Feature Extraction and Image Processing for Computer Vision
Description
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
Affiliations and Expertise
Ratings and Reviews
Latest reviews
(Total rating for all reviews)
IoannisStefanis Fri May 24 2019
Feature Extraction and Image Processing for Computer Vision
Great as expected