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
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Naive background subtraction


Let's start the background subtraction discussion from the beginning. What does a background subtraction process look like? Consider the following image:

The preceding image represents the background scene. Now, let's introduce a new object into this scene:

As shown in the preceding image, there is a new object in the scene. So, if we compute the difference between this image and our background model, you should be able to identify the location of the TV remote:

The overall process looks like this:

Does it work well?

There's a reason why we call it the naive approach. It works under ideal conditions, and as we know, nothing is ideal in the real world. It does a reasonably good job of computing the shape of the given object, but it does so under some constraints. One of the main requirements of this approach is that the color and intensity of the object should be sufficiently different from that of the background. Some of the factors that affect these kinds of algorithms...