Book Image

Building Computer Vision Projects with OpenCV 4 and C++

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

Building Computer Vision Projects with OpenCV 4 and C++

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

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. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

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 8Video Surveillance, Background Modeling, and Morphological Operations), and you must choose a model that gives the best predictions.

If we don't get the correct predictions, we have to check each one of these concepts to find the issue.

In the...