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

Segmenting our input image

Now, we will introduce you to the following two techniques used to segment our thresholded image:

  • The connected components

  • The findContours function

With these two techniques, we will be allowed to extract each region of interest of our image where our target objects appear; in our case, a nut, screw, and ring.

The connected component algorithm

The connected component is a very common algorithm used to segment and identify parts in binary images. A connected component is an iterative algorithm used for the purpose of labeling an image using an 8- or 4-connectivity pixel. Two pixels are connected if they have the same value and are neighbors. In the following figure, each pixel has eight neighbor pixels:

A 4-connectivity means that only the 2, 4, 5, and 7 neighbors can be connected to the center if they have the same value. In the case of 8-connectivity, 1, 2, 3, 4, 5, 6, 7, and 8 can be connected if they have the same value.

In the following example, we can see the difference...