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

What are integral images?

In order to extract these Haar features, we need to calculate the sum of the pixel values enclosed in many rectangular regions of the image. To make it scale invariant, we need to compute these areas at multiple scales (that is, for various rectangle sizes). If implemented naively, this would be a very computationally intensive process. We would have to iterate over all the pixels of each rectangle, including reading the same pixels multiple times if they are contained in different overlapping rectangles. If you want to build a system that can run in real time, you cannot spend so much time in computation. We need to find a way to avoid this huge redundancy during the area computation because we iterate over the same pixels multiple times. To avoid this, we can use something called integral images. These images can be initialized in a linear time (by iterating only twice over the image) and can then be provided with the sum of pixels inside any rectangle of any...