Book Overview & Buying
Table Of Contents

Mastering OpenCV 3 - Second Edition

By : Jason Saragih

Buy this Book

Mastering OpenCV 3

By: Jason Saragih

Buy this Book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Cartoonifier and Skin Changer for Raspberry Pi

Accessing the webcam

Main camera processing loop for a desktop app

Implementation of the skin color changer

Summary

Exploring Structure from Motion Using OpenCV

Structure from Motion concepts

Estimating the camera motion from a pair of images

Reconstructing the scene

Reconstruction from many views

Refinement of the reconstruction

Using the example code

Summary

References

Number Plate Recognition using SVM and Neural Network

Introduction to ANPR

ANPR algorithm

Plate detection

Plate recognition

Summary

Non-Rigid Face Tracking

Overview

Utilities

Geometrical constraints

Facial feature detectors

Face detection and initialization

Face tracking

Summary

References

3D Head Pose Estimation Using AAM and POSIT

Active Appearance Models overview

Active Shape Models

Model Instantiation - playing with the AAM

AAM search and fitting

POSIT

Summary

References

Face Recognition Using Eigenfaces or Fisherfaces

Introduction to face recognition and face detection

Summary

References

3D Head Pose Estimation Using AAM and POSIT

A good computer vision algorithm can't be complete without great, robust capabilities, as well as wide generalization and a solid math foundation. All these features accompany the work mainly developed by Timothy Cootes with Active Appearance Models. This chapter will teach you how to create an Active Appearance Model (AAM) of your own using OpenCV as well as how to use it to search for the closest position your model is located at in a given frame. Besides, you will learn how to use the POSIT algorithm and how to fit your 3D model in the posed image. With all these tools, you will be able to track a 3D model in a video, in real time--ain't it great? Although the examples focus on head pose, virtually any deformable model could use the same approach.

This chapter will cover the following topics: