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

Morphological filters


Let's talk about morphological image processing, which is related to the shape of the in an image. Morphological operations are according to a pixel's relative order among its neighbors.

Morphological filters are used to modify structures in binary images. Morphological operators modify the shape of pixel groups instead of their amplitude or value.

Morphological reconstructions are based on recurring uses of dilation or erosion until a marker point is moved. We can perform object boundary detection, hole filling, and region flooding.

The most common morphological operations are erosion and dilation. Dilation adds pixels to the boundaries of objects in an image. Erosion removes pixels on the object boundaries. Kernel size assigns the amount of pixels that are added or subtracted from the objects during the process.

Combinations of dilation and erosion are often used to implement different kinds of morphological processing operations. For instance, the definition of the...