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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,…mehr

Produktbeschreibung
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.
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.
  • Produktdetails
  • Verlag: Wiley John + Sons / Wiley, John, & Sons, Inc
  • 1. Auflage
  • Seitenzahl: 344
  • Erscheinungstermin: Januar 2011
  • Englisch
  • Abmessung: 244mm x 169mm x 20mm
  • Gewicht: 538g
  • ISBN-13: 9780470844731
  • ISBN-10: 0470844736
  • Artikelnr.: 14852072
Inhaltsangabe
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.