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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

Marker-based versus marker-less AR


From the previous chapter you've learned how to use special images called markers to augment a real scene. The strong aspects of the markers are as follows:

  • Cheap detection algorithm

  • Robust against lighting changes

Markers also have several weaknesses. They are as follows:

  • Doesn't work if partially overlapped

  • Marker image has to be black and white

  • Has square form in most cases (because it's easy to detect)

  • Non-esthetic visual look of the marker

  • Has nothing in common with real-world objects

So, markers are a good point to start working with augmented reality; but if you want more, it's time to move on from marker-based to marker-less AR. Marker-less AR is a technique that is based on recognition of objects that exist in the real world. A few examples of a target for marker-less AR are: magazine covers, company logos, toys, and so on. In general, any object that has enough descriptive and discriminative information regarding the rest of the scene can be a target...

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