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Mastering OpenCV with Practical Computer Vision Projects

You're reading from  Mastering OpenCV with Practical Computer Vision Projects

Product type Book
Published in Dec 2012
Publisher Packt
ISBN-13 9781849517829
Pages 340 pages
Edition 1st Edition
Languages

Table of Contents (15) Chapters

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

Structure from Motion concepts


The first discrimination we should make is the difference between stereo (or indeed any multiview), 3D reconstruction using calibrated rigs, and SfM. While a rig of two or more cameras assume we already know what the motion between the cameras is, in SfM we don't actually know this motion 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 function.

Let us 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. Assuming we already know the...

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