Image Processing: Dealing with Texture

The 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 two-level mathematical approach: light math is covered in the main level of the book, with harder math identified in separate boxes. This important text: Contains an update of the classic advanced text that reviews practical image processing methods and theory for image texture analysis Puts the focus exclusively on an in-depth exploration of texture Contains a companion website with exercises and algorithms Includes examples that are fully worked to enhance the learning experience Written for students and researchers of image processing, the second edition of Image Processing has been revised and updated to incorporate the foundational information on the topic and information on the latest advances.

Table of Contents:
Preface 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