#### 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.
Title Page
Mastering OpenCV 3 Second Edition
Credits
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Cartoonifier and Skin Changer for Raspberry Pi
Exploring Structure from Motion Using OpenCV
Number Plate Recognition using SVM and Neural Network
Non-Rigid Face Tracking
3D Head Pose Estimation Using AAM and POSIT
Face Recognition Using Eigenfaces or Fisherfaces

## POSIT

After we have found the 2D position of our landmark points, we can derive the 3D pose of our model using the POSIT. The pose P of a 3D object is defined as the 3 x 3 rotation matrix R and the 3D translation vector T; hence, P is equal to [ R | T ].

### Note

Most of this section is based on the OpenCV POSIT tutorial by Javier Barandiaran.

As the name implies, POSIT uses the Pose from Orthography and Scaling (POS) algorithm in several iterations, so it is an acronym for POS with iterations. The hypothesis for its working is that we can detect and match in the image four or more non-coplanar feature points of the object and that we know their relative geometry on the object.

The main idea of the algorithm is that we can find a good approximation to the object pose, supposing that all the model points are in the same plane, since their depths are not very different from one another if compared to the distance from the camera to a face. After the initial pose is obtained, the rotation matrix and...