Image Processing

Image Processing, Analysis, and
Machine Vision
Second Edition
Milan Sonka

Vaclav Hlavac

Roger Boyle

Contents
List of algorithms

xiii

List of symbols and abbreviations

xvii

Preface

xix

Course contents

xxiii

1 Introduction

1

1.1 Summary
1.2 Exercises
1.3 References

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2 The digitized image and its properties

2.1 Basic concepts
2.1.1 Image functions
2.1.2 The Dirac distribution and convolution
2.1.3 The Fourier transform
2.1.4 Images as a stochastic process
2.1.5 Images as linear systems
2.2 Image digitization
2.2.1 Sampling
2.2.2 Quantization
2.2.3 Color images
2.3 Digital image properties
2.3.1 Metric and topological properties of digital images
2.3.2 Histograms
2.3.3 Visual perception of the image
2.3.4 Image quality
2.3.5 Noise in images
2.4 Summary
2.5 Exercises
2.6 References v 10

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vi Contents

3 Data structures for image analysis

42

4 Image pre-processing

57

3.1 Levels of image data representation
3.2 Traditional image data structures
3.2.1 Matrices
3.2.2 Chains
3.2.3 Topological data structures
3.2.4 Relational structures
3.3 Hierarchical data structures
3.3.1 Pyramids
3.3.2 Quadtrees
3.3.3 Other pyramidical structures
3.4 Summary
3.5 Exercises
3.6 References
4.1 Pixel brightness transformations
4.1.1 Position-dependent brightness correction
4.1.2 Gray-scale transformation
4.2 Geometric transformations
4.2.1 Pixel co-ordinate transformations
4.2.2 Brightness interpolation
4.3 Local pre-processing
4.3.1 Image smoothing
4.3.2 Edge detectors
4.3.3 Zero-crossings of the second derivative
4.3.4 Scale in image processing
4.3.5 Canny edge detection
4.3.6 Parametric edge models
4.3.7 Edges in multi-spectral images
4.3.8 Other local pre-processing operators
4.3.9 Adaptive neighborhood pre-processing
4.4 Image restoration
4.4.1 Degradations that are easy to restore