《数字图像处理》第四版配套资源指南

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"Digital Image Processing 4th Edition 是一本关于计算机图像处理的专业书籍，由Rafael C. Gonzalez和Richard E. Woods撰写。本书提供了一系列的支持包，包括概率和向量的复习、与书中内容相关的教程、图像数据库等额外资源。读者可以通过访问www.ImageProcessingPlace.com并按照指示注册来获取这些配套资源。请注意，序列号仅可使用一次，订阅不可转让。" 《数字图像处理》第四版是计算机视觉和数字图像处理领域的一本经典著作。这本书深入浅出地介绍了图像处理的基础理论和方法，适合对图像处理感兴趣或正在学习相关课程的学生。作者Rafael C. Gonzalez和Richard E. Woods是该领域的权威专家，他们的作品被广泛应用于教学和研究。本书的内容可能涵盖以下几个核心知识点： 1. **图像基础知识**：包括图像的类型（如灰度图像、彩色图像）、像素的概念、图像的表示与存储方式（如像素矩阵、位图、JPEG、PNG等格式）。 2. **图像处理基本操作**：如图像的平滑（滤波）、锐化、边缘检测、阈值分割、直方图均衡化等，这些都是图像预处理的重要步骤。 3. **数字图像变换**：傅立叶变换在图像处理中的应用，如离散傅立叶变换（DFT）、快速傅立叶变换（FFT），以及频域分析对图像特征的影响。 4. **图像增强**：通过调整图像的亮度、对比度，或者应用特定的滤波器来改善图像质量。 5. **图像恢复与复原**：处理图像失真、噪声，如运动模糊的校正、去噪技术（如高斯滤波、中值滤波、Wiener滤波）。 6. **图像编码与压缩**：介绍图像数据的压缩标准，如JPEG、JPEG2000、MPEG等，以及无损和有损压缩的区别。 7. **计算机视觉基础**：可能涉及到特征检测（如角点、边缘、斑点）、模板匹配、物体识别和跟踪等内容。 8. **图像分析与理解**：包括图像分割、区域生长、形态学操作以及更高级的图像理解算法。 9. **支持包中的资源**：额外提供的概率和向量复习可以帮助读者巩固数学基础，相关的教程可能涵盖最新技术和理论，而图像数据库则为实践提供了丰富的素材。通过这本书，读者不仅可以掌握数字图像处理的基本原理和技术，还能通过配套资源进行实践操作，提升理论知识与实际应用能力。对于想要深入学习计算机视觉或图像处理的人来说，这是一本不可或缺的参考资料。

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 University of

Florida, Gainesville, in 1967 and 1970, respectively. He joined the Electrical and

Computer Science Department at the University of Tennessee, Knoxville (UTK) in

1970, where he became Associate Professor in 1973, Professor in 1978, and Distin-

guished Service Professor in 1984. He served as Chairman 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 contract with

Westinghouse Corporation, who acquired the company in 1989.

Under his direction, Perceptics became highly successful in image processing,

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 advanced Macintosh computers; and a line of trillion-

byte laser disk products.

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

recognition, image processing, and machine learning. His academic honors 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 Service 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 Engi-

neer Award for Commercial Development in Tennessee; the 1988 Albert Rose

National 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 Outstanding

Engineer Award; and the 1993 Automated Imaging Association National Award for

Technology Development.

Gonzalez is author or co-author of over 100 technical articles, two edited books,

and four textbooks in the ﬁelds of pattern recognition, image processing, and robot-

ics. His books are used in over 1000 universities and research institutions throughout

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18 Chapter 1 Introduction

1.1 WHAT IS DIGITAL IMAGE PROCESSING?

An image may be defined as a two-dimensional function, fxy(,), where x and y are

spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (,)xy

is called the intensity or gray level of the image at that point. When x, y, and the

intensity values of f are all finite, discrete quantities, we call the image a digital image.

The field of digital image processing refers to processing digital images by means of

a digital computer. Note that a digital image is composed of a finite number of ele-

ments, each of which has a particular location and value. These elements are called

picture elements, image elements, pels, and pixels. Pixel is the term used most widely

to denote the elements of a digital image. We will 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) spectrum, 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 ultrasound, electron microscopy, and com-

puter-generated images. Thus, digital image processing encompasses a wide and var-

ied ﬁeld of applications.

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

ing stops and other related areas, such as image analysis and computer vision, 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 sin-

gle number) would not be considered an image processing operation. On the other

hand, there are ﬁelds such as computer vision whose ultimate goal is to use comput-

ers 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 intel-

ligence (AI) whose objective is to emulate 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 anticipated. The area of image analysis (also called image

understanding) is in between 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 con-

sider three types of computerized processes in this continuum: low-, mid-, and high-

level processes. Low-level processes involve primitive operations 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 of 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

1.1

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