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Mastering OpenCV with Practical Computer Vision Projects

You're reading from   Mastering OpenCV with Practical Computer Vision Projects This is the definitive advanced tutorial for OpenCV, designed for those with basic C++ skills. The computer vision projects are divided into easily assimilated chapters with an emphasis on practical involvement for an easier learning curve.

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Product type Paperback
Published in Dec 2012
Publisher Packt
ISBN-13 9781849517829
Length 340 pages
Edition 1st Edition
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Table of Contents (15) Chapters Close

Mastering OpenCV with Practical Computer Vision Projects
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Cartoonifier and Skin Changer for Android FREE CHAPTER 2. Marker-based Augmented Reality on iPhone or iPad 3. Marker-less Augmented Reality 4. Exploring Structure from Motion Using OpenCV 5. Number Plate Recognition Using SVM and Neural Networks 6. Non-rigid Face Tracking 7. 3D Head Pose Estimation Using AAM and POSIT 8. Face Recognition using Eigenfaces or Fisherfaces Index

Placing a marker in 3D


Augmented Reality tries to fuse the real-world object with virtual content. To place a 3D model in a scene, we need to know its pose with regard to a camera that we use to obtain the video frames. We will use a Euclidian transformation in the Cartesian coordinate system to represent such a pose.

The position of the marker in 3D and its corresponding projection in 2D is restricted by the following equation:

P = A * [R|T] * M;

Where:

  • M denotes a point in a 3D space

  • [R|T] denotes a [3|4] matrix representing a Euclidian transformation

  • A denotes a camera matrix or a matrix of intrinsic parameters

  • P denotes projection of M in screen space

After performing the marker detection step we now know the position of the four marker corners in 2D (projections in screen space). In the next section you will learn how to obtain the A matrix and M vector parameters and calculate the [R|T] transformation.

Camera calibration

Each camera lens has unique parameters, such as focal length, principal...

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