Mastering OpenCV 3: Get hands-on with practical Computer Vision using OpenCV 3 , Second Edition

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Exploring Structure from Motion Using OpenCV

In this chapter, we will discuss the notion of Structure from Motion (SfM),or better put, extracting geometric structures from images taken with a camera under motion, using OpenCV's API to help us. First, let's constrain the otherwise very b road approach to SfM using a single camera, usually called a monocular approach, and a discrete and sparse set of frames rather than a continuous video stream. These two constrains will greatly simplify the system we will sketch out in the coming pages, and help us understand the fundamentals of any SfM method. To implement our method, we will follow in the footsteps of Hartley and Zisserman (hereafter referred to as H&Z, for brevity), as documented in Chapters 9 through 12 of their seminal book Multiple View Geometry in Computer Vision.

In this chapter, we will cover the following:

Structure from Motion concepts
Estimating...

Key benefits

Updated for OpenCV 3, this book covers new features that will help you unlock the full potential of OpenCV 3

Written by a team of 7 experts, each chapter explores a new aspect of OpenCV to help you make amazing computer-vision aware applications

Project-based approach with each chapter being a complete tutorial, showing you how to apply OpenCV to solve complete problems

Description

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.

What you will learn

Execute basic image processing operations and cartoonify an image

Build an OpenCV project natively with Raspberry Pi and cross-compile it for Raspberry Pi.text

Extend the natural feature tracking algorithm to support the tracking of multiple image targets on a video

Use OpenCV 3's new 3D visualization framework to illustrate the 3D scene geometry

Create an application for Automatic Number Plate Recognition (ANPR) using a support vector machine and Artificial Neural Networks

Train and predict pattern-recognition algorithms to decide whether an image is a number plate

Use POSIT for the six degrees of freedom head pose

Train a face recognition database using deep learning and recognize faces from that database

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