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
About the Authors
About the Reviewers


In this chapter, we learned the basics of the machine learning model and how to apply a small sample application to understand all the basic tips required to create our own ML application.

Machine learning is complex and involves different techniques for each use case (supervised learning, unsupervised, clustering, and so on), and we learned how to create the most typical ML application and the supervised learning with an SVM.

The most important concepts in supervised machine learning are: first, we need to have an appropriate number of samples or datasets; and second, we need to correctly choose the features that describe our objects correctly. For more information on image features, refer to Chapter 8, Video Surveillance, Background Modeling, and Morphological Operations. Third, choose the best model that gives us the best predictions.

If we don't reach the correct predictions we have to check each one of these concepts to look for where the issue is.

In the next chapter, we will introduce...