デジタル画像処理の基礎:Matlabの例による実践的アプローチ<br>Fundamentals of Digital Image Processing : A Practical Approach with Examples in Matlab

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デジタル画像処理の基礎:Matlabの例による実践的アプローチ
Fundamentals of Digital Image Processing : A Practical Approach with Examples in Matlab

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  • 製本 Hardcover:ハードカバー版/ページ数 328 p.
  • 言語 ENG
  • 商品コード 9780470844724
  • DDC分類 621.367

Full Description


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

Preface                                            xi
Using the book website xv
1 Representation 1 (20)
1.1 What is an image? 1 (2)
1.1.1 Image layout 1 (1)
1.1.2 Image colour 2 (1)
1.2 Resolution and quantization 3 (2)
1.2.1 Bit-plane splicing 4 (1)
1.3 Image formats 5 (4)
1.3.1 Image data types 6 (1)
1.3.2 Image compression 7 (2)
1.4 Colour spaces 9 (5)
1.4.1 RGB 10 (1)
1.4.1.1 RGB to grey-scale image 11 (1)
conversion
1.4.2 Perceptual colour space 12 (2)
1.5 Images in Matlab 14 (4)
1.5.1 Reading, writing and querying 14 (1)
images
1.5.2 Basic display of images 15 (1)
1.5.3 Accessing pixel values 16 (1)
1.5.4 Converting image types 17 (1)
Exercises 18 (3)
2 Formation 21 (28)
2.1 How is an image formed? 21 (1)
2.2 The mathematics of image formation 22 (15)
2.2.1 Introduction 22 (1)
2.2.2 Linear imaging systems 23 (1)
2.2.3 Linear superposition integral 24 (1)
2.2.4 The Dirac delta or impulse 25 (3)
function
2.2.5 The point-spread function 28 (1)
2.2.6 Linear shift-invariant systems 29 (1)
and the convolution integral
2.2.7 Convolution: its importance and 30 (4)
meaning
2.2.8 Multiple convolution: N imaging 34 (1)
elements in a linear shift-invariant
system
2.2.9 Digital convolution 34 (3)
2.3 The engineering of image formation 37 (9)
2.3.1 The camera 38 (2)
2.3.2 The digitization process 40 (1)
2.3.2.1 Quantization 40 (2)
2.3.2.2 Digitization hardware 42 (1)
2.3.2.3 Resolution versus performance 43 (1)
2.3.3 Noise 44 (2)
Exercises 46 (3)
3 Pixels 49 (36)
3.1 What is a pixel? 49 (1)
3.2 Operations upon pixels 50 (7)
3.2.1 Arithmetic operations on images 51 (1)
3.2.1.1 Image addition and subtraction 51 (2)
3.2.1.2 Multiplication and division 53 (1)
3.2.2 Logical operations on images 54 (1)
3.2.3 Thresholding 55 (2)
3.3 Point-based operations on images 57 (6)
3.3.1 Logarithmic transform 57 (2)
3.3.2 Exponential transform 59 (2)
3.3.3 Power-law (gamma) transform 61 (1)
3.3.3.1 Application: gamma correction 62 (1)
3.4 Pixel distributions: histograms 63 (18)
3.4.1 Histograms for threshold selection 65 (1)
3.4.2 Adaptive thresholding 66 (1)
3.4.3 Contrast stretching 67 (2)
3.4.4 Histogram equalization 69 (1)
3.4.4.1 Histogram equalization theory 69 (1)
3.4.4.2 Histogram equalization theory: 70 (1)
discrete case
3.4.4.3 Histogram equalization in 71 (2)
practice
3.4.5 Histogram matching 73 (1)
3.4.5.1 Histogram-matching theory 73 (1)
3.4.5.2 Histogram-matching theory: 74 (1)
discrete case
3.4.5.3 Histogram matching in practice 75 (1)
3.4.6 Adaptive histogram equalization 76 (3)
3.4.7 Histogram operations on colour 79 (2)
images
Exercises 81 (4)
4 Enhancement 85 (28)
4.1 Why perform enhancement? 85 (1)
4.1.1 Enhancement via image filtering 85 (1)
4.2 Pixel neighbourhoods 86 (1)
4.3 FiLter kernels and the mechanics of 87 (3)
linear filtering
4.3.1 Nonlinear spatial filtering 90 (1)
4.4 Filtering for noise removal 90 (7)
4.4.1 Mean filtering 91 (1)
4.4.2 Median filtering 92 (2)
4.4.3 Rank filtering 94 (1)
4.4.4 Gaussian filtering 95 (2)
4.5 Filtering for edge detection 97 (8)
4.5.1 Derivative filters for 97 (2)
discontinuities
4.5.2 First-order edge detection 99 (2)
4.5.2.1 Linearly separable filtering 101(1)
4.5.3 Second-order edge detection 102(1)
4.5.3.1 Laplacian edge detection 102(1)
4.5.3.2 Laplacian of Gaussian 103(1)
4.5.3.3 Zero-crossing detector 104(1)
4.6 Edge enhancement 105(4)
4.6.1 Laplacian edge sharpening 105(2)
4.6.2 The unsharp mask filter 107(2)
Exercises 109(4)
5 Fourier transforms and frequency-domain 113(28)
processing
5.1 Frequency space: a friendly 113(1)
introduction
5.2 Frequency space: the fundamental idea 114(4)
5.2.1 The Fourier series 115(3)
5.3 Calculation of the Fourier spectrum 118(1)
5.4 CompLex Fourier series 118(1)
5.5 The 1-D Fourier transform 119(2)
5.6 The inverse Fourier transform and 121(2)
reciprocity
5.7 The 2-D Fourier transform 123(3)
5.8 Understanding the Fourier transform: 126(3)
frequency-space filtering
5.9 Linear systems and Fourier transforms 129(1)
5.10 The convolution theorem 129(2)
5.11 The optical transfer function 131(3)
5.12 Digital Fourier transforms: the 134(1)
discrete fast Fourier transform
5.13 Sampled data: the discrete Fourier 135(1)
transform
5.14 The centred discrete Fourier 136(5)
transform
6 Image restoration 141(28)
6.1 Imaging models 141(1)
6.2 Nature of the point-spread function 142(1)
and noise
6.3 Restoration by the inverse Fourier 143(3)
filter
6.4 The Wiener-Helstrom Filter 146(1)
6.5 Origin of the Wiener-Helstrom filter 147(4)
6.6 Acceptable solutions to the imaging 151(1)
equation
6.7 Constrained deconvolution 151(3)
6.8 Estimating an unknown point-spread 154(2)
function or optical transfer function
6.9 Blind deconvolution 156(2)
6.10 Iterative deconvolution and the 158(3)
Lucy-Richardson algorithm
6.11 Matrix formulation of image 161(1)
restoration
6.12 The standard least-squares solution 162(1)
6.13 Constrained least-squares restoration 163(2)
6.14 Stochastic input distributions and 165(1)
Bayesian estimators
6.15 The generalized Gauss-Markov 165(4)
estimator
7 Geometry 169(28)
7.1 The description of shape 169(1)
7.2 Shape-preserving transformations 170(1)
7.3 Shape transformation and homogeneous 171(2)
coordinates
7.4 The general 2-D affine transformation 173(1)
7.5 Affine transformation in homogeneous 174(1)
coordinates
7.6 The Procrustes transformation 175(1)
7.7 Procrustes alignment 176(4)
7.8 The projective transform 180(4)
7.9 Nonlinear transformations 184(2)
7.10 Warping: the spatial transformation 186(3)
of an image
7.11 Overdetermined spatial 189(2)
transformations
7.12 The piecewise warp 191(1)
7.13 The piecewise affine warp 191(3)
7.14 Warping: forward and reverse mapping 194(3)
8 Morphological processing 197(38)
8.1 Introduction 197(1)
8.2 Binary images: foreground, background 197(1)
and connectedness
8.3 Structuring elements and 198(2)
neighbourhoods
8.4 Dilation and erosion 200(1)
8.5 Dilation, erosion and structuring 201(1)
elements within Matlab
8.6 Structuring element decomposition and 202(2)
Matlab
8.7 Effects and uses of erosion and 204(5)
dilation
8.7.1 Application of erosion to 207(2)
particle sizing
8.8 Morphological opening and closing 209(3)
8.8.1 The rolling-ball analogy 210(2)
8.9 Boundary extraction 212(1)
8.10 Extracting connected components 213(2)
8.11 Region filling 215(1)
8.12 The hit-or-miss transformation 216(4)
8.12.1 Generalization of hit-or-miss 219(1)
8.13 Relaxing constraints in hit-or-miss: 220(2)
`don't care' pixels
8.13.1 Morphological thinning 222(1)
8.14 Skeletonization 222(2)
8.15 Opening by reconstruction 224(3)
8.16 Grey-scale erosion and dilation 227(1)
8.17 Grey-scale structuring elements: 227(1)
general case
8.18 Grey-scale erosion and dilation with 228(1)
flat structuring elements
8.19 Grey-scale opening and closing 229(1)
8.20 The top-hat transformation 230(1)
8.21 Summary 231(2)
Exercises 233(2)
9 Features 235(28)
9.1 Landmarks and shape vectors 235(2)
9.2 Single-parameter shape descriptors 237(2)
9.3 Signatures and the radial Fourier 239(4)
expansion
9.4 Statistical moments as region 243(3)
descriptors
9.5 Texture features based on statistical 246(1)
measures
9.6 Principal component analysis 247(1)
9.7 Principal component analysis: an 247(3)
illustrative example
9.8 Theory of principal component 250(1)
analysis: version 1
9.9 Theory of principal component 251(2)
analysis: version 2
9.10 Principal axes and principal 253(1)
components
9.11 Summary of properties of principal 253(3)
component analysis
9.12 Dimensionality reduction: the 256(1)
purpose of principal component analysis
9.13 Principal components analysis on an 257(1)
ensemble of digital images
9.14 Representation of out-of-sample 257(2)
examples using principal component
analysis
9.15 Key example: eigenfaces and the 259(4)
human face
10 Image Segmentation 263(28)
10.1 Image segmentation 263(1)
10.2 Use of image properties and features 263(2)
in segmentation
10.3 Intensity thresholding 265(2)
10.3.1 Problems with global thresholding 266(1)
10.4 Region growing and region splitting 267(1)
10.5 Split-and-merge algorithm 267(3)
10.6 The challenge of edge detection 270(1)
10.7 The Laplacian of Gaussian and 270(1)
difference of Gaussians filters
10.8 The Canny edge detector 271(3)
10.9 Interest operators 274(5)
10.10 Watershed segmentation 279(1)
10.11 Segmentation functions 280(6)
10.12 Image segmentation with Markov 286(5)
random fields
10.12.1 Parameter estimation 288(1)
10.12.2 Neighbourhood weighting 289(1)
parameter θn
10.12.3 Minimizing U(x|y): the iterated 290(1)
conditional modes algorithm
11 Classification 291(26)
11.1 The purpose of automated 291(1)
classification
11.2 Supervised and unsupervised 292(1)
classification
11.3 Classification: a simple example 292(2)
11.4 Design of classification systems 294(2)
11.5 Simple classifiers: prototypes and 296(1)
minimum distance criteria
11.6 Linear discriminant functions 297(4)
11.7 Linear discriminant functions in N 301(1)
dimensions
11.8 Extension of the minimum distance 302(1)
classifier and the Mahalanobis distance
11.9 Bayesian classification: definitions 303(1)
11.10 The Bayes decision rule 304(2)
11.11 The multivariate normal density 306(1)
11.12 Bayesian classifiers for 307(5)
multivariate normal distributions
11.12.1 The Fisher linear discriminant 310(1)
11.12.2 Risk and cost functions 311(1)
11.13 Ensemble classifiers 312(1)
11.13.1 Combining weak classifiers: the 313(1)
AdaBoost method
11.14 Unsupervised learning: k-means 313(4)
clustering
Further reading 317(2)
Index 319