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)

Summary

In this chapter, we learned about the basics of machine learning and applied them to a small sample application. This allowed us to understand the basic techniques that we can use to create our own machine learning application. Machine learning is complex and involves different techniques for each use case (supervised learning, unsupervised, clustering, and so on). We also learned how to create the most typical machine learning application, the supervised learning application, with SVM. The most important concepts in supervised machine learning are as follows: you must have an appropriate number of samples or a dataset, you must accurately choose the features that describe our objects (for more information on image features, go to Chapter 8, Video Surveillance, Background Modeling, and Morphological Operations), and you must choose a model that gives the best predictions...