Pearson-Digital Image Processing， 4th.Edition-2018.pdf

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Digital Image Processing 4th Edition [Rafael C. Gonzalez]

Cover

Digital Image Processing 4th Edition [Rafael C. Gonzalez]

Contents

Digital Image Processing

Preface

Acknowledgments

The Book Website

The DIP4E Support Packages

About the Authors

1 Introduction

1.1 What is Digital Image Processing_

1.2 The Origins of Digital Image Processing

1.3 Examples of Fields that Use Digital Image Processing

1.4 Fundamental Steps in Digital Image Processing

1.5 Components of an Image Processing System

2 Digital Image Fundamentals

2.1 Elements of Visual Perception

2.2 Light and the Electromagnetic Spectrum

2.3 Image Sensing and Acquisition

2.4 Image Sampling and Quantization

2.5 Some Basic Relationships Between Pixels

2.6 Introduction to the Basic Mathematical Tools Used in Digital Image Processing

3 Intensity Transformations and Spatial Filtering

3.1 Background

3.2 Some Basic Intensity Transformation Functions

3.3 Histogram Processing

3.4 Fundamentals of Spatial Filtering

3.5 Smoothing (Lowpass) Spatial Filters

3.6 Sharpening (Highpass) Spatial Filters

3.7 Highpass, Bandreject, and Bandpass Filters from Lowpass Filters

3.8 Combining Spatial Enhancement Methods

3.9 Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering

4 Filtering in the Frequency Domain

4.1 Background

4.2 Preliminary Concepts

4.3 Sampling and the Fourier Transform of Sampled Functions

4.4 The Discrete Fourier Transform of One Variable

4.5 Extensions to Functions of Two Variables

4.6 Some Properties of the 2-D DFT and IDFT

4.7 The Basics of Filtering in the Frequency Domain

4.8 Image Smoothing Using Lowpass Frequency Domain Filters

4.9 Image Sharpening Using Highpass Filters

4.10 Selective Filtering

4.11 The Fast Fourier Transform

5 Image Restoration and Reconstruction

5.1 A Model of the Image Degradation Restoration Process

5.2 Noise Models

5.3 Restoration in the Presence of Noise Only—Spatial Filtering

5.4 Periodic Noise Reduction Using Frequency Domain Filtering

5.5 Linear, Position-Invariant Degradations

5.6 Estimating the Degradation Function

5.7 Inverse Filtering

5.8 Minimum Mean Square Error (Wiener) Filtering

5.9 Constrained Least Squares Filtering

5.10 Geometric Mean Filter

5.11 Image Reconstruction From Projections

6 Wavelet and Other Image Transforms

6.1 Preliminaries

6.2 Matrix-Based Transforms

6.3 Correlation

6.4 Basis Functions in the Time-Frequency Plane

6.5 Basis Images

6.6 Fourier-Related Transforms

6.7 Walsh-Hadamard Transforms

6.8 Slant Transform

6.9 Haar Transform

6.10 Wavelet Transforms

7 Color Image Processing

7.1 Color Fundamentals

7.2 Color Models

7.3 Pseudocolor Image Processing

7.4 Basics of Full-Color Image Processing

7.5 Color Transformations

7.6 Color Image Smoothing and Sharpening

7.7 Using Color in Image Segmentation

7.8 Noise in Color Images

7.9 Color Image Compression

8 Image Compression and Watermarking

8.1 Fundamentals

8.2 Huffman Coding

8.3 Golomb Coding

8.4 Arithmetic Coding

8.5 LZW Coding

8.6 Run-Length Coding

8.7 Symbol-Based Coding

8.8 Bit-Plane Coding

8.9 Block Transform Coding

8.10 Predictive Coding

9 Morphological Image Processing

9.1 Preliminaries

9.2 Erosion and Dilation

9.3 Opening and Closing

9.4 The Hit-Or-Miss Transform

9.5 Some Basic Morphological Algorithms

9.6 Morphological Reconstruction

9.7 Summary of Morphological Operations on Binary Images

9.8 Grayscale Morphology

10 Image Segmentation I Edge Detection, Thresholding, and Region Detection

10.1 Fundamentals

10.2 Point, Line, and Edge Detection

10.3 Thresholding

10.4 Segmentation by Region Growing and by Region Splitting and Merging

10.5 Region Segmentation using Clustering and Superpixels

10.6 Region Segmentation Using Graph Cuts

10.7 Segmentation using Morphological Watersheds

10.8 The Use of Motion in Segmentation

11 Image Segmentation II Active Contours_ Snakes and Level Sets

11.1 Background

11.2 Image Segmentation Using Snakes

11.3 Segmentation Using Level Sets

12 Feature Extraction

12.1 Background

12.2 Boundary Preprocessing

12.3 Boundary Feature Descriptors

12.4 Region Feature Descriptors

12.5 Principal Components as Feature Descriptors

12.6 Whole-Image Features

12.7 Scale-Invariant Feature Transform (SIFT)

13 Image Pattern Classification

13.1 Background

13.2 Patterns and Pattern Classes

13.3 Pattern Classification by Prototype Matching

13.4 Optimum (Bayes) Statistical Classifiers

13.5 Neural Networks and Deep Learning

13.6 Deep Convolutional Neural Networks

13.7 Some Additional Details of Implementation

Bibiography

Digital Image Processing Rafael C. Gonzalez University of Tennessee Richard E. Woods Interapptics 330 Hudson Street, New York, NY 10013 330 Hudson Street, New York, NY 10013

Senior Vice President Courseware Portfolio Management: Marcia J. Horton Director, Portfolio Management: Engineering, Computer Science & Global Editions: Julian Partridge Portfolio Manager: Julie Bai Field Marketing Manager: Demetrius Hall Product Marketing Manager: Yvonne Vannatta Marketing Assistant: Jon Bryant Content Managing Producer, ECS and Math: Scott Disanno Content Producer: Michelle Bayman Project Manager: Rose Kernan Operations Specialist: Maura Zaldivar-Garcia Manager, Rights and Permissions: Ben Ferrini Cover Designer: Black Horse Designs Cover Photo: MRI image—Author supplied; Rose—Author supplied; Satellite image of Washington, D.C.—Courtesy of NASA; Bottles—Author supplied; Fingerprint— Courtesy of the National Institute of Standards and Technology; Moon IO of Jupiter— Courtesy of NASA Composition: Richard E. Woods Copyright © 2018, 2008 by Pearson Education, Inc. Hoboken, NJ 07030. All rights reserved. Manufactured in the United States of America. This publication is protected by copyright and permissions should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise. For information regarding permissions, request forms and the appropriate contacts within the Pearson Education Global Rights & Permissions department, please visit www.pearsoned.com/ permissions/.

Many of the designations by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed in initial caps or all caps. The author and publisher of this book have used their best efforts in preparing this book. These efforts include the development, research, and testing of theories and programs to determine their effectiveness. The author and publisher make no warranty of any kind, expressed or implied, with regard to these programs or the documentation contained in this book. The author and publisher shall not be liable in any event for incidental or consequential damages with, or arising out of, the furnishing, performance, or use of these programs. Pearson Education Ltd., London Pearson Education Singapore, Pte. Ltd Pearson Education Canada, Inc. Pearson Education Japan Pearson Education Australia PTY, Ltd Pearson Education North Asia, Ltd., Hong Kong Pearson Education de Mexico, S.A. de C.V. Pearson Education Malaysia, Pte. Ltd. Pearson Education, Inc., Hoboken MATLAB is a registered trademark of The MathWorks, Inc., 1 Apple Hill Drive, Natick, MA 01760-2098. Library of Congress Cataloging-in-Publication Data Names: Gonzalez, Rafael C., author. | Woods, Richard E. (Richard Eugene), author. Title: Digital Image Processing / Rafael C. Gonzalez, University of Tennessee, Richard E. Woods, Interapptics. Description: New York, NY : Pearson, [2018] | Includes bibliographical references and index.

Identifiers: LCCN 2017001581 | ISBN 9780133356724 Subjects: LCSH: Image processing--Digital techniques. Classification: LCC TA1632 .G66 2018 | DDC 621.36/7--dc23 LC record available at https://lccn.loc.gov/2017001581 1 16 ISBN-10: 0-13-335672-8 ISBN-13: 9780133356724 www.pearsonhighered.com

Contents Cover Title Page Copyright Dedication Preface ix Acknowledgments xiii The Book Website xiv The DIP4E Support Packages xiv About the Authors xv 1 Introduction 1 What is Digital Image Processing? 2 The Origins of Digital Image Processing 3 Examples of Fields that Use Digital Image Processing 7 Fundamental Steps in Digital Image Processing 25 Components of an Image Processing System 28 2 Digital Image Fundamentals 31 Elements of Visual Perception 32 Light and the Electromagnetic Spectrum 38 Image Sensing and Acquisition 41 Image Sampling and Quantization 47 Some Basic Relationships Between Pixels 63 Introduction to the Basic Mathematical Tools Used in Digital Image Processing 67 3 Intensity Transformations and Spatial Filtering 133 Background 134 Some Basic Intensity Transformation Functions 136

Histogram Processing 147 Fundamentals of Spatial Filtering 177 Smoothing (Lowpass) Spatial Filters 188 Sharpening (Highpass) Spatial Filters 199 Highpass, Bandreject, and Bandpass Filters from Lowpass Filters 212 Combining Spatial Enhancement Methods 216 Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering 217 4 Filtering in the Frequency Domain 249 Background 250 Preliminary Concepts 253 Sampling and the Fourier Transform of Sampled Functions 261 The Discrete Fourier Transform of One Variable 271 Extensions to Functions of Two Variables 276 Some Properties of the 2-D DFT and IDF 286 The Basics of Filtering in the Frequency Domain 306 Image Smoothing Using Lowpass Frequency Domain Filters 318 Image Sharpening Using Highpass Filters 330 Selective Filtering 342 The Fast Fourier Transform 349 5 Image Restoration and Reconstruction 365 A Model of the Image Degradation/Restoration process 366 Noise Models 366 Restoration in the Presence of Noise Only—Spatial Filtering 375 Periodic Noise Reduction Using Frequency Domain Filtering 388 Linear, Position-Invariant Degradations 396 Estimating the Degradation Function 400 Inverse Filtering 404 Minimum Mean Square Error (Wiener) Filtering 406

Constrained Least Squares Filtering 411 Geometric Mean Filter 415 Image Reconstruction from Projections 416 6 Wavelet and Other Image Transforms 451 Preliminaries 452 Matrix-based Transforms 454 Correlation 466 Basis Functions in the Time-Frequency Plane 467 Basis Images 471 Fourier-Related Transforms 472 Walsh-Hadamard Transforms 484 Slant Transform 488 Haar Transform 490 Wavelet Transforms 492 7 Color Image Processing 529 Color Fundamentals 530 Color Models 535 Pseudocolor Image Processing 550 Basics of Full-Color Image Processing 559 Color Transformations 560 Color Image Smoothing and Sharpening 572 Using Color in Image Segmentation 575 Noise in Color Images 582 Color Image Compression 585 8 Image Compression and Watermarking 595 Fundamentals 596 Huffman Coding 609 Golomb Coding 612 Arithmetic Coding 617

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