Feature Extraction and Image Processing for Computer Vision - 3rd Edition - ISBN: 9780123965493, 9780123978240

Feature Extraction and Image Processing for Computer Vision

3rd Edition

Authors: Mark Nixon
Paperback ISBN: 9780123965493
eBook ISBN: 9780123978240
Imprint: Academic Press
Published Date: 3rd August 2012
Page Count: 632
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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.

Key Features

  • 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.

Table of Contents



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



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About the Author

Mark Nixon

Mark Nixon is the Professor in Computer Vision at the University of Southampton UK. His research interests are in image processing and computer vision. His team develops new techniques for static and moving shape extraction which have found application in biometrics and in medical image analysis. His team were early workers in automatic face recognition, later came to pioneer gait recognition and more recently joined the pioneers of ear biometrics. With Tieniu Tan and Rama Chellappa, their book Human ID based on Gait is part of the Springer Series on Biometrics and was published in 2005. He has chaired/ program chaired many conferences (BMVC 98, AVBPA 03, IEEE Face and Gesture FG06, ICPR 04, ICB 09, IEEE BTAS 2010) and given many invited talks. Dr. Nixon is a Fellow IET and a Fellow IAPR.

Affiliations and Expertise

Professor of Electronics and Computer Science at the University of Southampton, UK.


Notable Computer Books 2012: Computing Methodologies, Computing Reviews


"…the book is well written and is easy to follow. In fact, the presentation order is the logical order of any actual computer vision system processing pipeline. The authors have done a great job grouping related topics together and touching upon recent techniques."--IAPR Newsletter, October 2013
"The mathematical element is presented in a non-mathematical way thus making the content more accessible…this edition is a very welcome addition to vision extraction."--IMA.org, August 2013
"All in all, I highly recommend this 600 pager as an introduction for students, and as a reference for practitioners. The latter audience will find an abundance of use references in each chapter…"--ComputingReviews.com, April 18, 2013
"After reviewing the human vision system, Nixon…and Aguardo…introduce signal processing theory for computer vision and current digital techniques for edge detection within an image, fixed shape matching, and deformable shape analysis. The undergraduate engineering textbook also explains the characterization of objects by boundary, region, and texture descriptions."--Reference and Research Book News, February 2013