Feature Extraction and Image Processing for Computer Vision

This book is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the exemplar code of the algorithms."

Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Adaboost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended.

• Named a 2012 Notable Computer Book for Computing Methodologies by Computing Reviews
• Essential reading for engineers and students working in this cutting-edge field
• Ideal module text and background reference for courses in image processing and computer vision
• The only currently available text to concentrate on feature extraction with working implementation and worked through derivation

University researchers, Research & Development engineers, graduate students.

Dedication

Preface

About the authors

Chapter 1. Introduction

1.1 Overview

1.2 Human and computer vision

1.3 The human vision system

1.4 Computer vision systems

1.5 Mathematical systems

1.6 Associated literature

1.7 Conclusions

1.8 References

Chapter 2. Images, sampling, and frequency domain processing

2.1 Overview

2.2 Image formation

2.3 The Fourier transform

2.4 The sampling criterion

2.5 The discrete Fourier transform

2.6 Other properties of the Fourier transform

2.7 Transforms other than Fourier

2.8 Applications using frequency domain properties

2.9 Further reading

2.10 References

Chapter 3. Basic image processing operations

3.1 Overview

3.2 Histograms

3.3 Point operators

3.4 Group operations

3.5 Other statistical operators

3.6 Mathematical morphology

3.7 Further reading

3.8 References

Chapter 4. Low-level feature extraction (including edge detection)

4.1 Overview

4.2 Edge detection

4.3 Phase congruency

4.4 Localized feature extraction

4.5 Describing image motion

4.6 Further reading

4.7 References

Chapter 5. High-level feature extraction: fixed shape matching

5.1 Overview

5.2 Thresholding and subtraction

5.3 Template matching

5.4 Feature extraction by low-level features

5.5 Hough transform

5.6 Further reading

5.7 References

Chapter 6. High-level feature extraction: deformable shape analysis

6.1 Overview

6.2 Deformable shape analysis

6.3 Active contours (snakes)

6.4 Shape skeletonization

6.5 Flexible shape models—active shape and active appearance

6.6 Further reading

6.7 References

Chapter 7. Object description

7.1 Overview

7.2 Boundary descriptions

7.3 Region descriptors

7.4 Further reading

7.5 References

Chapter 8. Introduction to texture description, segmentation, and classification

8.1 Overview

8.2 What is texture?

8.3 Texture description

8.4 Classification

8.5 Segmentation

8.6 Further reading

8.7 References

Chapter 9. Moving object detection and description

9.1 Overview

9.2 Moving object detection

9.3 Tracking moving features

9.4 Moving feature extraction and description

9.5 Further reading

9.6 References

Chapter 10. Appendix 1: Camera geometry fundamentals

10.1 Image geometry

10.2 Perspective camera

10.3 Perspective camera model

10.4 Affine camera

10.5 Weak perspective model

10.6 Example of camera models

10.7 Discussion

10.8 References

Chapter 11. Appendix 2: Least squares analysis

11.1 The least squares criterion

11.2 Curve fitting by least squares

Chapter 12. Appendix 3: Principal components analysis

12.1 Principal components analysis

12.2 Data

12.3 Covariance

12.4 Covariance matrix

12.5 Data transformation

12.6 Inverse transformation

12.7 Eigenproblem

12.8 Solving the eigenproblem

12.9 PCA method summary

12.10 Example

12.11 References

Chapter 13. Appendix 4: Color images

13.1 Color images

13.2 Tristimulus theory

13.3 Color models

13.4 References

Index