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Book SynopsisFollowing the success of the first edition, this thoroughly updated second edition of Image Processing: The Fundamentals will ensure that it remains the ideal text for anyone seeking an introduction to the essential concepts of image processing. New material includes image processing and colour, sine and cosine transforms, Independent Component Analysis (ICA), phase congruency and the monogenic signal and several other new topics. These updates are combined with coverage of classic topics in image processing, such as orthogonal transforms and image enhancement, making this a truly comprehensive text on the subject. Key features: Presents material at two levels of difficulty: the main text addresses the fundamental concepts and presents a broad view of image processing, whilst more advanced material is interleaved in boxes throughout the text, providing further reference for those who wish to examine each technique in depth. Contains a largTrade Review"Although many books are available in the area of image processing, this book Image Processing, the Fundamentals by Maria Petrou and Costas Petrou explains this subject in a distinctive way. In this book, the authors have attempted to capture new insights with a lot of examples in all the fundamental topics of image processing." (IAPR Newsletter, 1 January 2011) "This book is an ideal teaching resource for both undergraduate and postgraduate students. It will also be of value to researchers of various disciplines from medicine to mathematics with a professional interest in image processing." (Zentralblatt MATH, 2010) Table of ContentsPreface xxiii 1 Introduction 1 2 Image Transformations 47 2.1 Singular value decomposition 51 2.2 Haar, Walsh and Hadamard transforms 74 2.3 Discrete Fourier transform 94 2.4 The even symmetric discrete cosine transform (EDCT) 138 2.5 The odd symmetric discrete cosine transform (ODCT) 149 2.6 The even antisymmetric discrete sine transform (EDST) 157 2.7 The odd antisymmetric discrete sine transform (ODST) 167 3 Statistical Description of Images 177 3.1 Random fields 178 3.2 Karhunen-Loeve transform 201 3.3 Independent component analysis 234 4 Image Enhancement 293 4.1 Elements of linear filter theory 294 4.2 Reducing high frequency noise 311 4.3 Reducing low frequency interference 351 4.4 Histogram manipulation 367 4.5 Generic deblurring algorithms 383 5 Image Restoration 395 5.1 Homogeneous linear image restoration: inverse filtering 396 5.2 Homogeneous linear image restoration: Wiener filtering 419 5.3 Homogeneous linear image restoration: Constrained matrix inversion 436 5.4 Inhomogeneous linear image restoration: the whirl transform 468 5.5 Nonlinear image restoration: MAP estimation 490 5.6 Geometric image restoration 513 6 Image Segmentation and Edge Detection 527 6.1 Image segmentation 528 6.2 Edge detection 591 6.3 Phase congruency and the monogenic signal 625 7 Image Processing for Multispectral Images 669 7.1 Image preprocessing for multispectral images 671 7.2 The physics and psychophysics of colour vision 700 7.3 Colour image processing in practice 742 Bibliographical notes 775 References 777 Index 781

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Book SynopsisThe classic text that covers practical image processing methods and theory for image texture analysis, updated second edition The revised second edition of Image Processing: Dealing with Textures updates the classic work on texture analysis theory and methods without abandoning the foundational essentials of this landmark work. Like the first, the new edition offers an analysis of texture in digital images that are essential to a diverse range of applications such as: robotics, defense, medicine and the geo-sciences. Designed to easily locate information on specific problems, the text is structured around a series of helpful questions and answers. Updated to include the most recent developments in the field, many chapters have been completely revised including: Fractals and Multifractals, Image Statistics, Texture Repair, Local Phase Features, Dual Tree Complex Wavelet Transform, Ridgelets and Curvelets and Deep Texture Features. The book takes a Table of ContentsPreface to the Second Edition vii Preface to the First Edition viii Acknowledgements ix About the Companion Website x 1 Introduction 1 2 Binary Textures 11 2.1 Shape Grammars 13 2.2 Boolean Models 21 2.3 Mathematical Morphology 51 3 Stationary Grey Texture Images 79 3.1 Image Binarisation 81 3.2 Grey Scale Mathematical Morphology 88 3.3 Fractals and Multifractals 104 3.4 Image Statistics 174 3.5 Texture Features from the Fourier Transform 227 3.6 Markov Random Fields 263 3.7 Gibbs Distributions 301 3.8 Texture Repair 348 4 Non-stationary Grey Texture Images 371 4.1 The Uncertainty Principle and its Implications in Signal and Image Processing 371 4.2 Gabor Functions 399 4.3 Prolate Spheroidal Sequence Functions 450 4.4 Local Phase Features 503 4.5 Wavelets 518 4.6 The Dual Tree Complex Wavelet Transform 594 4.7 Ridgelets and Curvelets 621 4.8 Where Image Processing and Pattern Recognition Meet 673 4.9 Laws’ Masks and the “What Looks Like Where” Space 697 4.10 Local Binary Patterns 727 4.11 The Wigner Distribution 735 4.12 Convolutional Neural Networks for Textures Feature Extraction 754 Bibliographical Notes 793 References 795 Index 801

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