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Instructor’s Instructor’s Manual Manual

NOTICE This manual is intended for your personal use only. Copying, printing, posting, or any form of printed or electronic distribution of any part of this manual constitutes a violation of copyright law. As a security measure, this manual was encrypted during download with the serial number of your book, and with your personal information. Any printed or electronic copies of this file will bear that encryption, which will tie the copy to you. Please help us defeat piracy of intellectual property, one of the principal reasons for the increase in the cost of books. -------------------------------

Digital Image Processing Third Edition Instructor's Manual Version 3.0 Rafael C. Gonzalez Richard E. Woods Prentice Hall Upper Saddle River, NJ 07458 www.imageprocessingplace.com Copyright © 1992-2008 R. C. Gonzalez and R. E. Woods

Chapter 1 Introduction The purpose of this chapter is to present suggested guidelines for teaching mate- rial from Digital Image Processing at the senior and ﬁrst-year graduate levels. We also discuss use of the book web site. Although the book is totally self-contained, the web site offers, among other things, complementary review material and computer projects that can be assigned in conjunction with classroom work. Detailed solutions to all problems in the book also are included in the remain- ing chapters of this manual. 1.1 Teaching Features of the Book Undergraduate programs that offer digital image processing typically limit cov- erage to one semester. Graduate programs vary, and can include one or two semesters of the material. In the following discussion we give general guidelines for a one-semester senior course, a one-semester graduate course, and a full- year course of study covering two semesters. We assume a 15-week program per semester with three lectures per week. In order to provide ﬂexibility for exams and review sessions, the guidelines discussed in the following sections are based on forty, 50-minute lectures per semester. The background assumed on the part of the student is senior-level preparation in mathematical analysis, matrix the- ory, probability, and computer programming. The Tutorials section in the book web site contains review materials on matrix theory and probability, and has a brief introduction to linear systems. PowerPoint classroom presentation mate- rial on the review topics is available in the Faculty section of the web site. The suggested teaching guidelines are presented in terms of general objec- tives, and not as time schedules. There is so much variety in the way image pro- cessing material is taught that it makes little sense to attempt a breakdown of the material by class period. In particular, the organization of the present edition of 1

2 CHAPTER1. INTRODUCTION the book is such that it makes it much easier than before to adopt signiﬁcantly different teaching strategies, depending on course objectives and student back- ground. For example, it is possible with the new organization to offer a course that emphasizes spatial techniques and covers little or no transform material. This is not something we recommend, but it is an option that often is attractive in programs that place little emphasis on the signal processing aspects of the ﬁeld and prefer to focus more on the implementation of spatial techniques. 1.2 One Semester Senior Course A basic strategy in teaching a senior course is to focus on aspects of image pro- cessing in which both the inputs and outputs of those processes are images. In the scope of a senior course, this usually means the material contained in Chapters 1 through 6. Depending on instructor preferences, wavelets (Chap- ter 7) usually are beyond the scope of coverage in a typical senior curriculum. However, we recommend covering at least some material on image compres- sion (Chapter 8) as outlined below. We have found in more than three decades of teaching this material to se- niors in electrical engineering, computer science, and other technical disciplines, that one of the keys to success is to spend at least one lecture on motivation and the equivalent of one lecture on review of background material, as the need arises. The motivational material is provided in the numerous application areas dis1.2 One Semester Senior Coursecussed in Chapter 1. This chapter was pre- pared with this objective in mind. Some of this material can be covered in class in the ﬁrst period and the rest assigned as independent reading. Background re- view should cover probability theory (of one random variable) before histogram processing (Section 3.3). A brief review of vectors and matrices may be required later, depending on the material covered. The review material in the book web site was designed for just this purpose. Chapter 2 should be covered in its entirety. Some of the material (Sections 2.1 through 2.3.3) can be assigned as independent reading, but more detailed explanation (combined with some additional independent reading) of Sections 2.3.4 and 2.4 through 2.6 is time well spent. The material in Section 2.6 covers concepts that are used throughout the book and provides a number of image processing applications that are useful as motivational background for the rest of the book Chapter 3 covers spatial intensity transformations and spatial correlation and convolution as the foundation of spatial ﬁltering. The chapter also covers a number of different uses of spatial transformations and spatial ﬁltering for im- age enhancement. These techniques are illustrated in the context enhancement

1.2. ONESEMESTERSENIORCOURSE 3 (as motivational aids), but it is pointed out several times in the chapter that the methods developed have a much broader range of application. For a se- nior course, we recommend covering Sections 3.1 through 3.3.1, and Sections 3.4 through 3.6. Section 3.7 can be assigned as independent reading, depending on time. The key objectives of Chapter 4 are (1) to start from basic principles of signal sampling and from these derive the discrete Fourier transform; and (2) to illus- trate the use of ﬁltering in the frequency domain. As in Chapter 3, we use mostly examples from image enhancement, but make it clear that the Fourier trans- form has a much broader scope of application. The early part of the chapter through Section 4.2.2 can be assigned as independent reading. We recommend careful coverage of Sections 4.2.3 through 4.3.4. Section 4.3.5 can be assigned as independent reading. Section 4.4 should be covered in detail. The early part of Section 4.5 deals with extending to 2-D the material derived in the earlier sections of this chapter. Thus, Sections 4.5.1 through 4.5.3 can be assigned as independent reading and then devote part of the period following the assign- ment to summarizing that material. We recommend class coverage of the rest of the section. In Section 4.6, we recommend that Sections 4.6.1-4.6.6 be cov- ered in class. Section 4.6.7 can be assigned as independent reading. Sections 4.7.1-4.7.3 should be covered and Section 4.7.4 can be assigned as independent reading. In Sections 4.8 through 4.9 we recommend covering one ﬁlter (like the ideal lowpass and highpass ﬁlters) and assigning the rest of those two sections as independent reading. In a senior course, we recommend covering Section 4.9 through Section 4.9.3 only. In Section 4.10, we also recommend covering one ﬁlter and assigning the rest as independent reading. In Section 4.11, we recom- mend covering Sections 4.11.1 and 4.11.2 and mentioning the existence of FFT algorithms. The log 2 computational advantage of the FFT discussed in the early part of Section 4.11.3 should be mentioned, but in a senior course there typically is no time to cover development of the FFT in detail. Chapter 5 can be covered as a continuation of Chapter 4. Section 5.1 makes this an easy approach. Then, it is possible to give the student a “ﬂavor” of what restoration is (and still keep the discussion brief) by covering only Gaussian and impulse noise in Section 5.2.1, and two of the spatial ﬁlters in Section 5.3. This latter section is a frequent source of confusion to the student who, based on dis- cussions earlier in the chapter, is expecting to see a more objective approach. It is worthwhile to emphasize at this point that spatial enhancement and restora- tion are the same thing when it comes to noise reduction by spatial ﬁltering. A good way to keep it brief and conclude coverage of restoration is to jump at this point to inverse ﬁltering (which follows directly from the model in Section 5.1) and show the problems with this approach. Then, with a brief explanation

4 CHAPTER1. INTRODUCTION regarding the fact that much of restoration centers around the instabilities in- herent in inverse ﬁltering, it is possible to introduce the “interactive” form of the Wiener ﬁlter in Eq. (5.8-3) and discuss Examples 5.12 and 5.13. At a minimum, we recommend a brief discussion on image reconstruction by covering Sections 5.11.1-5.11-2 and mentioning that the rest of Section 5.11 deals with ways to generated projections in which blur is minimized. Coverage of Chapter 6 also can be brief at the senior level by focusing on enough material to give the student a foundation on the physics of color (Sec- tion 6.1), two basic color models (RGB and CMY/CMYK), and then concluding with a brief coverage of pseudocolor processing (Section 6.3). We typically con- clude a senior course by covering some of the basic aspects of image compres- sion (Chapter 8). Interest in this topic has increased signiﬁcantly as a result of the heavy use of images and graphics over the Internet, and students usually are easily motivated by the topic. The amount of material covered depends on the time left in the semester. 1.3 One Semester Graduate Course (No Background in DIP) The main difference between a senior and a ﬁrst-year graduate course in which neither group has formal background in image processing is mostly in the scope of the material covered, in the sense that we simply go faster in a graduate course and feel much freer in assigning independent reading. In a graduate course we add the following material to the material suggested in the previous section. Sections 3.3.2-3.3.4 are added as is Section 3.3.8 on fuzzy image processing. We cover Chapter 4 in its entirety (with appropriate sections assigned as inde- pendent readying, depending on the level of the class). To Chapter 5 we add Sec- tions 5.6-5.8 and cover Section 5.11 in detail. In Chapter 6 we add the HSI model (Section 6.3.2) , Section 6.4, and Section 6.6. A nice introduction to wavelets (Chapter 7) can be achieved by a combination of classroom discussions and in- dependent reading. The minimum number of sections in that chapter are 7.1, 7.2, 7.3, and 7.5, with appropriate (but brief) mention of the existence of fast wavelet transforms. Sections 8.1 and 8.2 through Section 8.2.8 provide a nice introduction to image compression. If additional time is available, a natural topic to cover next is morphological image processing (Chapter 9). The material in this chapter begins a transition from methods whose inputs and outputs are images to methods in which the in- puts are images, but the outputs are attributes about those images, in the sense deﬁned in Section 1.1. We recommend coverage of Sections 9.1 through 9.4, and

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冈萨雷斯数字图像处理第三版课后习题答案绝对完整版.pdf

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