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Fundamentals of Digital Image Processing Share ITS
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Fundamentals of Digital,Image Processing,A Practical Approach. with Examples in Matlab,Chris Solomon, School of Physical Sciences University of Kent Canterbury UK. Toby Breckon, School of Engineering Cranfield University Bedfordshire UK. Fundamentals of Digital,Image Processing,Fundamentals of Digital. Image Processing,A Practical Approach,with Examples in Matlab.
Chris Solomon, School of Physical Sciences University of Kent Canterbury UK. Toby Breckon, School of Engineering Cranfield University Bedfordshire UK. This edition first published 2011 2011 by John Wiley Sons Ltd. Wiley Blackwell is an imprint of John Wiley Sons formed by the merger of Wiley s global Scientific. Technical and Medical business with Blackwell Publishing. Registered office John Wiley Sons Ltd The Atrium Southern Gate Chichester. West Sussex PO19 8SQ UK,Editorial Offices,9600 Garsington Road Oxford OX4 2DQ UK. 111 River Street Hoboken NJ 07030 5774 USA, For details of our global editorial offices for customer services and for information about how to. apply for permission to reuse the copyright material in this book please see our website at. www wiley com wiley blackwell, The right of the author to be identified as the author of this work has been asserted in accordance with.
the Copyright Designs and Patents Act 1988, All rights reserved No part of this publication may be reproduced stored in a retrieval system or. transmitted in any form or by any means electronic mechanical photocopying recording or otherwise. except as permitted by the UK Copyright Designs and Patents Act 1988 without the prior permission. of the publisher, Wiley also publishes its books in a variety of electronic formats Some content that appears in. print may not be available in electronic books, Designations used by companies to distinguish their products are often claimed as trademarks All brand. names and product names used in this book are trade names service marks trademarks or registered. trademarks of their respective owners The publisher is not associated with any product or vendor mentioned. in this book This publication is designed to provide accurate and authoritative information in regard to. the subject matter covered It is sold on the understanding that the publisher is not engaged in rendering. professional services If professional advice or other expert assistance is required the services of a. competent professional should be sought, MATLAB is a trademark of The MathWorks Inc and is used with permission The MathWorks does not warrant. the accuracy of the text or exercises in this book This book s use or discussion of MATLAB software or related. products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical. approach or particular use of the MATLAB software, Library of Congress Cataloguing in Publication Data.
Solomon Chris and Breckon Toby, Fundamentals of digital image processing a practical approach with examples in Matlab Chris Solomon and. Toby Breckon,Includes index, Summary Fundamentals of Digital Image Processing is an introductory text on the science of image processing. and 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 Provided by publisher, ISBN 978 0 470 84472 4 hardback ISBN 978 0 470 84473 1 pbk. 1 Image processing Digital techniques 2 Matlab I Breckon Toby II Title. TA1637 S65154 2010,621 36 7 dc22,2010025730, This book is published in the following electronic formats eBook 9780470689783 Wiley Online Library. 9780470689776, A catalogue record for this book is available from the British Library.
Set in 10 12 5 pt Minion by Thomson Digital Noida India. Preface xi,Using the book website xv,1 Representation 1. 1 1 What is an image 1,1 1 1 Image layout 1,1 1 2 Image colour 2. 1 2 Resolution and quantization 3,1 2 1 Bit plane splicing 4. 1 3 Image formats 5,1 3 1 Image data types 6,1 3 2 Image compression 7. 1 4 Colour spaces 9,1 4 1 RGB 10,1 4 1 1 RGB to grey scale image conversion 11.
1 4 2 Perceptual colour space 12,1 5 Images in Matlab 14. 1 5 1 Reading writing and querying images 14,1 5 2 Basic display of images 15. 1 5 3 Accessing pixel values 16,1 5 4 Converting image types 17. Exercises 18,2 Formation 21,2 1 How is an image formed 21. 2 2 The mathematics of image formation 22,2 2 1 Introduction 22.
2 2 2 Linear imaging systems 23,2 2 3 Linear superposition integral 24. 2 2 4 The Dirac delta or impulse function 25,2 2 5 The point spread function 28. vi CONTENTS, 2 2 6 Linear shift invariant systems and the convolution. integral 29,2 2 7 Convolution its importance and meaning 30. 2 2 8 Multiple convolution N imaging elements,in a linear shift invariant system 34.
2 2 9 Digital convolution 34,2 3 The engineering of image formation 37. 2 3 1 The camera 38,2 3 2 The digitization process 40. 2 3 2 1 Quantization 40,2 3 2 2 Digitization hardware 42. 2 3 2 3 Resolution versus performance 43,2 3 3 Noise 44. Exercises 46,3 Pixels 49,3 1 What is a pixel 49,3 2 Operations upon pixels 50.
3 2 1 Arithmetic operations on images 51,3 2 1 1 Image addition and subtraction 51. 3 2 1 2 Multiplication and division 53,3 2 2 Logical operations on images 54. 3 2 3 Thresholding 55,3 3 Point based operations on images 57. 3 3 1 Logarithmic transform 57,3 3 2 Exponential transform 59. 3 3 3 Power law gamma transform 61,3 3 3 1 Application gamma correction 62.
3 4 Pixel distributions histograms 63,3 4 1 Histograms for threshold selection 65. 3 4 2 Adaptive thresholding 66,3 4 3 Contrast stretching 67. 3 4 4 Histogram equalization 69,3 4 4 1 Histogram equalization theory 69. 3 4 4 2 Histogram equalization theory discrete case 70. 3 4 4 3 Histogram equalization in practice 71,3 4 5 Histogram matching 73. 3 4 5 1 Histogram matching theory 73, 3 4 5 2 Histogram matching theory discrete case 74.
3 4 5 3 Histogram matching in practice 75,3 4 6 Adaptive histogram equalization 76. 3 4 7 Histogram operations on colour images 79,Exercises 81. CONTENTS vii,4 Enhancement 85,4 1 Why perform enhancement 85. 4 1 1 Enhancement via image filtering 85,4 2 Pixel neighbourhoods 86. 4 3 Filter kernels and the mechanics of linear filtering 87. 4 3 1 Nonlinear spatial filtering 90,4 4 Filtering for noise removal 90.
4 4 1 Mean filtering 91,4 4 2 Median filtering 92,4 4 3 Rank filtering 94. 4 4 4 Gaussian filtering 95,4 5 Filtering for edge detection 97. 4 5 1 Derivative filters for discontinuities 97,4 5 2 First order edge detection 99. 4 5 2 1 Linearly separable filtering 101,4 5 3 Second order edge detection 102. 4 5 3 1 Laplacian edge detection 102,4 5 3 2 Laplacian of Gaussian 103.
4 5 3 3 Zero crossing detector 104,4 6 Edge enhancement 105. 4 6 1 Laplacian edge sharpening 105,4 6 2 The unsharp mask filter 107. Exercises 109, 5 Fourier transforms and frequency domain processing 113. 5 1 Frequency space a friendly introduction 113,5 2 Frequency space the fundamental idea 114. 5 2 1 The Fourier series 115,5 3 Calculation of the Fourier spectrum 118.
5 4 Complex Fourier series 118,5 5 The 1 D Fourier transform 119. 5 6 The inverse Fourier transform and reciprocity 121. 5 7 The 2 D Fourier transform 123, 5 8 Understanding the Fourier transform frequency space filtering 126. 5 9 Linear systems and Fourier transforms 129,5 10 The convolution theorem 129. 5 11 The optical transfer function 131, 5 12 Digital Fourier transforms the discrete fast Fourier transform 134. 5 13 Sampled data the discrete Fourier transform 135. 5 14 The centred discrete Fourier transform 136,6 Image restoration 141.
6 1 Imaging models 141, 6 2 Nature of the point spread function and noise 142. viii CONTENTS,6 3 Restoration by the inverse Fourier filter 143. 6 4 The Wiener Helstrom Filter 146,6 5 Origin of the Wiener Helstrom filter 147. 6 6 Acceptable solutions to the imaging equation 151. 6 7 Constrained deconvolution 151, 6 8 Estimating an unknown point spread function or optical transfer. function 154,6 9 Blind deconvolution 156, 6 10 Iterative deconvolution and the Lucy Richardson algorithm 158.
6 11 Matrix formulation of image restoration 161,6 12 The standard least squares solution 162. 6 13 Constrained least squares restoration 163, 6 14 Stochastic input distributions and Bayesian estimators 165. 6 15 The generalized Gauss Markov estimator 165,7 Geometry 169. 7 1 The description of shape 169,7 2 Shape preserving transformations 170. 7 3 Shape transformation and homogeneous coordinates 171. 7 4 The general 2 D affine transformation 173, 7 5 Affine transformation in homogeneous coordinates 174.
7 6 The Procrustes transformation 175,7 7 Procrustes alignment 176. 7 8 The projective transform 180,7 9 Nonlinear transformations 184. 7 10 Warping the spatial transformation of an image 186. 7 11 Overdetermined spatial transformations 189,7 12 The piecewise warp 191. 7 13 The piecewise affine warp 191,7 14 Warping forward and reverse mapping 194. 8 Morphological processing 197,8 1 Introduction 197.
8 2 Binary images foreground background and connectedness 197. 8 3 Structuring elements and neighbourhoods 198,8 4 Dilation and erosion 200. 8 5 Dilation erosion and structuring elements within Matlab 201. 8 6 Structuring element decomposition and Matlab 202. 8 7 Effects and uses of erosion and dilation 204, 8 7 1 Application of erosion to particle sizing 207. 8 8 Morphological opening and closing 209,8 8 1 The rolling ball analogy 210. 8 9 Boundary extraction 212,8 10 Extracting connected components 213. CONTENTS ix,8 11 Region filling 215,8 12 The hit or miss transformation 216.
8 12 1 Generalization of hit or miss 219, 8 13 Relaxing constraints in hit or miss don t care pixels 220. 8 13 1 Morphological thinning 222,8 14 Skeletonization 222. 8 15 Opening by reconstruction 224,8 16 Grey scale erosion and dilation 227. 8 17 Grey scale structuring elements general case 227. 8 18 Grey scale erosion and dilation with flat structuring elements 228. 8 19 Grey scale opening and closing 229,8 20 The top hat transformation 230. 8 21 Summary 231,Exercises 233,9 Features 235,9 1 Landmarks and shape vectors 235.
9 2 Single parameter shape descriptors 237, 9 3 Signatures and the radial Fourier expansion 239. 9 4 Statistical moments as region descriptors 243, 9 5 Texture features based on statistical measures 246. 9 6 Principal component analysis 247, 9 7 Principal component analysis an illustrative example 247. 9 8 Theory of principal component analysis version 1 250. 9 9 Theory of principal component analysis version 2 251. 9 10 Principal axes and principal components 253, 9 11 Summary of properties of principal component analysis 253. 9 12 Dimensionality reduction the purpose of principal. component analysis 256, 9 13 Principal components analysis on an ensemble of digital images 257.
9 14 Representation of out of sample examples using principal. component analysis 257, 9 15 Key example eigenfaces and the human face 259. 10 Image Segmentation 263,10 1 Image segmentation 263. 10 2 Use of image properties and features in segmentation 263. 10 3 Intensity thresholding 265,10 3 1 Problems with global thresholding 266. 10 4 Region growing and region splitting 267,10 5 Split and merge algorithm 267. 10 6 The challenge of edge detection 270, 10 7 The Laplacian of Gaussian and difference of Gaussians filters 270.
10 8 The Canny edge detector 271,x CONTENTS,10 9 Interest operators 274. 10 10 Watershed segmentation 279,10 11 Segmentation functions 280. 10 12 Image segmentation with Markov random fields 286. 10 12 1 Parameter estimation 288,10 12 2 Neighbourhood weighting parameter un 289. 10 12 3 Minimizing U x y the iterated conditional,modes algorithm 290. 11 Classification 291,11 1 The purpose of automated classification 291.
11 2 Supervised and unsupervised classification 292. 11 3 Classification a simple example 292,11 4 Design of classification systems 294. 11 5 Simple classifiers prototypes and minimum distance. criteria 296,11 6 Linear discriminant functions 297. 11 7 Linear discriminant functions in N dimensions 301. 11 8 Extension of the minimum distance classifier and the. Mahalanobis distance 302,11 9 Bayesian classification definitions 303. 11 10 The Bayes decision rule 304,11 11 The multivariate normal density 306. 11 12 Bayesian classifiers for multivariate normal distributions 307. 11 12 1 The Fisher linear discriminant 310,11 12 2 Risk and cost functions 311.
11 13 Ensemble classifiers 312, 11 13 1 Combining weak classifiers the AdaBoost method 313. 11 14 Unsupervised learning k means clustering 313. Further reading 317,Scope of this book, This is an introductory text on the science and art of image processing The book also. employs the Matlab programming language and toolboxes to illuminate and consolidate. some of the elementary but key concepts in modern image processing and pattern. recognition, The authors are firm believers in the old adage Hear and forget See and remember. Do and know For most of us it is through good examples and gently guided experimenta. tion that we really learn Accordingly the book has a large number of carefully chosen. examples graded exercises and computer experiments designed to help the reader get a real. grasp of the material All the program code m files used in the book corresponding to the. examples and exercises are made available to the reader course instructor and may be. downloaded from the book s dedicated web site www fundipbook com. Who is this book for, For undergraduate and graduate students in the technical disciplines for technical. professionals seeking a direct introduction to the field of image processing and for. instructors looking to provide a hands on structured course This book intentionally. starts with simple material but we also hope that relative experts will nonetheless find some. interesting and useful material in the latter parts. What then are the specific aims of this book Two of the principal aims are. To introduce the reader to some of the key concepts and techniques of modern image. processing, To provide a framework within which these concepts and techniques can be understood.
by a series of examples exercises and computer experiments. xii PREFACE, These are perhaps aims which one might reasonably expect from any book on a technical. subject However we have one further aim namely to provide the reader with the fastest. most direct route to acquiring a real hands on understanding of image processing We hope. this book will give you a real fast start in the field. Assumptions, We make no assumptions about the reader s mathematical background beyond that. expected at the undergraduate level in the technical sciences ie reasonable competence. in calculus matrix algebra and basic statistics,Why write this book. There are already a number of excellent and comprehensive texts on image processing and. pattern recognition and we refer the interested reader to a number in the appendices of this. book There are also some exhaustive and well written books on the Matlab language What. the authors felt was lacking was an image processing book which combines a simple exposition. of principles with a means to quickly test verify and experiment with them in an instructive and. interactive way, In our experience formed over a number of years Matlab and the associated image. processing toolbox are extremely well suited to help achieve this aim It is simple but. powerful and its key feature in this context is that it enables one to concentrate on the image. processing concepts and techniques i e the real business at hand while keeping concerns. about programming syntax and data management to a minimum. What is Matlab, Matlab is a programming language with an associated set of specialist software toolboxes.
It is an industry standard in scientific computing and used worldwide in the scientific. technical industrial and educational sectors Matlab is a commercial product and. information on licences and their cost can be obtained direct by enquiry at the. web site www mathworks com Many Universities all over the world provide site licenses. for their students, What knowledge of Matlab is required for this book. Matlab is very much part of this book and we use it extensively to demonstrate how. certain processing tasks and approaches can be quickly implemented and tried out in. practice Throughout the book we offer comments on the Matlab language and the best. way to achieve certain image processing tasks in that language Thus the learning of. concepts in image processing and their implementation within Matlab go hand in hand.

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