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Feature Extraction and Image Processing for Computer Vision
- 3rd Edition - August 3, 2012
- Author: Mark Nixon
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 3 9 6 5 4 9 - 3
- eBook ISBN:9 7 8 - 0 - 1 2 - 3 9 7 8 2 4 - 0
Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introd… Read more
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Request a sales quoteFeature 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.
- 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
- No. of pages: 632
- Language: English
- Edition: 3
- Published: August 3, 2012
- Imprint: Academic Press
- Paperback ISBN: 9780123965493
- eBook ISBN: 9780123978240
MN
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, UKRead Feature Extraction and Image Processing for Computer Vision on ScienceDirect