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

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1 Extended Contents List Preface... xi About the authors... xvii CHAPTER 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 Matlab, Hello Images! Hello Mathcad! Associated Literature Journals, Magazines and Conferences Textbooks The Web Conclusions Chapter 1 References CHAPTER 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 Gabor Wavelet Haar Wavelet i

2 2.7.4 Other Transforms Applications using Frequency Domain Properties Further Reading Chapter 2 References CHAPTER 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 More on Averaging Other Statistical Operators 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 CHAPTER 4 Low-Level Feature Extraction (including Edge Detection) Overview Edge Detection 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 ii

3 4.2.5 Further Reading on Edge Detection Phase Congruency Localised Feature Extraction Detecting Image Curvature (Corner Extraction) 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 Scale Invariant Feature Transform (SIFT) Speeded Up Robust Features (SURF) Saliency Other Techniques and Performance Issues Describing Image Motion Area-based approach Differential approach Further Reading on Optical Flow Further Reading Chapter 4 References CHAPTER 5 High-Level Feature Extraction: Fixed Shape Matching Overview Thresholding and Subtraction Template Matching Definition Fourier Transform Implementation Discussion of Template Matching Feature Extraction by Low Level Features Appearance-Based Approaches Object Detection by Templates Object Detection by Combinations of Parts Distribution-Based Descriptors Description by Interest Points Characterising Object Appearance and Shape 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 iii

4 Invariant GHT Other Extensions to the HT Further Reading Chapter 5 References CHAPTER 6 High-Level Feature Extraction: Deformable Shape Analysis Overview Deformable Shape Analysis Deformable Templates Parts-based Shape Analysis Active Contours (Snakes) Basics The Greedy Algorithm for Snakes Complete (Kass) Snake Implementation Other Snake Approaches Further Snake Developments Geometric Active Contours (Level-Set Based Approaches) Shape Skeletonisation Distance Transforms Symmetry Flexible Shape Models Active Shape and Active Appearance Further Reading Chapter 6 References CHAPTER 7 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 iv

5 CHAPTER 8 Intro. to Texture Description, Segmentation and Classification Overview What is Texture? Texture Description Performance Requirements Structural Approaches Statistical Approaches Combination Approaches Local Binary Patterns Other Approaches Classification Distance Measures The k-nearest Neighbour Rule Other Classification Approaches Segmentation Further Reading Chapter 8 References CHAPTER 9 Moving Object Detection and Description Overview Moving Object Detection Basic Approaches Detection by Subtracting the Background Improving Quality by Morphology Modelling and Adapting to the (Static) Background Background Segmentation by Thresholding Problems and Advances Tracking Moving Features Tracking Moving Objects Tracking by Local Search Problems in Tracking Approaches to Tracking MeanShift and Camshift Kernel-Based Density Estimation MeanShift Tracking Camshift Technique Recent Approaches Moving Feature Extraction and Description Moving (Biological) Shape Analysis Detecting Moving Shapes by Shape Matching in Image Sequences Moving Shape Description Further Reading Chapter 9 References CHAPTER 10 Appendix 1: Camera Geometry Fundamentals Image Geometry Perspective Camera Perspective Camera Model... v

6 Homogeneous co-ordinates and Projective Geometry Representation of a Line and Duality Ideal Points Transformations in the Projective Space Perspective Camera Model Analysis 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 1 References... CHAPTER 11 Appendix 2: Least Squares Analysis The Least Squares Criterion Curve Fitting by Least Squares... CHAPTER 12 Appendix 3: 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... CHAPTER 13 Appendix 4: Colour Images Colour Images 13.2 Tristimulus Theory 13.3 Colour Models The Colorimetric Equation Luminosity Function Perception based Colour Models: The CIE RGB and CIE XYZ CIE RGB Colour Model: Wright-Guild Data CIE RGB Colour Matching Functions CIE RGB Chromaticity Diagram and Chromaticity Coordinates CIE XYZ Colour Model CIE XYZ Colour Matching Functions XYZ Chromaticity Diagram Uniform Colour Spaces: CIE LUV and CIE LAB... vi

7 Additive and Subtractive Colour Models: RGB and CMY RGB and CMY Transformation between RGB Colour Models Transformation between RGB and CMY Colour Models Luminance and Chrominance Colour Models: YUV, YIQ and YCbCr Luminance and Gamma Correction Chrominance Transformations between YUV, YIQ and RGB Colour Models Colour Model for Component Video: YPbPr Colour Model for Digital Video: YCbCr Perceptual Colour Models: HSV and HLS The Hexagonal Model: HSV The Triangular Model: HSI More Colour Models References... Index vii

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

Feature Extraction and Image Processing, 2 nd Edition. Contents. Preface , 2 nd Edition Preface ix 1 Introduction 1 1.1 Overview 1 1.2 Human and Computer Vision 1 1.3 The Human Vision System 3 1.3.1 The Eye 4 1.3.2 The Neural System 7 1.3.3 Processing 7 1.4 Computer Vision