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Learn OpenCV 4 by Building Projects

Learn OpenCV 4 by Building Projects

By : Millán Escrivá, Vinícius G. Mendonça, Joshi
2.5 (2)
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Learn OpenCV 4 by Building Projects

Learn OpenCV 4 by Building Projects

2.5 (2)
By: Millán Escrivá, Vinícius G. Mendonça, 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)
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Tracking objects of a specific color

In order to build a good object tracker, we need to understand what characteristics can be used to make our tracking robust and accurate. So, let's take a baby step in that direction and see whether we can use colorspace information to come up with a good visual tracker. One thing to keep in mind is that color information is sensitive to lighting conditions. In real-world applications, you will have to do some preprocessing to take care of that. But for now, let's assume that somebody else is doing that and we are getting clean color images.

There are many different colorspaces, and picking a good one will depend on the different applications that a user is using. While RGB is the native representation on a computer screen, it's not necessarily ideal for humans. When it comes to humans, we give names to colors more naturally...

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