Book Image

OpenCV By Example

By : Prateek Joshi, David Millán Escrivá, Vinícius G. Mendonça
Book Image

OpenCV By Example

By: Prateek Joshi, David Millán Escrivá, Vinícius G. Mendonça

Overview of this book

Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects. Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch. By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.
Table of Contents (18 chapters)
OpenCV By Example
About the Authors
About the Reviewers

Understanding Haar cascades

Haar cascades are cascade classifiers that are based on Haar features. What is a cascade classifier? It is simply a concatenation of a set of weak classifiers that can be used to create a strong classifier. Now, what do we mean by weak and strong classifiers? Weak classifiers are classifiers whose performances are limited. They don't have the ability to classify everything correctly. If you keep the problem really simple, they might perform at an acceptable level. Strong classifiers, on the other hand, are really good at classifying our data correctly. We will see how it all comes together in the next couple of paragraphs. Another important part of Haar cascades is Haar features. These features are simple summations of rectangles and differences of those areas across the image. Let's consider the following figure:

If we want to compute the Haar features of the region ABCD, we just need to compute the difference between the white pixels and the colored pixels in...