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)

Face tracking


The problem of face tracking can be posed as that of finding an efficient and robust way to combine the independent detections of various facial features with the geometrical dependencies they exhibit in order to arrive at an accurate estimate of facial feature locations in each image of a sequence. With this in mind, it is perhaps worth considering whether geometrical dependencies are at all necessary. In the following figure, the results of detecting the facial features with and without geometrical constraints are shown. These results clearly highlight the benefit of capturing the spatial inter-dependencies between facial features. The relative performance of these two approaches is typical, whereby relying strictly on the detections leads to overly noisy solutions. The reason for this is that the response maps for each facial feature cannot be expected to always peak at the correct location. Whether due to image noise, lighting changes, or expression variation, the only...