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)

What this book covers

Chapter 1, Cartoonifier and Skin Changer for Raspberry Pi, contains a complete tutorial and source code for both a desktop application and a Raspberry Pi that automatically generates a cartoon or painting from a real camera image, with several possible types of cartoons, including a skin color changer.

Chapter 2, Exploring Structure from Motion Using OpenCV, contains an introduction to Structure from Motion (SfM) via an implementation of SfM concepts in OpenCV. The reader will learn how to reconstruct 3D geometry from multiple 2D images and estimate camera positions.

Chapter 3, Number Plate Recognition Using SVM and Neural Networks, includes a complete tutorial and source code to build an automatic number plate recognition application using pattern recognition algorithms and also using a support vector machine and Artificial Neural Networks. The reader will learn how to train and predict pattern-recognition algorithms to decide whether an image is a number plate or not. It will also help classify a set of features into a character.

Chapter 4, Non-Rigid Face Tracking, contains a complete tutorial and source code to build a dynamic face tracking system that can model and track the many complex parts of a person's face.

Chapter 5, 3D Head Pose Estimation Using AAM and POSIT, includes all the background required to understand what Active Appearance Models (AAMs) are and how to create them with OpenCV using a set of face frames with different facial expressions. Besides, this chapter explains how to match a given frame through fitting capabilities offered by AAMs. Then, by applying the POSIT algorithm, one can find the 3D head pose.

Chapter 6, Face Recognition Using Eigenfaces or Fisherfaces, contains a complete tutorial and source code for a real-time face-recognition application that includes basic face and eye detection to handle the rotation of faces and varying lighting conditions in the images.

Chapter 7, Natural Feature Tracking for Augmented Reality, includes a complete tutorial on how to build a marker-based Augmented Reality (AR) application for iPad and iPhone devices with an explanation of each step and source code. It also contains a complete tutorial on how to develop a marker-less augmented reality desktop application with an explanation of what marker-less AR is and the source code.

You can download this chapter from: h t t p s ://w w w . p a c k t p u b . c o m /s i t e s /d e f a u l t /f i l e s /d o w n l o a d s /N a t u r a l F e a t u r e T r a c k i n g f o r A u g m e n t e d R e a l i t y . p d f.