Book Image

Arduino Computer Vision Programming

By : Özen Özkaya, Giray Yıllıkçı
Book Image

Arduino Computer Vision Programming

By: Özen Özkaya, Giray Yıllıkçı

Overview of this book

<p>Most technologies are developed with an inspiration of human capabilities. Most of the time, the hardest to implement capability is vision. Development of highly capable computer vision applications in an easy way requires a generic approach. In this approach, Arduino is a perfect tool for interaction with the real world. Moreover, the combination of OpenCV and Arduino boosts the level and quality of practical computer vision applications.</p> <p>Computer vision is the next level of sensing the environment. The purpose of this book is to teach you how to develop Arduino-supported computer vision systems that can interact with real life by seeing it.</p> <p>This book will combine the powers of Arduino and computer vision in a generalized, well-defined, and applicable way. The practices and approaches in the book can be used for any related problems and on any platforms. At the end of the book, you should be able to solve any types of real life vision problems with all its components by using the presented approach. Each component will extend your vision with the best practices on the topic.</p> <p>In each chapter, you will find interesting real life practical application examples about the topics in the chapter. To make it grounded, we will build a vision-enabled robot step by step towards the end of the book. You will observe that, even though the contexts of the problems are very different, the approaches to solve them are the same and very easy!</p> <p>&nbsp;</p>
Table of Contents (16 chapters)
Arduino Computer Vision Programming
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
5
Processing Vision Data with OpenCV
Index

Color conversions


We use color conversion for better information representations. Some major color spaces supported in OpenCV are blue, green, red (BGR), hue, saturation and brightness (HSB), grayscale, and binary. The most frequently used color model is the BGR color scheme. Blue, green and red are primary colors and their combinations represent full color images.

The HSB (also known as hue, saturation, value or HSV) model separates color information from luminance. Hue represents the dominant color as seen by the observer, saturation refers to the amount of dilution of the color with white light, and brightness defines the average luminance. HSB representation is frequently used in color image processing because hue is relatively independent from light intensity. It is used to detect an object with a known color. HSB representation works in different lighting conditions! This is difficult to do with BGR values.

Grayscale is another frequently used color space where image information is...