Feature Extraction and Image Processing, 2 nd Edition. Contents. Preface

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Lorena Lindsey
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1 , 2 nd Edition Preface ix 1 Introduction Overview Human and Computer Vision The Human Vision System The Eye The Neural System Processing Computer Vision Systems Cameras Computer Interfaces Processing an Image Mathematical Systems Mathematical Tools Hello Mathcad, Hello Images! Hello Matlab! Associated Literature Journals and Magazines Textbooks The Web Conclusions Chapter 1 References 28 2 Images, Sampling and Frequency Domain Processing Overview Image Formation The Fourier Transform The Sampling Criterion The Discrete Fourier Transform (DFT) One Dimensional Transform Two Dimensional Transform Other Properties of the Fourier Transform Shift Invariance Rotation Frequency Scaling Superposition (Linearity) Transforms other than Fourier Discrete Cosine Transform Discrete Hartley Transform Introductory Wavelets; The Gabor Wavelet Other Transforms Applications using Frequency Domain Properties Further Reading 63 v

2 2.10 Chapter 2 References 64 3 Basic Image Processing Operations Overview Histograms Point Operators Basic Point Operations Histogram Normalisation Histogram Equalisation Thresholding Group Operations Template Convolution Averaging Operator On Different Template Size Gaussian Averaging Operator Other Statistical Operators More on Averaging Median Filter Mode Filter Anisotropic Diffusion Force Field Transform Comparison of Statistical Operators Mathematical Morphology Morphological Operators Grey Level Morphology Grey Level Erosion and Dilation Minkowski Operators Further Reading Chapter 3 References Low-Level Feature Extraction (including Edge Detection) Overview First Order Edge Detection Operators Basic Operators Analysis of the Basic Operators Prewitt Edge Detection Operator Sobel Edge Detection Operator The Canny Edge Detector Second Order Edge Detection Operators Motivation Basic Operators: The Laplacian The Marr-Hildreth Operator Other Edge Detection Operators Comparison of Edge Detection Operators Further Reading on Edge Detection Phase Congruency Localised Feature Extraction Detecting Image Curvature (Corner Extraction) Definition of Curvature 150 vi

3 Computing Differences in Edge Direction Measuring Curvature by Changes in Intensity Moravec and Harris Detectors Further Reading on Curvature Modern Approaches; Region/Patch Analysis Scale Invariant Feature Transform Saliency Other Techniques and Performance Issues Describing Image Motion Area-based approach Differential approach Further Reading on Optical Flow Conclusions Chapter 4 References Feature Extraction by Shape Matching Overview Thresholding and Subtraction Template Matching Definition Fourier Transform Implementation Discussion of Template Matching Hough Transform (HT) Overview Lines HT for Circles HT for Ellipses Parameter Space Decomposition Parameter space reduction for lines Parameter space reduction for circles Parameter space reduction for ellipses Generalised Hough Transform (GHT) Formal Definition of the GHT Polar definition The GHT Technique Invariant GHT Other Extensions to the HT Further Reading Chapter 5 References Flexible Shape Extraction (Snakes and Other Techniques) Overview Deformable Templates Active Contours (Snakes) Basics The Greedy Algorithm for Snakes Complete (Kass) Snake Implementation Other Snake Approaches Further Snake Developments 245 vii

4 6.3.6 Recent Developments in Contour Models Shape Skeletonisation Distance Transforms Symmetry Flexible Shape Models Active Shape and Active Appearance Further Reading Chapter 6 References Object Description Overview Boundary Descriptions Boundary and Region Chain Codes Fourier Descriptors Basis of Fourier Descriptors Fourier Expansion Shift invariance Discrete computation Cumulative Angular Function Elliptic Fourier Descriptors Invariance Region Descriptors Basic Region Descriptors Moments Basic Properties Invariant Moments Zernike Moments Other Moments Further Reading Chapter 7 References Introduction to Texture Description, Segmentation and Classification Overview What is Texture? Texture Description Performance Requirements Structural Approaches Statistical Approaches Combination Approaches Classification The k-nearest Neighbour Rule Other Classification Approaches Segmentation Further Reading Chapter 8 References Appendix 1 Example Worksheets Example Mathcad Worksheet for Chapter viii

5 9.2 Example Matlab Worksheet for Chapter Appendix 2 Camera Geometry Fundamentals Image Geometry Perspective Camera Perspective Camera Homogeneous co-ordinates and Projective Geometry Representation of a Line and Duality Ideal Points Transformations in the Projective Space Perspective Camera Model Parameters of the Perspective Camera Model Affine Camera Affine Camera Model Affine Camera Model and the Perspective Projection Parameters of the Affine Camera Model Weak Perspective Model Example of Camera Models Discussion Appendix 2 References Appendix 3 Least Squares Analysis The Least Squares Criterion Appendix 2.2 Curve Fitting by Least Squares Appendix 4: Principal Components Analysis Principal Components Analysis (PCA) Data Covariance Covariance Matrix Data Transformation Inverse Transformation Eigenproblem Solving the Eigenproblem PCA Method Summary Example Appendix 4 References 377 Index 378 ix

CHAPTER 1 Introduction 1. CHAPTER 2 Images, Sampling and Frequency Domain Processing 37

CHAPTER 1 Introduction 1. CHAPTER 2 Images, Sampling and Frequency Domain Processing 37 Extended Contents List Preface... xi About the authors... xvii CHAPTER 1 Introduction 1 1.1 Overview... 1 1.2 Human and Computer Vision... 2 1.3 The Human Vision System... 4 1.3.1 The Eye... 5 1.3.2 The