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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

Active Appearance Models overview


In few words, Active Appearance Models are a nice model parameterization of combined texture and shape coupled to an efficient search algorithm that can tell exactly where and how a model is located in a picture frame. In order to do that, we will start with the Active Shape Models section and will see that they are more closely related to landmark positions. A principal component analysis and some hands-on experience will be better described in the following sections. Then, we will be able to get some help from OpenCV's Delaunay functions and learn some triangulation. From that we will evolve to applying piecewise affine warps in the triangle texture warping section, where we can get information from an object's texture.

As we get enough background to build a good model, we can play with the techniques in the model instantiation section. We will then be able to solve the inverse problem through AAM search and fitting. These by themselves are already very...

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