algebra, and it assumes some familiarity with solution methods to least-squares optimization problems as
well as some basic knowledge of Gaussian distributions, Bayes’ law, and derivatives of simple functions.
The math is in support of developing intuition for the algorithms. The reader may skip the math and the
algorithm descriptions, using only the function definitions and code examples to get vision applications up
and running.
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; read the function’s description if you want the gist of how it works “under the hood”. The intent of
this book is more tutorial, however. It gives you a basic understanding of computer vision along with
details of how and when to use selected algorithms.
This book was written to allow its use as an adjunct or as a primary textbook for an undergraduate 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 reading 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 Chapter 1–Chapter 3 in the first sitting, then just hit the appropriate chapters or sections as
you need them. This book does not have to be read in sequence, except for Chapter 11 and Chapter 12
(Calibration and Stereo).
Good Progress
Read just two chapters a week until you’ve covered Chapter 1–Chapter 12 in six weeks (Chapter 13 is
a special case, as discussed shortly). Start on projects and dive into details on selected areas in the
field, using additional texts and papers as appropriate.
The Sprint
Just cruise through the book as fast as your comprehension allows, covering Chapter 1–Chapter 12.
Then get started on projects and go into details on selected areas in the field using additional texts and
papers. This is probably the choice for professionals, but it might also suit a more advanced computer
vision course.
Chapter 13 is a long chapter that gives a general background to machine learning in addition to details
behind 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. This chapter should
probably be considered optional for a typical computer vision class.
This is how the authors 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 meaningful class projects while the
instructor supplies depth and formal rigor in selected areas by drawing from other texts or papers in the
field. This same method works for quarter, semester, or two-term classes. Students can get quickly up and
running with a general understanding of their vision task and working code to match. As they begin more
challenging and time-consuming projects, the instructor helps them develop and debug complex systems.
For longer courses, the projects themselves can become instructional in terms of project management.
Build up working systems first; refine them with more knowledge, detail, and research later. The goal in
such courses is for each project to aim at being worthy of a conference publication and with a few project
papers being published subsequent to further (postcourse) work.
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