Chris Solomon, Toby Breckon
Fundamentals of Digital Image Processing
A Practical Approach with Examples in Matlab
Chris Solomon, Toby Breckon
Fundamentals of Digital Image Processing
A Practical Approach with Examples in Matlab
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Fundamentals of Digital Image Processing is an introductory text on the science of image processing. The stand-alone text employs the Matlab programming language to illustrate some of the elementary, key concepts in modern image processing and pattern recognition, drawing on specific examples from within science, medicine, and electronics. Here, authors Chris Solomon and Stuart Gibson provide a comprehensive introduction to some of the key concepts and techniques of modern image processing and offer a framework within which these concepts can be understood by a series of well-chosen examples, exercises and computer experiments.…mehr
Fundamentals of Digital Image Processing is an introductory text on the science of image processing. The stand-alone text employs the Matlab programming language to illustrate some of the elementary, key concepts in modern image processing and pattern recognition, drawing on specific examples from within science, medicine, and electronics. Here, authors Chris Solomon and Stuart Gibson provide a comprehensive introduction to some of the key concepts and techniques of modern image processing and offer a framework within which these concepts can be understood by a series of well-chosen examples, exercises and computer experiments.
Produktdetails
- Produktdetails
- Verlag: Wiley & Sons
- 1. Auflage
- Seitenzahl: 344
- Erscheinungstermin: 22. Februar 2011
- Englisch
- Abmessung: 244mm x 169mm x 20mm
- Gewicht: 534g
- ISBN-13: 9780470844731
- ISBN-10: 0470844736
- Artikelnr.: 14852072
- Verlag: Wiley & Sons
- 1. Auflage
- Seitenzahl: 344
- Erscheinungstermin: 22. Februar 2011
- Englisch
- Abmessung: 244mm x 169mm x 20mm
- Gewicht: 534g
- ISBN-13: 9780470844731
- ISBN-10: 0470844736
- Artikelnr.: 14852072
Dr Chris Solomon, Applied Optics Group, School of Physical Sciences, The University of Kent, Canterbury, Kent, UK. Dr Stuart Gibson, VisionMetric, Canterbury, Kent, UK.
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. 1.4.2 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. 2.3.3 Noise. Exercises. 3 Pixels. 3.1 What is a pixel? 3.2
Operations upon pixels. 3.2.1 Arithmetic operations on images. 3.2.1.2
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.
3.4 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. 3.4.5 Histogram matching. 3.4.6 Adaptive histogram
equalization. 3.4.7 Histogram operations on colour images. Exercises. 4
Enhancement. 4.1 Why perform enhancement? 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. 4.5.3 Second-order edge detection. 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 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. 7.10Warping: 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 thetan 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.
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. 1.4.2 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. 2.3.3 Noise. Exercises. 3 Pixels. 3.1 What is a pixel? 3.2
Operations upon pixels. 3.2.1 Arithmetic operations on images. 3.2.1.2
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.
3.4 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. 3.4.5 Histogram matching. 3.4.6 Adaptive histogram
equalization. 3.4.7 Histogram operations on colour images. Exercises. 4
Enhancement. 4.1 Why perform enhancement? 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. 4.5.3 Second-order edge detection. 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 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. 7.10Warping: 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 thetan 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.
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. 1.4.2 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. 2.3.3 Noise. Exercises. 3 Pixels. 3.1 What is a pixel? 3.2
Operations upon pixels. 3.2.1 Arithmetic operations on images. 3.2.1.2
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.
3.4 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. 3.4.5 Histogram matching. 3.4.6 Adaptive histogram
equalization. 3.4.7 Histogram operations on colour images. Exercises. 4
Enhancement. 4.1 Why perform enhancement? 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. 4.5.3 Second-order edge detection. 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 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. 7.10Warping: 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 thetan 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.
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. 1.4.2 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. 2.3.3 Noise. Exercises. 3 Pixels. 3.1 What is a pixel? 3.2
Operations upon pixels. 3.2.1 Arithmetic operations on images. 3.2.1.2
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.
3.4 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. 3.4.5 Histogram matching. 3.4.6 Adaptive histogram
equalization. 3.4.7 Histogram operations on colour images. Exercises. 4
Enhancement. 4.1 Why perform enhancement? 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. 4.5.3 Second-order edge detection. 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 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. 7.10Warping: 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 thetan 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.