冈萨雷斯《数字图像处理第三版》详解

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Preface

When something can be read without effort,

great effort has gone into its writing.

Enrique Jardiel Poncela

This edition of Digital Image Processing is a major revision of the book. As in

the 1977 and 1987 editions by Gonzalez and Wintz, and the 1992 and 2002 edi-

tions by Gonzalez and Woods, this fifth-generation edition was prepared with

students and instructors in mind.The principal objectives of the book continue

to be to provide an introduction to basic concepts and methodologies for digi-

tal image processing, and to develop a foundation that can be used as the basis

for further study and research in this field. To achieve these objectives, we

focused again on material that we believe is fundamental and whose scope of

application is not limited to the solution of specialized problems. The mathe-

matical complexity of the book remains at a level well within the grasp of

college seniors and first-year graduate students who have introductory prepa-

ration in mathematical analysis, vectors, matrices, probability, statistics, linear

systems, and computer programming. The book Web site provides tutorials to

support readers needing a review of this background material.

One of the principal reasons this book has been the world leader in its field

for more than 30 years is the level of attention we pay to the changing educa-

tional needs of our readers.The present edition is based on the most extensive

survey we have ever conducted. The survey involved faculty, students, and in-

dependent readers of the book in 134 institutions from 32 countries.The major

findings of the survey indicated a need for:

● A more comprehensive introduction early in the book to the mathemati-

cal tools used in image processing.

● An expanded explanation of histogram processing techniques.

● Stating complex algorithms in step-by-step summaries.

● An expanded explanation of spatial correlation and convolution.

● An introduction to fuzzy set theory and its application to image processing.

● A revision of the material dealing with the frequency domain, starting

with basic principles and showing how the discrete Fourier transform fol-

lows from data sampling.

● Coverage of computed tomography (CT).

● Clarification of basic concepts in the wavelets chapter.

● A revision of the data compression chapter to include more video com-

pression techniques, updated standards, and watermarking.

● Expansion of the chapter on morphology to include morphological recon-

struction and a revision of gray-scale morphology.

xvi ■ Preface

● Expansion of the coverage on image segmentation to include more ad-

vanced edge detection techniques such as Canny’s algorithm, and a more

comprehensive treatment of image thresholding.

● An update of the chapter dealing with image representation and description.

● Streamlining the material dealing with structural object recognition.

The new and reorganized material that resulted in the present edition is our

attempt at providing a reasonable degree of balance between rigor, clarity of

presentation, and the findings of the market survey, while at the same time

keeping the length of the book at a manageable level. The major changes in

this edition of the book are as follows.

Chapter 1: A few figures were updated and part of the text was rewritten to

correspond to changes in later chapters.

Chapter 2: Approximately 50% of this chapter was revised to include new

images and clearer explanations. Major revisions include a new section on

image interpolation and a comprehensive new section summarizing the

principal mathematical tools used in the book. Instead of presenting “dry”

mathematical concepts one after the other, however, we took this opportu-

nity to bring into Chapter 2 a number of image processing applications that

were scattered throughout the book. For example, image averaging and

image subtraction were moved to this chapter to illustrate arithmetic opera-

tions.This follows a trend we began in the second edition of the book to move

as many applications as possible early in the discussion not only as illustra-

tions, but also as motivation for students. After finishing the newly organized

Chapter 2, a reader will have a basic understanding of how digital images are

manipulated and processed.This is a solid platform upon which the rest of the

book is built.

Chapter 3: Major revisions of this chapter include a detailed discussion of

spatial correlation and convolution, and their application to image filtering

using spatial masks. We also found a consistent theme in the market survey

asking for numerical examples to illustrate histogram equalization and specifi-

cation, so we added several such examples to illustrate the mechanics of these

processing tools. Coverage of fuzzy sets and their application to image pro-

cessing was also requested frequently in the survey. We included in this chap-

ter a new section on the foundation of fuzzy set theory, and its application to

intensity transformations and spatial filtering, two of the principal uses of this

theory in image processing.

Chapter 4: The topic we heard most about in comments and suggestions

during the past four years dealt with the changes we made in Chapter 4 from

the first to the second edition. Our objective in making those changes was to

simplify the presentation of the Fourier transform and the frequency domain.

Evidently, we went too far, and numerous users of the book complained that

the new material was too superficial.We corrected that problem in the present

edition.The material now begins with the Fourier transform of one continuous

variable and proceeds to derive the discrete Fourier transform starting with

basic concepts of sampling and convolution. A byproduct of the flow of this

■ Preface xvii

material is an intuitive derivation of the sampling theorem and its implica-

tions.The 1-D material is then extended to 2-D, where we give a number of ex-

amples to illustrate the effects of sampling on digital images, including aliasing

and moiré patterns. The 2-D discrete Fourier transform is then illustrated and

a number of important properties are derived and summarized. These con-

cepts are then used as the basis for filtering in the frequency domain. Finally,

we discuss implementation issues such as transform decomposition and the

derivation of a fast Fourier transform algorithm.At the end of this chapter, the

reader will have progressed from sampling of 1-D functions through a clear

derivation of the foundation of the discrete Fourier transform and some of its

most important uses in digital image processing.

Chapter 5: The major revision in this chapter was the addition of a section

dealing with image reconstruction from projections, with a focus on computed

tomography (CT). Coverage of CT starts with an intuitive example of the un-

derlying principles of image reconstruction from projections and the various

imaging modalities used in practice. We then derive the Radon transform and

the Fourier slice theorem and use them as the basis for formulating the con-

cept of filtered backprojections. Both parallel- and fan-beam reconstruction

are discussed and illustrated using several examples. Inclusion of this material

was long overdue and represents an important addition to the book.

Chapter 6: Revisions to this chapter were limited to clarifications and a few

corrections in notation. No new concepts were added.

Chapter 7: We received numerous comments regarding the fact that the

transition from previous chapters into wavelets was proving difficult for be-

ginners. Several of the foundation sections were rewritten in an effort to make

the material clearer.

Chapter 8: This chapter was rewritten completely to bring it up to date. New

coding techniques, expanded coverage of video, a revision of the section on

standards, and an introduction to image watermarking are among the major

changes. The new organization will make it easier for beginning students to

follow the material.

Chapter 9: The major changes in this chapter are the inclusion of a new sec-

tion on morphological reconstruction and a complete revision of the section

on gray-scale morphology. The inclusion of morphological reconstruction for

both binary and gray-scale images made it possible to develop more complex

and useful morphological algorithms than before.

Chapter 10: This chapter also underwent a major revision. The organization

is as before, but the new material includes greater emphasis on basic principles

as well as discussion of more advanced segmentation techniques. Edge models

are discussed and illustrated in more detail, as are properties of the gradient.

The Marr-Hildreth and Canny edge detectors are included to illustrate more

advanced edge detection techniques.The section on thresholding was rewritten

also to include Otsu’s method, an optimum thresholding technique whose pop-

ularity has increased significantly over the past few years. We introduced this

approach in favor of optimum thresholding based on the Bayes classifica-

tion rule, not only because it is easier to understand and implement, but also

xviii ■ Preface

because it is used considerably more in practice. The Bayes approach was

moved to Chapter 12, where the Bayes decision rule is discussed in more detail.

We also added a discussion on how to use edge information to improve thresh-

olding and several new adaptive thresholding examples. Except for minor clar-

ifications, the sections on morphological watersheds and the use of motion for

segmentation are as in the previous edition.

Chapter 11: The principal changes in this chapter are the inclusion of a

boundary-following algorithm, a detailed derivation of an algorithm to fit a

minimum-perimeter polygon to a digital boundary, and a new section on co-

occurrence matrices for texture description. Numerous examples in Sections

11.2 and 11.3 are new, as are all the examples in Section 11.4.

Chapter 12: Changes in this chapter include a new section on matching by

correlation and a new example on using the Bayes classifier to recognize re-

gions of interest in multispectral images. The section on structural classifica-

tion now limits discussion only to string matching.

All the revisions just mentioned resulted in over 400 new images, over 200

new line drawings and tables, and more than 80 new homework problems.

Where appropriate, complex processing procedures were summarized in the

form of step-by-step algorithm formats. The references at the end of all chap-

ters were updated also.

The book Web site, established during the launch of the second edition, has

been a success, attracting more than 20,000 visitors each month. The site was

redesigned and upgraded to correspond to the launch of this edition. For more

details on features and content, see The Book Web Site, following the

Acknowledgments.

This edition of Digital Image Processing is a reflection of how the educa-

tional needs of our readers have changed since 2002. As is usual in a project

such as this, progress in the field continues after work on the manuscript stops.

One of the reasons why this book has been so well accepted since it first ap-

peared in 1977 is its continued emphasis on fundamental concepts—an ap-

proach that, among other things, attempts to provide a measure of stability in

a rapidly-evolving body of knowledge. We have tried to follow the same prin-

ciple in preparing this edition of the book.

R. C. G.

R. E. W.

Acknowledgments

We are indebted to a number of individuals in academic circles as well as in in-

dustry and government who have contributed to this edition of the book.Their

contributions have been important in so many different ways that we find it

difficult to acknowledge them in any other way but alphabetically. In particu-

lar, we wish to extend our appreciation to our colleagues Mongi A. Abidi,

Steven L. Eddins, Yongmin Kim, Bryan Morse,Andrew Oldroyd,Ali M. Reza,

Edgardo Felipe Riveron, Jose Ruiz Shulcloper, and Cameron H. G.Wright for

their many suggestions on how to improve the presentation and/or the scope

of coverage in the book.

Numerous individuals and organizations provided us with valuable assis-

tance during the writing of this edition. Again, we list them alphabetically. We

are particularly indebted to Courtney Esposito and Naomi Fernandes at The

Mathworks for providing us with MATLAB software and support that were

important in our ability to create or clarify many of the examples and experi-

mental results included in this edition of the book.A significant percentage of

the new images used in this edition (and in some cases their history and inter-

pretation) were obtained through the efforts of individuals whose contribu-

tions are sincerely appreciated. In particular, we wish to acknowledge the

efforts of Serge Beucher, Melissa D. Binde, James Blankenship, Uwe Boos,

Ernesto Bribiesca, Michael E. Casey, Michael W. Davidson, Susan L. Forsburg,

Thomas R. Gest, Lalit Gupta, Daniel A. Hammer, Zhong He, Roger Heady,

Juan A. Herrera, John M. Hudak, Michael Hurwitz, Chris J. Johannsen, Rhon-

da Knighton, Don P. Mitchell, Ashley Mohamed, A. Morris, Curtis C. Ober,

Joseph E. Pascente, David. R. Pickens, Michael Robinson, Barrett A. Schaefer,

Michael Shaffer, Pete Sites, Sally Stowe, Craig Watson, David K. Wehe, and

Robert A. West. We also wish to acknowledge other individuals and organiza-

tions cited in the captions of numerous figures throughout the book for their

permission to use that material.

Special thanks go to Vince O’Brien, Rose Kernan, Scott Disanno, Michael

McDonald, Joe Ruddick, Heather Scott, and Alice Dworkin, at Prentice Hall.

Their creativity, assistance, and patience during the production of this book

are truly appreciated.

R.C.G.

R.E.W.

xix

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