冈萨雷斯第四版《数字图像处理》Kindle电子书转PDF教程

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《数字图像处理第四版》(Digital Image Processing, Fourth Edition)是由拉斐尔·冈萨雷斯(Rafael C. Gonzalez)和理查德·伍兹(Richard E. Woods)共同编著的一本经典教材，由皮尔逊教育公司(Pearson Education)出版，于2018年和2008年首次发行。该书专为大学计算机科学、电子工程以及图像处理领域的学生和专业人士设计，深入浅出地讲解了数字图像处理的各种理论和技术。本书共四版，是图像处理领域的重要参考资料，涵盖了图像获取、表示、增强、分析、编码与压缩等核心内容。作者们以其丰富的学术背景，结合实际应用案例，使读者能够理解和掌握诸如像素操作、滤波器、边缘检测、色彩空间转换、图像分割、特征提取、机器学习在图像识别中的应用等内容。此外，书中还包含了大量清晰的插图和练习题，便于读者通过实践巩固所学知识。 Kindle版本的电子书被转化为PDF格式，方便读者在不同设备上阅读，无论是在电脑、平板或手机上都能轻松查阅。书中涉及的版权信息表明，所有权利受到保护，未经许可不得复制或传播。书中的封面设计出自BlackHorseDesigns之手，包括MRI图像、卫星照片、指纹图像和木星的月面图像等视觉元素，增强了书籍的专业性和吸引力。该书的团队成员包括课程内容管理负责人Marcia J. Horton、工程及计算机科学全球版块总监Julian Partridge，以及负责市场营销的Yvonne Vannatta等人，他们共同确保了教材的质量和市场推广。《数字图像处理第四版》是一本全面且实用的教材，对于希望在图像处理领域深入研究或从事相关工作的读者来说，无论是用于课堂教学还是个人自学，都是不可或缺的参考资料。它不仅传授了基础知识，还展示了最新技术和前沿发展，是推动数字化世界中图像处理技术进步的重要工具。

A discussion of clustering, superpixels, and their use in region segmentation.

New material on active contours that includes snakes and level sets, and their use in

image segmentation.

Coverage of maximally stable extremal regions.

Expanded coverage of feature extraction to include the Scale Invariant Feature Transform

(SIFT).

Expanded coverage of neural networks to include deep neural networks, back-propagation,

deep learning, and, especially, deep convolutional neural networks.

More homework problems at the end of the chapters.

MATLAB computer projects.

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

providing a reasonable balance between rigor, clarity of presentation, and the findings of the

survey. In addition to new material, earlier portions of the text were updated and clarified. This

edition contains 425 new images, 135 new drawings, and 220 new exercises. For the first

time, we have included MATLAB projects at the end of every chapter. In total there are 120 new

MATLAB projects that cover a broad range of the material in the book. Although the solutions

we provide are in MATLAB, the projects themselves are written in such a way that they can be

implemented in other languages. Projects are an important addition because they will allow

students to experiment with material they learn in the classroom. A large database of digital

images is provided for this purpose.

New to This Edition

The highlights of this edition are as follows.

Chapter 1: Some figures were updated, and parts of the text were rewritten to

correspond to changes in later chapters.

Chapter 2: Many of the sections and examples were rewritten for clarity. We added a

new section dealing with random numbers and probability, with an emphasis on their

application to image processing. We included 12 new examples, 31 new images, 22 new

drawings, 32 new exercises, and 10 new MATLAB projects.

Chapter 3: Major revisions of the topics in this chapter include a new section on exact

histogram matching. Fundamental concepts of spatial filtering were rewritten to include a

discussion on separable filter kernels, expanded coverage of the properties of lowpass

Gaussian kernels, and expanded coverage of highpass, bandreject, and bandpass filters,

including numerous new examples that illustrate their use. In addition to revisions in the text,

including 6 new examples, the chapter has 67 new images, 18 new line drawings, 31 new

exercises, and 10 new MATLAB projects.

Chapter 4: Several of the sections of this chapter were revised to improve the clarity of

presentation. We replaced dated graphical material with 35 new images and 4 new line

drawings. We added 25 new exercises and 10 new MATLAB projects.

Chapter 5: Revisions to this chapter were limited to clarifications and a few

corrections in notation. We added 6 new images, 17 new exercises, and 10 new MATLAB

projects.

Chapter 6: This is a new chapter that brings together wavelets, several new

transforms, and many of the image transforms that were scattered throughout the book. The

emphasis of this new chapter is on the presentation of these transforms from a unified point of

view. We added 24 new images, 20 new drawings, 25 new exercises and 10 new MATLAB

projects.

Chapter 7: The material dealing with color image processing was moved to this

chapter. Several sections were clarified, and the explanation of the CMY and CMYK color

models was expanded. We added 2 new images and 10 new MATLAB projects.

Chapter 8: In addition to numerous clarifications and minor improvements to the

presentation, we added 10 new MATLAB projects to this chapter.

Chapter 9: Revisions of this chapter included a complete rewrite of several sections,

including redrafting of several line drawings. We added 18 new exercises and 10 new

MATLAB projects.

Chapter 10: Several of the sections were rewritten for clarity. We updated the chapter

by adding coverage of finite differences, K-means clustering, superpixels, and graph cuts.

The new topics are illustrated with 4 new examples. In total, we added 31 new images, 3 new

drawings, 8 new exercises, and 10 new MATLAB projects.

Chapter 11: This is a new chapter dealing with active contours for image

segmentation, including snakes and level sets. An important feature in this chapter is that it

presents a derivation of the fundamental snake equation. Similarly, we provide a derivation of

the level set equation. Both equations are derived starting from basic principles, and the

methods are illustrated with numerous examples. The strategy when we prepared this chapter

was to bring this material to a level that could be understood by beginners in our field. To that

end, we complemented the text material with 17 new examples, 141 new images, 19 new

drawings, 37 new problems, and 10 new MATLAB projects.

Chapter 12: This is the chapter on feature extraction, which was moved from its 11th

position in the previous edition. The chapter was updated with numerous topics, beginning

with a more detailed classification of feature types and their uses. In addition to improvements

in the clarity of presentation, we added coverage of slope change codes, expanded the

explanation of skeletons, medial axes, and the distance transform, and added several new

basic descriptors of compactness, circularity, and eccentricity. New material includes

coverage of the Harris-Stephens corner detector, and a presentation of maximally stable

extremal regions. A major addition to the chapter is a comprehensive discussion dealing with

the Scale-Invariant Feature Transform (SIFT). The new material is complemented by 65 new

images, 15 new drawings, 4 new examples, and 15 new exercises. We also added 10 new

MATLAB projects.

Chapter 13: This is the image pattern classification chapter that was Chapter

12 in the previous edition. This chapter underwent a major revision to include an

extensive rewrite of neural networks and deep learning, an area that has grown significantly

since the last edition of the book. We added a comprehensive discussion on fully connected,

deep neural networks that includes derivation of backpropagation starting from basic

principles. The equations of backpropagation were expressed in “traditional” scalar terms,

and then generalized into a compact set of matrix equations ideally suited for implementation

of deep neural nets. The effectiveness of fully connected networks was demonstrated with

several examples that included a comparison with the Bayes classifier. One of the most-

requested topics in the survey was coverage of deep convolutional neural networks. We added

an extensive section on this, following the same blueprint we used for deep, fully connected

nets. That is, we derived the equations of backpropagation for convolutional nets, and showed

how they are different from “traditional” backpropagation. We then illustrated the use of

convolutional networks with simple images, and applied them to large image databases of

numerals and natural scenes. The written material is complemented by 23 new images, 28

new drawings, and 12 new exercises. We also included 10 new MATLAB projects.

Also for the first time, we have created student and faculty support packages that can be

downloaded from the book website. The Student Support Package contains all the original

images in the book, answers to selected exercises, detailed answers (including MATLAB

code) to selected MATLAB projects, and instructions for using a set of utility functions that

complement the projects. The Faculty Support Package contains solutions to all exercises

and projects, teaching suggestions, and all the art in the book in the form of modifiable

PowerPoint slides. One support package is made available with every new book, free of

charge.

MATLAB projects are structured in a unique way that gives instructors significant flexibility in

how projects are assigned. The MATLAB functions required to solve all the projects in the

book are provided in executable, p-code format. These functions run just like the original

functions, but the source code is not visible, and the files cannot be modified. The availability

of these functions as a complete package makes it possible for projects to be assigned solely

for the purpose of experimenting with image processing concepts, without having to write a

single line of code. In other words, the complete set of MATLAB functions is available as a

stand-alone p-code toolbox, ready to use without further development. When instructors elect

to assign projects that involve MATLAB code development, we provide students enough

answers to form a good base that they can expand, thus gaining experience with developing

software solutions to image processing problems. Instructors have access to detailed

answers to all projects.

The book website, established during the launch of the 2002 edition, continues to be a

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冈萨雷斯第四版《数字图像处理》Kindle电子书转PDF教程

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