Book Image

Mastering OpenCV 3 - Second Edition

By : Jason Saragih
Book Image

Mastering OpenCV 3 - Second Edition

By: Jason Saragih

Overview of this book

As we become more capable of handling data in every kind, we are becoming more reliant on visual input and what we can do with those self-driving cars, face recognition, and even augmented reality applications and games. This is all powered by Computer Vision. This book will put you straight to work in creating powerful and unique computer vision applications. Each chapter is structured around a central project and deep dives into an important aspect of OpenCV such as facial recognition, image target tracking, making augmented reality applications, the 3D visualization framework, and machine learning. You’ll learn how to make AI that can remember and use neural networks to help your applications learn. By the end of the book, you will have created various working prototypes with the projects in the book and will be well versed with the new features of OpenCV3.
Table of Contents (14 chapters)
Title Page
Mastering OpenCV 3 Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Summary


In this chapter, we discussed how Active Appearance Models can be combined with the POSIT algorithm in order to obtain a 3D head pose. An overview on how to create, train, and manipulate AAMs has been given, and you can use this background for any other field, such as medical, imaging, or industry. Besides dealing with AAMs, we got familiar with Delaunay subdivisions and learned how to use such an interesting structure as a triangulated mesh. We also showed you how to perform texture mapping in the triangles using OpenCV functions. Another interesting topic was approached in AAM fitting. Although only the inverse compositional project-out algorithm was described, we could easily obtain the results of years of research by simply using its output.

After enough theory and practice of AAMs, we dived into the details of POSIT in order to couple 2D measurements to 3D ones, explaining how to fit a 3D model using matchings between model points. We concluded the chapter by showing how to use...