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Mastering OpenCV 3
Mastering OpenCV 3

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

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Mastering OpenCV 3

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...

Structure from Motion concepts

The first discrimination we should make is the difference between stereo (or indeed any multiview) and 3D reconstruction using calibrated rigs and SfM. A rig of two or more cameras assumes that we already know the motion between the cameras, while in SfM, we don't know what this motion is and we wish to find it. Calibrated rigs, from a simplistic point of view, allow a much more accurate reconstruction of 3D geometry because there is no error in estimating the distance and rotation between the cameras, it is already known. The first step in implementing an SfM system is finding the motion between the cameras. OpenCV may help us in a number of ways to obtain this motion, specifically using the findFundamentalMat and findEssentialMat functions.

Let's think for one moment of the goal behind choosing an SfM algorithm. In most cases, we wish to obtain the geometry of...

Estimating the camera motion from a pair of images

Before we set out to actually find the motion between two cameras, let's examine the inputs and the tools we have at hand to perform this operation. First, we have two images of the same scene from (hopefully not extremely) different positions in space. This is a powerful asset, and we will make sure that we use it. As for tools, we should take a look at mathematical objects that impose constraints over our images, cameras, and the scene.

Two very useful mathematical objects are the fundamental matrix (denoted by F) and the essential matrix (denoted by E), which impose a constraint over corresponding 2D points in two images of the scene. They are mostly similar, except that the essential matrix is assuming usage of calibrated cameras; this is the case for us, so we will choose it. OpenCV allows us to find the fundamental matrix via the findFundamentalMat function...

Reconstructing the scene

Next, we look into the matter of recovering the 3D structure of the scene from the information we have acquired so far. As we had done before, we should look at the tools and information we have at hand to achieve this. In the preceding section, we obtained two camera matrices from the essential matrix; we already discussed how these tools would be useful for obtaining the 3D position of a point in space. Then, we can go back to our matched point pairs to fill in our equations with numerical data. The point pairs will also be useful in calculating the error we get from all our approximate calculations.

This is the time to see how we can perform triangulation using OpenCV. Luckily, OpenCV supplies us with a number of functions that make this process easy to implement: triangulatePoints, undistortPoints, and convertPointsFromHomogeneous.

Remember we had two key equations arising from the 2D...

Reconstruction from many views

Now that we know how to recover the motion and scene geometry from two cameras, it would seem simple to get the parameters of additional cameras and more scene points simply by applying the same process. This matter is in fact not so simple, as we can only get a reconstruction that is upto scale, and each pair of pictures has a different scale.

There are a number of ways to correctly reconstruct the 3D scene data from multiple views. One way to achieve camera pose estimation or camera resectioning, is the Perspective N-Point(PnP) algorithm, where we try to solve for the position of a new camera using N 3D scene points, which we have already found and their respective 2D image points. Another way is to triangulate more points and see how they fit into our existing scene geometry; this will tell us the position of the new camera by means of point cloud registration. In this...

Structure from Motion concepts


The first discrimination we should make is the difference between stereo (or indeed any multiview) and 3D reconstruction using calibrated rigs and SfM. A rig of two or more cameras assumes that we already know the motion between the cameras, while in SfM, we don't know what this motion is and we wish to find it. Calibrated rigs, from a simplistic point of view, allow a much more accurate reconstruction of 3D geometry because there is no error in estimating the distance and rotation between the cameras, it is already known. The first step in implementing an SfM system is finding the motion between the cameras. OpenCV may help us in a number of ways to obtain this motion, specifically using the findFundamentalMat and findEssentialMat functions.

Let's think for one moment of the goal behind choosing an SfM algorithm. In most cases, we wish to obtain the geometry of the scene, for example, where objects are in relation to the camera and what their form is. Having...

Estimating the camera motion from a pair of images


Before we set out to actually find the motion between two cameras, let's examine the inputs and the tools we have at hand to perform this operation. First, we have two images of the same scene from (hopefully not extremely) different positions in space. This is a powerful asset, and we will make sure that we use it. As for tools, we should take a look at mathematical objects that impose constraints over our images, cameras, and the scene.

Two very useful mathematical objects are the fundamental matrix (denoted by F) and the essential matrix (denoted by E), which impose a constraint over corresponding 2D points in two images of the scene. They are mostly similar, except that the essential matrix is assuming usage of calibrated cameras; this is the case for us, so we will choose it. OpenCV allows us to find the fundamental matrix via the findFundamentalMat function and the essential matrix via the findEssentialMatrix function. Finding the essential...

Reconstructing the scene


Next, we look into the matter of recovering the 3D structure of the scene from the information we have acquired so far. As we had done before, we should look at the tools and information we have at hand to achieve this. In the preceding section, we obtained two camera matrices from the essential matrix; we already discussed how these tools would be useful for obtaining the 3D position of a point in space. Then, we can go back to our matched point pairs to fill in our equations with numerical data. The point pairs will also be useful in calculating the error we get from all our approximate calculations.

This is the time to see how we can perform triangulation using OpenCV. Luckily, OpenCV supplies us with a number of functions that make this process easy to implement: triangulatePoints, undistortPoints, and convertPointsFromHomogeneous.

Remember we had two key equations arising from the 2D point matching and P matrices: x=PX and x'= P'X, where x and x' are matching...

Reconstruction from many views


Now that we know how to recover the motion and scene geometry from two cameras, it would seem simple to get the parameters of additional cameras and more scene points simply by applying the same process. This matter is in fact not so simple, as we can only get a reconstruction that is upto scale, and each pair of pictures has a different scale.

There are a number of ways to correctly reconstruct the 3D scene data from multiple views. One way to achieve camera pose estimation or camera resectioning, is the Perspective N-Point(PnP) algorithm, where we try to solve for the position of a new camera using N 3D scene points, which we have already found and their respective 2D image points. Another way is to triangulate more points and see how they fit into our existing scene geometry; this will tell us the position of the new camera by means of point cloud registration. In this section, we will discuss using OpenCV's solvePnP functions that implements the first method...

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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.

Who is this book for?

This book is for those who have a basic knowledge of OpenCV and are competent C++ programmers. You need to have an understanding of some of the more theoretical/mathematical concepts, as we move quite quickly throughout the book.

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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Publication date : Apr 28, 2017
Length: 250 pages
Edition : 2nd
Language : English
ISBN-13 : 9781786467171
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Length: 250 pages
Edition : 2nd
Language : English
ISBN-13 : 9781786467171
Vendor :
Intel
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Tools :

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Table of Contents

6 Chapters
Cartoonifier and Skin Changer for Raspberry Pi Chevron down icon Chevron up icon
Exploring Structure from Motion Using OpenCV Chevron down icon Chevron up icon
Number Plate Recognition using SVM and Neural Network Chevron down icon Chevron up icon
Non-Rigid Face Tracking Chevron down icon Chevron up icon
3D Head Pose Estimation Using AAM and POSIT Chevron down icon Chevron up icon
Face Recognition Using Eigenfaces or Fisherfaces Chevron down icon Chevron up icon
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