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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

Refinement of the reconstruction


One of the most important parts of an SfM method is refining and optimizing the reconstructed scene, also known as the process of Bundle Adjustment (BA). This is an optimizing step where all the data we gathered is fitted to a monolithic model. Both the position of the 3D points and the positions of cameras are optimized, so reprojection errors are minimized (that is, approximated 3D points are projected on the image close to the position of originating 2D points). This process usually entails the solving of very big linear equations of the order of tens of thousands of parameters. The process may be slightly laborious, but the steps we took earlier will allow for an easy integration with the bundle adjuster. Some things that seemed strange earlier may become clear; for example, the reason we retain the origin 2D points for each 3D point in the cloud.

One implementation of a bundle adjustment algorithm is the Simple Sparse Bundle Adjustment (SSBA) library...

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