Learning Open CV Computer Vision in C++ with the OpenCV Library

Abstract

This book provides a working guide to the Open Source Computer Vision Library (OpenCV) and also provides a general background to the field of computer vision sufficient to use OpenCV effectively. Purpose Computer vision is a rapidly growing field, partly as a result of both cheaper and more capable cameras, partly because of affordable processing power, and partly because vision algorithms are starting to mature. OpenCV itself has played a role in the growth of computer vision by enabling thousands of people to do more productive work in vision. With its focus on real-time vision, OpenCV helps students and professionals efficiently implement projects and jump-start research by providing them with a computer vision and machine learning infrastructure that was previously available only in a few mature research labs. The purpose of this text is to: • Better document OpenCV—detail what function calling conventions really mean and how to use them correctly. • Rapidly give the reader an intuitive understanding of how the vision algorithms work. • Give the reader some sense of what algorithm to use and when to use it. • Give the reader a boost in implementing computer vision and machine learning algorithms by providing many working coded examples to start from. • Provide intuitions about how to fix some of the more advanced routines when something goes wrong. Simply put, this is the text the authors wished we had in school and the coding reference book we wished we had at work. This book documents a tool kit, OpenCV, that allows the reader to do interesting and fun things rapidly in computer vision. It gives an intuitive understanding as to how the algorithms work, which serves to guide the reader in designing and debugging vision applications and also to make the formal descriptions of computer vision and machine learning algorithms in other texts easier to comprehend and remember. After all, it is easier to understand complex algorithms and their associated math when you start with an intuitive grasp of how those algorithms work.