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

Computer Vision and the machine learning workflow

The Computer Vision applications with machine learning have a common basic structure. This structure is divided into different steps that are repeated in almost all Computer Vision applications, and some others are omitted. In the following diagram, we show you the different steps involved:

Almost any Computer Vision application starts with a preprocessing stage that is applied to the input image. Preprocessing involves light removal conditions and noise, thresholding, blur, and so on.

After we apply all the preprocessing steps required to the input image, the second step is segmentation. In the segmentation step, we need to extract the regions of interest of an image and isolate each one as a unique object of interest. For example, in a face detection system, we need to separate the faces from the rest of the parts in the scene.

After getting the objects inside the image, we continue with the next step. We need to extract all the features of...