Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab

✍ Scribed by Chris Solomon, Toby Breckon

• Publisher: Wiley
• Year: 2011
• Tongue: English
• Leaves: 354
• Edition: 1
• Category: Library

✦ Synopsis

This is an introductory to intermediate level text on the science of image processing, which employs the Matlab programming language to illustrate some of the elementary, key concepts in modern image processing and pattern recognition. The approach taken is essentially practical and the book offers a framework within which the concepts can be understood by a series of well chosen examples, exercises and computer experiments, drawing on specific examples from within science, medicine and engineering.

Clearly divided into eleven distinct chapters, the book begins with a fast-start introduction to image processing  to enhance the accessibility of later topics. Subsequent chapters offer increasingly advanced discussion of topics involving more challenging concepts, with  the final chapter  looking at the application of automated image classification (with Matlab examples) .

Matlab is frequently used in the book as a tool for demonstrations, conducting experiments and for solving problems, as it is both ideally suited to this role and is widely available. Prior experience of Matlab is not required and those without access to Matlab can still benefit from the independent presentation of topics and numerous examples.

• Features a companion website www.wiley.com/go/solomon/fundamentals containing a Matlab fast-start primer, further  exercises, examples, instructor resources and accessibility to all files corresponding to the examples and exercises within the book itself.
• Includes numerous examples, graded exercises and computer experiments to support both students and instructors alike.

✦ Table of Contents

Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab
Contents
Preface
Using the book website
1 Representation
1.1 What is an image?
1.1.1 Image layout
1.1.2 Image colour
1.2 Resolution and quantization
1.2.1 Bit-plane splicing
1.3 Image formats
1.3.1 Image data types
1.3.2 Image compression
1.4 Colour spaces
1.4.1 RGB
1.4.1.1 RGB to grey-scale image conversion
1.4.2 Perceptual colour space
1.5 Images in Matlab
1.5.1 Reading, writing and querying images
1.5.2 Basic display of images
1.5.3 Accessing pixel values
1.5.4 Converting image types
Exercises
2 Formation
2.1 How is an image formed?
2.2 The mathematics of image formation
2.2.1 Introduction
2.2.2 Linear imaging systems
2.2.3 Linear superposition integral
2.2.4 The Dirac delta or impulse function
2.2.5 The point-spread function
2.2.6 Linear shift-invariant systems and the convolution integral
2.2.7 Convolution: its importance and meaning
2.2.8 Multiple convolution: N imaging elements in a linear shift-invariant system
2.2.9 Digital convolution
2.3 The engineering of image formation
2.3.1 The camera
2.3.2 The digitization process
2.3.2.1 Quantization
2.3.2.2 Digitization hardware
2.3.2.3 Resolution versus performance
2.3.3 Noise
Exercises
3 Pixels
3.1 What is a pixel?
3.2 Operations upon pixels
3.2.1 Arithmetic operations on images
3.2.1.1 Image addition and subtraction
3.2.1.2 Image multiplication and division
3.2.2 Logical operations on images
3.2.3 Thresholding
3.3 Point-based operations on images
3.3.1 Logarithmic transform
3.3.2 Exponential transform
3.3.3 Power-law (gamma) transform
3.3.3.1 Application: gamma correction
3.4 Pixel distributions: histograms
3.4.1 Histograms for threshold selection
3.4.2 Adaptive thresholding
3.4.3 Contrast stretching
3.4.4 Histogram equalization
3.4.4.1 Histogram equalization theory
3.4.4.2 Histogram equalization theory: discrete case
3.4.4.3 Histogram equalization in practice
3.4.5 Histogram matching
3.4.5.1 Histogram matching theory
3.4.5.2 Histogram matching theory: discrete case
3.4.5.3 Histogram matching in practice
3.4.6 Adaptive histogram equalization
3.4.7 Histogram operations on colour images
Exercises
4 Enhancement
4.1 Why perform enhancement?
4.1.1 Enhancement via image filtering
4.2 Pixel neighbourhoods
4.3 Filter kernels and the mechanics of linear filtering
4.3.1 Nonlinear spatial filtering
4.4 Filtering for noise removal
4.4.1 Mean filtering
4.4.2 Median filtering
4.4.3 Rank filtering
4.4.4 Gaussian filtering
4.5 Filtering for edge detection
4.5.1 Derivative filters for discontinuities
4.5.2 First-order edge detection
4.5.2.1 Linearly separable filtering
4.5.3 Second-order edge detection
4.5.3.1 Laplacian edge detection
4.5.3.2 Laplacian of Gaussian
4.5.3.3 Zero-crossing detector
4.6 Edge enhancement
4.6.1 Laplacian edge sharpening
4.6.2 The unsharp mask filter
Exercises
5 Fourier transforms and frequency-domain processing
5.1 Frequency space: a friendly introduction
5.2 Frequency space: the fundamental idea
5.2.1 The Fourier series
5.3 Calculation of the Fourier spectrum
5.4 Complex Fourier series
5.5 The 1-D Fourier transform
5.6 The inverse Fourier transform and reciprocity
5.7 The 2-D Fourier transform
5.8 Understanding the Fourier transform: frequency-space filtering
5.9 Linear systems and Fourier transforms
5.10 The convolution theorem
5.11 The optical transfer function
5.12 Digital Fourier transforms: the discrete fast Fourier transform
5.13 Sampled data: the discrete Fourier transform
5.14 The centred discrete Fourier transform
6 Image restoration
6.1 Imaging models
6.2 Nature of the point-spread function and noise
6.3 Restoration by the inverse Fourier filter
6.4 The Wiener–Helstrom filter
6.5 Origin of the Wiener–Helstrom filter
6.6 Acceptable solutions to the imaging equation
6.7 Constrained deconvolution
6.8 Estimating an unknown point-spread function or optical transfer function
6.9 Blind deconvolution
6.10 Iterative deconvolution and the Lucy–Richardson algorithm
6.11 Matrix formulation of image restoration
6.12 The standard least-squares solution
6.13 Constrained least-squares restoration
6.14 Stochastic input distributions and Bayesian estimators
6.15 The generalized Gauss–Markov estimator
7 Geometry
7.1 The description of shape
7.2 Shape-preserving transformations
7.3 Shape transformation and homogeneous coordinates
7.4 The general 2-D affine transformation
7.5 Affine transformation in homogeneous coordinates
7.6 The procrustes transformation
7.7 Procrustes alignment
7.8 The projective transform
7.9 Nonlinear transformations
7.10 Warping: the spatial transformation of an image
7.11 Overdetermined spatial transformations
7.12 The piecewise warp
7.13 The piecewise affine warp
7.14 Warping: forward and reverse mapping
8 Morphological processing
8.1 Introduction
8.2 Binary images: foreground, background and connectedness
8.3 Structuring elements and neighbourhoods
8.4 Dilation and erosion
8.5 Dilation, erosion and structuring elements within Matlab
8.6 Structuring element decomposition and Matlab
8.7 Effects and uses of erosion and dilation
8.7.1 Application of erosion to particle sizing
8.8 Morphological opening and closing
8.8.1 The rolling-ball analogy
8.9 Boundary extraction
8.10 Extracting connected components
8.11 Region filling
8.12 The hit-or-miss transformation
8.12.1 Generalization of hit-or-miss
8.13 Relaxing constraints in hit-or-miss: ‘don’t care’ pixels
8.13.1 Morphological thinning
8.14 Skeletonization
8.15 Opening by reconstruction
8.16 Grey-scale erosion and dilation
8.17 Grey-scale structuring elements: general case
8.18 Grey-scale erosion and dilation with flat structuring elements
8.19 Grey-scale opening and closing
8.20 The top-hat transformation
8.21 Summary
Exercises
9 Features
9.1 Landmarks and shape vectors
9.2 Single-parameter shape descriptors
9.3 Signatures and the radial fourier expansion
9.4 Statistical moments as region descriptors
9.5 Texture features based on statistical measures
9.6 Principal component analysis
9.7 Principal component analysis: an illustrative example
9.8 Theory of principal component analysis: version 1
9.9 Theory of principal component analysis: version 2
9.10 Principal axes and principal components
9.11 Summary of properties of principal component analysis
9.12 Dimensionality reduction: the purpose of principal component analysis
9.13 Principal components analysis on an ensemble of digital images
9.14 Representation of out-of-sample examples using principal component analysis
9.15 Key example: eigenfaces and the human face
10 Image segmentation
10.1 Image segmentation
10.2 Use of image properties and features in segmentation
10.3 Intensity thresholding
10.3.1 Problems with global thresholding
10.4 Region growing and region splitting
10.5 Split-and-merge algorithm
10.6 The challenge of edge detection
10.7 The laplacian of Gaussian and difference of Gaussians filters
10.8 The Canny edge detector
10.9 Interest operators
10.10 Watershed segmentation
10.11 Segmentation functions
10.12 Image segmentation with markov random fields
10.12.1 Parameter estimation
10.12.2 Neighbourhood weighting parameter θn
10.12.3 Minimizing U(x | y): the iterated conditional modes algorithm
11 Classification
11.1 The purpose of automated classification
11.2 Supervised and unsupervised classification
11.3 Classification: a simple example
11.4 Design of classification systems
11.5 Simple classifiers: prototypes and minimum distance criteria
11.6 Linear discriminant functions
11.7 Linear discriminant functions in N dimensions
11.8 Extension of the minimum distance classifier and the Mahalanobis distance
11.9 Bayesian classification: definitions
11.10 The Bayes decision rule
11.11 The multivariate normal density
11.12 Bayesian classifiers for multivariate normal distributions
11.12.1 The Fisher linear discriminant
11.12.2 Risk and cost functions
11.13 Ensemble classifiers
11.13.1 Combining weak classifiers: the AdaBoost method
11.14 Unsupervised learning: k-means clustering
Further reading
Index
Colour Plates