Feature Extraction and Image Processing for Computer Vision

Feature Extraction and Image Processing for Computer Vision

3rd Edition - August 3, 2012

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  • Author: Mark Nixon
  • eBook ISBN: 9780123978240
  • Paperback ISBN: 9780123965493

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Description

Feature Extraction and Image Processing for Computer Vision 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

Readership

University researchers, Research & Development engineers, graduate students

Table of Contents

  • 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

Product details

  • No. of pages: 632
  • Language: English
  • Copyright: © Academic Press 2012
  • Published: August 3, 2012
  • Imprint: Academic Press
  • eBook ISBN: 9780123978240
  • Paperback ISBN: 9780123965493

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, University of Southampton, UK

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  • IoannisStefanis Fri May 24 2019

    Feature Extraction and Image Processing for Computer Vision

    Great as expected