Feature Extraction and Image Processing Second edition Mark S. Nixon Alberto S. Aguado • : *авш JBK IIP™ AMSTERDAM • BOSTON • HEIDELBERG • LONDON • NEW YORK • OXFORD PARIS • SAN DIEGO • SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO ELSEVIER Academic Press is an imprint of Elsevier
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Feature Extraction and
Image Processing Second edition
Mark S. Nixon
Alberto S. Aguado
•
:*авш J B K IIP™ AMSTERDAM • BOSTON • HEIDELBERG • LONDON • N E W YORK • OXFORD
PARIS • SAN DIEGO • SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO
ELSEVIER Academic Press is an imprint of Elsevier
Contents
Preface XI
1 Introduction
1.1 1.2 1.3
1.4
1.5
1.6
1.7 1.8
Overview Human and computer vision The human vision system 1.3.1 The eye 1.3.2 The neural system 1.3.3 Processing Computer vision systems 1.4.1 Cameras 1.4.2 Computer interfaces 1.4.3 Processing an image Mathematical systems 1.5.1 Mathematical tools 1.5.2 Hello Mathcad, hello images! 1.5.3 Hello Matlab! Associated literature 1.6.1 Journals and magazines 1.6.2 Textbooks 1.6.3 The web Conclusions References
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
Overview Histograms Point operators 3.3.1 Basic point operations 3.3.2 Histogram normalization 3.3.3 Histogram equalization 3.3.4 Thresholding Group operations 3.4.1 Template convolution 3.4.2 Averaging operator 3.4.3 On different template size 3.4.4 Gaussian averaging operator Other statistical operators 3.5.1 More on averaging 3.5.2 Median filter 3.5.3 Mode filter 3.5.4 Anisotropic diffusion 3.5.5 Force field transform 3.5.6 Comparison of statistical operators Mathematical morphology 3.6.1 Morphological operators 3.6.2 Grey-level morphology 3.6.3 Grey-level erosion and dilation 3.6.4 Minkowski operators Further reading References
vi Contents
4.3
4.4 4.5 4.6 4.7 4.8
Second order edge detection operators 4.3.1 Motivation 4.3.2 Basic operators: the Laplacian 4.3.3 Marr-Hildreth operator Other edge detection operators Comparison of edge detection operators Further reading on edge detection Phase congruency Localized feature extraction 4.8.1 Detecting image curvature (corner extraction)
4.8.1.1 Definition of curvature Computing differences in edge direction Measuring curvature by changes in intensity (differentiation) Moravec and Harris detectors Further reading on curvature
Modern approaches: region/patch analysis 4.8.2.1 Scale invariant feature transform 4.8.2.2 Saliency 4.8.2.3 Other techniques and performance issues
4.8.2
4.8.1.2 4.8.1.3
4.8.1.4 4.8.1.5
4.9 Describing image motion 4.9.1 Area-based approach 4.9.2 Differential approach 4.9.3 Further reading on optical flow
4.10 Conclusions 4.11 References
5 Feature extraction by shape matching
5.1 Overview 5.2 Thresholding and subtraction 5.3 Template matching
8 Introduction to texture description, segmentation and classification
8.1 8.2 8.3
8.4
8.5 8.6 8.7
Overview What is texture? Texture description 8.3.1 Performance requirements 8.3.2 Structural approaches 8.3.3 Statistical approaches 8.3.4 Combination approaches Classification 8.4.1 The fc-nearest neighbour rule 8.4.2 Other classification approaches Segmentation Further reading References
9 Appendix 1: Example worksheets
9.1 Example Mathcad worksheet for Chapter 3 9.2 Example Matlab worksheet for Chapter 4
10 Appendix 2: Camera geometry fundamentals
10.1 Image geometry 10.2 Perspective camera 10.3 Perspective camera model
10.3.1 Homogeneous coordinates and projective geometry 10.3.1.1 Representation of a line and duality 10.3.1.2 Ideal points 10.3.1.3 Transformations in the projective space
10.3.2 Perspective camera model analysis 10.3.3 Parameters of the perspective camera model
10.4 Affine camera 10.4.1 Affine camera model 10.4.2 Affine camera model and the perspective projection 10.4.3 Parameters of the affine camera model
10.5 Weak perspective model 10.6 Example of camera models 10.7 Discussion 10.8 References
11 Appendix 3: Least squares analysis
11.1 The least squares criterion 11.2 Curve fitting by least squares
Introduction Data Covariance Covariance matrix Data transformation Inverse transformation Eigenproblem Solving the eigenproblem PCA method summary Example References