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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

Overview


Non-rigid face tracking was first popularized in the early to mid 90s with the advent of active shape models (ASM) by Cootes and Taylor. Since then, a tremendous amount of research has been dedicated to solving the difficult problem of generic face tracking with many improvements over the original method that ASM proposed. The first milestone was the extension of ASM to active appearance models (AAM) in 2001, also by Cootes and Taylor. This approach was later formalized though the principled treatment of image warps by Baker and colleges in the the mid 2000s. Another strand of work along these lines was the 3D Morphable Model (3DMM) by Blanz and Vetter, which like AAM, not only modeled image textures as opposed to profiles along object boundaries as in ASM, but took it one step further by representing the models with a highly dense 3D data learned from laser scans of faces. From the mid to the late 2000s, the focus of research on face tracking shifted away from how the face was...

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