Learning OpenCV

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 Chapters 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 Chapters Chapter 11 and Chapter 12 (Calibration and Stereo).

Good Progress

Read just two chapters a week until you've covered Chapters Chapter 1–Chapter 12 in six weeks (Chapter 13 is a special case, as discussed shortly). Start on projects and start in detail 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 Chapters Chapter 1–Chapter 12. 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 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.