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 CHAPTER 1. 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
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