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

Chapter 6. Learning Object Classification

In the previous chapter, we introduced you to the basic concepts of object segmentation and detection. This means isolating the objects that appear in an image for future processing and analysis.

This chapter covers how to classify each of these isolated objects. In order to allow us to classify each object, we need to train our system to be capable of learning the required parameters to decide which specific label should be assigned to the detected object (depending on the different categories taken into account during the training phase).

This chapter is going to introduce you to the basic concepts of machine learning to classify images with different labels.

We will create a basic application based on the segmentation algorithm, as discussed in Chapter 5, Automated Optical Inspection, Object Segmentation, and Detection. This segmentation algorithm extracts parts of an image, which contains objects. For each object, we will extract the different features...