Digital Image Processing

Preface

Contents

Part I Foundation

1 Applications and Tools

1.1 A Tool for Science and Technique

1.2 Examples of Applications

1.3 Hierarchy of Image Processing Operations

1.4 Image Processing and Computer Graphics

1.5 Cross-disciplinary Nature of Image Processing

1.6 Human and Computer Vision

1.7 Components of an Image Processing System

1.8 Exercises

1.9 Further Readings

2 Image Representation

2.1 Introduction

2.2 Spatial Representation of Digital Images

2.3 Wave Number Space and Fourier Transform

2.4 Discrete Unitary Transforms

2.5 Fast Algorithms for Unitary Transforms

2.6 Exercises

2.7 Further Readings

3 Random Variables and Fields

3.1 Introduction

3.2 Random Variables

3.3 Multiple Random Variables

3.4 Probability Density Functions

3.5 Stochastic Processes and Random Fields

3.6 Exercises

3.7 Further Readings

4 Neighborhood Operations

4.1 Basic Properties and Purpose

4.2 Linear Shift-Invariant Filters

4.3 Rank Value Filters

4.4 LSI-Filters: Further Properties

4.5 Recursive Filters

4.6 Recursive Filters

4.7 Further Readings

5 Multiscale Representation

5.1 Scale

5.2 Multigrid Representations

5.3 Scale Spaces

5.4 Exercises

5.5 Further Readings

Part II Image Formation and Preprocessing

6 Quantitative Visualization

6.1 Introduction

6.2 Radiometry, Photometry, Spectroscopy, and Color

6.3 Waves and Particles

6.4 Interactions of Radiation with Matter

6.5 Exercises

6.6 Further Readings

7 Image Formation

7.1 Introduction

7.2 World and Camera Coordinates

7.3 Ideal Imaging: Perspective Projection

7.4 Real Imaging

7.5 Radiometry of Imaging

7.6 Linear System Theory of Imaging

7.7 Homogeneous Coordinates

7.8 Exercises

7.9 Further Readings

8 3-D Imaging

8.1 Basics

8.2 Depth from Triangulation

8.3 Depth from Time-of-Flight

8.4 Depth from Phase: Interferometry

8.5 Shape from Shading

8.6 Depth from Multiple Projections: Tomography

8.7 Exercises

8.8 Further Readings

9 Digitization, Sampling, Quantization

9.1 Definition and E.ects of Digitization

9.2 Image Formation, Sampling, Windowing

9.3 Reconstruction from Samples

9.4 Multidimensional Sampling on Nonorthogonal Grits

9.5 Quantization

9.6 Exercises

9.7 Further Readings

10 Pixel Processing

10.1 Introduction

10.2 Homogeneous Point Operations

10.3 Inhomogeneous Point Operations

10.4 Geometric Transformations

10.5 Interpolation

10.6 Optimized Interpolation

10.7 Multichannel Point Operations

10.8 Exercises

10.9 Further Redings

Part III Feature Extraction

11 Averaging

11.1 Introduction

11.2 General Properties of Averaging Filters

11.3 Box Filter

11.4 Binomial Filter

11.5 Effcient Large-Scale Averaging

11.6 Nonlinear Averaging

11.7 Averaging in Multichannel Images

11.8 Exercises

11.9 Further Redings

12 Edges

12.1 Introduction

12.2 Differential Description of Signal Changes

12.3 General Properties of Edge Filters

12.4 Gradient-Based Edge Detection

12.5 Edge Detection by Zero Crossings

12.6 Optimized Edge Detection

12.7 Regularized Edge Detection

12.8 Edges in Multichannel Images

12.9 Exercises

12.10 Further Redings

13 Simple Neighborhoods

13.1 Introduction

13.2 Properties of Simple Neighborhoods

13.3 First-Order Tensor Representation

13.4 Local Wave Number and Phase

13.5 Further Tensor Representations

13.6 Exercises

13.7 Further Redings

14 Motion

14.1 Introduction

14.2 Basics

14.3 First-Order Di.erential Methods

14.4 Tensor Methods

14.5 Correlation Methods

14.6 Phase Method

14.7 Additional Methods

14.8 Exercises

14.9 Fruther Readings

15 Texture

15.1 Introduction

15.2 First-Order Statistics

15.3 Rotation and Scale Variant Texture Features

15.4 Exercises

15.5 Further Readings

Part IV Image Analysis

16 Segmentation

16.1 Introduction

16.2 Pixel-Based Segmentation

16.3 Edge-Based Segmentation

16.4 Region-Based Segmentation

16.5 Model-Based Segmentation

16.6 Exercises

16.7 Further Readings

17 Regularization and Modeling

17.1 Introduction

17.2 Continuous Modeling I: Veriational Approach

17.3 Continuous Modeling II: Diffusion

17.4 Discrete Modeling: Inverse Problems

17.5 Inverse Filtering

17.6 Further Equivalent Approaches

17.7 Exercises

17.8 Further Redings

18 Morphology

18.1 Introduction

18.2 Neighborhood Operations on Binary Images

18.3 General Properties

18.4 Composite Morphological Operators

18.5 Exercises

18.6 Furtheer Readings

19 Shape Presentation and Analysis

19.1 Introduction

19.2 Representation of Shape

19.3 Moment-Based Shape Features

19.4 Fourier Descriptors

19.5 Shape Parameters

19.6 Exercises

19.7 Further Readings

20 Classification

20.1 Introduction

20.2 Feature Space

20.3 Simple Classi.cation Techniques

20.4 Exercises

20.5 Further Readings

Part V Reference Part

A Reference Material

B Notation

Bibliography

Index