.6.1 System Identification

5.6.2 Observability, Controllability and Stability

5.6.3 Computational Issues

5.6.4 Consistency Checks

5.7 Selected Bibliography

6 Supervised Learning

6.1 Training Sets

6.2 Parametric Learning

6.2.1 Gaussian Distribution, Mean Unknown

6.2.2 Gaussian Distribution, Covariance Matrix Unknown

6.2.3 Gaussian Distribution, Mean and Covariance Matrix Both Unknown

6.2.4 Estimation of the Prior Probabilities

6.2.5 Binary Measurements

6.3 Non-parametric Learning

6.3.1 Parzen Estimation and Histogramming

6.3.2 Nearest Neighbour Classification

6.3.3 Linear Discriminant Functions

6.3.4 The Support Vector Classifier

6.3.5 The Feedforward Neural Network

6.4 Adaptive Boosting - Adaboost

6.5 Convolutional Neural Networks (CNNs)

6.5.1 Convolutional Neural Network Structure

6.5.2 Computation and Training of CNNs

6.6 Empirical Evaluation

6.7 Selected Bibliography

7 Feature Extraction and Selection

7.1 Criteria for Selection and Extraction

7.1.1 Interclass/Intraclass Distance

7.1.2 Chernoff-Bhattacharyya Distance

7.1.3 Other Criteria

7.2 Feature Selection

7.2.1 Branch-and-Bound

7.2.2 Suboptimal Search

7.2.3 Several New Methods of Feature Selection

7.2.4 Implementation Issues.

7.3 Linear Feature Extraction

7.3.1 Feature Extraction Based on the Bhattacharyya Distance with Gaussian Distributions

7.3.2 Feature Extraction Based on InterIntra Class Distance

7.4 References

8 Unsupervised Learning

8.1 Feature Reduction

8.1.1 Principal Component Analysis

8.1.2 Multidimensional Scaling

8.1.3 Kernel Principal Component Analysis

8.2 Clustering

8.2.1 Hierarchical Clustering

8.2.2 K-Means Clustering

8.2.3 Mixture of Gaussians

8.2.4 Mixture of probabilistic PCA

8.2.5 Self-Organizing Maps

8.2.6 Generative Topographic Mapping

8.3 References

9 Worked Out Examples

9.1 Example on Image Classification with PRTools

9.1.1 Example on Image Classification

9.1.2 Example on Face Classification

9.1.3 Example on Silhouette Classification

9.2 Boston Housing Classification Problem

9.2.1 Dataset Description

9.2.2 Simple Classification Methods

9.2.3 Feature Extraction

9.2.4 Feature Selection

9.2.5 Complex Classifiers

9.2.6 Conclusions

9.3 Time-of-Flight Estimation of an Acoustic Tone Burst

9.3.1 Models of the Observed Waveform

9.3.2 Heuristic Methods for Determining the ToF

9.3.3 Curve Fitting

9.3.4 Matched Filtering

9.3.5 ML Estimation Using Covariance Models for the Reflections

9.3.6 Optimization and Evaluation

9.4 Online Level Estimation in a Hydraulic System

9.4.1 Linearized Kalman Filte

9.4.2 Extended Kalman Filtering

9.4.3 Particle Filtering

9.4.4 Discussion

9.5 References

Appendix A Topics Selected from Functional Analysis

A.1 Linear Spaces

A.1.1 Normed Linear Spaces

A.1.2 Euclidean Spaces or Inner Product Spaces

A.2 Metric Spaces

A.3 Orthonormal Systems and Fourier Series

A.4 Linear Operators

A.5 Selected Bibliography

Appendix B Topics Selected from Linear Algebra and Matrix Theory.

B.1 Vectors and Matrices

B.2 Convolution

B.3 Trace and Determinant

B.4 Differentiation of Vector and Matrix Functions

B.5 Diagonalization of Self-Adjoint Matrices

B.6 Singular Value Decomposition (SVD)

B.7 Selected Bibliography

Appendix C Probability Theory

C.1 Probability Theory and Random Variables

C.1.1 Moments

C.1.2 Poisson Distribution

C.1.3 Binomial Distribution

C.1.4 Normal Distribution

C.1.5 The Chi-Square Distribution

C.2 Bivariate Random Variables

C.3 Random Vectors

C.3.1 Linear Operations on Gaussian Random Vectors

C.3.2 Decorrelation

C.4 Selected Bibliography

Appendix D Discrete-Time Dynamic Systems

D.1 Discrete-Time Dynamic Systems

D.2 Linear Systems

D.3 Linear Time-Invariant Systems

D.3.1 Diagonalization of a System

D.3.2 Stability

D.4 Selected Bibliography

Index

EULA.

Notes:

Revised edition of: Classification, parameter estimation, and state estimation : an engineering approach using MATLAB / F. van der Heijden ... [et al.]. 2004.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781119152453

1119152453

9781119152446

1119152445

9781119152484

1119152488

OCLC:

967457469