Book Image

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

By : Robert Laganiere
Book Image

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

By: Robert Laganiere

Overview of this book

Making your applications see has never been easier with OpenCV. With it, you can teach your robot how to follow your cat, write a program to correctly identify the members of One Direction, or even help you find the right colors for your redecoration. OpenCV 3 Computer Vision Application Programming Cookbook Third Edition provides a complete introduction to the OpenCV library and explains how to build your first computer vision program. You will be presented with a variety of computer vision algorithms and exposed to important concepts in image and video analysis that will enable you to build your own computer vision applications. This book helps you to get started with the library, and shows you how to install and deploy the OpenCV library to write effective computer vision applications following good programming practices. You will learn how to read and write images and manipulate their pixels. Different techniques for image enhancement and shape analysis will be presented. You will learn how to detect specific image features such as lines, circles or corners. You will be introduced to the concepts of mathematical morphology and image filtering. The most recent methods for image matching and object recognition are described, and you’ll discover how to process video from files or cameras, as well as how to detect and track moving objects. Techniques to achieve camera calibration and perform multiple-view analysis will also be explained. Finally, you’ll also get acquainted with recent approaches in machine learning and object classification.
Table of Contents (21 chapters)
OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Introduction


We learned in the previous chapter how a camera captures a 3D scene by projecting light rays on a 2D sensor plane. The image produced is an accurate representation of what the scene looks like from a particular point of view, at the instant the image was captured. However, by its nature, the process of image formation eliminates all information concerning the depth of the represented scene elements. This chapter will teach how, under specific conditions, the 3D structure of the scene and the 3D pose of the cameras that captured it, can be recovered. We will see how a good understanding of projective geometry concepts allows us to devise methods that enable 3D reconstruction. We will therefore revisit the principle of image formation introduced in the previous chapter; in particular, we will now take into consideration that our image is composed of pixels.

Digital image formation

Let's now redraw a new version of the figure shown in Chapter 10 , Estimating Projective Relations...