Book Image

Mastering OpenCV with Practical Computer Vision Projects

Book Image

Mastering OpenCV with Practical Computer Vision Projects

Overview of this book

Computer Vision is fast becoming an important technology and is used in Mars robots, national security systems, automated factories, driver-less cars, and medical image analysis to new forms of human-computer interaction. OpenCV is the most common library for computer vision, providing hundreds of complex and fast algorithms. But it has a steep learning curve and limited in-depth tutorials.Mastering OpenCV with Practical Computer Vision Projects is the perfect book for developers with just basic OpenCV skills who want to try practical computer vision projects, as well as the seasoned OpenCV experts who want to add more Computer Vision topics to their skill set or gain more experience with OpenCV's new C++ interface before migrating from the C API to the C++ API.Each chapter is a separate project including the necessary background knowledge, so try them all one-by-one or jump straight to the projects you're most interested in.Create working prototypes from this book including real-time mobile apps, Augmented Reality, 3D shape from video, or track faces & eyes, fluid wall using Kinect, number plate recognition and so on. Mastering OpenCV with Practical Computer Vision Projects gives you rapid training in nine computer vision areas with useful projects.
Table of Contents (15 chapters)
Mastering OpenCV with Practical Computer Vision Projects
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Chapter 7. 3D Head Pose Estimation Using AAM and POSIT

A good computer vision algorithm can't be complete without great, robust capabilities as well as wide generalization and a solid math foundation. All these features accompany the work mainly developed by Tim Cootes with Active Appearance Models. This chapter will teach you how to create an Active Appearance Model of your own using OpenCV as well as how to use it to search for the closest position your model is located at in a given frame. Besides, you will learn how to use the POSIT algorithm and how to fit your 3D model in the "posed" image. With all these tools, you will be able to track a 3D model in a video, in real time; ain't it great? Although the examples focus on head pose, virtually any deformable model could use the same approach.

As you read the sections, you will come across the following topics:

  • Active Appearance Models overview

  • Active Shape Models overview

  • Model instantiation—playing with the Active Appearance Model

  • AAM search...