Book Image

Learn OpenCV 4 By Building Projects - Second Edition

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

Learn OpenCV 4 By Building Projects - Second Edition

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

Overview of this book

OpenCV is one of the best open source libraries available, and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you’re completely new to computer vision, or have a basic understanding of its concepts, Learn OpenCV 4 by Building Projects – Second edition will be your guide to understanding OpenCV concepts and algorithms through real-world examples and projects. You’ll begin with the installation of OpenCV and the basics of image processing. Then, you’ll cover user interfaces and get deeper into image processing. As you progress through the book, you'll learn complex computer vision algorithms and explore machine learning and face detection. The book then guides you in creating optical flow video analysis and background subtraction in complex scenes. In the concluding chapters, you'll also learn about text segmentation and recognition and understand the basics of the new and improved deep learning module. By the end of this book, you'll be familiar with the basics of Open CV, such as matrix operations, filters, and histograms, and you'll have mastered commonly used computer vision techniques to build OpenCV projects from scratch.
Table of Contents (14 chapters)

Naive background subtraction

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

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

As we can see, 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 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...