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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

Summary


In this chapter we have seen how OpenCV can help us approach Structure from Motion in a manner that is both simple to code and to understand. OpenCV's API contains a number of useful functions and data structures that make our lives easier and also assist in a cleaner implementation.

However, the state-of-the-art SfM methods are far more complex. There are many issues we choose to disregard in favor of simplicity, and plenty more error examinations that are usually in place. Our chosen methods for the different elements of SfM can also be revisited. For one, H and Z propose a highly accurate triangulation method that minimizes the reprojection error in the image domain. Some methods even use the N-view triangulation once they understand the relationship between the features in multiple images.

If we would like to extend and deepen our familiarity with SfM, we will certainly benefit from looking at other open-source SfM libraries. One particularly interesting project is libMV, which...

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