探索数字图像处理：应用与基础原理

冈萨雷斯

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《数字图像处理》第三版，由拉斐尔·C·冈萨雷斯和理查德·E·伍兹合著，是专为理解与应用数字图像处理领域的经典教材。该书针对初学者和专业人士，深入探讨了从图像的基本概念到高级技术的方方面面。全书分为两个主要部分： **第1章：引言** 1.1 **数字图像处理定义**：章节阐述了什么是数字图像处理，它如何利用计算机来捕捉、分析和处理图像数据，包括像素操作和信号处理方法。 1.2 **数字图像处理的起源**：介绍了这项技术的发展历史，从早期的摄影技术发展到现代的数字化图像处理系统，如扫描仪和数码相机。 1.3 **广泛应用领域**： - **伽马射线成像**：用于医学检测和安全检查中的辐射成像。 - **X射线成像**：医学影像学中的基础，如CT和MRI扫描。 - **紫外线、可见光和红外成像**：涉及安防、遥感和光学通信等领域。 - **微波和无线电波成像**：航空航天和雷达技术。 - **多模态成像结合示例**：展示不同成像技术的互补性。 1.4 **数字图像处理基本步骤**：概述了图像预处理、增强、分割、特征提取、识别和重建等关键步骤。 1.5 **图像处理系统的组成部分**：讲解了硬件设备（如传感器、处理器和存储器）、软件算法以及它们之间的交互。 **第2章：数字图像基础** 2.1 **视觉感知元素**：讨论了人眼的工作原理，包括视网膜结构和视觉适应性。 2.2 **光与电磁谱**：介绍了光的本质，及其在不同频率下的应用对图像的影响。 2.3 **图像获取**：详细解释了单个传感器、多行传感器阵列和不同类型的图像采集方法。 2.4 **图像采样与量化**：阐述了数字化图像是如何从连续的光信号转换为离散的数据表示的。这些章节为读者提供了全面的理论框架，涵盖了数字图像处理的各个方面，旨在帮助读者掌握从图像采集到最终分析和应用的全过程。无论是研究者还是工程师，都能从中获得丰富的实践指导和理论知识。

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■ 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

About the Authors

Rafael C. Gonzalez

R. C. Gonzalez received the B.S.E.E. degree from the University of Miami in

1965 and the M.E. and Ph.D. degrees in electrical engineering from the Univer-

sity of Florida, Gainesville, in 1967 and 1970, respectively. He joined the Elec-

trical and Computer Engineering Department at the University of Tennessee,

Knoxville (UTK) in 1970, where he became Associate Professor in 1973, Pro-

fessor in 1978, and Distinguished Service Professor in 1984. He served as Chair-

man of the department from 1994 through 1997. He is currently a Professor

Emeritus at UTK.

Gonzalez is the founder of the Image & Pattern Analysis Laboratory and the

Robotics & Computer Vision Laboratory at the University of Tennessee. He

also founded Perceptics Corporation in 1982 and was its president until 1992.

The last three years of this period were spent under a full-time employment con-

tract with Westinghouse Corporation, who acquired the company in 1989.

Under his direction, Perceptics became highly successful in image process-

ing, computer vision, and laser disk storage technology. In its initial ten years,

Perceptics introduced a series of innovative products, including: The world’s

first commercially-available computer vision system for automatically reading

license plates on moving vehicles; a series of large-scale image processing and

archiving systems used by the U.S. Navy at six different manufacturing sites

throughout the country to inspect the rocket motors of missiles in the Trident

II Submarine Program; the market-leading family of imaging boards for ad-

vanced Macintosh computers; and a line of trillion-byte laser disk products.

He is a frequent consultant to industry and government in the areas of pat-

tern recognition, image processing, and machine learning. His academic hon-

ors for work in these fields include the 1977 UTK College of Engineering

Faculty Achievement Award; the 1978 UTK Chancellor’s Research Scholar

Award; the 1980 Magnavox Engineering Professor Award; and the 1980 M.E.

Brooks Distinguished Professor Award. In 1981 he became an IBM Professor

at the University of Tennessee and in 1984 he was named a Distinguished Ser-

vice Professor there. He was awarded a Distinguished Alumnus Award by the

University of Miami in 1985, the Phi Kappa Phi Scholar Award in 1986, and

the University of Tennessee’s Nathan W. Dougherty Award for Excellence in

Engineering in 1992.

Honors for industrial accomplishment include the 1987 IEEE Outstanding

Engineer Award for Commercial Development in Tennessee; the 1988 Albert

Rose Nat’l Award for Excellence in Commercial Image Processing; the 1989 B.

Otto Wheeley Award for Excellence in Technology Transfer; the 1989 Coopers

and Lybrand Entrepreneur of the Year Award; the 1992 IEEE Region 3 Out-

standing Engineer Award; and the 1993 Automated Imaging Association Na-

tional Award for Technology Development.

xxi

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数字图像处理第二版中文版 by 冈萨雷斯 /Digital Image Processing by Gonzalez Part1/3

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