Book Image

Mastering OpenCV 3 - Second Edition

By : Saragih
Book Image

Mastering OpenCV 3 - Second Edition

By: 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 (7 chapters)

Geometrical constraints


In face tracking, geometry refers to the spatial configuration of a predefined set of points that correspond to physically consistent locations on the human face (such as eye corners, nose tips, and eyebrow edges). A particular choice of these points is application dependent, with some applications requiring a dense set of over 100 points and others requiring only a sparser selection. However, the robustness of face-tracking algorithms generally improves with an increased number of points, as their separate measurements can reinforce each other through their relative spatial dependencies. For example, the location of an eye corner is a good indication of where to expect the nose to be located. However, there are limits to improvements in robustness gained by increasing the number of points, where performance typically plateaus after around 100 points. Furthermore, increasing the point set used to describe a face carries with it a linear increase in computational complexity...