数字图像处理：gonzalez第二版概览与应用

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"Digital Image Processing By Gonzalez 2nd Edition 2002" 是一本由 Rafael C. Gonzalez 和 Richard E. Woods 合著的关于数字图像处理的经典教材。这本书的第二版受到了Prentice Hall进行的市场调查的影响，对内容进行了重大调整，以满足读者的需求。在这次市场调查中，主要发现了以下几个关键点： 1. 引言部分需要更多的应用实例，以激发读者对数字图像处理的兴趣，展示其在各种领域广泛的应用。 2. 提倡早期章节内容的简化和压缩，以便快速进入主题，让读者更快地接触核心概念。 3. 在一些领域如图像变换和图像恢复，提供更直观的讲解，使读者更容易理解和掌握。 4. 将颜色图像处理、小波理论和图像形态学单独成章，以深入探讨这些重要的子领域。 5. 每章末尾增加更多种类的问题，以增强读者的实践能力和理论理解。书中的内容涵盖了数字图像处理的基础和高级主题。作者Rafael C. Gonzalez来自田纳西大学，而Richard E. Woods则与MedData Interactive有关联，他们的专业知识和经验使得这本书成为该领域的权威之作。书中可能包括了图像的获取、表示、分析、增强、复原、压缩和编码等方面的知识，同时可能还讨论了色彩空间转换、滤波器、频域分析、图像分割、特征提取等技术。此外，书中还包括了参考文献，便于读者进一步研究。提供的ISBN号和LC分类号表明这是一本正式出版的学术著作，并且可能已经通过了严格的同行评审。这本书的生产和制造团队也体现了Prentice Hall的专业水平，确保了内容的质量和读者的阅读体验。总结来说，《Digital Image Processing By Gonzalez 2nd Edition 2002》是一本面向初学者和专业人士的数字图像处理教材，它不仅提供了丰富的理论知识，还注重实践应用，通过改进的教学方法帮助读者更好地理解和应用这一领域的内容。书中的问题集可以帮助读者巩固所学，并提升解决实际问题的能力。对于那些对图像处理、计算机视觉或相关领域感兴趣的读者，这是一本不可多得的参考资料。

11.2 Boundary Descriptors 653

11.2.1 Some Simple Descriptors 653

11.2.2 Shape Numbers 654

11.2.3 Fourier Descriptors 655

11.2.4 Statistical Moments 659

11.3 Regional Descriptors 660

11.3.1 Some Simple Descriptors 661

11.3.2 Topological Descriptors 661

11.3.3 Texture 665

11.3.4 Moments of Two-Dimensional Functions 672

11.4 Use of Principal Components for Description 675

11.5 Relational Descriptors 683

Summary 687

References and Further Reading 687

Problems 689

Object Recognition 693

12.1 Patterns and Pattern Classes 693

12.2 Recognition Based on Decision-Theoretic Methods 698

12.2.1 Matching 698

12.2.2 Optimum Statistical Classifiers 704

12.2.3 Neural Networks 712

12.3 Structural Methods 732

12.3.1 Matching Shape Numbers 732

12.3.2 String Matching 734

12.3.3 Syntactic Recognition of Strings 735

12.3.4 Syntactic Recognition of Trees 740

Summary 750

References and Further Reading 750

Problems 750

Bibliography 755

Index 779

xiv ■ Contents

GONZFM-i-xxii. 5-10-2001 14:22 Page xiv

2 Chapter 1 ■ Introduction

value. These elements are referred to as picture elements, image elements, pels,

and pixels. Pixel is the term most widely used to denote the elements of a digi-

tal image. We consider these definitions in more formal terms in Chapter 2.

Vision is the most advanced of our senses, so it is not surprising that images

play the single most important role in human perception. However, unlike

humans, who are limited to the visual band of the electromagnetic (EM) spec-

trum, imaging machines cover almost the entire EM spectrum, ranging from

gamma to radio waves. They can operate on images generated by sources that

humans are not accustomed to associating with images. These include ultra-

sound, electron microscopy, and computer-generated images.Thus, digital image

processing encompasses a wide and varied field of applications.

There is no general agreement among authors regarding where image pro-

cessing stops and other related areas, such as image analysis and computer vi-

sion, start. Sometimes a distinction is made by defining image processing as a

discipline in which both the input and output of a process are images.We believe

this to be a limiting and somewhat artificial boundary. For example, under this

definition, even the trivial task of computing the average intensity of an image

(which yields a single number) would not be considered an image processing op-

eration. On the other hand, there are fields such as computer vision whose ul-

timate goal is to use computers to emulate human vision, including learning

and being able to make inferences and take actions based on visual inputs.This

area itself is a branch of artificial intelligence (AI) whose objective is to emu-

late human intelligence.The field of AI is in its earliest stages of infancy in terms

of development, with progress having been much slower than originally antic-

ipated. The area of image analysis (also called image understanding) is in be-

tween image processing and computer vision.

There are no clear-cut boundaries in the continuum from image processing

at one end to computer vision at the other. However, one useful paradigm is

to consider three types of computerized processes in this continuum: low-,

mid-, and high-level processes. Low-level processes involve primitive opera-

tions such as image preprocessing to reduce noise, contrast enhancement, and

image sharpening. A low-level process is characterized by the fact that both

its inputs and outputs are images. Mid-level processing on images involves

tasks such as segmentation (partitioning an image into regions or objects),

description of those objects to reduce them to a form suitable for computer

processing, and classification (recognition) of individual objects. A mid-level

process is characterized by the fact that its inputs generally are images, but its

outputs are attributes extracted from those images (e.g., edges, contours, and

the identity of individual objects). Finally, higher-level processing involves

“making sense” of an ensemble of recognized objects, as in image analysis,

and, at the far end of the continuum, performing the cognitive functions nor-

mally associated with vision.

Based on the preceding comments, we see that a logical place of overlap be-

tween image processing and image analysis is the area of recognition of indi-

vidual regions or objects in an image. Thus, what we call in this book digital

image processing encompasses processes whose inputs and outputs are images

GONZ01-001-033.II 29-08-2001 14:42 Page 2

1.2 ■ The Origins of Digital Image Processing

†

References in the Bibliography at the end of the book are listed in alphabetical order by authors’ last

names.

and, in addition, encompasses processes that extract attributes from images, up

to and including the recognition of individual objects. As a simple illustration

to clarify these concepts, consider the area of automated analysis of text. The

processes of acquiring an image of the area containing the text, preprocessing

that image, extracting (segmenting) the individual characters, describing the

characters in a form suitable for computer processing, and recognizing those

individual characters are in the scope of what we call digital image processing

in this book. Making sense of the content of the page may be viewed as being

in the domain of image analysis and even computer vision, depending on the

level of complexity implied by the statement “making sense.” As will become

evident shortly, digital image processing, as we have defined it, is used success-

fully in a broad range of areas of exceptional social and economic value.The con-

cepts developed in the following chapters are the foundation for the methods

used in those application areas.

The Origins of Digital Image Processing

One of the first applications of digital images was in the newspaper industry,

when pictures were first sent by submarine cable between London and New

York. Introduction of the Bartlane cable picture transmission system in the

early 1920s reduced the time required to transport a picture across the Atlantic

from more than a week to less than three hours. Specialized printing equipment

coded pictures for cable transmission and then reconstructed them at the re-

ceiving end. Figure 1.1 was transmitted in this way and reproduced on a tele-

graph printer fitted with typefaces simulating a halftone pattern.

Some of the initial problems in improving the visual quality of these early dig-

ital pictures were related to the selection of printing procedures and the distri-

bution of intensity levels. The printing method used to obtain Fig. 1.1 was

abandoned toward the end of 1921 in favor of a technique based on photo-

graphic reproduction made from tapes perforated at the telegraph receiving

terminal. Figure 1.2 shows an image obtained using this method. The improve-

ments over Fig. 1.1 are evident, both in tonal quality and in resolution.

1.2

FIGURE 1.1

digital picture

produced in 1921

from a coded tape

by a telegraph

printer with

special type faces.

(McFarlane.

†

)

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