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How This Book Is Best Used
This text need not be read in order. It can serve as a kind of user manual: look up the
function when you need it, and read the function’s description if you want the gist of
how it works “under the hood.” However, the intent of this book is tutorial. It gives
you a basic understanding of computer vision along with details of how and when to
use selected algorithms.
This book is written to allow its use as an adjunct or primary textbook for an under‐
graduate or graduate course in computer vision. The basic strategy with this method
is for students to read the book for a rapid overview and then supplement that read‐
ing with more formal sections in other textbooks and with papers in the field. There
are exercises at the end of each chapter to help test the student’s knowledge and to
develop further intuitions.
You could approach this text in any of the following ways:
Grab bag
Go through Chapters 1–5 in the first sitting, and then just hit the appropriate
chapters or sections as you need them. This book does not have to be read in
sequence, except for Chapters 18 and 19 (which cover camera calibration and
stereo imaging) and Chapters 20, 21, and 22 (which cover machine learning).
Entrepreneurs and students doing project-based courses might go this way.
Good progress
Read just two chapters a week until you’ve covered Chapters 1–22 in 11 weeks
(Chapter 23 will go by in an instant). Start on projects and dive into details on
selected areas in the field, using additional texts and papers as appropriate.
The sprint
Cruise through the book as fast as your comprehension allows, covering Chap‐
ters 1–23. Then get started on projects and go into detail on selected areas in the
field using additional texts and papers. This is probably the choice for professio‐
nals, but it might also suit a more advanced computer vision course.
Chapter 20 is a brief chapter that gives general background on machine learning,
which is followed by Chapters 21 and 22, which give more details on the machine
learning algorithms implemented in OpenCV and how to use them. Of course,
machine learning is integral to object recognition and a big part of computer vision,
but it’s a field worthy of its own book. Professionals should find this text a suitable
launching point for further explorations of the literature—or for just getting down to
business with the code in that part of the library. The machine learning interface has
been substantially simplified and unified in OpenCV 3.x.
This is how we like to teach computer vision: sprint through the course content at a
level where the students get the gist of how things work; then get students started on
xviii | Preface